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@commands.command(name='hoogle', brief='search hoogle') async def hoogle(self, ctx, *query: str): 'Searches Hoggle and returns first two options\n Click title to see full search' url = f"https://hoogle.haskell.org?mode=json&hoogle={'+'.join(query)}&start=1&count=1" (result, error) = (await get_json(url)) if error: (await ctx.send(error)) return embed = discord.Embed(title=f"Definition of {' '.join(query)}", url=f"https://hoogle.haskell.org/?hoogle={'+'.join(query)}", color=self.bot.embed_color) embed.set_thumbnail(url='https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Lambda-letter-lowercase-symbol-Garamond.svg/1200px-Lambda-letter-lowercase-symbol-Garamond.svg.png') if (not result): embed.add_field(name='No results found', value='*undefined*', inline=False) else: for l in result: val = (('*Module:* ' + l['module']['name']) + '\n') val += sub('\\n{2,}', '\n\n', sub('\\n +', '\n', html2text(l['docs']))) embed.add_field(name=html2text(l['item']), value=val, inline=False) embed.set_footer(text='first option in Hoogle (Click title for more)') (await ctx.send(embed=embed))
-1,525,945,134,972,282,000
Searches Hoggle and returns first two options Click title to see full search
extensions/api.py
hoogle
JoseFilipeFerreira/JBB.py
python
@commands.command(name='hoogle', brief='search hoogle') async def hoogle(self, ctx, *query: str): 'Searches Hoggle and returns first two options\n Click title to see full search' url = f"https://hoogle.haskell.org?mode=json&hoogle={'+'.join(query)}&start=1&count=1" (result, error) = (await get_json(url)) if error: (await ctx.send(error)) return embed = discord.Embed(title=f"Definition of {' '.join(query)}", url=f"https://hoogle.haskell.org/?hoogle={'+'.join(query)}", color=self.bot.embed_color) embed.set_thumbnail(url='https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Lambda-letter-lowercase-symbol-Garamond.svg/1200px-Lambda-letter-lowercase-symbol-Garamond.svg.png') if (not result): embed.add_field(name='No results found', value='*undefined*', inline=False) else: for l in result: val = (('*Module:* ' + l['module']['name']) + '\n') val += sub('\\n{2,}', '\n\n', sub('\\n +', '\n', html2text(l['docs']))) embed.add_field(name=html2text(l['item']), value=val, inline=False) embed.set_footer(text='first option in Hoogle (Click title for more)') (await ctx.send(embed=embed))
def generateRequestData(self, offset, timestamp, chunkSize, isGroupConversation=False): 'Generate the data for the POST request.\n :return: the generated data\n ' ids_type = ('thread_fbids' if isGroupConversation else 'user_ids') dataForm = {'messages[{}][{}][offset]'.format(ids_type, self._convID): str(offset), 'messages[{}][{}][timestamp]'.format(ids_type, self._convID): timestamp, 'messages[{}][{}][limit]'.format(ids_type, self._convID): str(chunkSize), 'client': 'web_messenger', '__a': '', '__dyn': '', '__req': '', 'fb_dtsg': self._fb_dtsg} return dataForm
-2,812,959,289,043,096,600
Generate the data for the POST request. :return: the generated data
src/util/conversationScraper.py
generateRequestData
5agado/conversation-analyzer
python
def generateRequestData(self, offset, timestamp, chunkSize, isGroupConversation=False): 'Generate the data for the POST request.\n :return: the generated data\n ' ids_type = ('thread_fbids' if isGroupConversation else 'user_ids') dataForm = {'messages[{}][{}][offset]'.format(ids_type, self._convID): str(offset), 'messages[{}][{}][timestamp]'.format(ids_type, self._convID): timestamp, 'messages[{}][{}][limit]'.format(ids_type, self._convID): str(chunkSize), 'client': 'web_messenger', '__a': , '__dyn': , '__req': , 'fb_dtsg': self._fb_dtsg} return dataForm
def executeRequest(self, requestData): 'Executes the POST request and retrieves the correspondent response content.\n Request headers are generated here\n :return: the response content\n ' headers = {'Host': 'www.facebook.com', 'Origin': 'https://www.facebook.com', 'Referer': 'https://www.facebook.com', 'accept-encoding': 'gzip,deflate', 'accept-language': 'en-US,en;q=0.8', 'cookie': self._cookie, 'pragma': 'no-cache', 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.122 Safari/537.36', 'content-type': 'application/x-www-form-urlencoded', 'accept': '*/*', 'cache-control': 'no-cache'} url = 'https://www.facebook.com/ajax/mercury/thread_info.php' start = time.time() response = requests.post(url, data=requestData, headers=headers) end = time.time() logger.info('Retrieved in {0:.2f}s'.format((end - start))) msgsData = response.text[9:] return msgsData
-2,882,766,584,559,465,500
Executes the POST request and retrieves the correspondent response content. Request headers are generated here :return: the response content
src/util/conversationScraper.py
executeRequest
5agado/conversation-analyzer
python
def executeRequest(self, requestData): 'Executes the POST request and retrieves the correspondent response content.\n Request headers are generated here\n :return: the response content\n ' headers = {'Host': 'www.facebook.com', 'Origin': 'https://www.facebook.com', 'Referer': 'https://www.facebook.com', 'accept-encoding': 'gzip,deflate', 'accept-language': 'en-US,en;q=0.8', 'cookie': self._cookie, 'pragma': 'no-cache', 'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/37.0.2062.122 Safari/537.36', 'content-type': 'application/x-www-form-urlencoded', 'accept': '*/*', 'cache-control': 'no-cache'} url = 'https://www.facebook.com/ajax/mercury/thread_info.php' start = time.time() response = requests.post(url, data=requestData, headers=headers) end = time.time() logger.info('Retrieved in {0:.2f}s'.format((end - start))) msgsData = response.text[9:] return msgsData
def scrapeConversation(self, merge, offset, timestampOffset, chunkSize, limit, isGroupConversation): 'Retrieves conversation messages and stores them in a JSON file\n If merge is specified, the new messages will be merged with the previous version of the conversation, if present\n ' if merge: if (not os.path.exists(os.path.join(self._directory, 'conversation.json'))): logger.error('Conversation not present. Merge operation not possible') return with open(os.path.join(self._directory, 'conversation.json')) as conv: convMessages = json.load(conv) numMergedMsgs = 0 if (not os.path.exists(self._directory)): os.makedirs(self._directory) logger.info('Starting scraping of conversation {}'.format(self._convID)) messages = [] msgsData = '' timestamp = ('' if (timestampOffset == 0) else str(timestampOffset)) while (self.CONVERSATION_ENDMARK not in msgsData): requestChunkSize = (chunkSize if (limit <= 0) else min(chunkSize, (limit - len(messages)))) reqData = self.generateRequestData(offset, timestamp, requestChunkSize, isGroupConversation) logger.info('Retrieving messages {}-{}'.format(offset, (requestChunkSize + offset))) msgsData = self.executeRequest(reqData) jsonData = json.loads(msgsData) if (jsonData and ('payload' in jsonData) and jsonData['payload']): if (('actions' in jsonData['payload']) and jsonData['payload']['actions']): actions = jsonData['payload']['actions'] if (merge and (convMessages[(- 1)]['timestamp'] > actions[0]['timestamp'])): for (i, action) in enumerate(actions): if (convMessages[(- 1)]['timestamp'] == actions[i]['timestamp']): numMergedMsgs = (len(actions[(i + 1):(- 1)]) + len(messages)) messages = ((convMessages + actions[(i + 1):(- 1)]) + messages) break break if (len(messages) == 0): messages = actions else: messages = (actions[:(- 1)] + messages) timestamp = str(actions[0]['timestamp']) else: if ('errorSummary' in jsonData): logger.error(('Response error: ' + jsonData['errorSummary'])) else: logger.error('Response error. No messages found') logger.error(msgsData) return else: logger.error('Response error. Empty data or payload') logger.error(msgsData) logger.info('Retrying in {} seconds'.format(self.ERROR_WAIT)) time.sleep(self.ERROR_WAIT) continue offset += chunkSize if ((limit != 0) and (len(messages) >= limit)): break time.sleep(self.REQUEST_WAIT) if merge: logger.info('Successfully merged {} new messages'.format(numMergedMsgs)) logger.info('Conversation total message count = {}'.format(len(messages))) else: logger.info('Conversation scraped successfully. {} messages retrieved'.format(len(messages))) self.writeMessages(messages)
-4,032,054,314,809,956,000
Retrieves conversation messages and stores them in a JSON file If merge is specified, the new messages will be merged with the previous version of the conversation, if present
src/util/conversationScraper.py
scrapeConversation
5agado/conversation-analyzer
python
def scrapeConversation(self, merge, offset, timestampOffset, chunkSize, limit, isGroupConversation): 'Retrieves conversation messages and stores them in a JSON file\n If merge is specified, the new messages will be merged with the previous version of the conversation, if present\n ' if merge: if (not os.path.exists(os.path.join(self._directory, 'conversation.json'))): logger.error('Conversation not present. Merge operation not possible') return with open(os.path.join(self._directory, 'conversation.json')) as conv: convMessages = json.load(conv) numMergedMsgs = 0 if (not os.path.exists(self._directory)): os.makedirs(self._directory) logger.info('Starting scraping of conversation {}'.format(self._convID)) messages = [] msgsData = timestamp = ( if (timestampOffset == 0) else str(timestampOffset)) while (self.CONVERSATION_ENDMARK not in msgsData): requestChunkSize = (chunkSize if (limit <= 0) else min(chunkSize, (limit - len(messages)))) reqData = self.generateRequestData(offset, timestamp, requestChunkSize, isGroupConversation) logger.info('Retrieving messages {}-{}'.format(offset, (requestChunkSize + offset))) msgsData = self.executeRequest(reqData) jsonData = json.loads(msgsData) if (jsonData and ('payload' in jsonData) and jsonData['payload']): if (('actions' in jsonData['payload']) and jsonData['payload']['actions']): actions = jsonData['payload']['actions'] if (merge and (convMessages[(- 1)]['timestamp'] > actions[0]['timestamp'])): for (i, action) in enumerate(actions): if (convMessages[(- 1)]['timestamp'] == actions[i]['timestamp']): numMergedMsgs = (len(actions[(i + 1):(- 1)]) + len(messages)) messages = ((convMessages + actions[(i + 1):(- 1)]) + messages) break break if (len(messages) == 0): messages = actions else: messages = (actions[:(- 1)] + messages) timestamp = str(actions[0]['timestamp']) else: if ('errorSummary' in jsonData): logger.error(('Response error: ' + jsonData['errorSummary'])) else: logger.error('Response error. No messages found') logger.error(msgsData) return else: logger.error('Response error. Empty data or payload') logger.error(msgsData) logger.info('Retrying in {} seconds'.format(self.ERROR_WAIT)) time.sleep(self.ERROR_WAIT) continue offset += chunkSize if ((limit != 0) and (len(messages) >= limit)): break time.sleep(self.REQUEST_WAIT) if merge: logger.info('Successfully merged {} new messages'.format(numMergedMsgs)) logger.info('Conversation total message count = {}'.format(len(messages))) else: logger.info('Conversation scraped successfully. {} messages retrieved'.format(len(messages))) self.writeMessages(messages)
def check_num_list(prompt: str, max_length: int=0, min_length: int=0) -> List[float]: 'Function to check if users input is a number, splitting number\n by spaces and checking that the correct amount of numbers are\n entered, returning them in a list' while True: try: num = input(prompt) num = num.split(' ') if min_length: assert (len(num) >= min_length), f'Please enter at least {min_length} numbers' if max_length: assert (len(num) <= max_length), f'Please enter no more than {max_length} numbers' for (index, value) in enumerate(num): num[index] = float(value) return num except Exception as e: print(e)
-5,256,720,275,051,967,000
Function to check if users input is a number, splitting number by spaces and checking that the correct amount of numbers are entered, returning them in a list
150-Challenges/Challenges 27 - 34/Challenge 33.py
check_num_list
DGrifferty/Python
python
def check_num_list(prompt: str, max_length: int=0, min_length: int=0) -> List[float]: 'Function to check if users input is a number, splitting number\n by spaces and checking that the correct amount of numbers are\n entered, returning them in a list' while True: try: num = input(prompt) num = num.split(' ') if min_length: assert (len(num) >= min_length), f'Please enter at least {min_length} numbers' if max_length: assert (len(num) <= max_length), f'Please enter no more than {max_length} numbers' for (index, value) in enumerate(num): num[index] = float(value) return num except Exception as e: print(e)
def get_imdb(name): 'Get an imdb (image database) by name.' if (name not in __sets): raise KeyError('Unknown dataset: {}'.format(name)) return __sets[name]()
-3,263,413,934,054,098,000
Get an imdb (image database) by name.
lib/datasets/factory.py
get_imdb
wangvation/torch-mobilenet
python
def get_imdb(name): if (name not in __sets): raise KeyError('Unknown dataset: {}'.format(name)) return __sets[name]()
def list_imdbs(): 'List all registered imdbs.' return list(__sets.keys())
4,693,669,182,354,276,000
List all registered imdbs.
lib/datasets/factory.py
list_imdbs
wangvation/torch-mobilenet
python
def list_imdbs(): return list(__sets.keys())
def main(*args): '\n Process command line arguments and invoke bot.\n\n If args is an empty list, sys.argv is used.\n\n @param args: command line arguments\n @type args: str\n ' filename = 'fb2w.nt.gz' for arg in pywikibot.handle_args(args): if arg.startswith('-filename'): filename = arg[11:] bot = FreebaseMapperRobot(filename) bot.run()
8,901,002,085,312,635,000
Process command line arguments and invoke bot. If args is an empty list, sys.argv is used. @param args: command line arguments @type args: str
scripts/freebasemappingupload.py
main
5j9/pywikibot-core
python
def main(*args): '\n Process command line arguments and invoke bot.\n\n If args is an empty list, sys.argv is used.\n\n @param args: command line arguments\n @type args: str\n ' filename = 'fb2w.nt.gz' for arg in pywikibot.handle_args(args): if arg.startswith('-filename'): filename = arg[11:] bot = FreebaseMapperRobot(filename) bot.run()
def __init__(self, filename): 'Initializer.' self.repo = pywikibot.Site('wikidata', 'wikidata').data_repository() self.filename = filename if (not os.path.exists(self.filename)): pywikibot.output(('Cannot find %s. Try providing the absolute path.' % self.filename)) sys.exit(1)
8,991,422,563,458,918,000
Initializer.
scripts/freebasemappingupload.py
__init__
5j9/pywikibot-core
python
def __init__(self, filename): self.repo = pywikibot.Site('wikidata', 'wikidata').data_repository() self.filename = filename if (not os.path.exists(self.filename)): pywikibot.output(('Cannot find %s. Try providing the absolute path.' % self.filename)) sys.exit(1)
def run(self): 'Run the bot.' self.claim = pywikibot.Claim(self.repo, 'P646') self.statedin = pywikibot.Claim(self.repo, 'P248') freebasedumpitem = pywikibot.ItemPage(self.repo, 'Q15241312') self.statedin.setTarget(freebasedumpitem) self.dateofpub = pywikibot.Claim(self.repo, 'P577') oct28 = pywikibot.WbTime(site=self.repo, year=2013, month=10, day=28, precision='day') self.dateofpub.setTarget(oct28) for line in gzip.open(self.filename): self.processLine(line.strip())
-7,101,836,632,625,595,000
Run the bot.
scripts/freebasemappingupload.py
run
5j9/pywikibot-core
python
def run(self): self.claim = pywikibot.Claim(self.repo, 'P646') self.statedin = pywikibot.Claim(self.repo, 'P248') freebasedumpitem = pywikibot.ItemPage(self.repo, 'Q15241312') self.statedin.setTarget(freebasedumpitem) self.dateofpub = pywikibot.Claim(self.repo, 'P577') oct28 = pywikibot.WbTime(site=self.repo, year=2013, month=10, day=28, precision='day') self.dateofpub.setTarget(oct28) for line in gzip.open(self.filename): self.processLine(line.strip())
def processLine(self, line): 'Process a single line.' if ((not line) or line.startswith('#')): return (mid, sameas, qid, dot) = line.split() if (sameas != '<https://www.w3.org/2002/07/owl#sameAs>'): return if (dot != '.'): return if (not mid.startswith('<https://rdf.freebase.com/ns/m')): return mid = ('/m/' + mid[30:(- 1)]) if (not qid.startswith('<https://www.wikidata.org/entity/Q')): return qid = ('Q' + qid[33:(- 1)]) data = pywikibot.ItemPage(self.repo, qid) data.get() if (not data.labels): label = '' elif ('en' in data.labels): label = data.labels['en'] else: label = list(data.labels.values())[0] pywikibot.output('Parsed: {} <--> {}'.format(qid, mid)) pywikibot.output('{} is {}'.format(data.getID(), label)) if (data.claims and ('P646' in data.claims)): if (mid != data.claims['P646'][0].getTarget()): pywikibot.output('Mismatch: expected {}, has {} instead'.format(mid, data.claims['P646'][0].getTarget())) else: pywikibot.output('Already has mid set, is consistent.') else: pywikibot.output('Going to add a new claim.') self.claim.setTarget(mid) data.addClaim(self.claim) self.claim.addSources([self.statedin, self.dateofpub]) pywikibot.output('Claim added!')
3,031,414,387,276,538,400
Process a single line.
scripts/freebasemappingupload.py
processLine
5j9/pywikibot-core
python
def processLine(self, line): if ((not line) or line.startswith('#')): return (mid, sameas, qid, dot) = line.split() if (sameas != '<https://www.w3.org/2002/07/owl#sameAs>'): return if (dot != '.'): return if (not mid.startswith('<https://rdf.freebase.com/ns/m')): return mid = ('/m/' + mid[30:(- 1)]) if (not qid.startswith('<https://www.wikidata.org/entity/Q')): return qid = ('Q' + qid[33:(- 1)]) data = pywikibot.ItemPage(self.repo, qid) data.get() if (not data.labels): label = elif ('en' in data.labels): label = data.labels['en'] else: label = list(data.labels.values())[0] pywikibot.output('Parsed: {} <--> {}'.format(qid, mid)) pywikibot.output('{} is {}'.format(data.getID(), label)) if (data.claims and ('P646' in data.claims)): if (mid != data.claims['P646'][0].getTarget()): pywikibot.output('Mismatch: expected {}, has {} instead'.format(mid, data.claims['P646'][0].getTarget())) else: pywikibot.output('Already has mid set, is consistent.') else: pywikibot.output('Going to add a new claim.') self.claim.setTarget(mid) data.addClaim(self.claim) self.claim.addSources([self.statedin, self.dateofpub]) pywikibot.output('Claim added!')
def _parse_decl_specs_simple(self, outer: str, typed: bool) -> ASTDeclSpecsSimple: 'Just parse the simple ones.' storage = None threadLocal = None inline = None virtual = None explicit = None constexpr = None volatile = None const = None friend = None attrs = [] while 1: self.skip_ws() if (not storage): if (outer in ('member', 'function')): if self.skip_word('static'): storage = 'static' continue if self.skip_word('extern'): storage = 'extern' continue if (outer == 'member'): if self.skip_word('mutable'): storage = 'mutable' continue if self.skip_word('register'): storage = 'register' continue if ((not threadLocal) and (outer == 'member')): threadLocal = self.skip_word('thread_local') if threadLocal: continue if (outer == 'function'): if (not inline): inline = self.skip_word('inline') if inline: continue if (not friend): friend = self.skip_word('friend') if friend: continue if (not virtual): virtual = self.skip_word('virtual') if virtual: continue if (not explicit): explicit = self.skip_word('explicit') if explicit: continue if ((not constexpr) and (outer in ('member', 'function'))): constexpr = self.skip_word('constexpr') if constexpr: continue if ((not volatile) and typed): volatile = self.skip_word('volatile') if volatile: continue if ((not const) and typed): const = self.skip_word('const') if const: continue attr = self._parse_attribute() if attr: attrs.append(attr) continue break return ASTDeclSpecsSimple(storage, threadLocal, inline, virtual, explicit, constexpr, volatile, const, friend, attrs)
489,535,564,035,447,300
Just parse the simple ones.
