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| import json | |
| import cv2 | |
| import numpy as np | |
| import os | |
| from torch.utils.data import Dataset | |
| from PIL import Image | |
| import cv2 | |
| from .data_utils import * | |
| from .base import BaseDataset | |
| import albumentations as A | |
| class VitonHDDataset(BaseDataset): | |
| def __init__(self, image_dir): | |
| self.image_root = image_dir | |
| self.data = os.listdir(self.image_root) | |
| self.size = (512,512) | |
| self.clip_size = (224,224) | |
| self.dynamic = 2 | |
| def __len__(self): | |
| return 20000 | |
| def check_region_size(self, image, yyxx, ratio, mode = 'max'): | |
| pass_flag = True | |
| H,W = image.shape[0], image.shape[1] | |
| H,W = H * ratio, W * ratio | |
| y1,y2,x1,x2 = yyxx | |
| h,w = y2-y1,x2-x1 | |
| if mode == 'max': | |
| if h > H and w > W: | |
| pass_flag = False | |
| elif mode == 'min': | |
| if h < H and w < W: | |
| pass_flag = False | |
| return pass_flag | |
| def get_sample(self, idx): | |
| ref_image_path = os.path.join(self.image_root, self.data[idx]) | |
| tar_image_path = ref_image_path.replace('/cloth/', '/image/') | |
| ref_mask_path = ref_image_path.replace('/cloth/','/cloth-mask/') | |
| tar_mask_path = ref_image_path.replace('/cloth/', '/image-parse-v3/').replace('.jpg','.png') | |
| # Read Image and Mask | |
| ref_image = cv2.imread(ref_image_path) | |
| ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) | |
| tar_image = cv2.imread(tar_image_path) | |
| tar_image = cv2.cvtColor(tar_image, cv2.COLOR_BGR2RGB) | |
| ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] | |
| tar_mask = Image.open(tar_mask_path ).convert('P') | |
| tar_mask= np.array(tar_mask) | |
| tar_mask = tar_mask == 5 | |
| item_with_collage = self.process_pairs(ref_image, ref_mask, tar_image, tar_mask, max_ratio = 1.0) | |
| sampled_time_steps = self.sample_timestep() | |
| item_with_collage['time_steps'] = sampled_time_steps | |
| return item_with_collage | |