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import os
import pandas as pd
import numpy as np
from PIL import Image
import torch
from torch.utils.data import Dataset, DataLoader
import json
import random
import glob
import torch
import torchvision.transforms.functional as TF
def image_resize(img, max_size=512):
w, h = img.size
if w >= h:
new_w = max_size
new_h = int((max_size / w) * h)
else:
new_h = max_size
new_w = int((max_size / h) * w)
return img.resize((new_w, new_h))
def c_crop(image):
width, height = image.size
new_size = min(width, height)
left = (width - new_size) / 2
top = (height - new_size) / 2
right = (width + new_size) / 2
bottom = (height + new_size) / 2
return image.crop((left, top, right, bottom))
def crop_to_aspect_ratio(image, ratio="16:9"):
width, height = image.size
ratio_map = {
"16:9": (16, 9),
"4:3": (4, 3),
"1:1": (1, 1)
}
target_w, target_h = ratio_map[ratio]
target_ratio_value = target_w / target_h
current_ratio = width / height
if current_ratio > target_ratio_value:
new_width = int(height * target_ratio_value)
offset = (width - new_width) // 2
crop_box = (offset, 0, offset + new_width, height)
else:
new_height = int(width / target_ratio_value)
offset = (height - new_height) // 2
crop_box = (0, offset, width, offset + new_height)
cropped_img = image.crop(crop_box)
return cropped_img
class CustomImageDataset(Dataset):
def __init__(self, img_dir, img_size=512, caption_type='json', random_ratio=False):
self.images = [os.path.join(img_dir, i) for i in os.listdir(img_dir) if '.jpg' in i or '.png' in i]
# self.images = glob.glob(img_dir +'**/*.jpg', recursive=True) + glob.glob(img_dir +'**/*.png', recursive=True) + glob.glob(img_dir +'**/*.jpeg', recursive=True)
self.images.sort()
self.img_size = img_size
self.caption_type = caption_type
self.random_ratio = random_ratio
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
try:
img = Image.open(self.images[idx]).convert('RGB')
if self.random_ratio:
ratio = random.choice(["16:9", "default", "1:1", "4:3"])
if ratio != "default":
img = crop_to_aspect_ratio(img, ratio)
img = image_resize(img, self.img_size)
w, h = img.size
new_w = (w // 32) * 32
new_h = (h // 32) * 32
img = img.resize((new_w, new_h))
img = torch.from_numpy((np.array(img) / 127.5) - 1)
img = img.permute(2, 0, 1)
json_path = self.images[idx].split('.')[0] + '.' + self.caption_type
if self.caption_type == "json":
prompt = json.load(open(json_path))['caption']
else:
prompt = open(json_path).read()
return img, prompt
except Exception as e:
print(e)
return self.__getitem__(random.randint(0, len(self.images) - 1))
def loader(train_batch_size, num_workers, **args):
dataset = CustomImageDataset(**args)
return DataLoader(dataset, batch_size=train_batch_size, num_workers=num_workers, shuffle=True)
class ImageEditPairDataset(Dataset):
def __init__(self, img_dir, img_size=512, caption_type='json', random_ratio=False, grayscale_editing=False, zoom_camera=False):
# self.images = [os.path.join(img_dir, i) for i in os.listdir(img_dir) if '.jpg' in i or '.png' in i]
self.images = glob.glob(img_dir +'**/*.jpg', recursive=True) + glob.glob(img_dir +'**/*.png', recursive=True) + glob.glob(img_dir +'**/*.jpeg', recursive=True)
self.images.sort()
self.img_size = img_size
self.caption_type = caption_type
self.random_ratio = random_ratio
self.grayscale_editing = grayscale_editing
self.zoom_camera = zoom_camera
if "ByteMorph-Bench" or "InstructMove" in img_dir:
self.eval = True
else:
self.eval = False
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
try:
img = Image.open(self.images[idx]).convert('RGB')
ori_width, ori_height = img.size
left_half = (0, 0, ori_width // 2, ori_height)
right_half = (ori_width // 2, 0, ori_width, ori_height)
src_image = img.crop(left_half) # Left half
tgt_image = img.crop(right_half) # Right half
# print("ori_width, ori_height: ",ori_width, ori_height)
if self.random_ratio:
ratio = random.choice(["16:9", "default", "1:1", "4:3"])
if ratio != "default":
src_image = crop_to_aspect_ratio(src_image, ratio)
tgt_image = crop_to_aspect_ratio(tgt_image, ratio)
