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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
import cv2
import albumentations
import random
from ldm.data.util import new_process_im_base #, imagenet_process_im
from glob import glob
import random
import base64
from io import BytesIO
class LaionOCRCLDataset(Dataset):
def __init__(self,
img_folder,
ocr_folder,
data_info_file,
max_num_samples = -1,
no_hint = False,
first_stage_key = "jpg",
cond_stage_key = "txt",
control_key = "hint",
BLIP_caption = False, #True,
filter_ocr_data = False,
filter_way = 0, #0, 1, 2
ocr_threshold = 0.5,
ocr_area_ths = 0.1,
max_token_num = 3,
rendered_txt_in_caption = False,
caption_choices = ["original", "w_rend_text", "wo_rend_text"],
caption_drop_rates = [0.1, 0.5, 0.1],
postprocess=None,
new_proc_config = None,
add_glyph_control = False, # TODO
rm_text_from_cp = False,
replace_token = "",
) -> None:
"""Create a dataset from a folder of images.
If you pass in a root directory it will be searched for images
ending in ext (ext can be a list)
"""
with open(data_info_file, "r") as f:
data_infos = f.readlines()
if max_num_samples > 0:
data_infos = random.sample(data_infos, max_num_samples)
self.data_infos = data_infos
self.img_folder = img_folder
self.ocr_folder = ocr_folder
self.ocr_threshold = ocr_threshold
self.no_hint = no_hint
self.filter_ocr_data = filter_ocr_data
self.filter_way = filter_way
self.max_token_num = max_token_num
self.ocr_area_ths =ocr_area_ths
self.caption_choices = caption_choices
self.caption_drop_rates = caption_drop_rates
self.rendered_txt_in_caption = rendered_txt_in_caption
self.BLIP_caption = BLIP_caption
self.first_stage_key = first_stage_key
self.cond_stage_key = cond_stage_key
self.control_key = control_key
self.add_glyph_control = add_glyph_control
# postprocess
if isinstance(postprocess, DictConfig):
postprocess = instantiate_from_config(postprocess)
self.postprocess = postprocess
# image transform
if new_proc_config is not None:
self.new_proc_func = instantiate_from_config(new_proc_config)
else:
self.new_proc_func = new_process_im_base()
self.filtered_data_list = []
self.rm_text_from_cp = rm_text_from_cp
self.replace_token = replace_token
def __len__(self):
return len(self.data_infos)
def __getitem__(self, index):
data = {}
# data info
data_info = self.data_infos[index]
info_split = [di.strip() for di in data_info.split("\t")]
try:
assert len(info_split) == 5
except:
print("data_info_error", len(info_split))
return self.__getitem__(np.random.choice(self.__len__()))
tsv_name = info_split[2]
path_split = tsv_name.split("/")
try:
assert len(path_split) <= 2
except:
print("wrong tsv path", tsv_name)
return self.__getitem__(np.random.choice(self.__len__()))
tsv_name = path_split[-1]
if len(path_split) == 2:
img_folder = os.path.join(self.img_folder, path_split[0])
ocr_folder = os.path.join(
self.ocr_folder,
path_split[0].rstrip("_with_new_caption").replace("ori", "ocr")
)
else:
img_folder = self.img_folder
ocr_folder = self.ocr_folder
file_pos = eval(info_split[3])
idx_in_tsv = eval(info_split[4])
img_id = "\t".join(info_split[:2])
if self.filter_ocr_data and img_id in self.filtered_data_list:
return self.__getitem__(np.random.choice(self.__len__()))
# original image
ori_tsv_file = os.path.join(img_folder, tsv_name)
with open(ori_tsv_file, "r") as f:
f.seek(file_pos)
img_info = f.readline()
img_info_split = [di.strip() for di in img_info.split("\t")]
try:
assert len(img_info_split) >= 4 #=4
assert img_id == "\t".join(img_info_split[:2])
except:
print("image_info_error", len(img_info_split), img_id, "\t".join(img_info_split[:2]))
return self.__getitem__(np.random.choice(self.__len__()))
img_code = img_info_split[2] #[-2]
try:
ori_img = Image.open(BytesIO(base64.b64decode(img_code)))
except:
print("can't open original image: {}".format(img_id))
return self.__getitem__(np.random.choice(self.__len__()))
if self.BLIP_caption:
try:
assert len(img_info_split) == 5
except:
print("caption_error", len(img_info_split), img_id, "\t".join(img_info_split[:2]), img_info_split[-1])
return self.__getitem__(np.random.choice(self.__len__()))
caption_ori = img_info_split[-1]
else:
caption_ori = img_info_split[3]
img_size = ori_img.size
# ocr info
name_split = os.path.splitext(tsv_name)[0].split("_")
ocr_infos_file = os.path.join(
ocr_folder,
"_".join(name_split[:-1] + ["ocr_info"] + [name_split[-1]]) + ".json"
)
try:
with open(ocr_infos_file, "r") as f:
ocr_infos = json.load(f)
except:
print("can't open ocr info file {}".format(ocr_infos_file))
return self.__getitem__(np.random.choice(self.__len__()))
try:
ocr_info = ocr_infos[img_id]
