GlyphControl / ldm /data /textcaps_control.py
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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, imagenet_process_im
class TextCapsCLDataset(Dataset):
def __init__(self,
img_folder,
caption_file=None,
image_transforms=[],
first_stage_key = "jpg", cond_stage_key = "txt",
OneCapPerImage = False,
default_caption="",
ext="jpg",
postprocess=None,
return_paths=False,
filter_data=False,
filter_words=["sign", "poster"],
ocr_file=None,
no_hint = False,
hint_folder = None,
control_key = "hint",
# aug4hint = True,
do_tutorial_proc = False,
imagenet_proc = False,
imagenet_proc_config = None,
filter_ocr_tokens = False,
do_new_proc = True,
new_proc_config = None,
random_drop_caption = False,
drop_caption_p = 0.5,
new_ocr_info = True,
sep_cap_for_2b = False,
rendered_txt_in_caption = False,
filter_token_num = False,
max_token_num = 3,
random_drop_sd_caption = False,
drop_sd_caption_p = 0.1,
) -> 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)
"""
self.root_dir = Path(img_folder)
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
self.imagenet_proc = imagenet_proc
self.do_new_proc = do_new_proc
self.do_tutorial_proc = do_tutorial_proc
# self.aug4hint = aug4hint
if self.do_new_proc:
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()
elif not self.do_tutorial_proc:
if self.imagenet_proc:
if imagenet_proc_config is not None:
self.imagenet_proc_func = instantiate_from_config(imagenet_proc_config)
else:
self.imagenet_proc_func = imagenet_process_im()
self.process_im = self.imagenet_proc_func
else:
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.process_im = self.simple_process_im
# caption
if caption_file is not None:
with open(caption_file, "rt") as f:
ext = Path(caption_file).suffix.lower()
if ext == ".json":
captions = json.load(f)
# elif ext == ".jsonl":
# lines = f.readlines()
# lines = [json.loads(x) for x in lines]
# captions = {x["file_name"]: x["text"].strip("\n") for x in lines}
else:
raise ValueError(f"Unrecognised format: {ext}")
self.captions = captions["data"]
if OneCapPerImage and ocr_file is None:
new_captions = []
taken_images = []
for caption_data in self.captions:
if caption_data["image_id"] in taken_images:
continue
else:
new_captions.append(caption_data)
taken_images.append(caption_data["image_id"])
self.captions = new_captions
else:
self.captions = None
if not isinstance(ext, (tuple, list, ListConfig)):
ext = [ext]
# Only used if there is no caption file
self.paths = []
for e in ext:
self.paths.extend(list(self.root_dir.rglob(f"*.{e}")))
self.default_caption = default_caption
self.return_paths = return_paths
self.filter_data = filter_data
self.filter_words = filter_words
self.ocr_file = ocr_file
self.ocr_data = []
if ocr_file is not None:
assert self.captions is not None
with open(ocr_file, "r") as f:
ocrs = json.loads(f.read())
ocr_data = ocrs['data']
self.ocr_data = ocr_data
self.no_hint = no_hint
self.control_key = control_key
self.hint_folder = None
if not self.no_hint:
if hint_folder is None:
print("Warning: The folder of hint images is not provided! No hint will be used")
self.no_hint = True
else:
self.hint_folder = Path(hint_folder)
self.filter_ocr_tokens = filter_ocr_tokens
self.random_drop_caption = random_drop_caption
self.drop_caption_p = drop_caption_p
self.new_ocr_info = new_ocr_info
self.sep_cap_for_2b = sep_cap_for_2b
self.rendered_txt_in_caption = rendered_txt_in_caption
self.filter_token_num = filter_token_num
self.max_token_num = max_token_num
self.random_drop_sd_caption = random_drop_sd_caption