src_image = image_resize(src_image, self.img_size)
tgt_image = image_resize(tgt_image, self.img_size)
w, h = src_image.size
new_w = (w // 32) * 32
new_h = (h // 32) * 32
# print("new_w, new_h: ",new_w, new_h)
src_image = src_image.resize((new_w, new_h))
src_image = torch.from_numpy((np.array(src_image) / 127.5) - 1)
src_image = src_image.permute(2, 0, 1)
tgt_image = tgt_image.resize((new_w, new_h))
tgt_image = torch.from_numpy((np.array(tgt_image) / 127.5) - 1)
tgt_image = tgt_image.permute(2, 0, 1)
json_path = self.images[idx].split('.')[0] + '.' + self.caption_type
if self.eval:
image_name = self.images[idx].split('.')[0].split("/")[-1]
edit_type = self.images[idx].split('.')[0].split("/")[-2]
if self.caption_type == "json":
if not self.eval:
prompt = json.load(open(json_path))['caption']
edit_prompt = json.load(open(json_path))['edit']
else:
prompt = [] #json.load(open(json_path))['caption']
edit_prompt = json.load(open(json_path))['edit']
else:
raise NotImplementedError
# prompt = open(json_path).read()
if (not self.grayscale_editing) and (not self.zoom_camera):
if not self.eval:
return src_image, tgt_image, prompt, edit_prompt
else:
return src_image, tgt_image, prompt, edit_prompt, image_name, edit_type
if self.grayscale_editing and (not self.zoom_camera):
# Grayscale = 0.2989 * R + 0.5870 * G + 0.1140 * B
grayscale_image = 0.2989 * src_image[0, :, :] + 0.5870 * src_image[1, :, :] + 0.1140 * src_image[2, :, :]
tgt_image = grayscale_image.unsqueeze(0).repeat(3, 1, 1)
edit_prompt = "Convert the input image to a black and white grayscale image while maintaining the original composition and details."
if not self.eval:
return src_image, tgt_image, prompt, edit_prompt
else:
return src_image, tgt_image, prompt, edit_prompt, image_name, edit_type
if (not self.grayscale_editing) and self.zoom_camera:
cropped = TF.center_crop(src_image, (256, 256))
tgt_image = TF.resize(cropped, (512, 512))
edit_prompt = "The central area of the input image is zoomed. The camera transitions from a wide shot to a closer position, narrowing its view."
if not self.eval:
return src_image, tgt_image, prompt, edit_prompt
else:
return src_image, tgt_image, prompt, edit_prompt, image_name, edit_type
if self.grayscale_editing and self.zoom_camera:
grayscale_image = 0.2989 * src_image[0, :, :] + 0.5870 * src_image[1, :, :] + 0.1140 * src_image[2, :, :]
tgt_image = grayscale_image.unsqueeze(0).repeat(3, 1, 1)
tgt_image = TF.center_crop(tgt_image, (256, 256))
tgt_image = TF.resize(tgt_image, (512, 512))
edit_prompt = "Convert the input image to a black and white grayscale image while maintaining the original composition and details. And the central area of the input image is zoomed, the camera transitions from a wide shot to a closer position, narrowing its view."
if not self.eval:
return src_image, tgt_image, prompt, edit_prompt
else:
return src_image, tgt_image, prompt, edit_prompt, image_name, edit_type
except Exception as e:
print(e)
return self.__getitem__(random.randint(0, len(self.images) - 1))
def image_pair_loader(train_batch_size, num_workers, **args):
dataset = ImageEditPairDataset(**args)
return DataLoader(dataset, batch_size=train_batch_size, num_workers=num_workers, shuffle=True)
def eval_image_pair_loader(eval_batch_size, num_workers, **args):
dataset = ImageEditPairDataset(**args)
return DataLoader(dataset, batch_size=eval_batch_size, num_workers=num_workers, shuffle=False)
if __name__ == "__main__":
from src.flux.util import save_image
example_dataset = ImageEditPairDataset(
img_dir="",
img_size=512,
caption_type='json',
random_ratio=False,
grayscale_editing=False,
zoom_camera=False,
)
train_dataloader = DataLoader(
example_dataset,
batch_size=1,
num_workers=4,
shuffle=False,
)
for step, batch in enumerate(train_dataloader):
src_image, tgt_image, prompt, edit_prompt = batch
os.makedirs("./debug", exist_ok=True)
save_image(src_image, f"./debug/{step}-src_img.jpg")
save_image(tgt_image, f"./debug/{step}-tgt_img.jpg")
if step == 3:
breakpoint()
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