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import json |
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import cv2 |
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import numpy as np |
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import os |
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from torch.utils.data import Dataset |
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from PIL import Image |
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import cv2 |
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from .data_utils import * |
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from .base import BaseDataset |
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import albumentations as A |
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class DresscodeDataset(BaseDataset): |
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def __init__(self, image_dir): |
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self.image_root = image_dir |
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self.data = os.listdir(self.image_root) |
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self.size = (512,512) |
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self.clip_size = (224,224) |
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self.dynamic = 2 |
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def __len__(self): |
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return 20000 |
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def check_region_size(self, image, yyxx, ratio, mode = 'max'): |
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pass_flag = True |
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H,W = image.shape[0], image.shape[1] |
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H,W = H * ratio, W * ratio |
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y1,y2,x1,x2 = yyxx |
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h,w = y2-y1,x2-x1 |
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if mode == 'max': |
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if h > H and w > W: |
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pass_flag = False |
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elif mode == 'min': |
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if h < H and w < W: |
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pass_flag = False |
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return pass_flag |
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def get_sample(self, idx): |
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tar_mask_path = os.path.join(self.image_root, self.data[idx]) |
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tar_image_path = tar_mask_path.replace('label_maps/','images/').replace('_4.png','_0.jpg') |
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ref_image_path = tar_mask_path.replace('label_maps/','images/').replace('_4.png','_1.jpg') |
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ref_image = cv2.imread(ref_image_path) |
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ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) |
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tar_image = cv2.imread(tar_image_path) |
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tar_image = cv2.cvtColor(tar_image, cv2.COLOR_BGR2RGB) |
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ref_mask = (ref_image < 240).astype(np.uint8)[:,:,0] |
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tar_mask = Image.open(tar_mask_path ).convert('P') |
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tar_mask= np.array(tar_mask) |
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tar_mask = tar_mask == 4 |
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item_with_collage = self.process_pairs(ref_image, ref_mask, tar_image, tar_mask, max_ratio = 1.0) |
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sampled_time_steps = self.sample_timestep() |
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item_with_collage['time_steps'] = sampled_time_steps |
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return item_with_collage |
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