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from typing import Dict
import numpy as np
from omegaconf import DictConfig, ListConfig
import torch
from torch.utils.data import Dataset
from pathlib import Path
import json
from PIL import Image
from torchvision import transforms
from einops import rearrange
from ldm.util import instantiate_from_config
# from datasets import load_dataset
import os
from collections import defaultdict
from glob import glob
import re
from bisect import bisect_left, bisect_right
import albumentations, cv2
import time
class SynWhiteBoardDataset(Dataset):
def __init__(self,
img_folder,
caption_folder,
tsv_info_file,
corpus_type = "all_4gram",
image_transforms=[],
first_stage_key = "jpg",
cond_stage_key = "txt",
postprocess=None,
ext = "png",
img_class = "whiteboard",
caption_type = "regular", # "simple" or "regular" or "full"
lower_case = False,
max_num = None,
image_size = 512,
do_padding = True,
explict_arrangement = False,
) -> None:
self.root_dir = os.path.join(Path(img_folder), corpus_type)
self.caption_folder = caption_folder
assert os.path.exists(self.caption_folder) and os.path.exists(tsv_info_file)
with open(tsv_info_file, "r") as f:
tsv_info_dict = json.loads(f.read())
total_num = 0
rank_list = []
for _, value in tsv_info_dict.items():
total_num += len(value)
rank_list.append(total_num)
self.rank_list = rank_list
self.total_num = total_num if max_num is None else max_num
self.tsv_info_dict = tsv_info_dict
self.corpus_type = corpus_type
self.first_stage_key = first_stage_key
self.cond_stage_key = cond_stage_key
# postprocess
if isinstance(postprocess, DictConfig):
postprocess = instantiate_from_config(postprocess)
self.postprocess = postprocess
# image transform
if isinstance(image_transforms, ListConfig):
image_transforms = [instantiate_from_config(tt) for tt in image_transforms]
image_transforms.extend([transforms.ToTensor(), # to be checked
transforms.Lambda(lambda x: rearrange(x * 2. - 1., 'c h w -> h w c'))])
image_transforms = transforms.Compose(image_transforms)
self.tform = image_transforms
self.ext = ext
self.num_rank = eval((list(tsv_info_dict.keys())[0]).split("_")[-1].split(".")[0])
self.img_class = img_class
self.caption_type = caption_type
self.lower_case = lower_case
self.do_padding = do_padding
self.image_rescaler = albumentations.LongestMaxSize(max_size=image_size, interpolation=cv2.INTER_AREA)
self.image_size = image_size
self.pad = albumentations.PadIfNeeded(min_height= self.image_size, min_width=self.image_size,
border_mode=cv2.BORDER_CONSTANT, value= (255, 255, 255),
)
self.explict_arrangement = explict_arrangement
def __len__(self):
return self.total_num
def __getitem__(self, index):
pre = time.time()
data = {}
rank = bisect_right(self.rank_list, index)
index_in_tsv = index - ( self.rank_list[rank-1] if rank > 0 else 0 )
# rank = index % self.num_rank
# index_in_tsv = index // self.num_rank
tsv_name = "{}_{}_{}.tsv".format(
self.corpus_type, rank, self.num_rank
)
with open(os.path.join(self.caption_folder, tsv_name), "r") as f:
f.seek(
self.tsv_info_dict[tsv_name][index_in_tsv]
)
caption_info = f.readline().strip()
# print("open caption file", time.time() - pre)
info_list = caption_info.split("\t")
assert len(info_list) == 5
txt_content, font_file, arrange_, align, imagename= info_list
# imagename= str(index) + ".{}".format(self.ext)
filename = os.path.join(self.root_dir, imagename)
img_pret = time.time()
try:
im = Image.open(filename)
# print("open image time", time.time() - img_pret)
except:
return self.__getitem__(np.random.choice(self.__len__()))
im = self.process_im(im)
data[self.first_stage_key] = im
# print("img process time", time.time() - img_pret)
if self.caption_type == "simple":
caption = 'A {} that says {}'.format(
self.img_class, txt_content,
)
else:
# elif self.caption_type == "regular":
font_weight = ""
font_style = ""
font_width = ""
font_file = re.sub(u'\\[.*?\\]',"", font_file) # remove []
font_list = font_file[:-4].split("-")
if len(font_list) > 2:
