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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 panopticapi.utils import rgb2id | |
| from PIL import Image | |
| from .base import BaseDataset | |
| class VIPSegDataset(BaseDataset): | |
| def __init__(self, image_dir, anno): | |
| self.image_root = image_dir | |
| self.anno_root = anno | |
| video_dirs = [] | |
| video_dirs = os.listdir(self.image_root) | |
| self.data = video_dirs | |
| self.size = (512,512) | |
| self.clip_size = (224,224) | |
| self.dynamic = 1 | |
| def __len__(self): | |
| return 30000 | |
| 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 or w > W: | |
| pass_flag = False | |
| elif mode == 'min': | |
| if h < H or w < W: | |
| pass_flag = False | |
| return pass_flag | |
| def get_sample(self, idx): | |
| video_name = self.data[idx] | |
| video_path = os.path.join(self.image_root, video_name) | |
| frames = os.listdir(video_path) | |
| # Sampling frames | |
| min_interval = len(frames) // 100 | |
| start_frame_index = np.random.randint(low=0, high=len(frames) - min_interval) | |
| end_frame_index = start_frame_index + np.random.randint(min_interval, len(frames) - start_frame_index ) | |
| end_frame_index = min(end_frame_index, len(frames) - 1) | |
| # Get image path | |
| ref_image_name = frames[start_frame_index] | |
| tar_image_name = frames[end_frame_index] | |
| ref_image_path = os.path.join(self.image_root, video_name, ref_image_name) | |
| tar_image_path = os.path.join(self.image_root, video_name, tar_image_name) | |
| ref_mask_path = ref_image_path.replace('images','panomasksRGB').replace('.jpg', '.png') | |
| tar_mask_path = tar_image_path.replace('images','panomasksRGB').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 = np.array(Image.open(ref_mask_path).convert('RGB')) | |
| ref_mask = rgb2id(ref_mask) | |
| tar_mask = np.array(Image.open(tar_mask_path).convert('RGB')) | |
| tar_mask = rgb2id(tar_mask) | |
| ref_ids = np.unique(ref_mask) | |
| tar_ids = np.unique(tar_mask) | |
| common_ids = list(np.intersect1d(ref_ids, tar_ids)) | |
| common_ids = [ i for i in common_ids if i != 0 ] | |
| chosen_id = np.random.choice(common_ids) | |
| ref_mask = ref_mask == chosen_id | |
| tar_mask = tar_mask == chosen_id | |
| len_mask = len( self.check_connect( ref_mask.astype(np.uint8) ) ) | |
| assert len_mask == 1 | |
| 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 | |
| def check_connect(self, mask): | |
| contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) | |
| cnt_area = [cv2.contourArea(cnt) for cnt in contours] | |
| return cnt_area | |