self.drop_sd_caption_p = drop_sd_caption_p
def __len__(self):
if self.ocr_file is not None:
return len(self.ocr_data)
if self.captions is not None:
# return len(self.captions.keys())
return len(self.captions)
else:
return len(self.paths)
def __getitem__(self, index):
data = {}
if self.ocr_file is not None:
sample = self.ocr_data[index]
image_id = sample["image_id"]
ocr_tokens = sample["ocr_tokens"]
ocr_info = sample["ocr_info"]
chosen = image_id + ".jpg"
filename = self.root_dir/chosen
for d in self.captions:
if d["image_id"] == image_id:
image_captions = d["reference_strs"]
image_classes = d["image_classes"]
break
if not len(ocr_tokens) or not len(image_captions) or not len(image_classes):
return self.__getitem__(np.random.choice(self.__len__()))
if self.filter_ocr_tokens:
tokens_state=defaultdict(list)
for token in ocr_tokens:
token_info = [
caption for caption in image_captions if (token.lower() in caption.rstrip(".").lower().split(" "))
]
tokens_state[len(token_info)].append(token.lower())
max_n = max(tokens_state.keys())
if max_n > 0:
valid_tokens = list(set(tokens_state[max_n]))
pos_info = dict()
for token in valid_tokens:
for item in ocr_info:
if item['word'].lower() == token:
token_box = item['bounding_box']
tx, ty = token_box['top_left_x'], token_box['top_left_y']
pos_info[token] = tx+ty
break
# arrange_tokens = list(dict(sorted(pos_info.items(), key=lambda x: x[1])).keys())
arrange_tokens = [item[0] for item in (sorted(pos_info.items(), key=lambda x: x[1]))]
valid_words = " ".join(arrange_tokens)
class_name = ""
for word in self.filter_words:
if word in " ".join(image_classes).lower():
class_name = word
break
if class_name == "":
return self.__getitem__(np.random.choice(self.__len__()))
else:
caption = "A {} that says '{}'.".format(
class_name, valid_words
)
else:
return self.__getitem__(np.random.choice(self.__len__()))
else:
caption = random.choice(image_captions)
if self.filter_data:
if not len([word for word in self.filter_words if word in " ".join(image_classes).lower()]):
return self.__getitem__(np.random.choice(self.__len__()))
with Image.open(filename) as img:
im_w, im_h = img.size
pos_info_list = []
pos_info_dict = dict()
if self.filter_token_num and len(ocr_info) > self.max_token_num:
return self.__getitem__(np.random.choice(self.__len__()))
for item in ocr_info:
token_box = item['bounding_box']
lf, up = token_box['top_left_x'], token_box['top_left_y']
w, h = token_box['width'], token_box['height']
if not self.new_ocr_info:
# old version
rg, dn = lf + w, up + h
pos_info_list.append([lf, up, rg, dn])
else:
## fix the bug when rotation happens
# pos_info_dict[item["word"]] = 0.06 * lf + up
lf, w = int(lf * im_w), int(w * im_w)
up, h = int(up * im_h), int(h * im_h)
yaw = token_box['yaw']
# if yaw > 5:
# aa = 1
tf_xy = np.array([lf, up])
yaw = yaw * np.pi / 180
rotate_mx = np.array([
[np.cos(yaw), -np.sin(yaw)],
[np.sin(yaw), np.cos(yaw)]
])
rel_cord = np.matmul(rotate_mx, np.array(
[[0, 0],
[w, 0],
[0, h],
[w, h]]
).T)
min_xy = np.min(rel_cord, axis = 1).astype(int) + tf_xy
max_xy = np.max(rel_cord, axis = 1).astype(int) + tf_xy
pos_info_list.append(
[
min_xy[0], min_xy[1],
max_xy[0], max_xy[1]
]
)
mean_xy = rel_cord[:, -1] / 2 + tf_xy
pos_info_dict[item["word"]] = 0.2 * lf + mean_xy[1] #0.15
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]
if self.rendered_txt_in_caption:
assert self.filter_data
arrange_tokens = [item[0] for item in (sorted(pos_info_dict.items(), key=lambda x: x[1]))]
valid_words = " ".join(arrange_tokens)