sphinx/domains/cpp.py
_parse_decl_specs_simple
begolu2/sphinx
python
def _parse_decl_specs_simple(self, outer: str, typed: bool) -> ASTDeclSpecsSimple: storage = None threadLocal = None inline = None virtual = None explicit = None constexpr = None volatile = None const = None friend = None attrs = [] while 1: self.skip_ws() if (not storage): if (outer in ('member', 'function')): if self.skip_word('static'): storage = 'static' continue if self.skip_word('extern'): storage = 'extern' continue if (outer == 'member'): if self.skip_word('mutable'): storage = 'mutable' continue if self.skip_word('register'): storage = 'register' continue if ((not threadLocal) and (outer == 'member')): threadLocal = self.skip_word('thread_local') if threadLocal: continue if (outer == 'function'): if (not inline): inline = self.skip_word('inline') if inline: continue if (not friend): friend = self.skip_word('friend') if friend: continue if (not virtual): virtual = self.skip_word('virtual') if virtual: continue if (not explicit): explicit = self.skip_word('explicit') if explicit: continue if ((not constexpr) and (outer in ('member', 'function'))): constexpr = self.skip_word('constexpr') if constexpr: continue if ((not volatile) and typed): volatile = self.skip_word('volatile') if volatile: continue if ((not const) and typed): const = self.skip_word('const') if const: continue attr = self._parse_attribute() if attr: attrs.append(attr) continue break return ASTDeclSpecsSimple(storage, threadLocal, inline, virtual, explicit, constexpr, volatile, const, friend, attrs)
def _parse_type(self, named: Union[(bool, str)], outer: str=None) -> ASTType: "\n named=False|'maybe'|True: 'maybe' is e.g., for function objects which\n doesn't need to name the arguments\n\n outer == operatorCast: annoying case, we should not take the params\n " if outer: if (outer not in ('type', 'member', 'function', 'operatorCast', 'templateParam')): raise Exception(('Internal error, unknown outer "%s".' % outer)) if (outer != 'operatorCast'): assert named if (outer in ('type', 'function')): prevErrors = [] startPos = self.pos try: declSpecs = self._parse_decl_specs(outer=outer, typed=False) decl = self._parse_declarator(named=True, paramMode=outer, typed=False) self.assert_end() except DefinitionError as exUntyped: if (outer == 'type'): desc = 'If just a name' elif (outer == 'function'): desc = 'If the function has no return type' else: assert False prevErrors.append((exUntyped, desc)) self.pos = startPos try: declSpecs = self._parse_decl_specs(outer=outer) decl = self._parse_declarator(named=True, paramMode=outer) except DefinitionError as exTyped: self.pos = startPos if (outer == 'type'): desc = 'If typedef-like declaration' elif (outer == 'function'): desc = 'If the function has a return type' else: assert False prevErrors.append((exTyped, desc)) if True: if (outer == 'type'): header = 'Type must be either just a name or a ' header += 'typedef-like declaration.' elif (outer == 'function'): header = 'Error when parsing function declaration.' else: assert False raise self._make_multi_error(prevErrors, header) else: self.pos = startPos typed = True declSpecs = self._parse_decl_specs(outer=outer, typed=typed) decl = self._parse_declarator(named=True, paramMode=outer, typed=typed) else: paramMode = 'type' if (outer == 'member'): named = True elif (outer == 'operatorCast'): paramMode = 'operatorCast' outer = None elif (outer == 'templateParam'): named = 'single' declSpecs = self._parse_decl_specs(outer=outer) decl = self._parse_declarator(named=named, paramMode=paramMode) return ASTType(declSpecs, decl)
-6,564,301,317,631,929,000
named=False|'maybe'|True: 'maybe' is e.g., for function objects which doesn't need to name the arguments outer == operatorCast: annoying case, we should not take the params
sphinx/domains/cpp.py
_parse_type
begolu2/sphinx
python
def _parse_type(self, named: Union[(bool, str)], outer: str=None) -> ASTType: "\n named=False|'maybe'|True: 'maybe' is e.g., for function objects which\n doesn't need to name the arguments\n\n outer == operatorCast: annoying case, we should not take the params\n " if outer: if (outer not in ('type', 'member', 'function', 'operatorCast', 'templateParam')): raise Exception(('Internal error, unknown outer "%s".' % outer)) if (outer != 'operatorCast'): assert named if (outer in ('type', 'function')): prevErrors = [] startPos = self.pos try: declSpecs = self._parse_decl_specs(outer=outer, typed=False) decl = self._parse_declarator(named=True, paramMode=outer, typed=False) self.assert_end() except DefinitionError as exUntyped: if (outer == 'type'): desc = 'If just a name' elif (outer == 'function'): desc = 'If the function has no return type' else: assert False prevErrors.append((exUntyped, desc)) self.pos = startPos try: declSpecs = self._parse_decl_specs(outer=outer) decl = self._parse_declarator(named=True, paramMode=outer) except DefinitionError as exTyped: self.pos = startPos if (outer == 'type'): desc = 'If typedef-like declaration' elif (outer == 'function'): desc = 'If the function has a return type' else: assert False prevErrors.append((exTyped, desc)) if True: if (outer == 'type'): header = 'Type must be either just a name or a ' header += 'typedef-like declaration.' elif (outer == 'function'): header = 'Error when parsing function declaration.' else: assert False raise self._make_multi_error(prevErrors, header) else: self.pos = startPos typed = True declSpecs = self._parse_decl_specs(outer=outer, typed=typed) decl = self._parse_declarator(named=True, paramMode=outer, typed=typed) else: paramMode = 'type' if (outer == 'member'): named = True elif (outer == 'operatorCast'): paramMode = 'operatorCast' outer = None elif (outer == 'templateParam'): named = 'single' declSpecs = self._parse_decl_specs(outer=outer) decl = self._parse_declarator(named=named, paramMode=paramMode) return ASTType(declSpecs, decl)
def run(self) -> List[Node]: "\n On purpose this doesn't call the ObjectDescription version, but is based on it.\n Each alias signature may expand into multiple real signatures (an overload set).\n The code is therefore based on the ObjectDescription version.\n " if (':' in self.name): (self.domain, self.objtype) = self.name.split(':', 1) else: (self.domain, self.objtype) = ('', self.name) node = addnodes.desc() node.document = self.state.document node['domain'] = self.domain node['objtype'] = node['desctype'] = self.objtype node['noindex'] = True self.names = [] signatures = self.get_signatures() for (i, sig) in enumerate(signatures): node.append(AliasNode(sig, env=self.env)) contentnode = addnodes.desc_content() node.append(contentnode) self.before_content() self.state.nested_parse(self.content, self.content_offset, contentnode) self.env.temp_data['object'] = None self.after_content() return [node]
-4,870,467,569,921,633,000
On purpose this doesn't call the ObjectDescription version, but is based on it. Each alias signature may expand into multiple real signatures (an overload set). The code is therefore based on the ObjectDescription version.
sphinx/domains/cpp.py
run
begolu2/sphinx
python
def run(self) -> List[Node]: "\n On purpose this doesn't call the ObjectDescription version, but is based on it.\n Each alias signature may expand into multiple real signatures (an overload set).\n The code is therefore based on the ObjectDescription version.\n " if (':' in self.name): (self.domain, self.objtype) = self.name.split(':', 1) else: (self.domain, self.objtype) = (, self.name) node = addnodes.desc() node.document = self.state.document node['domain'] = self.domain node['objtype'] = node['desctype'] = self.objtype node['noindex'] = True self.names = [] signatures = self.get_signatures() for (i, sig) in enumerate(signatures): node.append(AliasNode(sig, env=self.env)) contentnode = addnodes.desc_content() node.append(contentnode) self.before_content() self.state.nested_parse(self.content, self.content_offset, contentnode) self.env.temp_data['object'] = None self.after_content() return [node]
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, dynamic_tags_json: Optional[pulumi.Input[str]]=None, is_push_enabled: Optional[pulumi.Input[bool]]=None, kind: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, slot: Optional[pulumi.Input[str]]=None, tag_whitelist_json: Optional[pulumi.Input[str]]=None, tags_requiring_auth: Optional[pulumi.Input[str]]=None, __props__=None, __name__=None, __opts__=None): "\n Push settings for the App.\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] dynamic_tags_json: Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint.\n :param pulumi.Input[bool] is_push_enabled: Gets or sets a flag indicating whether the Push endpoint is enabled.\n :param pulumi.Input[str] kind: Kind of resource.\n :param pulumi.Input[str] name: Name of web app.\n :param pulumi.Input[str] resource_group_name: Name of the resource group to which the resource belongs.\n :param pulumi.Input[str] slot: Name of web app slot. If not specified then will default to production slot.\n :param pulumi.Input[str] tag_whitelist_json: Gets or sets a JSON string containing a list of tags that are whitelisted for use by the push registration endpoint.\n :param pulumi.Input[str] tags_requiring_auth: Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint.\n Tags can consist of alphanumeric characters and the following:\n '_', '@', '#', '.', ':', '-'. \n Validation should be performed at the PushRequestHandler.\n " if (__name__ is not None): warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning) resource_name = __name__ if (__opts__ is not None): warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if (opts is None): opts = pulumi.ResourceOptions() if (not isinstance(opts, pulumi.ResourceOptions)): raise TypeError('Expected resource options to be a ResourceOptions instance') if (opts.version is None): opts.version = _utilities.get_version() if (opts.id is None): if (__props__ is not None): raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['dynamic_tags_json'] = dynamic_tags_json if ((is_push_enabled is None) and (not opts.urn)): raise TypeError("Missing required property 'is_push_enabled'") __props__['is_push_enabled'] = is_push_enabled __props__['kind'] = kind if ((name is None) and (not opts.urn)): raise TypeError("Missing required property 'name'") __props__['name'] = name if ((resource_group_name is None) and (not opts.urn)): raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name if ((slot is None) and (not opts.urn)): raise TypeError("Missing required property 'slot'") __props__['slot'] = slot __props__['tag_whitelist_json'] = tag_whitelist_json __props__['tags_requiring_auth'] = tags_requiring_auth __props__['system_data'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_='azure-nextgen:web/v20200901:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/latest:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/latest:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20160801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20160801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20180201:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20180201:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20181101:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20181101:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20190801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20190801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20200601:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20200601:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20201001:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20201001:WebAppSitePushSettingsSlot')]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(WebAppSitePushSettingsSlot, __self__).__init__('azure-native:web/v20200901:WebAppSitePushSettingsSlot', resource_name, __props__, opts)
-8,120,773,970,255,479,000
Push settings for the App. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] dynamic_tags_json: Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint. :param pulumi.Input[bool] is_push_enabled: Gets or sets a flag indicating whether the Push endpoint is enabled. :param pulumi.Input[str] kind: Kind of resource. :param pulumi.Input[str] name: Name of web app. :param pulumi.Input[str] resource_group_name: Name of the resource group to which the resource belongs. :param pulumi.Input[str] slot: Name of web app slot. If not specified then will default to production slot. :param pulumi.Input[str] tag_whitelist_json: Gets or sets a JSON string containing a list of tags that are whitelisted for use by the push registration endpoint. :param pulumi.Input[str] tags_requiring_auth: Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint. Tags can consist of alphanumeric characters and the following: '_', '@', '#', '.', ':', '-'. Validation should be performed at the PushRequestHandler.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
__init__
pulumi-bot/pulumi-azure-native
python
def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions]=None, dynamic_tags_json: Optional[pulumi.Input[str]]=None, is_push_enabled: Optional[pulumi.Input[bool]]=None, kind: Optional[pulumi.Input[str]]=None, name: Optional[pulumi.Input[str]]=None, resource_group_name: Optional[pulumi.Input[str]]=None, slot: Optional[pulumi.Input[str]]=None, tag_whitelist_json: Optional[pulumi.Input[str]]=None, tags_requiring_auth: Optional[pulumi.Input[str]]=None, __props__=None, __name__=None, __opts__=None): "\n Push settings for the App.\n\n :param str resource_name: The name of the resource.\n :param pulumi.ResourceOptions opts: Options for the resource.\n :param pulumi.Input[str] dynamic_tags_json: Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint.\n :param pulumi.Input[bool] is_push_enabled: Gets or sets a flag indicating whether the Push endpoint is enabled.\n :param pulumi.Input[str] kind: Kind of resource.\n :param pulumi.Input[str] name: Name of web app.\n :param pulumi.Input[str] resource_group_name: Name of the resource group to which the resource belongs.\n :param pulumi.Input[str] slot: Name of web app slot. If not specified then will default to production slot.\n :param pulumi.Input[str] tag_whitelist_json: Gets or sets a JSON string containing a list of tags that are whitelisted for use by the push registration endpoint.\n :param pulumi.Input[str] tags_requiring_auth: Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint.\n Tags can consist of alphanumeric characters and the following:\n '_', '@', '#', '.', ':', '-'. \n Validation should be performed at the PushRequestHandler.\n " if (__name__ is not None): warnings.warn('explicit use of __name__ is deprecated', DeprecationWarning) resource_name = __name__ if (__opts__ is not None): warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if (opts is None): opts = pulumi.ResourceOptions() if (not isinstance(opts, pulumi.ResourceOptions)): raise TypeError('Expected resource options to be a ResourceOptions instance') if (opts.version is None): opts.version = _utilities.get_version() if (opts.id is None): if (__props__ is not None): raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['dynamic_tags_json'] = dynamic_tags_json if ((is_push_enabled is None) and (not opts.urn)): raise TypeError("Missing required property 'is_push_enabled'") __props__['is_push_enabled'] = is_push_enabled __props__['kind'] = kind if ((name is None) and (not opts.urn)): raise TypeError("Missing required property 'name'") __props__['name'] = name if ((resource_group_name is None) and (not opts.urn)): raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name if ((slot is None) and (not opts.urn)): raise TypeError("Missing required property 'slot'") __props__['slot'] = slot __props__['tag_whitelist_json'] = tag_whitelist_json __props__['tags_requiring_auth'] = tags_requiring_auth __props__['system_data'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_='azure-nextgen:web/v20200901:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/latest:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/latest:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20160801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20160801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20180201:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20180201:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20181101:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20181101:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20190801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20190801:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20200601:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20200601:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-native:web/v20201001:WebAppSitePushSettingsSlot'), pulumi.Alias(type_='azure-nextgen:web/v20201001:WebAppSitePushSettingsSlot')]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(WebAppSitePushSettingsSlot, __self__).__init__('azure-native:web/v20200901:WebAppSitePushSettingsSlot', resource_name, __props__, opts)
@staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'WebAppSitePushSettingsSlot': "\n Get an existing WebAppSitePushSettingsSlot resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n " opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__['dynamic_tags_json'] = None __props__['is_push_enabled'] = None __props__['kind'] = None __props__['name'] = None __props__['system_data'] = None __props__['tag_whitelist_json'] = None __props__['tags_requiring_auth'] = None __props__['type'] = None return WebAppSitePushSettingsSlot(resource_name, opts=opts, __props__=__props__)
-3,682,211,527,570,844,700
Get an existing WebAppSitePushSettingsSlot resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
get
pulumi-bot/pulumi-azure-native
python
@staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions]=None) -> 'WebAppSitePushSettingsSlot': "\n Get an existing WebAppSitePushSettingsSlot resource's state with the given name, id, and optional extra\n properties used to qualify the lookup.\n\n :param str resource_name: The unique name of the resulting resource.\n :param pulumi.Input[str] id: The unique provider ID of the resource to lookup.\n :param pulumi.ResourceOptions opts: Options for the resource.\n " opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__['dynamic_tags_json'] = None __props__['is_push_enabled'] = None __props__['kind'] = None __props__['name'] = None __props__['system_data'] = None __props__['tag_whitelist_json'] = None __props__['tags_requiring_auth'] = None __props__['type'] = None return WebAppSitePushSettingsSlot(resource_name, opts=opts, __props__=__props__)
@property @pulumi.getter(name='dynamicTagsJson') def dynamic_tags_json(self) -> pulumi.Output[Optional[str]]: '\n Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint.\n ' return pulumi.get(self, 'dynamic_tags_json')
2,910,655,291,979,059,000
Gets or sets a JSON string containing a list of dynamic tags that will be evaluated from user claims in the push registration endpoint.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
dynamic_tags_json
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter(name='dynamicTagsJson') def dynamic_tags_json(self) -> pulumi.Output[Optional[str]]: '\n \n ' return pulumi.get(self, 'dynamic_tags_json')
@property @pulumi.getter(name='isPushEnabled') def is_push_enabled(self) -> pulumi.Output[bool]: '\n Gets or sets a flag indicating whether the Push endpoint is enabled.\n ' return pulumi.get(self, 'is_push_enabled')
4,037,824,550,830,096,400
Gets or sets a flag indicating whether the Push endpoint is enabled.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
is_push_enabled
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter(name='isPushEnabled') def is_push_enabled(self) -> pulumi.Output[bool]: '\n \n ' return pulumi.get(self, 'is_push_enabled')
@property @pulumi.getter def kind(self) -> pulumi.Output[Optional[str]]: '\n Kind of resource.\n ' return pulumi.get(self, 'kind')
-1,425,049,396,835,993,600
Kind of resource.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
kind
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter def kind(self) -> pulumi.Output[Optional[str]]: '\n \n ' return pulumi.get(self, 'kind')
@property @pulumi.getter def name(self) -> pulumi.Output[str]: '\n Resource Name.\n ' return pulumi.get(self, 'name')
1,193,115,514,403,237,400
Resource Name.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
name
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter def name(self) -> pulumi.Output[str]: '\n \n ' return pulumi.get(self, 'name')
@property @pulumi.getter(name='systemData') def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: '\n The system metadata relating to this resource.\n ' return pulumi.get(self, 'system_data')
-7,169,214,494,930,004,000
The system metadata relating to this resource.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
system_data
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter(name='systemData') def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: '\n \n ' return pulumi.get(self, 'system_data')
@property @pulumi.getter(name='tagWhitelistJson') def tag_whitelist_json(self) -> pulumi.Output[Optional[str]]: '\n Gets or sets a JSON string containing a list of tags that are whitelisted for use by the push registration endpoint.\n ' return pulumi.get(self, 'tag_whitelist_json')
-212,308,369,731,080,420
Gets or sets a JSON string containing a list of tags that are whitelisted for use by the push registration endpoint.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
tag_whitelist_json
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter(name='tagWhitelistJson') def tag_whitelist_json(self) -> pulumi.Output[Optional[str]]: '\n \n ' return pulumi.get(self, 'tag_whitelist_json')
@property @pulumi.getter(name='tagsRequiringAuth') def tags_requiring_auth(self) -> pulumi.Output[Optional[str]]: "\n Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint.\n Tags can consist of alphanumeric characters and the following:\n '_', '@', '#', '.', ':', '-'. \n Validation should be performed at the PushRequestHandler.\n " return pulumi.get(self, 'tags_requiring_auth')
-2,422,842,160,071,476,000
Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint. Tags can consist of alphanumeric characters and the following: '_', '@', '#', '.', ':', '-'. Validation should be performed at the PushRequestHandler.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
tags_requiring_auth
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter(name='tagsRequiringAuth') def tags_requiring_auth(self) -> pulumi.Output[Optional[str]]: "\n Gets or sets a JSON string containing a list of tags that require user authentication to be used in the push registration endpoint.\n Tags can consist of alphanumeric characters and the following:\n '_', '@', '#', '.', ':', '-'. \n Validation should be performed at the PushRequestHandler.\n " return pulumi.get(self, 'tags_requiring_auth')
@property @pulumi.getter def type(self) -> pulumi.Output[str]: '\n Resource type.\n ' return pulumi.get(self, 'type')
2,132,950,812,122,862,800
Resource type.
sdk/python/pulumi_azure_native/web/v20200901/web_app_site_push_settings_slot.py
type
pulumi-bot/pulumi-azure-native
python
@property @pulumi.getter def type(self) -> pulumi.Output[str]: '\n \n ' return pulumi.get(self, 'type')
def _define_structure(self): '\n Define the main sizers building to build this application.\n ' self.main_sizer = wx.BoxSizer(wx.VERTICAL) self.box_source = wx.StaticBox(self, (- 1), str('Kiessig Thickness Calculator')) self.boxsizer_source = wx.StaticBoxSizer(self.box_source, wx.VERTICAL) self.dq_name_sizer = wx.BoxSizer(wx.HORIZONTAL) self.thickness_size_sizer = wx.BoxSizer(wx.HORIZONTAL) self.hint_sizer = wx.BoxSizer(wx.HORIZONTAL) self.button_sizer = wx.BoxSizer(wx.HORIZONTAL)
4,143,718,605,268,381,000
Define the main sizers building to build this application.
