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on
A10G
Running
on
A10G
| 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 | |
| class DreamBoothDataset(BaseDataset): | |
| def __init__(self, fg_dir, bg_dir): | |
| self.bg_dir = bg_dir | |
| bg_data = os.listdir(self.bg_dir) | |
| self.bg_data = [i for i in bg_data if 'mask' in i] | |
| self.image_dir = fg_dir | |
| self.data = os.listdir(self.image_dir) | |
| self.size = (512,512) | |
| self.clip_size = (224,224) | |
| ''' | |
| Dynamic: | |
| 0: Static View, High Quality | |
| 1: Multi-view, Low Quality | |
| 2: Multi-view, High Quality | |
| ''' | |
| self.dynamic = 1 | |
| def __len__(self): | |
| return len(self.data) | |
| def __getitem__(self, idx): | |
| idx = np.random.randint(0, len(self.data)-1) | |
| item = self.get_sample(idx) | |
| return item | |
| 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_alpha_mask(self, mask_path): | |
| image = cv2.imread( mask_path, cv2.IMREAD_UNCHANGED) | |
| mask = (image[:,:,-1] > 128).astype(np.uint8) | |
| return mask | |
| def get_sample(self, idx): | |
| dir_name = self.data[idx] | |
| dir_path = os.path.join(self.image_dir, dir_name) | |
| images = os.listdir(dir_path) | |
| image_name = [i for i in images if '.png' in i][0] | |
| image_path = os.path.join(dir_path, image_name) | |
| image = cv2.imread( image_path, cv2.IMREAD_UNCHANGED) | |
| mask = (image[:,:,-1] > 128).astype(np.uint8) | |
| image = image[:,:,:-1] | |
| image = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB) | |
| ref_image = image | |
| ref_mask = mask | |
| ref_image, ref_mask = expand_image_mask(image, mask, ratio=1.4) | |
| bg_idx = np.random.randint(0, len(self.bg_data)-1) | |
| tar_mask_name = self.bg_data[bg_idx] | |
| tar_mask_path = os.path.join(self.bg_dir, tar_mask_name) | |
| tar_image_path = tar_mask_path.replace('_mask','_GT') | |
| tar_image = cv2.imread(tar_image_path).astype(np.uint8) | |
| tar_image = cv2.cvtColor(tar_image, cv2.COLOR_BGR2RGB) | |
| tar_mask = (cv2.imread(tar_mask_path) > 128).astype(np.uint8)[:,:,0] | |
| item_with_collage = self.process_pairs(ref_image, ref_mask, tar_image, tar_mask) | |
| sampled_time_steps = self.sample_timestep() | |
| item_with_collage['time_steps'] = sampled_time_steps | |
| return item_with_collage | |