Spaces:
Runtime error
Runtime error
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 | |
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.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) | |