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
_define_structure
andyfaff/sasview
python
def _define_structure(self): '\n \n ' self.main_sizer = wx.BoxSizer(wx.VERTICAL) self.box_source = wx.StaticBox(self, (- 1), str('Kiessig Thickness Calculator')) self.boxsizer_source = wx.StaticBoxSizer(self.box_source, wx.VERTICAL) self.dq_name_sizer = wx.BoxSizer(wx.HORIZONTAL) self.thickness_size_sizer = wx.BoxSizer(wx.HORIZONTAL) self.hint_sizer = wx.BoxSizer(wx.HORIZONTAL) self.button_sizer = wx.BoxSizer(wx.HORIZONTAL)
def _layout_dq_name(self): '\n Fill the sizer containing dq name\n ' dq_value = str(self.kiessig.get_deltaq()) dq_unit_txt = wx.StaticText(self, (- 1), '[1/A]') dq_name_txt = wx.StaticText(self, (- 1), 'Kiessig Fringe Width (Delta Q): ') self.dq_name_tcl = InputTextCtrl(self, (- 1), size=(_BOX_WIDTH, (- 1))) dq_hint = 'Type the Kiessig Fringe Width (Delta Q)' self.dq_name_tcl.SetValue(dq_value) self.dq_name_tcl.SetToolTipString(dq_hint) id = wx.NewId() self.compute_button = wx.Button(self, id, 'Compute') hint_on_compute = 'Compute the diameter/thickness in the real space.' self.compute_button.SetToolTipString(hint_on_compute) self.Bind(wx.EVT_BUTTON, self.on_compute, id=id) self.dq_name_sizer.AddMany([(dq_name_txt, 0, wx.LEFT, 15), (self.dq_name_tcl, 0, wx.LEFT, 15), (dq_unit_txt, 0, wx.LEFT, 10), (self.compute_button, 0, wx.LEFT, 30)])
1,429,371,075,089,788,000
Fill the sizer containing dq name
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
_layout_dq_name
andyfaff/sasview
python
def _layout_dq_name(self): '\n \n ' dq_value = str(self.kiessig.get_deltaq()) dq_unit_txt = wx.StaticText(self, (- 1), '[1/A]') dq_name_txt = wx.StaticText(self, (- 1), 'Kiessig Fringe Width (Delta Q): ') self.dq_name_tcl = InputTextCtrl(self, (- 1), size=(_BOX_WIDTH, (- 1))) dq_hint = 'Type the Kiessig Fringe Width (Delta Q)' self.dq_name_tcl.SetValue(dq_value) self.dq_name_tcl.SetToolTipString(dq_hint) id = wx.NewId() self.compute_button = wx.Button(self, id, 'Compute') hint_on_compute = 'Compute the diameter/thickness in the real space.' self.compute_button.SetToolTipString(hint_on_compute) self.Bind(wx.EVT_BUTTON, self.on_compute, id=id) self.dq_name_sizer.AddMany([(dq_name_txt, 0, wx.LEFT, 15), (self.dq_name_tcl, 0, wx.LEFT, 15), (dq_unit_txt, 0, wx.LEFT, 10), (self.compute_button, 0, wx.LEFT, 30)])
def _layout_thickness_size(self): '\n Fill the sizer containing thickness information\n ' thick_unit = (('[' + self.kiessig.get_thickness_unit()) + ']') thickness_size_txt = wx.StaticText(self, (- 1), 'Thickness (or Diameter): ') self.thickness_size_tcl = OutputTextCtrl(self, (- 1), size=(_BOX_WIDTH, (- 1))) thickness_size_hint = ' Estimated Size in Real Space' self.thickness_size_tcl.SetToolTipString(thickness_size_hint) thickness_size_unit_txt = wx.StaticText(self, (- 1), thick_unit) self.thickness_size_sizer.AddMany([(thickness_size_txt, 0, wx.LEFT, 15), (self.thickness_size_tcl, 0, wx.LEFT, 15), (thickness_size_unit_txt, 0, wx.LEFT, 10)])
3,358,032,383,524,434,400
Fill the sizer containing thickness information
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
_layout_thickness_size
andyfaff/sasview
python
def _layout_thickness_size(self): '\n \n ' thick_unit = (('[' + self.kiessig.get_thickness_unit()) + ']') thickness_size_txt = wx.StaticText(self, (- 1), 'Thickness (or Diameter): ') self.thickness_size_tcl = OutputTextCtrl(self, (- 1), size=(_BOX_WIDTH, (- 1))) thickness_size_hint = ' Estimated Size in Real Space' self.thickness_size_tcl.SetToolTipString(thickness_size_hint) thickness_size_unit_txt = wx.StaticText(self, (- 1), thick_unit) self.thickness_size_sizer.AddMany([(thickness_size_txt, 0, wx.LEFT, 15), (self.thickness_size_tcl, 0, wx.LEFT, 15), (thickness_size_unit_txt, 0, wx.LEFT, 10)])
def _layout_hint(self): '\n Fill the sizer containing hint \n ' hint_msg = 'This tool is to approximately estimate ' hint_msg += 'the thickness of a layer' hint_msg += ' or the diameter of particles\n ' hint_msg += 'from the Kiessig fringe width in SAS/NR data.' hint_msg += '' self.hint_txt = wx.StaticText(self, (- 1), hint_msg) self.hint_sizer.AddMany([(self.hint_txt, 0, wx.LEFT, 15)])
-8,116,110,328,013,586,000
Fill the sizer containing hint
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
_layout_hint
andyfaff/sasview
python
def _layout_hint(self): '\n \n ' hint_msg = 'This tool is to approximately estimate ' hint_msg += 'the thickness of a layer' hint_msg += ' or the diameter of particles\n ' hint_msg += 'from the Kiessig fringe width in SAS/NR data.' hint_msg += self.hint_txt = wx.StaticText(self, (- 1), hint_msg) self.hint_sizer.AddMany([(self.hint_txt, 0, wx.LEFT, 15)])
def _layout_button(self): '\n Do the layout for the button widgets\n ' id = wx.NewId() self.bt_help = wx.Button(self, id, 'HELP') self.bt_help.Bind(wx.EVT_BUTTON, self.on_help) self.bt_help.SetToolTipString('Help using the Kiessig fringe calculator.') self.bt_close = wx.Button(self, wx.ID_CANCEL, 'Close') self.bt_close.Bind(wx.EVT_BUTTON, self.on_close) self.bt_close.SetToolTipString('Close this window.') self.button_sizer.AddMany([(self.bt_help, 0, wx.LEFT, 260), (self.bt_close, 0, wx.LEFT, 20)])
-6,821,566,193,316,706,000
Do the layout for the button widgets
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
_layout_button
andyfaff/sasview
python
def _layout_button(self): '\n \n ' id = wx.NewId() self.bt_help = wx.Button(self, id, 'HELP') self.bt_help.Bind(wx.EVT_BUTTON, self.on_help) self.bt_help.SetToolTipString('Help using the Kiessig fringe calculator.') self.bt_close = wx.Button(self, wx.ID_CANCEL, 'Close') self.bt_close.Bind(wx.EVT_BUTTON, self.on_close) self.bt_close.SetToolTipString('Close this window.') self.button_sizer.AddMany([(self.bt_help, 0, wx.LEFT, 260), (self.bt_close, 0, wx.LEFT, 20)])
def _do_layout(self): '\n Draw window content\n ' self._define_structure() self._layout_dq_name() self._layout_thickness_size() self._layout_hint() self._layout_button() self.boxsizer_source.AddMany([(self.dq_name_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5), (self.thickness_size_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5), (self.hint_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5)]) self.main_sizer.AddMany([(self.boxsizer_source, 0, wx.ALL, 10), (self.button_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5)]) self.SetSizer(self.main_sizer) self.SetAutoLayout(True)
-3,224,533,211,146,210,300
Draw window content
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
_do_layout
andyfaff/sasview
python
def _do_layout(self): '\n \n ' self._define_structure() self._layout_dq_name() self._layout_thickness_size() self._layout_hint() self._layout_button() self.boxsizer_source.AddMany([(self.dq_name_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5), (self.thickness_size_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5), (self.hint_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5)]) self.main_sizer.AddMany([(self.boxsizer_source, 0, wx.ALL, 10), (self.button_sizer, 0, ((wx.EXPAND | wx.TOP) | wx.BOTTOM), 5)]) self.SetSizer(self.main_sizer) self.SetAutoLayout(True)
def on_help(self, event): '\n Bring up the Kiessig fringe calculator Documentation whenever\n the HELP button is clicked.\n Calls DocumentationWindow with the path of the location within the\n documentation tree (after /doc/ ....". Note that when using old\n versions of Wx (before 2.9) and thus not the release version of\n installers, the help comes up at the top level of the file as\n webbrowser does not pass anything past the # to the browser when it is\n running "file:///...."\n\n :param evt: Triggers on clicking the help button\n ' _TreeLocation = 'user/sasgui/perspectives/calculator/' _TreeLocation += 'kiessig_calculator_help.html' _doc_viewer = DocumentationWindow(self, (- 1), _TreeLocation, '', 'Density/Volume Calculator Help')
6,930,186,287,438,744,000
Bring up the Kiessig fringe calculator Documentation whenever the HELP button is clicked. Calls DocumentationWindow with the path of the location within the documentation tree (after /doc/ ....". Note that when using old versions of Wx (before 2.9) and thus not the release version of installers, the help comes up at the top level of the file as webbrowser does not pass anything past the # to the browser when it is running "file:///...." :param evt: Triggers on clicking the help button
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
on_help
andyfaff/sasview
python
def on_help(self, event): '\n Bring up the Kiessig fringe calculator Documentation whenever\n the HELP button is clicked.\n Calls DocumentationWindow with the path of the location within the\n documentation tree (after /doc/ ....". Note that when using old\n versions of Wx (before 2.9) and thus not the release version of\n installers, the help comes up at the top level of the file as\n webbrowser does not pass anything past the # to the browser when it is\n running "file:///...."\n\n :param evt: Triggers on clicking the help button\n ' _TreeLocation = 'user/sasgui/perspectives/calculator/' _TreeLocation += 'kiessig_calculator_help.html' _doc_viewer = DocumentationWindow(self, (- 1), _TreeLocation, , 'Density/Volume Calculator Help')
def on_close(self, event): '\n close the window containing this panel\n ' self.parent.Close() if (event is not None): event.Skip()
-8,261,816,066,434,704,000
close the window containing this panel
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
on_close
andyfaff/sasview
python
def on_close(self, event): '\n \n ' self.parent.Close() if (event is not None): event.Skip()
def on_compute(self, event): '\n Execute the computation of thickness\n ' if (event is not None): event.Skip() dq = self.dq_name_tcl.GetValue() self.kiessig.set_deltaq(dq) output = self.kiessig.compute_thickness() thickness = self.format_number(output) self.thickness_size_tcl.SetValue(str(thickness))
4,238,333,682,032,974,300
Execute the computation of thickness
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
on_compute
andyfaff/sasview
python
def on_compute(self, event): '\n \n ' if (event is not None): event.Skip() dq = self.dq_name_tcl.GetValue() self.kiessig.set_deltaq(dq) output = self.kiessig.compute_thickness() thickness = self.format_number(output) self.thickness_size_tcl.SetValue(str(thickness))
def format_number(self, value=None): '\n Return a float in a standardized, human-readable formatted string\n ' try: value = float(value) except: output = None return output output = ('%-7.4g' % value) return output.lstrip().rstrip()
3,595,117,808,034,005,000
Return a float in a standardized, human-readable formatted string
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
format_number
andyfaff/sasview
python
def format_number(self, value=None): '\n \n ' try: value = float(value) except: output = None return output output = ('%-7.4g' % value) return output.lstrip().rstrip()
def _onparamEnter(self, event=None): '\n On Text_enter_callback, perform compute\n ' self.on_compute(event)
-6,448,870,365,049,814,000
On Text_enter_callback, perform compute
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
_onparamEnter
andyfaff/sasview
python
def _onparamEnter(self, event=None): '\n \n ' self.on_compute(event)
def on_close(self, event): '\n Close event\n ' if (self.manager is not None): self.manager.kiessig_frame = None self.Destroy()
-8,310,142,565,720,282,000
Close event
src/sas/sasgui/perspectives/calculator/kiessig_calculator_panel.py
on_close
andyfaff/sasview
python
def on_close(self, event): '\n \n ' if (self.manager is not None): self.manager.kiessig_frame = None self.Destroy()
@classmethod def find_project_root_directory(cls, current_directory: Optional[Path]) -> Optional[Path]: "\n Given a directory (with ``None`` implying the current directory) assumed to be at or under this project's root,\n find the project root directory.\n This implementation attempts to find a directory having both a ``.git/`` child directory and a ``.env`` file.\n Parameters\n ----------\n current_directory\n Returns\n -------\n Optional[Path]\n The project root directory, or ``None`` if it fails to find it.\n " if (not current_directory): current_directory = TestGetGithub._current_dir abs_root = Path(current_directory.absolute().root) while (current_directory.absolute() != abs_root): if (not current_directory.is_dir()): current_directory = current_directory.parent continue git_sub_dir = current_directory.joinpath('.git') child_env_file = current_directory.joinpath('config.yaml') if (git_sub_dir.exists() and git_sub_dir.is_dir() and child_env_file.exists() and child_env_file.is_file()): return current_directory current_directory = current_directory.parent return None
-2,530,477,784,102,690,000
Given a directory (with ``None`` implying the current directory) assumed to be at or under this project's root, find the project root directory. This implementation attempts to find a directory having both a ``.git/`` child directory and a ``.env`` file. Parameters ---------- current_directory Returns ------- Optional[Path] The project root directory, or ``None`` if it fails to find it.
github_archive/test/test_get_github.py
find_project_root_directory
hellkite500/github_archive
python
@classmethod def find_project_root_directory(cls, current_directory: Optional[Path]) -> Optional[Path]: "\n Given a directory (with ``None`` implying the current directory) assumed to be at or under this project's root,\n find the project root directory.\n This implementation attempts to find a directory having both a ``.git/`` child directory and a ``.env`` file.\n Parameters\n ----------\n current_directory\n Returns\n -------\n Optional[Path]\n The project root directory, or ``None`` if it fails to find it.\n " if (not current_directory): current_directory = TestGetGithub._current_dir abs_root = Path(current_directory.absolute().root) while (current_directory.absolute() != abs_root): if (not current_directory.is_dir()): current_directory = current_directory.parent continue git_sub_dir = current_directory.joinpath('.git') child_env_file = current_directory.joinpath('config.yaml') if (git_sub_dir.exists() and git_sub_dir.is_dir() and child_env_file.exists() and child_env_file.is_file()): return current_directory current_directory = current_directory.parent return None
@classmethod def load_token(cls): "\n Read an API token from a configuration file, if none found, use '' for no auth\n " token = '' root_dir = cls.find_project_root_directory(None) if (not root_dir): return token config_file = (root_dir / 'config.yaml') if config_file.exists(): with open(config_file) as file: config = yaml.load(file, Loader=yaml.FullLoader) try: token = config['token'] except: print('Unable to load api-token from project root directory config.yaml') return token
7,484,951,432,618,107,000
Read an API token from a configuration file, if none found, use '' for no auth
github_archive/test/test_get_github.py
load_token
hellkite500/github_archive
python
@classmethod def load_token(cls): "\n \n " token = root_dir = cls.find_project_root_directory(None) if (not root_dir): return token config_file = (root_dir / 'config.yaml') if config_file.exists(): with open(config_file) as file: config = yaml.load(file, Loader=yaml.FullLoader) try: token = config['token'] except: print('Unable to load api-token from project root directory config.yaml') return token
def test_get_repo_meta(self): '\n Test the archive_repo function to ensure all meta data is properly captured\n ' meta = get_repo_meta(self.repo, self.time, TestGetGithub._current_dir) self.assertIsNotNone(meta) self.assertTrue(len(meta), 6) pattern = '{repo}_{name}_{time}.json'.format(repo=self.repo_string, name='{name}', time=self.time) self.assertEqual(meta[0].name, pattern.format(name='comments')) self.assertEqual(meta[1].name, pattern.format(name='issues')) self.assertEqual(meta[2].name, pattern.format(name='issue_comments')) self.assertEqual(meta[3].name, pattern.format(name='pulls')) self.assertEqual(meta[4].name, pattern.format(name='pulls_comments')) self.assertEqual(meta[5].name, pattern.format(name='pulls_review_comments')) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='comments')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='issues')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='issue_comments')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='pulls')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='pulls_comments')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='pulls_review_comments')).exists())
8,121,341,174,717,497,000
Test the archive_repo function to ensure all meta data is properly captured
github_archive/test/test_get_github.py
test_get_repo_meta
hellkite500/github_archive
python
def test_get_repo_meta(self): '\n \n ' meta = get_repo_meta(self.repo, self.time, TestGetGithub._current_dir) self.assertIsNotNone(meta) self.assertTrue(len(meta), 6) pattern = '{repo}_{name}_{time}.json'.format(repo=self.repo_string, name='{name}', time=self.time) self.assertEqual(meta[0].name, pattern.format(name='comments')) self.assertEqual(meta[1].name, pattern.format(name='issues')) self.assertEqual(meta[2].name, pattern.format(name='issue_comments')) self.assertEqual(meta[3].name, pattern.format(name='pulls')) self.assertEqual(meta[4].name, pattern.format(name='pulls_comments')) self.assertEqual(meta[5].name, pattern.format(name='pulls_review_comments')) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='comments')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='issues')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='issue_comments')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='pulls')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='pulls_comments')).exists()) self.assertTrue((TestGetGithub._current_dir / pattern.format(name='pulls_review_comments')).exists())
def test_clone_and_archive(self): '\n Test the clone functionality\n ' self.assertFalse(self.repo.has_wiki) clone_url = self.repo.clone_url archive_name = clone_and_archive(self.repo_string, clone_url, self.time, TestGetGithub._current_dir, []) name = '{repo}_github_archive_{time}.tar.gz'.format(repo=self.repo_string, time=self.time) self.assertEqual(archive_name.name, name) self.assertTrue((TestGetGithub._current_dir / name).exists())
-257,225,097,966,887,840
Test the clone functionality
github_archive/test/test_get_github.py
test_clone_and_archive
hellkite500/github_archive
python
def test_clone_and_archive(self): '\n \n ' self.assertFalse(self.repo.has_wiki) clone_url = self.repo.clone_url archive_name = clone_and_archive(self.repo_string, clone_url, self.time, TestGetGithub._current_dir, []) name = '{repo}_github_archive_{time}.tar.gz'.format(repo=self.repo_string, time=self.time) self.assertEqual(archive_name.name, name) self.assertTrue((TestGetGithub._current_dir / name).exists())
def test_clone_and_archive_1(self): '\n Test cloning a repo with a wiki\n ' self.assertTrue(self.wiki_repo.has_wiki) wiki_url = (self.wiki_repo.clone_url[:(- 3)] + 'wiki.git') clone_url = self.wiki_repo.clone_url archive_name = clone_and_archive(self.repo_w_wiki, clone_url, self.time, TestGetGithub._current_dir, [], wiki_url) name = '{repo}_github_archive_{time}.tar.gz'.format(repo=self.repo_w_wiki, time=self.time) self.assertEqual(archive_name.name, name) self.assertTrue((TestGetGithub._current_dir / name).exists())
-7,822,465,264,797,826,000
Test cloning a repo with a wiki
github_archive/test/test_get_github.py
test_clone_and_archive_1
hellkite500/github_archive
python
def test_clone_and_archive_1(self): '\n \n ' self.assertTrue(self.wiki_repo.has_wiki) wiki_url = (self.wiki_repo.clone_url[:(- 3)] + 'wiki.git') clone_url = self.wiki_repo.clone_url archive_name = clone_and_archive(self.repo_w_wiki, clone_url, self.time, TestGetGithub._current_dir, [], wiki_url) name = '{repo}_github_archive_{time}.tar.gz'.format(repo=self.repo_w_wiki, time=self.time) self.assertEqual(archive_name.name, name) self.assertTrue((TestGetGithub._current_dir / name).exists())
def policy_v0(): 'Autoaugment policy that was used in AutoAugment Detection Paper.' policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('Color', 1.0, 6)]] return policy
8,460,943,867,710,150,000
Autoaugment policy that was used in AutoAugment Detection Paper.
efficientdet/aug/autoaugment.py
policy_v0
datawowio/automl
python
def policy_v0(): policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('Color', 1.0, 6)]] return policy
def policy_v1(): 'Autoaugment policy that was used in AutoAugment Detection Paper.' policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('Color', 1.0, 6)], [('Color', 0.0, 0), ('ShearX_Only_BBoxes', 0.8, 4)], [('ShearY_Only_BBoxes', 0.8, 2), ('Flip_Only_BBoxes', 0.0, 10)], [('Equalize', 0.6, 10), ('TranslateX_BBox', 0.2, 2)], [('Color', 1.0, 10), ('TranslateY_Only_BBoxes', 0.4, 6)], [('Rotate_BBox', 0.8, 10), ('Contrast', 0.0, 10)], [('Cutout', 0.2, 2), ('Brightness', 0.8, 10)], [('Color', 1.0, 6), ('Equalize', 1.0, 2)], [('Cutout_Only_BBoxes', 0.4, 6), ('TranslateY_Only_BBoxes', 0.8, 2)], [('Color', 0.2, 8), ('Rotate_BBox', 0.8, 10)], [('Sharpness', 0.4, 4), ('TranslateY_Only_BBoxes', 0.0, 4)], [('Sharpness', 1.0, 4), ('SolarizeAdd', 0.4, 4)], [('Rotate_BBox', 1.0, 8), ('Sharpness', 0.2, 8)], [('ShearY_BBox', 0.6, 10), ('Equalize_Only_BBoxes', 0.6, 8)], [('ShearX_BBox', 0.2, 6), ('TranslateY_Only_BBoxes', 0.2, 10)], [('SolarizeAdd', 0.6, 8), ('Brightness', 0.8, 10)]] return policy
-8,715,538,783,788,513,000
Autoaugment policy that was used in AutoAugment Detection Paper.
efficientdet/aug/autoaugment.py
policy_v1
datawowio/automl
python
def policy_v1(): policy = [[('TranslateX_BBox', 0.6, 4), ('Equalize', 0.8, 10)], [('TranslateY_Only_BBoxes', 0.2, 2), ('Cutout', 0.8, 8)], [('Sharpness', 0.0, 8), ('ShearX_BBox', 0.4, 0)], [('ShearY_BBox', 1.0, 2), ('TranslateY_Only_BBoxes', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('Color', 1.0, 6)], [('Color', 0.0, 0), ('ShearX_Only_BBoxes', 0.8, 4)], [('ShearY_Only_BBoxes', 0.8, 2), ('Flip_Only_BBoxes', 0.0, 10)], [('Equalize', 0.6, 10), ('TranslateX_BBox', 0.2, 2)], [('Color', 1.0, 10), ('TranslateY_Only_BBoxes', 0.4, 6)], [('Rotate_BBox', 0.8, 10), ('Contrast', 0.0, 10)], [('Cutout', 0.2, 2), ('Brightness', 0.8, 10)], [('Color', 1.0, 6), ('Equalize', 1.0, 2)], [('Cutout_Only_BBoxes', 0.4, 6), ('TranslateY_Only_BBoxes', 0.8, 2)], [('Color', 0.2, 8), ('Rotate_BBox', 0.8, 10)], [('Sharpness', 0.4, 4), ('TranslateY_Only_BBoxes', 0.0, 4)], [('Sharpness', 1.0, 4), ('SolarizeAdd', 0.4, 4)], [('Rotate_BBox', 1.0, 8), ('Sharpness', 0.2, 8)], [('ShearY_BBox', 0.6, 10), ('Equalize_Only_BBoxes', 0.6, 8)], [('ShearX_BBox', 0.2, 6), ('TranslateY_Only_BBoxes', 0.2, 10)], [('SolarizeAdd', 0.6, 8), ('Brightness', 0.8, 10)]] return policy
def policy_vtest(): 'Autoaugment test policy for debugging.' policy = [[('TranslateX_BBox', 1.0, 4), ('Equalize', 1.0, 10)]] return policy
-9,018,532,416,153,881,000
Autoaugment test policy for debugging.
efficientdet/aug/autoaugment.py
policy_vtest
datawowio/automl
python
def policy_vtest(): policy = [[('TranslateX_BBox', 1.0, 4), ('Equalize', 1.0, 10)]] return policy
def policy_v2(): 'Additional policy that performs well on object detection.' policy = [[('Color', 0.0, 6), ('Cutout', 0.6, 8), ('Sharpness', 0.4, 8)], [('Rotate_BBox', 0.4, 8), ('Sharpness', 0.4, 2), ('Rotate_BBox', 0.8, 10)], [('TranslateY_BBox', 1.0, 8), ('AutoContrast', 0.8, 2)], [('AutoContrast', 0.4, 6), ('ShearX_BBox', 0.8, 8), ('Brightness', 0.0, 10)], [('SolarizeAdd', 0.2, 6), ('Contrast', 0.0, 10), ('AutoContrast', 0.6, 0)], [('Cutout', 0.2, 0), ('Solarize', 0.8, 8), ('Color', 1.0, 4)], [('TranslateY_BBox', 0.0, 4), ('Equalize', 0.6, 8), ('Solarize', 0.0, 10)], [('TranslateY_BBox', 0.2, 2), ('ShearY_BBox', 0.8, 8), ('Rotate_BBox', 0.8, 8)], [('Cutout', 0.8, 8), ('Brightness', 0.8, 8), ('Cutout', 0.2, 2)], [('Color', 0.8, 4), ('TranslateY_BBox', 1.0, 6), ('Rotate_BBox', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('BBox_Cutout', 1.0, 4), ('Cutout', 0.2, 8)], [('Rotate_BBox', 0.0, 0), ('Equalize', 0.6, 6), ('ShearY_BBox', 0.6, 8)], [('Brightness', 0.8, 8), ('AutoContrast', 0.4, 2), ('Brightness', 0.2, 2)], [('TranslateY_BBox', 0.4, 8), ('Solarize', 0.4, 6), ('SolarizeAdd', 0.2, 10)], [('Contrast', 1.0, 10), ('SolarizeAdd', 0.2, 8), ('Equalize', 0.2, 4)]] return policy
8,499,406,954,455,301,000
Additional policy that performs well on object detection.
