62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
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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# Read Image and Mask
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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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