class_name = ""
for word in self.filter_words:
if word in " ".join(image_classes).lower():
class_name = word
break
if class_name == "":
return self.__getitem__(np.random.choice(self.__len__()))
else:
out_caption = 'A {} that says "{}".'.format(
class_name, valid_words
)
else:
if self.captions is not None:
# chosen = list(self.captions.keys())[index]
# caption = self.captions.get(chosen, None)
caption_data = self.captions[index]
chosen = os.path.basename(caption_data["image_path"])
caption = caption_data["caption_str"]
if caption is None:
caption = self.default_caption
filename = self.root_dir/chosen
image_classes = caption_data["image_classes"]
# data[self.cond_stage_key] = caption
else:
filename = self.paths[index]
caption = self.default_caption
image_classes = [""]
# data[self.cond_stage_key] = self.default_caption
if self.filter_data:
if not len([word for word in self.filter_words if word in " ".join(image_classes).lower()]):
return self.__getitem__(np.random.choice(self.__len__()))
# if not len([word for word in self.filter_words if word in caption.rstrip(".").lower().split(" ")]):
# return self.__getitem__(np.random.choice(self.__len__()))
if not self.no_hint:
hint_filename = self.hint_folder/chosen
if not os.path.isfile(hint_filename):
print("Hint file {} does not exist".format(hint_filename))
return self.__getitem__(np.random.choice(self.__len__()))
else:
hint_filename = None
if self.do_tutorial_proc:
# to be aborted
im, im_hint = self.tutorial_process_im(filename, hint_filename)
elif self.do_new_proc:
# recommended
assert all_pos_info
im, im_hint = self.new_proc_func(filename, all_pos_info, hint_filename)
else:
im_hint = None
im = Image.open(filename)
im = self.process_im(im) # not supported for the flip option for now
if hint_filename is not None:
im_hint = Image.open(hint_filename)
im_hint = self.process_im(im_hint) #if self.aug4hint else self.noaug_process_im(im_hint)
if not self.no_hint:
assert im_hint is not None
data[self.control_key] = im_hint
data[self.first_stage_key] = im
if self.return_paths:
data["path"] = str(filename)
if not self.rendered_txt_in_caption:
out_caption = caption
if self.random_drop_caption:
if torch.rand(1) < self.drop_caption_p:
out_caption = ""
if self.random_drop_sd_caption:
assert self.sep_cap_for_2b
if torch.rand(1) < self.drop_sd_caption_p:
caption = ""
if not self.sep_cap_for_2b:
data[self.cond_stage_key] = out_caption
else:
data[self.cond_stage_key] = [caption, out_caption]
if self.postprocess is not None:
data = self.postprocess(data)
return data
def simple_process_im(self, im):
im = im.convert("RGB")
return self.tform(im)
# def noaug_process_im(self, im):
# # To be aborted: lack consideration of different image sizes
# im = im.convert("RGB")
# im_trans = [transforms.ToTensor(), # to be checked
# transforms.Lambda(lambda x: rearrange(x, 'c h w -> h w c'))]
# im_trans= transforms.Compose(im_trans)
# im = im_trans(im)
# return im
def tutorial_process_im(self, target_filename, source_filename = None):
# To be aborted: lack consideration of different image sizes
target = cv2.imread(target_filename)
target = cv2.cvtColor(target, cv2.COLOR_BGR2RGB)
target = (target.astype(np.float32) / 127.5) - 1.0 # Normalize target images to [-1, 1].
if source_filename is not None:
source = cv2.imread(source_filename)
# Do not forget that OpenCV read images in BGR order.
source = cv2.cvtColor(source, cv2.COLOR_BGR2RGB)
# Normalize source images to [0, 1].
source = source.astype(np.float32) / 255.0
else:
source = None
return target, source