efficientdet/aug/autoaugment.py
policy_v2
datawowio/automl
python
def policy_v2(): policy = [[('Color', 0.0, 6), ('Cutout', 0.6, 8), ('Sharpness', 0.4, 8)], [('Rotate_BBox', 0.4, 8), ('Sharpness', 0.4, 2), ('Rotate_BBox', 0.8, 10)], [('TranslateY_BBox', 1.0, 8), ('AutoContrast', 0.8, 2)], [('AutoContrast', 0.4, 6), ('ShearX_BBox', 0.8, 8), ('Brightness', 0.0, 10)], [('SolarizeAdd', 0.2, 6), ('Contrast', 0.0, 10), ('AutoContrast', 0.6, 0)], [('Cutout', 0.2, 0), ('Solarize', 0.8, 8), ('Color', 1.0, 4)], [('TranslateY_BBox', 0.0, 4), ('Equalize', 0.6, 8), ('Solarize', 0.0, 10)], [('TranslateY_BBox', 0.2, 2), ('ShearY_BBox', 0.8, 8), ('Rotate_BBox', 0.8, 8)], [('Cutout', 0.8, 8), ('Brightness', 0.8, 8), ('Cutout', 0.2, 2)], [('Color', 0.8, 4), ('TranslateY_BBox', 1.0, 6), ('Rotate_BBox', 0.6, 6)], [('Rotate_BBox', 0.6, 10), ('BBox_Cutout', 1.0, 4), ('Cutout', 0.2, 8)], [('Rotate_BBox', 0.0, 0), ('Equalize', 0.6, 6), ('ShearY_BBox', 0.6, 8)], [('Brightness', 0.8, 8), ('AutoContrast', 0.4, 2), ('Brightness', 0.2, 2)], [('TranslateY_BBox', 0.4, 8), ('Solarize', 0.4, 6), ('SolarizeAdd', 0.2, 10)], [('Contrast', 1.0, 10), ('SolarizeAdd', 0.2, 8), ('Equalize', 0.2, 4)]] return policy
def policy_v3(): '"Additional policy that performs well on object detection.' policy = [[('Posterize', 0.8, 2), ('TranslateX_BBox', 1.0, 8)], [('BBox_Cutout', 0.2, 10), ('Sharpness', 1.0, 8)], [('Rotate_BBox', 0.6, 8), ('Rotate_BBox', 0.8, 10)], [('Equalize', 0.8, 10), ('AutoContrast', 0.2, 10)], [('SolarizeAdd', 0.2, 2), ('TranslateY_BBox', 0.2, 8)], [('Sharpness', 0.0, 2), ('Color', 0.4, 8)], [('Equalize', 1.0, 8), ('TranslateY_BBox', 1.0, 8)], [('Posterize', 0.6, 2), ('Rotate_BBox', 0.0, 10)], [('AutoContrast', 0.6, 0), ('Rotate_BBox', 1.0, 6)], [('Brightness', 1.0, 2), ('TranslateY_BBox', 1.0, 6)], [('Contrast', 0.0, 2), ('ShearY_BBox', 0.8, 0)], [('AutoContrast', 0.8, 10), ('Contrast', 0.2, 10)], [('SolarizeAdd', 0.8, 6), ('Equalize', 0.8, 8)]] return policy
-2,631,217,006,608,270,000
"Additional policy that performs well on object detection.
efficientdet/aug/autoaugment.py
policy_v3
datawowio/automl
python
def policy_v3(): policy = [[('Posterize', 0.8, 2), ('TranslateX_BBox', 1.0, 8)], [('BBox_Cutout', 0.2, 10), ('Sharpness', 1.0, 8)], [('Rotate_BBox', 0.6, 8), ('Rotate_BBox', 0.8, 10)], [('Equalize', 0.8, 10), ('AutoContrast', 0.2, 10)], [('SolarizeAdd', 0.2, 2), ('TranslateY_BBox', 0.2, 8)], [('Sharpness', 0.0, 2), ('Color', 0.4, 8)], [('Equalize', 1.0, 8), ('TranslateY_BBox', 1.0, 8)], [('Posterize', 0.6, 2), ('Rotate_BBox', 0.0, 10)], [('AutoContrast', 0.6, 0), ('Rotate_BBox', 1.0, 6)], [('Brightness', 1.0, 2), ('TranslateY_BBox', 1.0, 6)], [('Contrast', 0.0, 2), ('ShearY_BBox', 0.8, 0)], [('AutoContrast', 0.8, 10), ('Contrast', 0.2, 10)], [('SolarizeAdd', 0.8, 6), ('Equalize', 0.8, 8)]] return policy
def blend(image1, image2, factor): 'Blend image1 and image2 using \'factor\'.\n\n Factor can be above 0.0. A value of 0.0 means only image1 is used.\n A value of 1.0 means only image2 is used. A value between 0.0 and\n 1.0 means we linearly interpolate the pixel values between the two\n images. A value greater than 1.0 "extrapolates" the difference\n between the two pixel values, and we clip the results to values\n between 0 and 255.\n\n Args:\n image1: An image Tensor of type uint8.\n image2: An image Tensor of type uint8.\n factor: A floating point value above 0.0.\n\n Returns:\n A blended image Tensor of type uint8.\n ' if (factor == 0.0): return tf.convert_to_tensor(image1) if (factor == 1.0): return tf.convert_to_tensor(image2) image1 = tf.to_float(image1) image2 = tf.to_float(image2) difference = (image2 - image1) scaled = (factor * difference) temp = (tf.to_float(image1) + scaled) if ((factor > 0.0) and (factor < 1.0)): return tf.cast(temp, tf.uint8) return tf.cast(tf.clip_by_value(temp, 0.0, 255.0), tf.uint8)
-7,071,893,540,884,844,000
Blend image1 and image2 using 'factor'. Factor can be above 0.0. A value of 0.0 means only image1 is used. A value of 1.0 means only image2 is used. A value between 0.0 and 1.0 means we linearly interpolate the pixel values between the two images. A value greater than 1.0 "extrapolates" the difference between the two pixel values, and we clip the results to values between 0 and 255. Args: image1: An image Tensor of type uint8. image2: An image Tensor of type uint8. factor: A floating point value above 0.0. Returns: A blended image Tensor of type uint8.
efficientdet/aug/autoaugment.py
blend
datawowio/automl
python
def blend(image1, image2, factor): 'Blend image1 and image2 using \'factor\'.\n\n Factor can be above 0.0. A value of 0.0 means only image1 is used.\n A value of 1.0 means only image2 is used. A value between 0.0 and\n 1.0 means we linearly interpolate the pixel values between the two\n images. A value greater than 1.0 "extrapolates" the difference\n between the two pixel values, and we clip the results to values\n between 0 and 255.\n\n Args:\n image1: An image Tensor of type uint8.\n image2: An image Tensor of type uint8.\n factor: A floating point value above 0.0.\n\n Returns:\n A blended image Tensor of type uint8.\n ' if (factor == 0.0): return tf.convert_to_tensor(image1) if (factor == 1.0): return tf.convert_to_tensor(image2) image1 = tf.to_float(image1) image2 = tf.to_float(image2) difference = (image2 - image1) scaled = (factor * difference) temp = (tf.to_float(image1) + scaled) if ((factor > 0.0) and (factor < 1.0)): return tf.cast(temp, tf.uint8) return tf.cast(tf.clip_by_value(temp, 0.0, 255.0), tf.uint8)
def cutout(image, pad_size, replace=0): 'Apply cutout (https://arxiv.org/abs/1708.04552) to image.\n\n This operation applies a (2*pad_size x 2*pad_size) mask of zeros to\n a random location within `img`. The pixel values filled in will be of the\n value `replace`. The located where the mask will be applied is randomly\n chosen uniformly over the whole image.\n\n Args:\n image: An image Tensor of type uint8.\n pad_size: Specifies how big the zero mask that will be generated is that\n is applied to the image. The mask will be of size\n (2*pad_size x 2*pad_size).\n replace: What pixel value to fill in the image in the area that has\n the cutout mask applied to it.\n\n Returns:\n An image Tensor that is of type uint8.\n ' image_height = tf.maximum(tf.shape(image)[0], 10) image_width = tf.maximum(tf.shape(image)[1], 10) cutout_center_height = tf.random_uniform(shape=[], minval=0, maxval=image_height, dtype=tf.int32) cutout_center_width = tf.random_uniform(shape=[], minval=0, maxval=image_width, dtype=tf.int32) lower_pad = tf.maximum(0, (cutout_center_height - pad_size)) upper_pad = tf.maximum(0, ((image_height - cutout_center_height) - pad_size)) left_pad = tf.maximum(0, (cutout_center_width - pad_size)) right_pad = tf.maximum(0, ((image_width - cutout_center_width) - pad_size)) cutout_shape = [(image_height - (lower_pad + upper_pad)), (image_width - (left_pad + right_pad))] padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]] mask = tf.pad(tf.zeros(cutout_shape, dtype=image.dtype), padding_dims, constant_values=1) mask = tf.expand_dims(mask, (- 1)) mask = tf.tile(mask, [1, 1, 3]) image = tf.where(tf.equal(mask, 0), (tf.ones_like(image, dtype=image.dtype) * replace), image) return image
-4,239,343,247,505,422,000
Apply cutout (https://arxiv.org/abs/1708.04552) to image. This operation applies a (2*pad_size x 2*pad_size) mask of zeros to a random location within `img`. The pixel values filled in will be of the value `replace`. The located where the mask will be applied is randomly chosen uniformly over the whole image. Args: image: An image Tensor of type uint8. pad_size: Specifies how big the zero mask that will be generated is that is applied to the image. The mask will be of size (2*pad_size x 2*pad_size). replace: What pixel value to fill in the image in the area that has the cutout mask applied to it. Returns: An image Tensor that is of type uint8.
efficientdet/aug/autoaugment.py
cutout
datawowio/automl
python
def cutout(image, pad_size, replace=0): 'Apply cutout (https://arxiv.org/abs/1708.04552) to image.\n\n This operation applies a (2*pad_size x 2*pad_size) mask of zeros to\n a random location within `img`. The pixel values filled in will be of the\n value `replace`. The located where the mask will be applied is randomly\n chosen uniformly over the whole image.\n\n Args:\n image: An image Tensor of type uint8.\n pad_size: Specifies how big the zero mask that will be generated is that\n is applied to the image. The mask will be of size\n (2*pad_size x 2*pad_size).\n replace: What pixel value to fill in the image in the area that has\n the cutout mask applied to it.\n\n Returns:\n An image Tensor that is of type uint8.\n ' image_height = tf.maximum(tf.shape(image)[0], 10) image_width = tf.maximum(tf.shape(image)[1], 10) cutout_center_height = tf.random_uniform(shape=[], minval=0, maxval=image_height, dtype=tf.int32) cutout_center_width = tf.random_uniform(shape=[], minval=0, maxval=image_width, dtype=tf.int32) lower_pad = tf.maximum(0, (cutout_center_height - pad_size)) upper_pad = tf.maximum(0, ((image_height - cutout_center_height) - pad_size)) left_pad = tf.maximum(0, (cutout_center_width - pad_size)) right_pad = tf.maximum(0, ((image_width - cutout_center_width) - pad_size)) cutout_shape = [(image_height - (lower_pad + upper_pad)), (image_width - (left_pad + right_pad))] padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]] mask = tf.pad(tf.zeros(cutout_shape, dtype=image.dtype), padding_dims, constant_values=1) mask = tf.expand_dims(mask, (- 1)) mask = tf.tile(mask, [1, 1, 3]) image = tf.where(tf.equal(mask, 0), (tf.ones_like(image, dtype=image.dtype) * replace), image) return image
def color(image, factor): 'Equivalent of PIL Color.' degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image)) return blend(degenerate, image, factor)
2,872,861,326,192,433,000
Equivalent of PIL Color.
efficientdet/aug/autoaugment.py
color
datawowio/automl
python
def color(image, factor): degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image)) return blend(degenerate, image, factor)
def contrast(image, factor): 'Equivalent of PIL Contrast.' degenerate = tf.image.rgb_to_grayscale(image) degenerate = tf.cast(degenerate, tf.int32) mean = tf.reduce_mean(tf.cast(degenerate, tf.float32)) degenerate = (tf.ones_like(degenerate, dtype=tf.float32) * mean) degenerate = tf.clip_by_value(degenerate, 0.0, 255.0) degenerate = tf.image.grayscale_to_rgb(tf.cast(degenerate, tf.uint8)) return blend(degenerate, image, factor)
3,150,907,722,058,286,000
Equivalent of PIL Contrast.
efficientdet/aug/autoaugment.py
contrast
datawowio/automl
python
def contrast(image, factor): degenerate = tf.image.rgb_to_grayscale(image) degenerate = tf.cast(degenerate, tf.int32) mean = tf.reduce_mean(tf.cast(degenerate, tf.float32)) degenerate = (tf.ones_like(degenerate, dtype=tf.float32) * mean) degenerate = tf.clip_by_value(degenerate, 0.0, 255.0) degenerate = tf.image.grayscale_to_rgb(tf.cast(degenerate, tf.uint8)) return blend(degenerate, image, factor)
def brightness(image, factor): 'Equivalent of PIL Brightness.' degenerate = tf.zeros_like(image) return blend(degenerate, image, factor)
-5,514,793,971,791,669,000
Equivalent of PIL Brightness.
efficientdet/aug/autoaugment.py
brightness
datawowio/automl
python
def brightness(image, factor): degenerate = tf.zeros_like(image) return blend(degenerate, image, factor)
def posterize(image, bits): 'Equivalent of PIL Posterize.' shift = (8 - bits) return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)
-7,653,707,230,299,955,000
Equivalent of PIL Posterize.
efficientdet/aug/autoaugment.py
posterize
datawowio/automl
python
def posterize(image, bits): shift = (8 - bits) return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)
def rotate(image, degrees, replace): 'Rotates the image by degrees either clockwise or counterclockwise.\n\n Args:\n image: An image Tensor of type uint8.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n be rotated counterclockwise.\n replace: A one or three value 1D tensor to fill empty pixels caused by\n the rotate operation.\n\n Returns:\n The rotated version of image.\n ' degrees_to_radians = (math.pi / 180.0) radians = (degrees * degrees_to_radians) image = image_ops.rotate(wrap(image), radians) return unwrap(image, replace)
9,033,878,547,422,465,000
Rotates the image by degrees either clockwise or counterclockwise. Args: image: An image Tensor of type uint8. degrees: Float, a scalar angle in degrees to rotate all images by. If degrees is positive the image will be rotated clockwise otherwise it will be rotated counterclockwise. replace: A one or three value 1D tensor to fill empty pixels caused by the rotate operation. Returns: The rotated version of image.
efficientdet/aug/autoaugment.py
rotate
datawowio/automl
python
def rotate(image, degrees, replace): 'Rotates the image by degrees either clockwise or counterclockwise.\n\n Args:\n image: An image Tensor of type uint8.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n be rotated counterclockwise.\n replace: A one or three value 1D tensor to fill empty pixels caused by\n the rotate operation.\n\n Returns:\n The rotated version of image.\n ' degrees_to_radians = (math.pi / 180.0) radians = (degrees * degrees_to_radians) image = image_ops.rotate(wrap(image), radians) return unwrap(image, replace)
def random_shift_bbox(image, bbox, pixel_scaling, replace, new_min_bbox_coords=None): 'Move the bbox and the image content to a slightly new random location.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n The potential values for the new min corner of the bbox will be between\n [old_min - pixel_scaling * bbox_height/2,\n old_min - pixel_scaling * bbox_height/2].\n pixel_scaling: A float between 0 and 1 that specifies the pixel range\n that the new bbox location will be sampled from.\n replace: A one or three value 1D tensor to fill empty pixels.\n new_min_bbox_coords: If not None, then this is a tuple that specifies the\n (min_y, min_x) coordinates of the new bbox. Normally this is randomly\n specified, but this allows it to be manually set. The coordinates are\n the absolute coordinates between 0 and image height/width and are int32.\n\n Returns:\n The new image that will have the shifted bbox location in it along with\n the new bbox that contains the new coordinates.\n ' image_height = tf.to_float(tf.maximum(tf.shape(image)[0], 10)) image_width = tf.to_float(tf.maximum(tf.shape(image)[1], 10)) def clip_y(val): return tf.clip_by_value(val, 0, (tf.to_int32(image_height) - 1)) def clip_x(val): return tf.clip_by_value(val, 0, (tf.to_int32(image_width) - 1)) min_y = tf.to_int32((image_height * bbox[0])) min_x = tf.to_int32((image_width * bbox[1])) max_y = clip_y(tf.to_int32((image_height * bbox[2]))) max_x = clip_x(tf.to_int32((image_width * bbox[3]))) (bbox_height, bbox_width) = (((max_y - min_y) + 1), ((max_x - min_x) + 1)) image_height = tf.to_int32(image_height) image_width = tf.to_int32(image_width) minval_y = clip_y((min_y - tf.to_int32(((pixel_scaling * tf.to_float(bbox_height)) / 2.0)))) maxval_y = clip_y((min_y + tf.to_int32(((pixel_scaling * tf.to_float(bbox_height)) / 2.0)))) minval_x = clip_x((min_x - tf.to_int32(((pixel_scaling * tf.to_float(bbox_width)) / 2.0)))) maxval_x = clip_x((min_x + tf.to_int32(((pixel_scaling * tf.to_float(bbox_width)) / 2.0)))) if (new_min_bbox_coords is None): unclipped_new_min_y = tf.random_uniform(shape=[], minval=minval_y, maxval=maxval_y, dtype=tf.int32) unclipped_new_min_x = tf.random_uniform(shape=[], minval=minval_x, maxval=maxval_x, dtype=tf.int32) else: (unclipped_new_min_y, unclipped_new_min_x) = (clip_y(new_min_bbox_coords[0]), clip_x(new_min_bbox_coords[1])) unclipped_new_max_y = ((unclipped_new_min_y + bbox_height) - 1) unclipped_new_max_x = ((unclipped_new_min_x + bbox_width) - 1) (new_min_y, new_min_x, new_max_y, new_max_x) = (clip_y(unclipped_new_min_y), clip_x(unclipped_new_min_x), clip_y(unclipped_new_max_y), clip_x(unclipped_new_max_x)) shifted_min_y = ((new_min_y - unclipped_new_min_y) + min_y) shifted_max_y = (max_y - (unclipped_new_max_y - new_max_y)) shifted_min_x = ((new_min_x - unclipped_new_min_x) + min_x) shifted_max_x = (max_x - (unclipped_new_max_x - new_max_x)) new_bbox = tf.stack([(tf.to_float(new_min_y) / tf.to_float(image_height)), (tf.to_float(new_min_x) / tf.to_float(image_width)), (tf.to_float(new_max_y) / tf.to_float(image_height)), (tf.to_float(new_max_x) / tf.to_float(image_width))]) bbox_content = image[shifted_min_y:(shifted_max_y + 1), shifted_min_x:(shifted_max_x + 1), :] def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask, content_tensor, image_): 'Applies mask to bbox region in image then adds content_tensor to it.' mask = tf.pad(mask, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=1) content_tensor = tf.pad(content_tensor, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=0) return ((image_ * mask) + content_tensor) mask = tf.zeros_like(image)[min_y:(max_y + 1), min_x:(max_x + 1), :] grey_tensor = (tf.zeros_like(mask) + replace[0]) image = mask_and_add_image(min_y, min_x, max_y, max_x, mask, grey_tensor, image) mask = tf.zeros_like(bbox_content) image = mask_and_add_image(new_min_y, new_min_x, new_max_y, new_max_x, mask, bbox_content, image) return (image, new_bbox)
-5,648,054,145,612,244,000
Move the bbox and the image content to a slightly new random location. Args: image: 3D uint8 Tensor. bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x) of type float that represents the normalized coordinates between 0 and 1. The potential values for the new min corner of the bbox will be between [old_min - pixel_scaling * bbox_height/2, old_min - pixel_scaling * bbox_height/2]. pixel_scaling: A float between 0 and 1 that specifies the pixel range that the new bbox location will be sampled from. replace: A one or three value 1D tensor to fill empty pixels. new_min_bbox_coords: If not None, then this is a tuple that specifies the (min_y, min_x) coordinates of the new bbox. Normally this is randomly specified, but this allows it to be manually set. The coordinates are the absolute coordinates between 0 and image height/width and are int32. Returns: The new image that will have the shifted bbox location in it along with the new bbox that contains the new coordinates.