print("font file name outlier: {}".format(font_file))
font_list = [
"-".join(font_list[:-1]),
font_list[-1]
]
if len(font_list) == 2:
font_name, font_type = font_list
if font_type == "VF":
font_style = "VF"
else:
# font_type = re.sub(u'\\[.*?\\]',"", font_type) # remove []
font_tlist = re.findall("[A-Z][a-z]*", font_type)
if "Regular" in font_tlist:
font_weight = "Regular"
font_style = "Regular"
else:
# style
if "Italic" in font_tlist:
font_style = "Italic"
font_tlist.remove("Italic")
elif "Oblique" in font_tlist:
font_style = "Oblique"
font_tlist.remove("Oblique")
elif "Cursive" in font_tlist:
font_style = "Cursive"
font_tlist.remove("Cursive")
elif "Book" in font_tlist:
font_style = "Book"
font_tlist.remove("Book")
# width
if "Condensed" in font_tlist:
font_width = "Condensed"
font_tlist.remove("Condensed")
# weight
if len(font_tlist):
font_weight = " ".join(font_tlist)
elif len(font_list) == 1:
font_name = font_list[0]
# font_name = re.sub(u'\\[.*?\\]',"", font_name) # remove []
if "Italic" in font_name:
font_name = font_name.replace("Italic","")
font_style = "Italic"
if "Bold" in font_name:
font_name = font_name.replace("Bold", "")
font_weight = "Bold"
else:
print("Invalid font file name: {}".format(font_file))
return self.__getitem__(np.random.choice(self.__len__()))
# Width
if "Condensed" in font_name:
if "Extra" in font_name or "Semi" in font_name or "Ultra" in font_name:
font_name_list = re.findall("[A-Z][a-z]*", font_name)
font_width = " ".join(font_name_list[-2:])
font_name = "".join(font_name_list[:-2])
else:
font_name = font_name.rstrip("Condensed")
font_width = "Condensed"
# if "ExtraCondensed" in font_name:
# font_width = "Extra Condensed"
# elif "SemiCondensed" in font_name:
# font_width = "Semi Condensed"
# elif "UltraCondensed" in font_name:
# font_width = "Ultra Condensed"
# else:
# font_width = "Condensed"
caption = 'A {} that says {} written in the font of {}'.format(
self.img_class, txt_content, font_name
)
addition_cond = 0
if font_weight != "":
font_weight = font_weight.lower() if self.lower_case else font_weight
caption += " {} {} stroke weight".format(
"with" if addition_cond == 0 else "and", font_weight
)
addition_cond += 1
if font_width != "":
font_width = font_width.lower() if self.lower_case else font_width
caption += " {} {} font width".format(
"with" if addition_cond == 0 else "and", font_width
)
addition_cond += 1
if font_style != "":
font_style = font_style.lower() if self.lower_case else font_style
caption += " {} {} font style".format(
"with" if addition_cond == 0 else "and", font_style
)
addition_cond += 1
if self.caption_type == "full":
words = txt_content.strip('"').split(" ")
assert len(words) == 4
frn, srn = arrange_.split("_")
frn, srn = eval(frn), eval(srn)
assert (frn + srn == 4 )
if frn == 0 or srn == 0:
caption += '. All the words are written in the same row.'
else:
if self.explict_arrangement:
caption += '. "{}" is written in the first row while "{}" is in the second row.'.format(
' '.join(words[:frn]),
' '.join(words[frn:])
)
else:
caption += '. The first {} written in the first row while the {} in the second row.'.format(
"{} words are".format(frn) if frn >1 else "word is",
"other {} words are".format(srn) if srn >1 else "last word is",
)
# print(caption)
# print(caption)
data[self.cond_stage_key] = caption
# if self.captions is not None:
# data[self.cond_stage_key] = caption
# else:
# data[self.cond_stage_key] = self.default_caption
if self.postprocess is not None:
data = self.postprocess(data)
# print("total time", time.time() - pre)
return data
def process_im(self, im):
im = im.convert("RGB")
if self.do_padding:
# pre = time.time()
im = self.padding_image(im)
# print("padding time", time.time() - pre)
return self.tform(im)
def padding_image(self, im):
# resize
im = np.array(im).astype(np.uint8)
im_rescaled = self.image_rescaler(image=im)["image"]
# padding
im_padded = self.pad(image=im_rescaled)["image"]
return im_padded
# im_out = Image.fromarray(im_padded)
# return im_out |