assert len(ocr_info) > 0
except:
print("the ocr info of the {} is missing in {}".format(img_id, ocr_infos_file))
return self.__getitem__(np.random.choice(self.__len__()))
if self.filter_ocr_data and self.filter_way == 0 and len(ocr_info) > self.max_token_num:
if img_id not in self.filtered_data_list:
self.filtered_data_list.append(img_id)
return self.__getitem__(np.random.choice(self.__len__()))
ocr_area = 0
pos_info_list = []
pos_info_tuples = []
for info in ocr_info:
bbox, (text, confidence) = info
if confidence > self.ocr_threshold:
xy_info = np.array(bbox)
min_x, min_y = np.min(xy_info, axis = 0).astype(int)
max_x, max_y = np.max(xy_info, axis = 0).astype(int)
pos_info_list.append(
[min_x, min_y, max_x, max_y]
)
mean_xy = (xy_info[0] + xy_info[2]) / 2
lf = xy_info[0, 0] # min_x
pos_info_tuples.append((text, 0.2 * lf + mean_xy[1])) #0.15
# ocr_txt = info[1]
if self.filter_ocr_data and self.filter_way == 1:
ocr_area += np.abs(
np.linalg.det(
[xy_info[1] - xy_info[0], xy_info[3] - xy_info[0]]
)
)
if self.filter_ocr_data and self.filter_way == 1:
if ocr_area < self.ocr_area_ths * (img_size[0] * img_size[1]):
if img_id not in self.filtered_data_list:
self.filtered_data_list.append(img_id)
return self.__getitem__(np.random.choice(self.__len__()))
pos_info_list = np.array(pos_info_list)
all_lf, all_up = np.min(pos_info_list[:, :2], axis = 0)
all_rg, all_dn = np.max(pos_info_list[:, 2:], axis = 0)
all_pos_info = [all_lf, all_up, all_rg, all_dn]
# the third way to filter ocr data
if self.filter_ocr_data and self.filter_way == 2:
if (all_rg - all_lf) * (all_dn - all_up) < self.ocr_area_ths * (img_size[0] * img_size[1]):
if img_id not in self.filtered_data_list:
self.filtered_data_list.append(img_id)
return self.__getitem__(np.random.choice(self.__len__()))
# hint image
if not self.no_hint:
hint_tsv_file = os.path.join(
ocr_folder,
"_".join(name_split[:-1] + ["rendered"] + [name_split[-1]]) + ".tsv"
)
with open(hint_tsv_file, "r") as f:
hint_img_infos = f.readlines()
hint_img_info = hint_img_infos[idx_in_tsv]
hint_img_info_split = [di.strip() for di in hint_img_info.split("\t")]
try:
assert len(hint_img_info_split) == 3
assert img_id == "\t".join(hint_img_info_split[:2])
except:
print("hint_image_info_error", len(hint_img_info_split), img_id, "\t".join(hint_img_info_split[:2]))
return self.__getitem__(np.random.choice(self.__len__()))
hint_img_code = hint_img_info_split[-1]
try:
hint_img = Image.open(BytesIO(base64.b64decode(hint_img_code)))
except:
print("can't open hint image: {}".format(img_id))
return self.__getitem__(np.random.choice(self.__len__()))
else:
hint_img = None
# return self.__getitem__(np.random.choice(self.__len__()))
assert all_pos_info
im, im_hint = self.new_proc_func(ori_img, all_pos_info, hint_img)
if not self.no_hint:
assert im_hint is not None
data[self.control_key] = im_hint
data[self.first_stage_key] = im
caption_wr_text = None
arrange_tokens = [item[0] for item in (sorted(pos_info_tuples, key=lambda x: x[1]))]
if self.rendered_txt_in_caption:
valid_words = " ".join(arrange_tokens)
caption_wr_text = caption_ori + '. Words in the image: "{}"'.format(valid_words)
# class_name = ""
# if class_name == "":
# return self.__getitem__(np.random.choice(self.__len__()))
# else:
# caption_wr_text = 'A {} that says "{}".'.format(
# class_name, valid_words
# )
# process the ori
caption_wo_text = None #
if self.rm_text_from_cp and self.BLIP_caption: # only generate the caption without the rendered words in it while using BLIP captions
# caption_wo_text = caption_ori
# for token in arrange_tokens:
# caption_wo_text = caption_wo_text.replace(token, self.replace_token)
caption_items = caption_ori.split(" ")
lower_arrange_tokens = [tk.lower() for tk in arrange_tokens]
caption_wo_text = []
for cp_item in caption_items:
if cp_item.lower() in lower_arrange_tokens:
if self.replace_token != "":
caption_wo_text.append(self.replace_token)
else:
caption_wo_text.append(cp_item)
caption_wo_text = " ".join(caption_wo_text)
prompt_list = []
for i in range(len(self.caption_choices)):
cc = self.caption_choices[i]
if cc == "original":
caption = caption_ori
elif cc == "w_rend_text":
caption = caption_wr_text if caption_wr_text is not None else caption_ori
elif cc == "wo_rend_text":
caption = caption_wo_text if caption_wo_text is not None else caption_ori
if torch.rand(1) < self.caption_drop_rates[i]:
caption = ""
prompt_list.append(caption)
data[self.cond_stage_key] = prompt_list if len(prompt_list) > 1 else prompt_list[0]
if self.postprocess is not None:
data = self.postprocess(data)
return data
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