efficientdet/aug/autoaugment.py
random_shift_bbox
datawowio/automl
python
def random_shift_bbox(image, bbox, pixel_scaling, replace, new_min_bbox_coords=None): 'Move the bbox and the image content to a slightly new random location.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n The potential values for the new min corner of the bbox will be between\n [old_min - pixel_scaling * bbox_height/2,\n old_min - pixel_scaling * bbox_height/2].\n pixel_scaling: A float between 0 and 1 that specifies the pixel range\n that the new bbox location will be sampled from.\n replace: A one or three value 1D tensor to fill empty pixels.\n new_min_bbox_coords: If not None, then this is a tuple that specifies the\n (min_y, min_x) coordinates of the new bbox. Normally this is randomly\n specified, but this allows it to be manually set. The coordinates are\n the absolute coordinates between 0 and image height/width and are int32.\n\n Returns:\n The new image that will have the shifted bbox location in it along with\n the new bbox that contains the new coordinates.\n ' image_height = tf.to_float(tf.maximum(tf.shape(image)[0], 10)) image_width = tf.to_float(tf.maximum(tf.shape(image)[1], 10)) def clip_y(val): return tf.clip_by_value(val, 0, (tf.to_int32(image_height) - 1)) def clip_x(val): return tf.clip_by_value(val, 0, (tf.to_int32(image_width) - 1)) min_y = tf.to_int32((image_height * bbox[0])) min_x = tf.to_int32((image_width * bbox[1])) max_y = clip_y(tf.to_int32((image_height * bbox[2]))) max_x = clip_x(tf.to_int32((image_width * bbox[3]))) (bbox_height, bbox_width) = (((max_y - min_y) + 1), ((max_x - min_x) + 1)) image_height = tf.to_int32(image_height) image_width = tf.to_int32(image_width) minval_y = clip_y((min_y - tf.to_int32(((pixel_scaling * tf.to_float(bbox_height)) / 2.0)))) maxval_y = clip_y((min_y + tf.to_int32(((pixel_scaling * tf.to_float(bbox_height)) / 2.0)))) minval_x = clip_x((min_x - tf.to_int32(((pixel_scaling * tf.to_float(bbox_width)) / 2.0)))) maxval_x = clip_x((min_x + tf.to_int32(((pixel_scaling * tf.to_float(bbox_width)) / 2.0)))) if (new_min_bbox_coords is None): unclipped_new_min_y = tf.random_uniform(shape=[], minval=minval_y, maxval=maxval_y, dtype=tf.int32) unclipped_new_min_x = tf.random_uniform(shape=[], minval=minval_x, maxval=maxval_x, dtype=tf.int32) else: (unclipped_new_min_y, unclipped_new_min_x) = (clip_y(new_min_bbox_coords[0]), clip_x(new_min_bbox_coords[1])) unclipped_new_max_y = ((unclipped_new_min_y + bbox_height) - 1) unclipped_new_max_x = ((unclipped_new_min_x + bbox_width) - 1) (new_min_y, new_min_x, new_max_y, new_max_x) = (clip_y(unclipped_new_min_y), clip_x(unclipped_new_min_x), clip_y(unclipped_new_max_y), clip_x(unclipped_new_max_x)) shifted_min_y = ((new_min_y - unclipped_new_min_y) + min_y) shifted_max_y = (max_y - (unclipped_new_max_y - new_max_y)) shifted_min_x = ((new_min_x - unclipped_new_min_x) + min_x) shifted_max_x = (max_x - (unclipped_new_max_x - new_max_x)) new_bbox = tf.stack([(tf.to_float(new_min_y) / tf.to_float(image_height)), (tf.to_float(new_min_x) / tf.to_float(image_width)), (tf.to_float(new_max_y) / tf.to_float(image_height)), (tf.to_float(new_max_x) / tf.to_float(image_width))]) bbox_content = image[shifted_min_y:(shifted_max_y + 1), shifted_min_x:(shifted_max_x + 1), :] def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask, content_tensor, image_): 'Applies mask to bbox region in image then adds content_tensor to it.' mask = tf.pad(mask, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=1) content_tensor = tf.pad(content_tensor, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=0) return ((image_ * mask) + content_tensor) mask = tf.zeros_like(image)[min_y:(max_y + 1), min_x:(max_x + 1), :] grey_tensor = (tf.zeros_like(mask) + replace[0]) image = mask_and_add_image(min_y, min_x, max_y, max_x, mask, grey_tensor, image) mask = tf.zeros_like(bbox_content) image = mask_and_add_image(new_min_y, new_min_x, new_max_y, new_max_x, mask, bbox_content, image) return (image, new_bbox)
def _clip_bbox(min_y, min_x, max_y, max_x): 'Clip bounding box coordinates between 0 and 1.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type float between 0 and 1.\n max_x: Normalized bbox coordinate of type float between 0 and 1.\n\n Returns:\n Clipped coordinate values between 0 and 1.\n ' min_y = tf.clip_by_value(min_y, 0.0, 1.0) min_x = tf.clip_by_value(min_x, 0.0, 1.0) max_y = tf.clip_by_value(max_y, 0.0, 1.0) max_x = tf.clip_by_value(max_x, 0.0, 1.0) return (min_y, min_x, max_y, max_x)
-5,820,401,531,858,483,000
Clip bounding box coordinates between 0 and 1. Args: min_y: Normalized bbox coordinate of type float between 0 and 1. min_x: Normalized bbox coordinate of type float between 0 and 1. max_y: Normalized bbox coordinate of type float between 0 and 1. max_x: Normalized bbox coordinate of type float between 0 and 1. Returns: Clipped coordinate values between 0 and 1.
efficientdet/aug/autoaugment.py
_clip_bbox
datawowio/automl
python
def _clip_bbox(min_y, min_x, max_y, max_x): 'Clip bounding box coordinates between 0 and 1.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type float between 0 and 1.\n max_x: Normalized bbox coordinate of type float between 0 and 1.\n\n Returns:\n Clipped coordinate values between 0 and 1.\n ' min_y = tf.clip_by_value(min_y, 0.0, 1.0) min_x = tf.clip_by_value(min_x, 0.0, 1.0) max_y = tf.clip_by_value(max_y, 0.0, 1.0) max_x = tf.clip_by_value(max_x, 0.0, 1.0) return (min_y, min_x, max_y, max_x)
def _check_bbox_area(min_y, min_x, max_y, max_x, delta=0.05): 'Adjusts bbox coordinates to make sure the area is > 0.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type float between 0 and 1.\n max_x: Normalized bbox coordinate of type float between 0 and 1.\n delta: Float, this is used to create a gap of size 2 * delta between\n bbox min/max coordinates that are the same on the boundary.\n This prevents the bbox from having an area of zero.\n\n Returns:\n Tuple of new bbox coordinates between 0 and 1 that will now have a\n guaranteed area > 0.\n ' height = (max_y - min_y) width = (max_x - min_x) def _adjust_bbox_boundaries(min_coord, max_coord): max_coord = tf.maximum(max_coord, (0.0 + delta)) min_coord = tf.minimum(min_coord, (1.0 - delta)) return (min_coord, max_coord) (min_y, max_y) = tf.cond(tf.equal(height, 0.0), (lambda : _adjust_bbox_boundaries(min_y, max_y)), (lambda : (min_y, max_y))) (min_x, max_x) = tf.cond(tf.equal(width, 0.0), (lambda : _adjust_bbox_boundaries(min_x, max_x)), (lambda : (min_x, max_x))) return (min_y, min_x, max_y, max_x)
68,563,721,215,404,140
Adjusts bbox coordinates to make sure the area is > 0. Args: min_y: Normalized bbox coordinate of type float between 0 and 1. min_x: Normalized bbox coordinate of type float between 0 and 1. max_y: Normalized bbox coordinate of type float between 0 and 1. max_x: Normalized bbox coordinate of type float between 0 and 1. delta: Float, this is used to create a gap of size 2 * delta between bbox min/max coordinates that are the same on the boundary. This prevents the bbox from having an area of zero. Returns: Tuple of new bbox coordinates between 0 and 1 that will now have a guaranteed area > 0.
efficientdet/aug/autoaugment.py
_check_bbox_area
datawowio/automl
python
def _check_bbox_area(min_y, min_x, max_y, max_x, delta=0.05): 'Adjusts bbox coordinates to make sure the area is > 0.\n\n Args:\n min_y: Normalized bbox coordinate of type float between 0 and 1.\n min_x: Normalized bbox coordinate of type float between 0 and 1.\n max_y: Normalized bbox coordinate of type float between 0 and 1.\n max_x: Normalized bbox coordinate of type float between 0 and 1.\n delta: Float, this is used to create a gap of size 2 * delta between\n bbox min/max coordinates that are the same on the boundary.\n This prevents the bbox from having an area of zero.\n\n Returns:\n Tuple of new bbox coordinates between 0 and 1 that will now have a\n guaranteed area > 0.\n ' height = (max_y - min_y) width = (max_x - min_x) def _adjust_bbox_boundaries(min_coord, max_coord): max_coord = tf.maximum(max_coord, (0.0 + delta)) min_coord = tf.minimum(min_coord, (1.0 - delta)) return (min_coord, max_coord) (min_y, max_y) = tf.cond(tf.equal(height, 0.0), (lambda : _adjust_bbox_boundaries(min_y, max_y)), (lambda : (min_y, max_y))) (min_x, max_x) = tf.cond(tf.equal(width, 0.0), (lambda : _adjust_bbox_boundaries(min_x, max_x)), (lambda : (min_x, max_x))) return (min_y, min_x, max_y, max_x)
def _scale_bbox_only_op_probability(prob): 'Reduce the probability of the bbox-only operation.\n\n Probability is reduced so that we do not distort the content of too many\n bounding boxes that are close to each other. The value of 3.0 was a chosen\n hyper parameter when designing the autoaugment algorithm that we found\n empirically to work well.\n\n Args:\n prob: Float that is the probability of applying the bbox-only operation.\n\n Returns:\n Reduced probability.\n ' return (prob / 3.0)
8,180,426,613,846,989,000
Reduce the probability of the bbox-only operation. Probability is reduced so that we do not distort the content of too many bounding boxes that are close to each other. The value of 3.0 was a chosen hyper parameter when designing the autoaugment algorithm that we found empirically to work well. Args: prob: Float that is the probability of applying the bbox-only operation. Returns: Reduced probability.
efficientdet/aug/autoaugment.py
_scale_bbox_only_op_probability
datawowio/automl
python
def _scale_bbox_only_op_probability(prob): 'Reduce the probability of the bbox-only operation.\n\n Probability is reduced so that we do not distort the content of too many\n bounding boxes that are close to each other. The value of 3.0 was a chosen\n hyper parameter when designing the autoaugment algorithm that we found\n empirically to work well.\n\n Args:\n prob: Float that is the probability of applying the bbox-only operation.\n\n Returns:\n Reduced probability.\n ' return (prob / 3.0)
def _apply_bbox_augmentation(image, bbox, augmentation_func, *args): 'Applies augmentation_func to the subsection of image indicated by bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n augmentation_func: Augmentation function that will be applied to the\n subsection of image.\n *args: Additional parameters that will be passed into augmentation_func\n when it is called.\n\n Returns:\n A modified version of image, where the bbox location in the image will\n have `ugmentation_func applied to it.\n ' image_height = tf.to_float(tf.maximum(tf.shape(image)[0], 10)) image_width = tf.to_float(tf.maximum(tf.shape(image)[1], 10)) min_y = tf.to_int32((image_height * bbox[0])) min_x = tf.to_int32((image_width * bbox[1])) max_y = tf.to_int32((image_height * bbox[2])) max_x = tf.to_int32((image_width * bbox[3])) image_height = tf.to_int32(image_height) image_width = tf.to_int32(image_width) max_y = tf.minimum(max_y, (image_height - 1)) max_x = tf.minimum(max_x, (image_width - 1)) bbox_content = image[min_y:(max_y + 1), min_x:(max_x + 1), :] augmented_bbox_content = augmentation_func(bbox_content, *args) augmented_bbox_content = tf.pad(augmented_bbox_content, [[min_y, ((image_height - 1) - max_y)], [min_x, ((image_width - 1) - max_x)], [0, 0]]) mask_tensor = tf.zeros_like(bbox_content) mask_tensor = tf.pad(mask_tensor, [[min_y, ((image_height - 1) - max_y)], [min_x, ((image_width - 1) - max_x)], [0, 0]], constant_values=1) image = ((image * mask_tensor) + augmented_bbox_content) return image
5,033,668,537,318,786,000
Applies augmentation_func to the subsection of image indicated by bbox. Args: image: 3D uint8 Tensor. bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x) of type float that represents the normalized coordinates between 0 and 1. augmentation_func: Augmentation function that will be applied to the subsection of image. *args: Additional parameters that will be passed into augmentation_func when it is called. Returns: A modified version of image, where the bbox location in the image will have `ugmentation_func applied to it.
efficientdet/aug/autoaugment.py
_apply_bbox_augmentation
datawowio/automl
python
def _apply_bbox_augmentation(image, bbox, augmentation_func, *args): 'Applies augmentation_func to the subsection of image indicated by bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n augmentation_func: Augmentation function that will be applied to the\n subsection of image.\n *args: Additional parameters that will be passed into augmentation_func\n when it is called.\n\n Returns:\n A modified version of image, where the bbox location in the image will\n have `ugmentation_func applied to it.\n ' image_height = tf.to_float(tf.maximum(tf.shape(image)[0], 10)) image_width = tf.to_float(tf.maximum(tf.shape(image)[1], 10)) min_y = tf.to_int32((image_height * bbox[0])) min_x = tf.to_int32((image_width * bbox[1])) max_y = tf.to_int32((image_height * bbox[2])) max_x = tf.to_int32((image_width * bbox[3])) image_height = tf.to_int32(image_height) image_width = tf.to_int32(image_width) max_y = tf.minimum(max_y, (image_height - 1)) max_x = tf.minimum(max_x, (image_width - 1)) bbox_content = image[min_y:(max_y + 1), min_x:(max_x + 1), :] augmented_bbox_content = augmentation_func(bbox_content, *args) augmented_bbox_content = tf.pad(augmented_bbox_content, [[min_y, ((image_height - 1) - max_y)], [min_x, ((image_width - 1) - max_x)], [0, 0]]) mask_tensor = tf.zeros_like(bbox_content) mask_tensor = tf.pad(mask_tensor, [[min_y, ((image_height - 1) - max_y)], [min_x, ((image_width - 1) - max_x)], [0, 0]], constant_values=1) image = ((image * mask_tensor) + augmented_bbox_content) return image
def _concat_bbox(bbox, bboxes): 'Helper function that concats bbox to bboxes along the first dimension.' bboxes_sum_check = tf.reduce_sum(bboxes) bbox = tf.expand_dims(bbox, 0) bboxes = tf.cond(tf.equal(bboxes_sum_check, (- 4.0)), (lambda : bbox), (lambda : tf.concat([bboxes, bbox], 0))) return bboxes
9,145,103,630,591,172,000
Helper function that concats bbox to bboxes along the first dimension.
efficientdet/aug/autoaugment.py
_concat_bbox
datawowio/automl
python
def _concat_bbox(bbox, bboxes): bboxes_sum_check = tf.reduce_sum(bboxes) bbox = tf.expand_dims(bbox, 0) bboxes = tf.cond(tf.equal(bboxes_sum_check, (- 4.0)), (lambda : bbox), (lambda : tf.concat([bboxes, bbox], 0))) return bboxes
def _apply_bbox_augmentation_wrapper(image, bbox, new_bboxes, prob, augmentation_func, func_changes_bbox, *args): 'Applies _apply_bbox_augmentation with probability prob.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n new_bboxes: 2D Tensor that is a list of the bboxes in the image after they\n have been altered by aug_func. These will only be changed when\n func_changes_bbox is set to true. Each bbox has 4 elements\n (min_y, min_x, max_y, max_x) of type float that are the normalized\n bbox coordinates between 0 and 1.\n prob: Float that is the probability of applying _apply_bbox_augmentation.\n augmentation_func: Augmentation function that will be applied to the\n subsection of image.\n func_changes_bbox: Boolean. Does augmentation_func return bbox in addition\n to image.\n *args: Additional parameters that will be passed into augmentation_func\n when it is called.\n\n Returns:\n A tuple. Fist element is a modified version of image, where the bbox\n location in the image will have augmentation_func applied to it if it is\n chosen to be called with probability `prob`. The second element is a\n Tensor of Tensors of length 4 that will contain the altered bbox after\n applying augmentation_func.\n ' should_apply_op = tf.cast(tf.floor((tf.random_uniform([], dtype=tf.float32) + prob)), tf.bool) if func_changes_bbox: (augmented_image, bbox) = tf.cond(should_apply_op, (lambda : augmentation_func(image, bbox, *args)), (lambda : (image, bbox))) else: augmented_image = tf.cond(should_apply_op, (lambda : _apply_bbox_augmentation(image, bbox, augmentation_func, *args)), (lambda : image)) new_bboxes = _concat_bbox(bbox, new_bboxes) return (augmented_image, new_bboxes)
-6,801,094,433,672,039,000
Applies _apply_bbox_augmentation with probability prob. Args: image: 3D uint8 Tensor. bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x) of type float that represents the normalized coordinates between 0 and 1. new_bboxes: 2D Tensor that is a list of the bboxes in the image after they have been altered by aug_func. These will only be changed when func_changes_bbox is set to true. Each bbox has 4 elements (min_y, min_x, max_y, max_x) of type float that are the normalized bbox coordinates between 0 and 1. prob: Float that is the probability of applying _apply_bbox_augmentation. augmentation_func: Augmentation function that will be applied to the subsection of image. func_changes_bbox: Boolean. Does augmentation_func return bbox in addition to image. *args: Additional parameters that will be passed into augmentation_func when it is called. Returns: A tuple. Fist element is a modified version of image, where the bbox location in the image will have augmentation_func applied to it if it is chosen to be called with probability `prob`. The second element is a Tensor of Tensors of length 4 that will contain the altered bbox after applying augmentation_func.
efficientdet/aug/autoaugment.py
_apply_bbox_augmentation_wrapper
datawowio/automl
python
def _apply_bbox_augmentation_wrapper(image, bbox, new_bboxes, prob, augmentation_func, func_changes_bbox, *args): 'Applies _apply_bbox_augmentation with probability prob.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n new_bboxes: 2D Tensor that is a list of the bboxes in the image after they\n have been altered by aug_func. These will only be changed when\n func_changes_bbox is set to true. Each bbox has 4 elements\n (min_y, min_x, max_y, max_x) of type float that are the normalized\n bbox coordinates between 0 and 1.\n prob: Float that is the probability of applying _apply_bbox_augmentation.\n augmentation_func: Augmentation function that will be applied to the\n subsection of image.\n func_changes_bbox: Boolean. Does augmentation_func return bbox in addition\n to image.\n *args: Additional parameters that will be passed into augmentation_func\n when it is called.\n\n Returns:\n A tuple. Fist element is a modified version of image, where the bbox\n location in the image will have augmentation_func applied to it if it is\n chosen to be called with probability `prob`. The second element is a\n Tensor of Tensors of length 4 that will contain the altered bbox after\n applying augmentation_func.\n ' should_apply_op = tf.cast(tf.floor((tf.random_uniform([], dtype=tf.float32) + prob)), tf.bool) if func_changes_bbox: (augmented_image, bbox) = tf.cond(should_apply_op, (lambda : augmentation_func(image, bbox, *args)), (lambda : (image, bbox))) else: augmented_image = tf.cond(should_apply_op, (lambda : _apply_bbox_augmentation(image, bbox, augmentation_func, *args)), (lambda : image)) new_bboxes = _concat_bbox(bbox, new_bboxes) return (augmented_image, new_bboxes)
def _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func, func_changes_bbox, *args): 'Applies aug_func to the image for each bbox in bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float.\n prob: Float that is the probability of applying aug_func to a specific\n bounding box within the image.\n aug_func: Augmentation function that will be applied to the\n subsections of image indicated by the bbox values in bboxes.\n func_changes_bbox: Boolean. Does augmentation_func return bbox in addition\n to image.\n *args: Additional parameters that will be passed into augmentation_func\n when it is called.\n\n Returns:\n A modified version of image, where each bbox location in the image will\n have augmentation_func applied to it if it is chosen to be called with\n probability prob independently across all bboxes. Also the final\n bboxes are returned that will be unchanged if func_changes_bbox is set to\n false and if true, the new altered ones will be returned.\n ' new_bboxes = tf.constant(_INVALID_BOX) bboxes = tf.cond(tf.equal(tf.shape(bboxes)[0], 0), (lambda : tf.constant(_INVALID_BOX)), (lambda : bboxes)) bboxes = tf.ensure_shape(bboxes, (None, 4)) wrapped_aug_func = (lambda _image, bbox, _new_bboxes: _apply_bbox_augmentation_wrapper(_image, bbox, _new_bboxes, prob, aug_func, func_changes_bbox, *args)) num_bboxes = tf.shape(bboxes)[0] idx = tf.constant(0) cond = (lambda _idx, _images_and_bboxes: tf.less(_idx, num_bboxes)) if (not func_changes_bbox): loop_bboxes = tf.random.shuffle(bboxes) else: loop_bboxes = bboxes body = (lambda _idx, _images_and_bboxes: [(_idx + 1), wrapped_aug_func(_images_and_bboxes[0], loop_bboxes[_idx], _images_and_bboxes[1])]) (_, (image, new_bboxes)) = tf.while_loop(cond, body, [idx, (image, new_bboxes)], shape_invariants=[idx.get_shape(), (image.get_shape(), tf.TensorShape([None, 4]))]) if func_changes_bbox: final_bboxes = new_bboxes else: final_bboxes = bboxes return (image, final_bboxes)
102,916,792,991,740,450
Applies aug_func to the image for each bbox in bboxes. Args: image: 3D uint8 Tensor. bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox has 4 elements (min_y, min_x, max_y, max_x) of type float. prob: Float that is the probability of applying aug_func to a specific bounding box within the image. aug_func: Augmentation function that will be applied to the subsections of image indicated by the bbox values in bboxes. func_changes_bbox: Boolean. Does augmentation_func return bbox in addition to image. *args: Additional parameters that will be passed into augmentation_func when it is called. Returns: A modified version of image, where each bbox location in the image will have augmentation_func applied to it if it is chosen to be called with probability prob independently across all bboxes. Also the final bboxes are returned that will be unchanged if func_changes_bbox is set to false and if true, the new altered ones will be returned.
