Spaces:
Running
Running
File size: 8,100 Bytes
f8bf7d4 b582a0e f8bf7d4 b582a0e f8bf7d4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 |
from PIL import Image
from PIL import ExifTags
import re
import datetime
import hashlib
import logging
import streamlit as st
import cv2
import numpy as np
m_logger = logging.getLogger(__name__)
# we can set the log level locally for funcs in this module
#g_m_logger.setLevel(logging.DEBUG)
m_logger.setLevel(logging.INFO)
'''
A module to setup the input handling for the whale observation guidance tool
both the UI elements (setup_input_UI) and the validation functions.
'''
#allowed_image_types = ['webp']
allowed_image_types = ['jpg', 'jpeg', 'png', 'webp']
# autogenerated class to hold the input data
class InputObservation:
def __init__(self, image=None, latitude=None, longitude=None, author_email=None, date=None, time=None, date_option=None, time_option=None, uploaded_filename=None):
self.image = image
self.latitude = latitude
self.longitude = longitude
self.author_email = author_email
self.date = date
self.time = time
self.date_option = date_option
self.time_option = time_option
self.uploaded_filename = uploaded_filename
def __str__(self):
return f"Observation: {self.image}, {self.latitude}, {self.longitude}, {self.author_email}, {self.date}, {self.time}, {self.date_option}, {self.time_option}, {self.uploaded_filename}"
def __repr__(self):
return f"Observation: {self.image}, {self.latitude}, {self.longitude}, {self.author_email}, {self.date}, {self.time}, {self.date_option}, {self.time_option}, {self.uploaded_filename}"
def __eq__(self, other):
return (self.image == other.image and self.latitude == other.latitude and self.longitude == other.longitude and
self.author_email == other.author_email and self.date == other.date and self.time == other.time and
self.date_option == other.date_option and self.time_option == other.time_option and self.uploaded_filename == other.uploaded_filename)
def __ne__(self, other):
return not self.__eq__(other)
def __hash__(self):
return hash((self.image, self.latitude, self.longitude, self.author_email, self.date, self.time, self.date_option, self.time_option, self.uploaded_filename))
def to_dict(self):
return {
#"image": self.image,
"image_filename": self.uploaded_filename.name if self.uploaded_filename else None,
"image_md5": hashlib.md5(self.uploaded_filename.read()).hexdigest() if self.uploaded_filename else None,
"latitude": self.latitude,
"longitude": self.longitude,
"author_email": self.author_email,
"date": self.date,
"time": self.time,
"date_option": self.date_option,
"time_option": self.time_option,
"uploaded_filename": self.uploaded_filename
}
@classmethod
def from_dict(cls, data):
return cls(data["image"], data["latitude"], data["longitude"], data["author_email"], data["date"], data["time"], data["date_option"], data["time_option"], data["uploaded_filename"])
@classmethod
def from_input(cls, input):
return cls(input.image, input.latitude, input.longitude, input.author_email, input.date, input.time, input.date_option, input.time_option, input.uploaded_filename)
@staticmethod
def from_input(input):
return InputObservation(input.image, input.latitude, input.longitude, input.author_email, input.date, input.time, input.date_option, input.time_option, input.uploaded_filename)
@staticmethod
def from_dict(data):
return InputObservation(data["image"], data["latitude"], data["longitude"], data["author_email"], data["date"], data["time"], data["date_option"], data["time_option"], data["uploaded_filename"])
# define function to validate number, allowing signed float
def is_valid_number(number:str) -> bool:
pattern = r'^[-+]?[0-9]*\.?[0-9]+$'
return re.match(pattern, number) is not None
# Function to validate email address
def is_valid_email(email):
pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
return re.match(pattern, email) is not None
# Function to extract date and time from image metadata
def get_image_datetime(image_file):
try:
image = Image.open(image_file)
exif_data = image._getexif()
if exif_data is not None:
for tag, value in exif_data.items():
if ExifTags.TAGS.get(tag) == 'DateTimeOriginal':
return value
except Exception as e:
st.warning("Could not extract date from image metadata.")
return None
# an arbitrary set of defaults so testing is less painful...
# ideally we add in some randomization to the defaults
spoof_metadata = {
"latitude": 23.5,
"longitude": 44,
"author_email": "[email protected]",
"date": None,
"time": None,
}
#def display_whale(whale_classes:List[str], i:int, viewcontainer=None):
def setup_input(viewcontainer: st.delta_generator.DeltaGenerator=None, _allowed_image_types: list=None, ):
if viewcontainer is None:
viewcontainer = st.sidebar
if _allowed_image_types is None:
_allowed_image_types = allowed_image_types
viewcontainer.title("Input image and data")
# 1. Image Selector
uploaded_filename = viewcontainer.file_uploader("Upload an image", type=allowed_image_types)
image_datetime = None # For storing date-time from image
if uploaded_filename is not None:
# Display the uploaded image
#image = Image.open(uploaded_filename)
# load image using cv2 format, so it is compatible with the ML models
file_bytes = np.asarray(bytearray(uploaded_filename.read()), dtype=np.uint8)
image = cv2.imdecode(file_bytes, 1)
viewcontainer.image(image, caption='Uploaded Image.', use_column_width=True)
# store the image in the session state
st.session_state.image = image
# Extract and display image date-time
image_datetime = get_image_datetime(uploaded_filename)
print(f"[D] image date extracted as {image_datetime}")
m_logger.debug(f"image date extracted as {image_datetime} (from {uploaded_filename})")
# 2. Latitude Entry Box
latitude = viewcontainer.text_input("Latitude", spoof_metadata.get('latitude', ""))
if latitude and not is_valid_number(latitude):
viewcontainer.error("Please enter a valid latitude (numerical only).")
m_logger.error(f"Invalid latitude entered: {latitude}.")
# 3. Longitude Entry Box
longitude = viewcontainer.text_input("Longitude", spoof_metadata.get('longitude', ""))
if longitude and not is_valid_number(longitude):
viewcontainer.error("Please enter a valid longitude (numerical only).")
m_logger.error(f"Invalid latitude entered: {latitude}.")
# 4. Author Box with Email Address Validator
author_email = viewcontainer.text_input("Author Email", spoof_metadata.get('author_email', ""))
if author_email and not is_valid_email(author_email):
viewcontainer.error("Please enter a valid email address.")
# 5. date/time
## first from image metadata
if image_datetime is not None:
time_value = datetime.datetime.strptime(image_datetime, '%Y:%m:%d %H:%M:%S').time()
date_value = datetime.datetime.strptime(image_datetime, '%Y:%m:%d %H:%M:%S').date()
else:
time_value = datetime.datetime.now().time() # Default to current time
date_value = datetime.datetime.now().date()
## if not, give user the option to enter manually
date_option = st.sidebar.date_input("Date", value=date_value)
time_option = st.sidebar.time_input("Time", time_value)
observation = InputObservation(image=uploaded_filename, latitude=latitude, longitude=longitude,
author_email=author_email, date=image_datetime, time=None,
date_option=date_option, time_option=time_option)
return observation
|