File size: 2,024 Bytes
f1d6334 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
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 cv2
def canny_processor(image, low_threshold=100, high_threshold=200):
image = np.array(image)
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
return canny_image
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))
class CustomImageDataset(Dataset):
def __init__(self, img_dir, img_size=512):
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
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
try:
img = Image.open(self.images[idx])
img = c_crop(img)
img = img.resize((self.img_size, self.img_size))
hint = canny_processor(img)
img = torch.from_numpy((np.array(img) / 127.5) - 1)
img = img.permute(2, 0, 1)
hint = torch.from_numpy((np.array(hint) / 127.5) - 1)
hint = hint.permute(2, 0, 1)
json_path = self.images[idx].split('.')[0] + '.json'
prompt = json.load(open(json_path))['caption']
return img, hint, 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)
|