efficientdet/aug/autoaugment.py
_apply_multi_bbox_augmentation
datawowio/automl
python
def _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func, func_changes_bbox, *args): 'Applies aug_func to the image for each bbox in bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float.\n prob: Float that is the probability of applying aug_func to a specific\n bounding box within the image.\n aug_func: Augmentation function that will be applied to the\n subsections of image indicated by the bbox values in bboxes.\n func_changes_bbox: Boolean. Does augmentation_func return bbox in addition\n to image.\n *args: Additional parameters that will be passed into augmentation_func\n when it is called.\n\n Returns:\n A modified version of image, where each bbox location in the image will\n have augmentation_func applied to it if it is chosen to be called with\n probability prob independently across all bboxes. Also the final\n bboxes are returned that will be unchanged if func_changes_bbox is set to\n false and if true, the new altered ones will be returned.\n ' new_bboxes = tf.constant(_INVALID_BOX) bboxes = tf.cond(tf.equal(tf.shape(bboxes)[0], 0), (lambda : tf.constant(_INVALID_BOX)), (lambda : bboxes)) bboxes = tf.ensure_shape(bboxes, (None, 4)) wrapped_aug_func = (lambda _image, bbox, _new_bboxes: _apply_bbox_augmentation_wrapper(_image, bbox, _new_bboxes, prob, aug_func, func_changes_bbox, *args)) num_bboxes = tf.shape(bboxes)[0] idx = tf.constant(0) cond = (lambda _idx, _images_and_bboxes: tf.less(_idx, num_bboxes)) if (not func_changes_bbox): loop_bboxes = tf.random.shuffle(bboxes) else: loop_bboxes = bboxes body = (lambda _idx, _images_and_bboxes: [(_idx + 1), wrapped_aug_func(_images_and_bboxes[0], loop_bboxes[_idx], _images_and_bboxes[1])]) (_, (image, new_bboxes)) = tf.while_loop(cond, body, [idx, (image, new_bboxes)], shape_invariants=[idx.get_shape(), (image.get_shape(), tf.TensorShape([None, 4]))]) if func_changes_bbox: final_bboxes = new_bboxes else: final_bboxes = bboxes return (image, final_bboxes)
def _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, aug_func, func_changes_bbox, *args): 'Checks to be sure num bboxes > 0 before calling inner function.' num_bboxes = tf.shape(bboxes)[0] (image, bboxes) = tf.cond(tf.equal(num_bboxes, 0), (lambda : (image, bboxes)), (lambda : _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func, func_changes_bbox, *args))) return (image, bboxes)
-2,794,229,445,988,702,000
Checks to be sure num bboxes > 0 before calling inner function.
efficientdet/aug/autoaugment.py
_apply_multi_bbox_augmentation_wrapper
datawowio/automl
python
def _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, aug_func, func_changes_bbox, *args): num_bboxes = tf.shape(bboxes)[0] (image, bboxes) = tf.cond(tf.equal(num_bboxes, 0), (lambda : (image, bboxes)), (lambda : _apply_multi_bbox_augmentation(image, bboxes, prob, aug_func, func_changes_bbox, *args))) return (image, bboxes)
def rotate_only_bboxes(image, bboxes, prob, degrees, replace): 'Apply rotate to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, rotate, func_changes_bbox, degrees, replace)
9,211,649,917,027,160,000
Apply rotate to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
rotate_only_bboxes
datawowio/automl
python
def rotate_only_bboxes(image, bboxes, prob, degrees, replace): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, rotate, func_changes_bbox, degrees, replace)
def shear_x_only_bboxes(image, bboxes, prob, level, replace): 'Apply shear_x to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_x, func_changes_bbox, level, replace)
5,920,012,366,805,690,000
Apply shear_x to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
shear_x_only_bboxes
datawowio/automl
python
def shear_x_only_bboxes(image, bboxes, prob, level, replace): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_x, func_changes_bbox, level, replace)
def shear_y_only_bboxes(image, bboxes, prob, level, replace): 'Apply shear_y to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_y, func_changes_bbox, level, replace)
7,922,629,001,002,101,000
Apply shear_y to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
shear_y_only_bboxes
datawowio/automl
python
def shear_y_only_bboxes(image, bboxes, prob, level, replace): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, shear_y, func_changes_bbox, level, replace)
def translate_x_only_bboxes(image, bboxes, prob, pixels, replace): 'Apply translate_x to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_x, func_changes_bbox, pixels, replace)
-460,229,343,365,831,800
Apply translate_x to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
translate_x_only_bboxes
datawowio/automl
python
def translate_x_only_bboxes(image, bboxes, prob, pixels, replace): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_x, func_changes_bbox, pixels, replace)
def translate_y_only_bboxes(image, bboxes, prob, pixels, replace): 'Apply translate_y to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_y, func_changes_bbox, pixels, replace)
-888,010,208,539,744,100
Apply translate_y to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
translate_y_only_bboxes
datawowio/automl
python
def translate_y_only_bboxes(image, bboxes, prob, pixels, replace): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, translate_y, func_changes_bbox, pixels, replace)
def flip_only_bboxes(image, bboxes, prob): 'Apply flip_lr to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, tf.image.flip_left_right, func_changes_bbox)
-5,039,183,936,218,054,000
Apply flip_lr to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
flip_only_bboxes
datawowio/automl
python
def flip_only_bboxes(image, bboxes, prob): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, tf.image.flip_left_right, func_changes_bbox)
def solarize_only_bboxes(image, bboxes, prob, threshold): 'Apply solarize to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, solarize, func_changes_bbox, threshold)
-7,773,348,117,398,861,000
Apply solarize to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
solarize_only_bboxes
datawowio/automl
python
def solarize_only_bboxes(image, bboxes, prob, threshold): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, solarize, func_changes_bbox, threshold)
def equalize_only_bboxes(image, bboxes, prob): 'Apply equalize to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, equalize, func_changes_bbox)
-6,543,237,160,596,510,000
Apply equalize to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
equalize_only_bboxes
datawowio/automl
python
def equalize_only_bboxes(image, bboxes, prob): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, equalize, func_changes_bbox)
def cutout_only_bboxes(image, bboxes, prob, pad_size, replace): 'Apply cutout to each bbox in the image with probability prob.' func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, cutout, func_changes_bbox, pad_size, replace)
-2,471,233,948,783,666,000
Apply cutout to each bbox in the image with probability prob.
efficientdet/aug/autoaugment.py
cutout_only_bboxes
datawowio/automl
python
def cutout_only_bboxes(image, bboxes, prob, pad_size, replace): func_changes_bbox = False prob = _scale_bbox_only_op_probability(prob) return _apply_multi_bbox_augmentation_wrapper(image, bboxes, prob, cutout, func_changes_bbox, pad_size, replace)
def _rotate_bbox(bbox, image_height, image_width, degrees): 'Rotates the bbox coordinated by degrees.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n image_width: Int, height of the image.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n be rotated counterclockwise.\n\n Returns:\n A tensor of the same shape as bbox, but now with the rotated coordinates.\n ' (image_height, image_width) = (tf.to_float(image_height), tf.to_float(image_width)) degrees_to_radians = (math.pi / 180.0) radians = (degrees * degrees_to_radians) min_y = (- tf.to_int32((image_height * (bbox[0] - 0.5)))) min_x = tf.to_int32((image_width * (bbox[1] - 0.5))) max_y = (- tf.to_int32((image_height * (bbox[2] - 0.5)))) max_x = tf.to_int32((image_width * (bbox[3] - 0.5))) coordinates = tf.stack([[min_y, min_x], [min_y, max_x], [max_y, min_x], [max_y, max_x]]) coordinates = tf.cast(coordinates, tf.float32) rotation_matrix = tf.stack([[tf.cos(radians), tf.sin(radians)], [(- tf.sin(radians)), tf.cos(radians)]]) new_coords = tf.cast(tf.matmul(rotation_matrix, tf.transpose(coordinates)), tf.int32) min_y = (- ((tf.to_float(tf.reduce_max(new_coords[0, :])) / image_height) - 0.5)) min_x = ((tf.to_float(tf.reduce_min(new_coords[1, :])) / image_width) + 0.5) max_y = (- ((tf.to_float(tf.reduce_min(new_coords[0, :])) / image_height) - 0.5)) max_x = ((tf.to_float(tf.reduce_max(new_coords[1, :])) / image_width) + 0.5) (min_y, min_x, max_y, max_x) = _clip_bbox(min_y, min_x, max_y, max_x) (min_y, min_x, max_y, max_x) = _check_bbox_area(min_y, min_x, max_y, max_x) return tf.stack([min_y, min_x, max_y, max_x])
6,211,981,566,521,351,000
Rotates the bbox coordinated by degrees. Args: bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x) of type float that represents the normalized coordinates between 0 and 1. image_height: Int, height of the image. image_width: Int, height of the image. degrees: Float, a scalar angle in degrees to rotate all images by. If degrees is positive the image will be rotated clockwise otherwise it will be rotated counterclockwise. Returns: A tensor of the same shape as bbox, but now with the rotated coordinates.
efficientdet/aug/autoaugment.py
_rotate_bbox
datawowio/automl
python
def _rotate_bbox(bbox, image_height, image_width, degrees): 'Rotates the bbox coordinated by degrees.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n image_width: Int, height of the image.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n be rotated counterclockwise.\n\n Returns:\n A tensor of the same shape as bbox, but now with the rotated coordinates.\n ' (image_height, image_width) = (tf.to_float(image_height), tf.to_float(image_width)) degrees_to_radians = (math.pi / 180.0) radians = (degrees * degrees_to_radians) min_y = (- tf.to_int32((image_height * (bbox[0] - 0.5)))) min_x = tf.to_int32((image_width * (bbox[1] - 0.5))) max_y = (- tf.to_int32((image_height * (bbox[2] - 0.5)))) max_x = tf.to_int32((image_width * (bbox[3] - 0.5))) coordinates = tf.stack([[min_y, min_x], [min_y, max_x], [max_y, min_x], [max_y, max_x]]) coordinates = tf.cast(coordinates, tf.float32) rotation_matrix = tf.stack([[tf.cos(radians), tf.sin(radians)], [(- tf.sin(radians)), tf.cos(radians)]]) new_coords = tf.cast(tf.matmul(rotation_matrix, tf.transpose(coordinates)), tf.int32) min_y = (- ((tf.to_float(tf.reduce_max(new_coords[0, :])) / image_height) - 0.5)) min_x = ((tf.to_float(tf.reduce_min(new_coords[1, :])) / image_width) + 0.5) max_y = (- ((tf.to_float(tf.reduce_min(new_coords[0, :])) / image_height) - 0.5)) max_x = ((tf.to_float(tf.reduce_max(new_coords[1, :])) / image_width) + 0.5) (min_y, min_x, max_y, max_x) = _clip_bbox(min_y, min_x, max_y, max_x) (min_y, min_x, max_y, max_x) = _check_bbox_area(min_y, min_x, max_y, max_x) return tf.stack([min_y, min_x, max_y, max_x])
def rotate_with_bboxes(image, bboxes, degrees, replace): 'Equivalent of PIL Rotate that rotates the image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n be rotated counterclockwise.\n replace: A one or three value 1D tensor to fill empty pixels.\n\n Returns:\n A tuple containing a 3D uint8 Tensor that will be the result of rotating\n image by degrees. The second element of the tuple is bboxes, where now\n the coordinates will be shifted to reflect the rotated image.\n ' image = rotate(image, degrees, replace) image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] wrapped_rotate_bbox = (lambda bbox: _rotate_bbox(bbox, image_height, image_width, degrees)) bboxes = tf.map_fn(wrapped_rotate_bbox, bboxes) return (image, bboxes)
-3,447,364,672,112,616,400
Equivalent of PIL Rotate that rotates the image and bbox. Args: image: 3D uint8 Tensor. bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox has 4 elements (min_y, min_x, max_y, max_x) of type float. degrees: Float, a scalar angle in degrees to rotate all images by. If degrees is positive the image will be rotated clockwise otherwise it will be rotated counterclockwise. replace: A one or three value 1D tensor to fill empty pixels. Returns: A tuple containing a 3D uint8 Tensor that will be the result of rotating image by degrees. The second element of the tuple is bboxes, where now the coordinates will be shifted to reflect the rotated image.
efficientdet/aug/autoaugment.py
rotate_with_bboxes
datawowio/automl
python
def rotate_with_bboxes(image, bboxes, degrees, replace): 'Equivalent of PIL Rotate that rotates the image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float.\n degrees: Float, a scalar angle in degrees to rotate all images by. If\n degrees is positive the image will be rotated clockwise otherwise it will\n be rotated counterclockwise.\n replace: A one or three value 1D tensor to fill empty pixels.\n\n Returns:\n A tuple containing a 3D uint8 Tensor that will be the result of rotating\n image by degrees. The second element of the tuple is bboxes, where now\n the coordinates will be shifted to reflect the rotated image.\n ' image = rotate(image, degrees, replace) image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] wrapped_rotate_bbox = (lambda bbox: _rotate_bbox(bbox, image_height, image_width, degrees)) bboxes = tf.map_fn(wrapped_rotate_bbox, bboxes) return (image, bboxes)
def translate_x(image, pixels, replace): 'Equivalent of PIL Translate in X dimension.' image = image_ops.translate(wrap(image), [(- pixels), 0]) return unwrap(image, replace)
-3,514,625,783,606,751,000
Equivalent of PIL Translate in X dimension.
efficientdet/aug/autoaugment.py
translate_x
datawowio/automl
python
def translate_x(image, pixels, replace): image = image_ops.translate(wrap(image), [(- pixels), 0]) return unwrap(image, replace)
def translate_y(image, pixels, replace): 'Equivalent of PIL Translate in Y dimension.' image = image_ops.translate(wrap(image), [0, (- pixels)]) return unwrap(image, replace)
-3,311,760,775,496,658,400
Equivalent of PIL Translate in Y dimension.
efficientdet/aug/autoaugment.py
translate_y
datawowio/automl
python
def translate_y(image, pixels, replace): image = image_ops.translate(wrap(image), [0, (- pixels)]) return unwrap(image, replace)
def _shift_bbox(bbox, image_height, image_width, pixels, shift_horizontal): 'Shifts the bbox coordinates by pixels.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n image_width: Int, width of the image.\n pixels: An int. How many pixels to shift the bbox.\n shift_horizontal: Boolean. If true then shift in X dimension else shift in\n Y dimension.\n\n Returns:\n A tensor of the same shape as bbox, but now with the shifted coordinates.\n ' pixels = tf.to_int32(pixels) min_y = tf.to_int32((tf.to_float(image_height) * bbox[0])) min_x = tf.to_int32((tf.to_float(image_width) * bbox[1])) max_y = tf.to_int32((tf.to_float(image_height) * bbox[2])) max_x = tf.to_int32((tf.to_float(image_width) * bbox[3])) if shift_horizontal: min_x = tf.maximum(0, (min_x - pixels)) max_x = tf.minimum(image_width, (max_x - pixels)) else: min_y = tf.maximum(0, (min_y - pixels)) max_y = tf.minimum(image_height, (max_y - pixels)) min_y = (tf.to_float(min_y) / tf.to_float(image_height)) min_x = (tf.to_float(min_x) / tf.to_float(image_width)) max_y = (tf.to_float(max_y) / tf.to_float(image_height)) max_x = (tf.to_float(max_x) / tf.to_float(image_width)) (min_y, min_x, max_y, max_x) = _clip_bbox(min_y, min_x, max_y, max_x) (min_y, min_x, max_y, max_x) = _check_bbox_area(min_y, min_x, max_y, max_x) return tf.stack([min_y, min_x, max_y, max_x])
-6,072,056,995,610,296,000
Shifts the bbox coordinates by pixels. Args: bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x) of type float that represents the normalized coordinates between 0 and 1. image_height: Int, height of the image. image_width: Int, width of the image. pixels: An int. How many pixels to shift the bbox. shift_horizontal: Boolean. If true then shift in X dimension else shift in Y dimension. Returns: A tensor of the same shape as bbox, but now with the shifted coordinates.
efficientdet/aug/autoaugment.py
_shift_bbox
datawowio/automl
python
def _shift_bbox(bbox, image_height, image_width, pixels, shift_horizontal): 'Shifts the bbox coordinates by pixels.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n image_width: Int, width of the image.\n pixels: An int. How many pixels to shift the bbox.\n shift_horizontal: Boolean. If true then shift in X dimension else shift in\n Y dimension.\n\n Returns:\n A tensor of the same shape as bbox, but now with the shifted coordinates.\n ' pixels = tf.to_int32(pixels) min_y = tf.to_int32((tf.to_float(image_height) * bbox[0])) min_x = tf.to_int32((tf.to_float(image_width) * bbox[1])) max_y = tf.to_int32((tf.to_float(image_height) * bbox[2])) max_x = tf.to_int32((tf.to_float(image_width) * bbox[3])) if shift_horizontal: min_x = tf.maximum(0, (min_x - pixels)) max_x = tf.minimum(image_width, (max_x - pixels)) else: min_y = tf.maximum(0, (min_y - pixels)) max_y = tf.minimum(image_height, (max_y - pixels)) min_y = (tf.to_float(min_y) / tf.to_float(image_height)) min_x = (tf.to_float(min_x) / tf.to_float(image_width)) max_y = (tf.to_float(max_y) / tf.to_float(image_height)) max_x = (tf.to_float(max_x) / tf.to_float(image_width)) (min_y, min_x, max_y, max_x) = _clip_bbox(min_y, min_x, max_y, max_x) (min_y, min_x, max_y, max_x) = _check_bbox_area(min_y, min_x, max_y, max_x) return tf.stack([min_y, min_x, max_y, max_x])
def translate_bbox(image, bboxes, pixels, replace, shift_horizontal): 'Equivalent of PIL Translate in X/Y dimension that shifts image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float with values\n between [0, 1].\n pixels: An int. How many pixels to shift the image and bboxes\n replace: A one or three value 1D tensor to fill empty pixels.\n shift_horizontal: Boolean. If true then shift in X dimension else shift in\n Y dimension.\n\n Returns:\n A tuple containing a 3D uint8 Tensor that will be the result of translating\n image by pixels. The second element of the tuple is bboxes, where now\n the coordinates will be shifted to reflect the shifted image.\n ' if shift_horizontal: image = translate_x(image, pixels, replace) else: image = translate_y(image, pixels, replace) image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] wrapped_shift_bbox = (lambda bbox: _shift_bbox(bbox, image_height, image_width, pixels, shift_horizontal)) bboxes = tf.map_fn(wrapped_shift_bbox, bboxes) return (image, bboxes)
-1,837,703,549,154,977,800
Equivalent of PIL Translate in X/Y dimension that shifts image and bbox. Args: image: 3D uint8 Tensor. bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox has 4 elements (min_y, min_x, max_y, max_x) of type float with values between [0, 1]. pixels: An int. How many pixels to shift the image and bboxes replace: A one or three value 1D tensor to fill empty pixels. shift_horizontal: Boolean. If true then shift in X dimension else shift in Y dimension. Returns: A tuple containing a 3D uint8 Tensor that will be the result of translating image by pixels. The second element of the tuple is bboxes, where now the coordinates will be shifted to reflect the shifted image.
efficientdet/aug/autoaugment.py
translate_bbox
datawowio/automl
python
def translate_bbox(image, bboxes, pixels, replace, shift_horizontal): 'Equivalent of PIL Translate in X/Y dimension that shifts image and bbox.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float with values\n between [0, 1].\n pixels: An int. How many pixels to shift the image and bboxes\n replace: A one or three value 1D tensor to fill empty pixels.\n shift_horizontal: Boolean. If true then shift in X dimension else shift in\n Y dimension.\n\n Returns:\n A tuple containing a 3D uint8 Tensor that will be the result of translating\n image by pixels. The second element of the tuple is bboxes, where now\n the coordinates will be shifted to reflect the shifted image.\n ' if shift_horizontal: image = translate_x(image, pixels, replace) else: image = translate_y(image, pixels, replace) image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] wrapped_shift_bbox = (lambda bbox: _shift_bbox(bbox, image_height, image_width, pixels, shift_horizontal)) bboxes = tf.map_fn(wrapped_shift_bbox, bboxes) return (image, bboxes)
def shear_x(image, level, replace): 'Equivalent of PIL Shearing in X dimension.' image = image_ops.transform(wrap(image), [1.0, level, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) return unwrap(image, replace)
-6,123,410,436,435,485,000
Equivalent of PIL Shearing in X dimension.
efficientdet/aug/autoaugment.py
shear_x
datawowio/automl
python
def shear_x(image, level, replace): image = image_ops.transform(wrap(image), [1.0, level, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]) return unwrap(image, replace)
def shear_y(image, level, replace): 'Equivalent of PIL Shearing in Y dimension.' image = image_ops.transform(wrap(image), [1.0, 0.0, 0.0, level, 1.0, 0.0, 0.0, 0.0]) return unwrap(image, replace)
-7,747,551,437,542,087,000
Equivalent of PIL Shearing in Y dimension.
efficientdet/aug/autoaugment.py
shear_y
datawowio/automl
python
def shear_y(image, level, replace): image = image_ops.transform(wrap(image), [1.0, 0.0, 0.0, level, 1.0, 0.0, 0.0, 0.0]) return unwrap(image, replace)
def _shear_bbox(bbox, image_height, image_width, level, shear_horizontal): 'Shifts the bbox according to how the image was sheared.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n image_width: Int, height of the image.\n level: Float. How much to shear the image.\n shear_horizontal: If true then shear in X dimension else shear in\n the Y dimension.\n\n Returns:\n A tensor of the same shape as bbox, but now with the shifted coordinates.\n ' (image_height, image_width) = (tf.to_float(image_height), tf.to_float(image_width)) min_y = tf.to_int32((image_height * bbox[0])) min_x = tf.to_int32((image_width * bbox[1])) max_y = tf.to_int32((image_height * bbox[2])) max_x = tf.to_int32((image_width * bbox[3])) coordinates = tf.stack([[min_y, min_x], [min_y, max_x], [max_y, min_x], [max_y, max_x]]) coordinates = tf.cast(coordinates, tf.float32) if shear_horizontal: translation_matrix = tf.stack([[1, 0], [(- level), 1]]) else: translation_matrix = tf.stack([[1, (- level)], [0, 1]]) translation_matrix = tf.cast(translation_matrix, tf.float32) new_coords = tf.cast(tf.matmul(translation_matrix, tf.transpose(coordinates)), tf.int32) min_y = (tf.to_float(tf.reduce_min(new_coords[0, :])) / image_height) min_x = (tf.to_float(tf.reduce_min(new_coords[1, :])) / image_width) max_y = (tf.to_float(tf.reduce_max(new_coords[0, :])) / image_height) max_x = (tf.to_float(tf.reduce_max(new_coords[1, :])) / image_width) (min_y, min_x, max_y, max_x) = _clip_bbox(min_y, min_x, max_y, max_x) (min_y, min_x, max_y, max_x) = _check_bbox_area(min_y, min_x, max_y, max_x) return tf.stack([min_y, min_x, max_y, max_x])
-6,425,782,020,536,974,000
Shifts the bbox according to how the image was sheared. Args: bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x) of type float that represents the normalized coordinates between 0 and 1. image_height: Int, height of the image. image_width: Int, height of the image. level: Float. How much to shear the image. shear_horizontal: If true then shear in X dimension else shear in the Y dimension. Returns: A tensor of the same shape as bbox, but now with the shifted coordinates.
efficientdet/aug/autoaugment.py
_shear_bbox
datawowio/automl
python
def _shear_bbox(bbox, image_height, image_width, level, shear_horizontal): 'Shifts the bbox according to how the image was sheared.\n\n Args:\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n image_height: Int, height of the image.\n image_width: Int, height of the image.\n level: Float. How much to shear the image.\n shear_horizontal: If true then shear in X dimension else shear in\n the Y dimension.\n\n Returns:\n A tensor of the same shape as bbox, but now with the shifted coordinates.\n ' (image_height, image_width) = (tf.to_float(image_height), tf.to_float(image_width)) min_y = tf.to_int32((image_height * bbox[0])) min_x = tf.to_int32((image_width * bbox[1])) max_y = tf.to_int32((image_height * bbox[2])) max_x = tf.to_int32((image_width * bbox[3])) coordinates = tf.stack([[min_y, min_x], [min_y, max_x], [max_y, min_x], [max_y, max_x]]) coordinates = tf.cast(coordinates, tf.float32) if shear_horizontal: translation_matrix = tf.stack([[1, 0], [(- level), 1]]) else: translation_matrix = tf.stack([[1, (- level)], [0, 1]]) translation_matrix = tf.cast(translation_matrix, tf.float32) new_coords = tf.cast(tf.matmul(translation_matrix, tf.transpose(coordinates)), tf.int32) min_y = (tf.to_float(tf.reduce_min(new_coords[0, :])) / image_height) min_x = (tf.to_float(tf.reduce_min(new_coords[1, :])) / image_width) max_y = (tf.to_float(tf.reduce_max(new_coords[0, :])) / image_height) max_x = (tf.to_float(tf.reduce_max(new_coords[1, :])) / image_width) (min_y, min_x, max_y, max_x) = _clip_bbox(min_y, min_x, max_y, max_x) (min_y, min_x, max_y, max_x) = _check_bbox_area(min_y, min_x, max_y, max_x) return tf.stack([min_y, min_x, max_y, max_x])
def shear_with_bboxes(image, bboxes, level, replace, shear_horizontal): 'Applies Shear Transformation to the image and shifts the bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float with values\n between [0, 1].\n level: Float. How much to shear the image. This value will be between\n -0.3 to 0.3.\n replace: A one or three value 1D tensor to fill empty pixels.\n shear_horizontal: Boolean. If true then shear in X dimension else shear in\n the Y dimension.\n\n Returns:\n A tuple containing a 3D uint8 Tensor that will be the result of shearing\n image by level. The second element of the tuple is bboxes, where now\n the coordinates will be shifted to reflect the sheared image.\n ' if shear_horizontal: image = shear_x(image, level, replace) else: image = shear_y(image, level, replace) image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] wrapped_shear_bbox = (lambda bbox: _shear_bbox(bbox, image_height, image_width, level, shear_horizontal)) bboxes = tf.map_fn(wrapped_shear_bbox, bboxes) return (image, bboxes)
-8,922,601,394,061,101,000
Applies Shear Transformation to the image and shifts the bboxes. Args: image: 3D uint8 Tensor. bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox has 4 elements (min_y, min_x, max_y, max_x) of type float with values between [0, 1]. level: Float. How much to shear the image. This value will be between -0.3 to 0.3. replace: A one or three value 1D tensor to fill empty pixels. shear_horizontal: Boolean. If true then shear in X dimension else shear in the Y dimension. Returns: A tuple containing a 3D uint8 Tensor that will be the result of shearing image by level. The second element of the tuple is bboxes, where now the coordinates will be shifted to reflect the sheared image.
efficientdet/aug/autoaugment.py
shear_with_bboxes
datawowio/automl
python
def shear_with_bboxes(image, bboxes, level, replace, shear_horizontal): 'Applies Shear Transformation to the image and shifts the bboxes.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float with values\n between [0, 1].\n level: Float. How much to shear the image. This value will be between\n -0.3 to 0.3.\n replace: A one or three value 1D tensor to fill empty pixels.\n shear_horizontal: Boolean. If true then shear in X dimension else shear in\n the Y dimension.\n\n Returns:\n A tuple containing a 3D uint8 Tensor that will be the result of shearing\n image by level. The second element of the tuple is bboxes, where now\n the coordinates will be shifted to reflect the sheared image.\n ' if shear_horizontal: image = shear_x(image, level, replace) else: image = shear_y(image, level, replace) image_height = tf.shape(image)[0] image_width = tf.shape(image)[1] wrapped_shear_bbox = (lambda bbox: _shear_bbox(bbox, image_height, image_width, level, shear_horizontal)) bboxes = tf.map_fn(wrapped_shear_bbox, bboxes) return (image, bboxes)
def autocontrast(image): 'Implements Autocontrast function from PIL using TF ops.\n\n Args:\n image: A 3D uint8 tensor.\n\n Returns:\n The image after it has had autocontrast applied to it and will be of type\n uint8.\n ' def scale_channel(image): 'Scale the 2D image using the autocontrast rule.' lo = tf.to_float(tf.reduce_min(image)) hi = tf.to_float(tf.reduce_max(image)) def scale_values(im): scale = (255.0 / (hi - lo)) offset = ((- lo) * scale) im = ((tf.to_float(im) * scale) + offset) im = tf.clip_by_value(im, 0.0, 255.0) return tf.cast(im, tf.uint8) result = tf.cond((hi > lo), (lambda : scale_values(image)), (lambda : image)) return result s1 = scale_channel(image[:, :, 0]) s2 = scale_channel(image[:, :, 1]) s3 = scale_channel(image[:, :, 2]) image = tf.stack([s1, s2, s3], 2) return image
303,571,217,608,186,900
Implements Autocontrast function from PIL using TF ops. Args: image: A 3D uint8 tensor. Returns: The image after it has had autocontrast applied to it and will be of type uint8.
efficientdet/aug/autoaugment.py
autocontrast
datawowio/automl
python
def autocontrast(image): 'Implements Autocontrast function from PIL using TF ops.\n\n Args:\n image: A 3D uint8 tensor.\n\n Returns:\n The image after it has had autocontrast applied to it and will be of type\n uint8.\n ' def scale_channel(image): 'Scale the 2D image using the autocontrast rule.' lo = tf.to_float(tf.reduce_min(image)) hi = tf.to_float(tf.reduce_max(image)) def scale_values(im): scale = (255.0 / (hi - lo)) offset = ((- lo) * scale) im = ((tf.to_float(im) * scale) + offset) im = tf.clip_by_value(im, 0.0, 255.0) return tf.cast(im, tf.uint8) result = tf.cond((hi > lo), (lambda : scale_values(image)), (lambda : image)) return result s1 = scale_channel(image[:, :, 0]) s2 = scale_channel(image[:, :, 1]) s3 = scale_channel(image[:, :, 2]) image = tf.stack([s1, s2, s3], 2) return image
def sharpness(image, factor): 'Implements Sharpness function from PIL using TF ops.' orig_image = image image = tf.cast(image, tf.float32) image = tf.expand_dims(image, 0) kernel = (tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0) kernel = tf.tile(kernel, [1, 1, 3, 1]) strides = [1, 1, 1, 1] with tf.device('/cpu:0'): degenerate = tf.nn.depthwise_conv2d(image, kernel, strides, padding='VALID', rate=[1, 1]) degenerate = tf.clip_by_value(degenerate, 0.0, 255.0) degenerate = tf.squeeze(tf.cast(degenerate, tf.uint8), [0]) mask = tf.ones_like(degenerate) padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]]) padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]]) result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_image) return blend(result, orig_image, factor)
-1,232,677,706,748,177,200
Implements Sharpness function from PIL using TF ops.
efficientdet/aug/autoaugment.py
sharpness
datawowio/automl
python
def sharpness(image, factor): orig_image = image image = tf.cast(image, tf.float32) image = tf.expand_dims(image, 0) kernel = (tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]], dtype=tf.float32, shape=[3, 3, 1, 1]) / 13.0) kernel = tf.tile(kernel, [1, 1, 3, 1]) strides = [1, 1, 1, 1] with tf.device('/cpu:0'): degenerate = tf.nn.depthwise_conv2d(image, kernel, strides, padding='VALID', rate=[1, 1]) degenerate = tf.clip_by_value(degenerate, 0.0, 255.0) degenerate = tf.squeeze(tf.cast(degenerate, tf.uint8), [0]) mask = tf.ones_like(degenerate) padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]]) padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]]) result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_image) return blend(result, orig_image, factor)
def equalize(image): 'Implements Equalize function from PIL using TF ops.' def scale_channel(im, c): 'Scale the data in the channel to implement equalize.' im = tf.cast(im[:, :, c], tf.int32) histo = tf.histogram_fixed_width(im, [0, 255], nbins=256) nonzero = tf.where(tf.not_equal(histo, 0)) nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)]) step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255) def build_lut(histo, step): lut = ((tf.cumsum(histo) + (step // 2)) // step) lut = tf.concat([[0], lut[:(- 1)]], 0) return tf.clip_by_value(lut, 0, 255) result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im))) return tf.cast(result, tf.uint8) s1 = scale_channel(image, 0) s2 = scale_channel(image, 1) s3 = scale_channel(image, 2) image = tf.stack([s1, s2, s3], 2) return image
-4,360,158,787,895,066,600
Implements Equalize function from PIL using TF ops.
efficientdet/aug/autoaugment.py
equalize
datawowio/automl
python
def equalize(image): def scale_channel(im, c): 'Scale the data in the channel to implement equalize.' im = tf.cast(im[:, :, c], tf.int32) histo = tf.histogram_fixed_width(im, [0, 255], nbins=256) nonzero = tf.where(tf.not_equal(histo, 0)) nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)]) step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255) def build_lut(histo, step): lut = ((tf.cumsum(histo) + (step // 2)) // step) lut = tf.concat([[0], lut[:(- 1)]], 0) return tf.clip_by_value(lut, 0, 255) result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im))) return tf.cast(result, tf.uint8) s1 = scale_channel(image, 0) s2 = scale_channel(image, 1) s3 = scale_channel(image, 2) image = tf.stack([s1, s2, s3], 2) return image
def wrap(image): "Returns 'image' with an extra channel set to all 1s." shape = tf.shape(image) extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype) extended = tf.concat([image, extended_channel], 2) return extended
-2,054,740,842,410,237,000
Returns 'image' with an extra channel set to all 1s.
efficientdet/aug/autoaugment.py
wrap
datawowio/automl
python
def wrap(image): shape = tf.shape(image) extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype) extended = tf.concat([image, extended_channel], 2) return extended
def unwrap(image, replace): "Unwraps an image produced by wrap.\n\n Where there is a 0 in the last channel for every spatial position,\n the rest of the three channels in that spatial dimension are grayed\n (set to 128). Operations like translate and shear on a wrapped\n Tensor will leave 0s in empty locations. Some transformations look\n at the intensity of values to do preprocessing, and we want these\n empty pixels to assume the 'average' value, rather than pure black.\n\n\n Args:\n image: A 3D Image Tensor with 4 channels.\n replace: A one or three value 1D tensor to fill empty pixels.\n\n Returns:\n image: A 3D image Tensor with 3 channels.\n " image_shape = tf.shape(image) flattened_image = tf.reshape(image, [(- 1), image_shape[2]]) alpha_channel = flattened_image[:, 3] replace = tf.concat([replace, tf.ones([1], image.dtype)], 0) flattened_image = tf.where(tf.equal(alpha_channel, 0), (tf.ones_like(flattened_image, dtype=image.dtype) * replace), flattened_image) image = tf.reshape(flattened_image, image_shape) image = tf.slice(image, [0, 0, 0], [image_shape[0], image_shape[1], 3]) return image
6,263,443,170,076,591,000
Unwraps an image produced by wrap. Where there is a 0 in the last channel for every spatial position, the rest of the three channels in that spatial dimension are grayed (set to 128). Operations like translate and shear on a wrapped Tensor will leave 0s in empty locations. Some transformations look at the intensity of values to do preprocessing, and we want these empty pixels to assume the 'average' value, rather than pure black. Args: image: A 3D Image Tensor with 4 channels. replace: A one or three value 1D tensor to fill empty pixels. Returns: image: A 3D image Tensor with 3 channels.
efficientdet/aug/autoaugment.py
unwrap
datawowio/automl
python
def unwrap(image, replace): "Unwraps an image produced by wrap.\n\n Where there is a 0 in the last channel for every spatial position,\n the rest of the three channels in that spatial dimension are grayed\n (set to 128). Operations like translate and shear on a wrapped\n Tensor will leave 0s in empty locations. Some transformations look\n at the intensity of values to do preprocessing, and we want these\n empty pixels to assume the 'average' value, rather than pure black.\n\n\n Args:\n image: A 3D Image Tensor with 4 channels.\n replace: A one or three value 1D tensor to fill empty pixels.\n\n Returns:\n image: A 3D image Tensor with 3 channels.\n " image_shape = tf.shape(image) flattened_image = tf.reshape(image, [(- 1), image_shape[2]]) alpha_channel = flattened_image[:, 3] replace = tf.concat([replace, tf.ones([1], image.dtype)], 0) flattened_image = tf.where(tf.equal(alpha_channel, 0), (tf.ones_like(flattened_image, dtype=image.dtype) * replace), flattened_image) image = tf.reshape(flattened_image, image_shape) image = tf.slice(image, [0, 0, 0], [image_shape[0], image_shape[1], 3]) return image
def _cutout_inside_bbox(image, bbox, pad_fraction): 'Generates cutout mask and the mean pixel value of the bbox.\n\n First a location is randomly chosen within the image as the center where the\n cutout mask will be applied. Note this can be towards the boundaries of the\n image, so the full cutout mask may not be applied.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n pad_fraction: Float that specifies how large the cutout mask should be in\n in reference to the size of the original bbox. If pad_fraction is 0.25,\n then the cutout mask will be of shape\n (0.25 * bbox height, 0.25 * bbox width).\n\n Returns:\n A tuple. Fist element is a tensor of the same shape as image where each\n element is either a 1 or 0 that is used to determine where the image\n will have cutout applied. The second element is the mean of the pixels\n in the image where the bbox is located.\n ' image_height = tf.maximum(tf.shape(image)[0], 10) image_width = tf.maximum(tf.shape(image)[1], 10) bbox = tf.squeeze(bbox) min_y = tf.to_int32((tf.to_float(image_height) * bbox[0])) min_x = tf.to_int32((tf.to_float(image_width) * bbox[1])) max_y = tf.to_int32((tf.to_float(image_height) * bbox[2])) max_x = tf.to_int32((tf.to_float(image_width) * bbox[3])) mean = tf.reduce_mean(image[min_y:(max_y + 1), min_x:(max_x + 1)], reduction_indices=[0, 1]) box_height = ((max_y - min_y) + 1) box_width = ((max_x - min_x) + 1) pad_size_height = tf.to_int32((pad_fraction * (box_height / 2))) pad_size_width = tf.to_int32((pad_fraction * (box_width / 2))) cutout_center_height = tf.random_uniform(shape=[], minval=min_y, maxval=(max_y + 1), dtype=tf.int32) cutout_center_width = tf.random_uniform(shape=[], minval=min_x, maxval=(max_x + 1), dtype=tf.int32) lower_pad = tf.maximum(0, (cutout_center_height - pad_size_height)) upper_pad = tf.maximum(0, ((image_height - cutout_center_height) - pad_size_height)) left_pad = tf.maximum(0, (cutout_center_width - pad_size_width)) right_pad = tf.maximum(0, ((image_width - cutout_center_width) - pad_size_width)) cutout_shape = [(image_height - (lower_pad + upper_pad)), (image_width - (left_pad + right_pad))] padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]] mask = tf.pad(tf.zeros(cutout_shape, dtype=image.dtype), padding_dims, constant_values=1) mask = tf.expand_dims(mask, 2) mask = tf.tile(mask, [1, 1, 3]) return (mask, mean)
-8,068,290,216,123,599,000
Generates cutout mask and the mean pixel value of the bbox. First a location is randomly chosen within the image as the center where the cutout mask will be applied. Note this can be towards the boundaries of the image, so the full cutout mask may not be applied. Args: image: 3D uint8 Tensor. bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x) of type float that represents the normalized coordinates between 0 and 1. pad_fraction: Float that specifies how large the cutout mask should be in in reference to the size of the original bbox. If pad_fraction is 0.25, then the cutout mask will be of shape (0.25 * bbox height, 0.25 * bbox width). Returns: A tuple. Fist element is a tensor of the same shape as image where each element is either a 1 or 0 that is used to determine where the image will have cutout applied. The second element is the mean of the pixels in the image where the bbox is located.
efficientdet/aug/autoaugment.py
_cutout_inside_bbox
datawowio/automl
python
def _cutout_inside_bbox(image, bbox, pad_fraction): 'Generates cutout mask and the mean pixel value of the bbox.\n\n First a location is randomly chosen within the image as the center where the\n cutout mask will be applied. Note this can be towards the boundaries of the\n image, so the full cutout mask may not be applied.\n\n Args:\n image: 3D uint8 Tensor.\n bbox: 1D Tensor that has 4 elements (min_y, min_x, max_y, max_x)\n of type float that represents the normalized coordinates between 0 and 1.\n pad_fraction: Float that specifies how large the cutout mask should be in\n in reference to the size of the original bbox. If pad_fraction is 0.25,\n then the cutout mask will be of shape\n (0.25 * bbox height, 0.25 * bbox width).\n\n Returns:\n A tuple. Fist element is a tensor of the same shape as image where each\n element is either a 1 or 0 that is used to determine where the image\n will have cutout applied. The second element is the mean of the pixels\n in the image where the bbox is located.\n ' image_height = tf.maximum(tf.shape(image)[0], 10) image_width = tf.maximum(tf.shape(image)[1], 10) bbox = tf.squeeze(bbox) min_y = tf.to_int32((tf.to_float(image_height) * bbox[0])) min_x = tf.to_int32((tf.to_float(image_width) * bbox[1])) max_y = tf.to_int32((tf.to_float(image_height) * bbox[2])) max_x = tf.to_int32((tf.to_float(image_width) * bbox[3])) mean = tf.reduce_mean(image[min_y:(max_y + 1), min_x:(max_x + 1)], reduction_indices=[0, 1]) box_height = ((max_y - min_y) + 1) box_width = ((max_x - min_x) + 1) pad_size_height = tf.to_int32((pad_fraction * (box_height / 2))) pad_size_width = tf.to_int32((pad_fraction * (box_width / 2))) cutout_center_height = tf.random_uniform(shape=[], minval=min_y, maxval=(max_y + 1), dtype=tf.int32) cutout_center_width = tf.random_uniform(shape=[], minval=min_x, maxval=(max_x + 1), dtype=tf.int32) lower_pad = tf.maximum(0, (cutout_center_height - pad_size_height)) upper_pad = tf.maximum(0, ((image_height - cutout_center_height) - pad_size_height)) left_pad = tf.maximum(0, (cutout_center_width - pad_size_width)) right_pad = tf.maximum(0, ((image_width - cutout_center_width) - pad_size_width)) cutout_shape = [(image_height - (lower_pad + upper_pad)), (image_width - (left_pad + right_pad))] padding_dims = [[lower_pad, upper_pad], [left_pad, right_pad]] mask = tf.pad(tf.zeros(cutout_shape, dtype=image.dtype), padding_dims, constant_values=1) mask = tf.expand_dims(mask, 2) mask = tf.tile(mask, [1, 1, 3]) return (mask, mean)
def bbox_cutout(image, bboxes, pad_fraction, replace_with_mean): 'Applies cutout to the image according to bbox information.\n\n This is a cutout variant that using bbox information to make more informed\n decisions on where to place the cutout mask.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float with values\n between [0, 1].\n pad_fraction: Float that specifies how large the cutout mask should be in\n in reference to the size of the original bbox. If pad_fraction is 0.25,\n then the cutout mask will be of shape\n (0.25 * bbox height, 0.25 * bbox width).\n replace_with_mean: Boolean that specified what value should be filled in\n where the cutout mask is applied. Since the incoming image will be of\n uint8 and will not have had any mean normalization applied, by default\n we set the value to be 128. If replace_with_mean is True then we find\n the mean pixel values across the channel dimension and use those to fill\n in where the cutout mask is applied.\n\n Returns:\n A tuple. First element is a tensor of the same shape as image that has\n cutout applied to it. Second element is the bboxes that were passed in\n that will be unchanged.\n ' def apply_bbox_cutout(image, bboxes, pad_fraction): 'Applies cutout to a single bounding box within image.' random_index = tf.random_uniform(shape=[], maxval=tf.shape(bboxes)[0], dtype=tf.int32) chosen_bbox = tf.gather(bboxes, random_index) (mask, mean) = _cutout_inside_bbox(image, chosen_bbox, pad_fraction) replace = (mean if replace_with_mean else 128) image = tf.where(tf.equal(mask, 0), tf.cast((tf.ones_like(image, dtype=image.dtype) * replace), dtype=image.dtype), image) return image image = tf.cond(tf.equal(tf.shape(bboxes)[0], 0), (lambda : image), (lambda : apply_bbox_cutout(image, bboxes, pad_fraction))) return (image, bboxes)
3,325,208,541,111,828,000
Applies cutout to the image according to bbox information. This is a cutout variant that using bbox information to make more informed decisions on where to place the cutout mask. Args: image: 3D uint8 Tensor. bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox has 4 elements (min_y, min_x, max_y, max_x) of type float with values between [0, 1]. pad_fraction: Float that specifies how large the cutout mask should be in in reference to the size of the original bbox. If pad_fraction is 0.25, then the cutout mask will be of shape (0.25 * bbox height, 0.25 * bbox width). replace_with_mean: Boolean that specified what value should be filled in where the cutout mask is applied. Since the incoming image will be of uint8 and will not have had any mean normalization applied, by default we set the value to be 128. If replace_with_mean is True then we find the mean pixel values across the channel dimension and use those to fill in where the cutout mask is applied. Returns: A tuple. First element is a tensor of the same shape as image that has cutout applied to it. Second element is the bboxes that were passed in that will be unchanged.
efficientdet/aug/autoaugment.py
bbox_cutout
datawowio/automl
python
def bbox_cutout(image, bboxes, pad_fraction, replace_with_mean): 'Applies cutout to the image according to bbox information.\n\n This is a cutout variant that using bbox information to make more informed\n decisions on where to place the cutout mask.\n\n Args:\n image: 3D uint8 Tensor.\n bboxes: 2D Tensor that is a list of the bboxes in the image. Each bbox\n has 4 elements (min_y, min_x, max_y, max_x) of type float with values\n between [0, 1].\n pad_fraction: Float that specifies how large the cutout mask should be in\n in reference to the size of the original bbox. If pad_fraction is 0.25,\n then the cutout mask will be of shape\n (0.25 * bbox height, 0.25 * bbox width).\n replace_with_mean: Boolean that specified what value should be filled in\n where the cutout mask is applied. Since the incoming image will be of\n uint8 and will not have had any mean normalization applied, by default\n we set the value to be 128. If replace_with_mean is True then we find\n the mean pixel values across the channel dimension and use those to fill\n in where the cutout mask is applied.\n\n Returns:\n A tuple. First element is a tensor of the same shape as image that has\n cutout applied to it. Second element is the bboxes that were passed in\n that will be unchanged.\n ' def apply_bbox_cutout(image, bboxes, pad_fraction): 'Applies cutout to a single bounding box within image.' random_index = tf.random_uniform(shape=[], maxval=tf.shape(bboxes)[0], dtype=tf.int32) chosen_bbox = tf.gather(bboxes, random_index) (mask, mean) = _cutout_inside_bbox(image, chosen_bbox, pad_fraction) replace = (mean if replace_with_mean else 128) image = tf.where(tf.equal(mask, 0), tf.cast((tf.ones_like(image, dtype=image.dtype) * replace), dtype=image.dtype), image) return image image = tf.cond(tf.equal(tf.shape(bboxes)[0], 0), (lambda : image), (lambda : apply_bbox_cutout(image, bboxes, pad_fraction))) return (image, bboxes)
def _randomly_negate_tensor(tensor): 'With 50% prob turn the tensor negative.' should_flip = tf.cast(tf.floor((tf.random_uniform([]) + 0.5)), tf.bool) final_tensor = tf.cond(should_flip, (lambda : tensor), (lambda : (- tensor))) return final_tensor
7,650,776,648,965,303,000
With 50% prob turn the tensor negative.
efficientdet/aug/autoaugment.py
_randomly_negate_tensor
datawowio/automl
python
def _randomly_negate_tensor(tensor): should_flip = tf.cast(tf.floor((tf.random_uniform([]) + 0.5)), tf.bool) final_tensor = tf.cond(should_flip, (lambda : tensor), (lambda : (- tensor))) return final_tensor
def _shrink_level_to_arg(level): 'Converts level to ratio by which we shrink the image content.' if (level == 0): return (1.0,) level = ((2.0 / (_MAX_LEVEL / level)) + 0.9) return (level,)
9,177,563,595,245,319,000
Converts level to ratio by which we shrink the image content.
efficientdet/aug/autoaugment.py
_shrink_level_to_arg
datawowio/automl
python
def _shrink_level_to_arg(level): if (level == 0): return (1.0,) level = ((2.0 / (_MAX_LEVEL / level)) + 0.9) return (level,)
def bbox_wrapper(func): 'Adds a bboxes function argument to func and returns unchanged bboxes.' def wrapper(images, bboxes, *args, **kwargs): return (func(images, *args, **kwargs), bboxes) return wrapper
7,986,334,951,161,487,000
Adds a bboxes function argument to func and returns unchanged bboxes.
efficientdet/aug/autoaugment.py
bbox_wrapper
datawowio/automl
python
def bbox_wrapper(func): def wrapper(images, bboxes, *args, **kwargs): return (func(images, *args, **kwargs), bboxes) return wrapper
def _parse_policy_info(name, prob, level, replace_value, augmentation_hparams): 'Return the function that corresponds to `name` and update `level` param.' func = NAME_TO_FUNC[name] args = level_to_arg(augmentation_hparams)[name](level) if ('prob' in inspect.getfullargspec(func)[0]): args = tuple(([prob] + list(args))) if ('replace' in inspect.getfullargspec(func)[0]): assert ('replace' == inspect.getfullargspec(func)[0][(- 1)]) args = tuple((list(args) + [replace_value])) if ('bboxes' not in inspect.getfullargspec(func)[0]): func = bbox_wrapper(func) return (func, prob, args)
1,299,395,002,175,213,300
Return the function that corresponds to `name` and update `level` param.
efficientdet/aug/autoaugment.py
_parse_policy_info
datawowio/automl
python
def _parse_policy_info(name, prob, level, replace_value, augmentation_hparams): func = NAME_TO_FUNC[name] args = level_to_arg(augmentation_hparams)[name](level) if ('prob' in inspect.getfullargspec(func)[0]): args = tuple(([prob] + list(args))) if ('replace' in inspect.getfullargspec(func)[0]): assert ('replace' == inspect.getfullargspec(func)[0][(- 1)]) args = tuple((list(args) + [replace_value])) if ('bboxes' not in inspect.getfullargspec(func)[0]): func = bbox_wrapper(func) return (func, prob, args)
def _apply_func_with_prob(func, image, args, prob, bboxes): 'Apply `func` to image w/ `args` as input with probability `prob`.' assert isinstance(args, tuple) assert ('bboxes' == inspect.getfullargspec(func)[0][1]) if ('prob' in inspect.getfullargspec(func)[0]): prob = 1.0 should_apply_op = tf.cast(tf.floor((tf.random_uniform([], dtype=tf.float32) + prob)), tf.bool) (augmented_image, augmented_bboxes) = tf.cond(should_apply_op, (lambda : func(image, bboxes, *args)), (lambda : (image, bboxes))) return (augmented_image, augmented_bboxes)
-935,833,796,455,203,700
Apply `func` to image w/ `args` as input with probability `prob`.
efficientdet/aug/autoaugment.py
_apply_func_with_prob
datawowio/automl
python
def _apply_func_with_prob(func, image, args, prob, bboxes): assert isinstance(args, tuple) assert ('bboxes' == inspect.getfullargspec(func)[0][1]) if ('prob' in inspect.getfullargspec(func)[0]): prob = 1.0 should_apply_op = tf.cast(tf.floor((tf.random_uniform([], dtype=tf.float32) + prob)), tf.bool) (augmented_image, augmented_bboxes) = tf.cond(should_apply_op, (lambda : func(image, bboxes, *args)), (lambda : (image, bboxes))) return (augmented_image, augmented_bboxes)
def select_and_apply_random_policy(policies, image, bboxes): 'Select a random policy from `policies` and apply it to `image`.' policy_to_select = tf.random_uniform([], maxval=len(policies), dtype=tf.int32) for (i, policy) in enumerate(policies): (image, bboxes) = tf.cond(tf.equal(i, policy_to_select), (lambda selected_policy=policy: selected_policy(image, bboxes)), (lambda : (image, bboxes))) return (image, bboxes)
-2,167,437,932,029,373,700
Select a random policy from `policies` and apply it to `image`.
efficientdet/aug/autoaugment.py
select_and_apply_random_policy
datawowio/automl
python
def select_and_apply_random_policy(policies, image, bboxes): policy_to_select = tf.random_uniform([], maxval=len(policies), dtype=tf.int32) for (i, policy) in enumerate(policies): (image, bboxes) = tf.cond(tf.equal(i, policy_to_select), (lambda selected_policy=policy: selected_policy(image, bboxes)), (lambda : (image, bboxes))) return (image, bboxes)
def build_and_apply_nas_policy(policies, image, bboxes, augmentation_hparams): 'Build a policy from the given policies passed in and apply to image.\n\n Args:\n policies: list of lists of tuples in the form `(func, prob, level)`, `func`\n is a string name of the augmentation function, `prob` is the probability\n of applying the `func` operation, `level` is the input argument for\n `func`.\n image: tf.Tensor that the resulting policy will be applied to.\n bboxes: tf.Tensor of shape [N, 4] representing ground truth boxes that are\n normalized between [0, 1].\n augmentation_hparams: Hparams associated with the NAS learned policy.\n\n Returns:\n A version of image that now has data augmentation applied to it based on\n the `policies` pass into the function. Additionally, returns bboxes if\n a value for them is passed in that is not None\n ' replace_value = [128, 128, 128] tf_policies = [] for policy in policies: tf_policy = [] for policy_info in policy: policy_info = (list(policy_info) + [replace_value, augmentation_hparams]) tf_policy.append(_parse_policy_info(*policy_info)) def make_final_policy(tf_policy_): def final_policy(image_, bboxes_): for (func, prob, args) in tf_policy_: (image_, bboxes_) = _apply_func_with_prob(func, image_, args, prob, bboxes_) return (image_, bboxes_) return final_policy tf_policies.append(make_final_policy(tf_policy)) (augmented_images, augmented_bboxes) = select_and_apply_random_policy(tf_policies, image, bboxes) return (augmented_images, augmented_bboxes)
-5,391,226,156,041,492,000
Build a policy from the given policies passed in and apply to image. Args: policies: list of lists of tuples in the form `(func, prob, level)`, `func` is a string name of the augmentation function, `prob` is the probability of applying the `func` operation, `level` is the input argument for `func`. image: tf.Tensor that the resulting policy will be applied to. bboxes: tf.Tensor of shape [N, 4] representing ground truth boxes that are normalized between [0, 1]. augmentation_hparams: Hparams associated with the NAS learned policy. Returns: A version of image that now has data augmentation applied to it based on the `policies` pass into the function. Additionally, returns bboxes if a value for them is passed in that is not None
efficientdet/aug/autoaugment.py
build_and_apply_nas_policy
datawowio/automl
python
def build_and_apply_nas_policy(policies, image, bboxes, augmentation_hparams): 'Build a policy from the given policies passed in and apply to image.\n\n Args:\n policies: list of lists of tuples in the form `(func, prob, level)`, `func`\n is a string name of the augmentation function, `prob` is the probability\n of applying the `func` operation, `level` is the input argument for\n `func`.\n image: tf.Tensor that the resulting policy will be applied to.\n bboxes: tf.Tensor of shape [N, 4] representing ground truth boxes that are\n normalized between [0, 1].\n augmentation_hparams: Hparams associated with the NAS learned policy.\n\n Returns:\n A version of image that now has data augmentation applied to it based on\n the `policies` pass into the function. Additionally, returns bboxes if\n a value for them is passed in that is not None\n ' replace_value = [128, 128, 128] tf_policies = [] for policy in policies: tf_policy = [] for policy_info in policy: policy_info = (list(policy_info) + [replace_value, augmentation_hparams]) tf_policy.append(_parse_policy_info(*policy_info)) def make_final_policy(tf_policy_): def final_policy(image_, bboxes_): for (func, prob, args) in tf_policy_: (image_, bboxes_) = _apply_func_with_prob(func, image_, args, prob, bboxes_) return (image_, bboxes_) return final_policy tf_policies.append(make_final_policy(tf_policy)) (augmented_images, augmented_bboxes) = select_and_apply_random_policy(tf_policies, image, bboxes) return (augmented_images, augmented_bboxes)
@tf.autograph.experimental.do_not_convert def distort_image_with_autoaugment(image, bboxes, augmentation_name): 'Applies the AutoAugment policy to `image` and `bboxes`.\n\n Args:\n image: `Tensor` of shape [height, width, 3] representing an image.\n bboxes: `Tensor` of shape [N, 4] representing ground truth boxes that are\n normalized between [0, 1].\n augmentation_name: The name of the AutoAugment policy to use. The available\n options are `v0`, `v1`, `v2`, `v3` and `test`. `v0` is the policy used for\n all of the results in the paper and was found to achieve the best results\n on the COCO dataset. `v1`, `v2` and `v3` are additional good policies\n found on the COCO dataset that have slight variation in what operations\n were used during the search procedure along with how many operations are\n applied in parallel to a single image (2 vs 3).\n\n Returns:\n A tuple containing the augmented versions of `image` and `bboxes`.\n ' logging.info('Using autoaugmention policy: %s', augmentation_name) available_policies = {'v0': policy_v0, 'v1': policy_v1, 'v2': policy_v2, 'v3': policy_v3, 'test': policy_vtest} if (augmentation_name not in available_policies): raise ValueError('Invalid augmentation_name: {}'.format(augmentation_name)) policy = available_policies[augmentation_name]() augmentation_hparams = hparams_config.Config(dict(cutout_max_pad_fraction=0.75, cutout_bbox_replace_with_mean=False, cutout_const=100, translate_const=250, cutout_bbox_const=50, translate_bbox_const=120)) return build_and_apply_nas_policy(policy, image, bboxes, augmentation_hparams)
6,681,142,781,319,894,000
Applies the AutoAugment policy to `image` and `bboxes`. Args: image: `Tensor` of shape [height, width, 3] representing an image. bboxes: `Tensor` of shape [N, 4] representing ground truth boxes that are normalized between [0, 1]. augmentation_name: The name of the AutoAugment policy to use. The available options are `v0`, `v1`, `v2`, `v3` and `test`. `v0` is the policy used for all of the results in the paper and was found to achieve the best results on the COCO dataset. `v1`, `v2` and `v3` are additional good policies found on the COCO dataset that have slight variation in what operations were used during the search procedure along with how many operations are applied in parallel to a single image (2 vs 3). Returns: A tuple containing the augmented versions of `image` and `bboxes`.
efficientdet/aug/autoaugment.py
distort_image_with_autoaugment
datawowio/automl
python
@tf.autograph.experimental.do_not_convert def distort_image_with_autoaugment(image, bboxes, augmentation_name): 'Applies the AutoAugment policy to `image` and `bboxes`.\n\n Args:\n image: `Tensor` of shape [height, width, 3] representing an image.\n bboxes: `Tensor` of shape [N, 4] representing ground truth boxes that are\n normalized between [0, 1].\n augmentation_name: The name of the AutoAugment policy to use. The available\n options are `v0`, `v1`, `v2`, `v3` and `test`. `v0` is the policy used for\n all of the results in the paper and was found to achieve the best results\n on the COCO dataset. `v1`, `v2` and `v3` are additional good policies\n found on the COCO dataset that have slight variation in what operations\n were used during the search procedure along with how many operations are\n applied in parallel to a single image (2 vs 3).\n\n Returns:\n A tuple containing the augmented versions of `image` and `bboxes`.\n ' logging.info('Using autoaugmention policy: %s', augmentation_name) available_policies = {'v0': policy_v0, 'v1': policy_v1, 'v2': policy_v2, 'v3': policy_v3, 'test': policy_vtest} if (augmentation_name not in available_policies): raise ValueError('Invalid augmentation_name: {}'.format(augmentation_name)) policy = available_policies[augmentation_name]() augmentation_hparams = hparams_config.Config(dict(cutout_max_pad_fraction=0.75, cutout_bbox_replace_with_mean=False, cutout_const=100, translate_const=250, cutout_bbox_const=50, translate_bbox_const=120)) return build_and_apply_nas_policy(policy, image, bboxes, augmentation_hparams)
def distort_image_with_randaugment(image, bboxes, num_layers, magnitude): 'Applies the RandAugment to `image` and `bboxes`.' replace_value = [128, 128, 128] tf.logging.info('Using RandAugment.') augmentation_hparams = hparams_config.Config(dict(cutout_max_pad_fraction=0.75, cutout_bbox_replace_with_mean=False, cutout_const=100, translate_const=250, cutout_bbox_const=50, translate_bbox_const=120)) available_ops = ['Equalize', 'Solarize', 'Color', 'Cutout', 'SolarizeAdd', 'TranslateX_BBox', 'TranslateY_BBox', 'ShearX_BBox', 'ShearY_BBox', 'Rotate_BBox'] if (bboxes is None): bboxes = tf.constant(0.0) for layer_num in range(num_layers): op_to_select = tf.random_uniform([], maxval=len(available_ops), dtype=tf.int32) random_magnitude = float(magnitude) with tf.name_scope('randaug_layer_{}'.format(layer_num)): for (i, op_name) in enumerate(available_ops): prob = tf.random_uniform([], minval=0.2, maxval=0.8, dtype=tf.float32) (func, _, args) = _parse_policy_info(op_name, prob, random_magnitude, replace_value, augmentation_hparams) (image, bboxes) = tf.cond(tf.equal(i, op_to_select), (lambda fn=func, fn_args=args: fn(image, bboxes, *fn_args)), (lambda : (image, bboxes))) return (image, bboxes)
6,479,833,031,204,732,000
Applies the RandAugment to `image` and `bboxes`.
efficientdet/aug/autoaugment.py
distort_image_with_randaugment
datawowio/automl
python
def distort_image_with_randaugment(image, bboxes, num_layers, magnitude): replace_value = [128, 128, 128] tf.logging.info('Using RandAugment.') augmentation_hparams = hparams_config.Config(dict(cutout_max_pad_fraction=0.75, cutout_bbox_replace_with_mean=False, cutout_const=100, translate_const=250, cutout_bbox_const=50, translate_bbox_const=120)) available_ops = ['Equalize', 'Solarize', 'Color', 'Cutout', 'SolarizeAdd', 'TranslateX_BBox', 'TranslateY_BBox', 'ShearX_BBox', 'ShearY_BBox', 'Rotate_BBox'] if (bboxes is None): bboxes = tf.constant(0.0) for layer_num in range(num_layers): op_to_select = tf.random_uniform([], maxval=len(available_ops), dtype=tf.int32) random_magnitude = float(magnitude) with tf.name_scope('randaug_layer_{}'.format(layer_num)): for (i, op_name) in enumerate(available_ops): prob = tf.random_uniform([], minval=0.2, maxval=0.8, dtype=tf.float32) (func, _, args) = _parse_policy_info(op_name, prob, random_magnitude, replace_value, augmentation_hparams) (image, bboxes) = tf.cond(tf.equal(i, op_to_select), (lambda fn=func, fn_args=args: fn(image, bboxes, *fn_args)), (lambda : (image, bboxes))) return (image, bboxes)
def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask, content_tensor, image_): 'Applies mask to bbox region in image then adds content_tensor to it.' mask = tf.pad(mask, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=1) content_tensor = tf.pad(content_tensor, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=0) return ((image_ * mask) + content_tensor)
-1,229,225,343,436,366,000
Applies mask to bbox region in image then adds content_tensor to it.
efficientdet/aug/autoaugment.py
mask_and_add_image
datawowio/automl
python
def mask_and_add_image(min_y_, min_x_, max_y_, max_x_, mask, content_tensor, image_): mask = tf.pad(mask, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=1) content_tensor = tf.pad(content_tensor, [[min_y_, ((image_height - 1) - max_y_)], [min_x_, ((image_width - 1) - max_x_)], [0, 0]], constant_values=0) return ((image_ * mask) + content_tensor)
def scale_channel(image): 'Scale the 2D image using the autocontrast rule.' lo = tf.to_float(tf.reduce_min(image)) hi = tf.to_float(tf.reduce_max(image)) def scale_values(im): scale = (255.0 / (hi - lo)) offset = ((- lo) * scale) im = ((tf.to_float(im) * scale) + offset) im = tf.clip_by_value(im, 0.0, 255.0) return tf.cast(im, tf.uint8) result = tf.cond((hi > lo), (lambda : scale_values(image)), (lambda : image)) return result
1,234,125,559,131,667,000
Scale the 2D image using the autocontrast rule.
efficientdet/aug/autoaugment.py
scale_channel
datawowio/automl
python
def scale_channel(image): lo = tf.to_float(tf.reduce_min(image)) hi = tf.to_float(tf.reduce_max(image)) def scale_values(im): scale = (255.0 / (hi - lo)) offset = ((- lo) * scale) im = ((tf.to_float(im) * scale) + offset) im = tf.clip_by_value(im, 0.0, 255.0) return tf.cast(im, tf.uint8) result = tf.cond((hi > lo), (lambda : scale_values(image)), (lambda : image)) return result
def scale_channel(im, c): 'Scale the data in the channel to implement equalize.' im = tf.cast(im[:, :, c], tf.int32) histo = tf.histogram_fixed_width(im, [0, 255], nbins=256) nonzero = tf.where(tf.not_equal(histo, 0)) nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)]) step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255) def build_lut(histo, step): lut = ((tf.cumsum(histo) + (step // 2)) // step) lut = tf.concat([[0], lut[:(- 1)]], 0) return tf.clip_by_value(lut, 0, 255) result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im))) return tf.cast(result, tf.uint8)
6,353,019,026,156,998,000
Scale the data in the channel to implement equalize.
efficientdet/aug/autoaugment.py
scale_channel
datawowio/automl
python
def scale_channel(im, c): im = tf.cast(im[:, :, c], tf.int32) histo = tf.histogram_fixed_width(im, [0, 255], nbins=256) nonzero = tf.where(tf.not_equal(histo, 0)) nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [(- 1)]) step = ((tf.reduce_sum(nonzero_histo) - nonzero_histo[(- 1)]) // 255) def build_lut(histo, step): lut = ((tf.cumsum(histo) + (step // 2)) // step) lut = tf.concat([[0], lut[:(- 1)]], 0) return tf.clip_by_value(lut, 0, 255) result = tf.cond(tf.equal(step, 0), (lambda : im), (lambda : tf.gather(build_lut(histo, step), im))) return tf.cast(result, tf.uint8)