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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 | |
| from pycocotools import mask as mask_utils | |
| class UVODataset(BaseDataset): | |
| def __init__(self, image_dir, video_json, image_json): | |
| json_path = video_json | |
| with open(json_path, 'r') as fcc_file: | |
| data = json.load(fcc_file) | |
| image_json_path = image_json | |
| with open(image_json_path , 'r') as image_file: | |
| video_dict = json.load(image_file) | |
| self.image_root = image_dir | |
| self.data = data['annotations'] | |
| self.video_dict = video_dict | |
| self.size = (512,512) | |
| self.clip_size = (224,224) | |
| self.dynamic = 1 | |
| def __len__(self): | |
| return 25000 | |
| 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): | |
| ins_anno = self.data[idx] | |
| video_id = str(ins_anno['video_id']) | |
| video_names = self.video_dict[video_id] | |
| masks = ins_anno['segmentations'] | |
| frames = video_names | |
| # Sampling frames | |
| min_interval = len(frames) // 10 | |
| 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, ref_image_name) | |
| tar_image_path = os.path.join(self.image_root, tar_image_name) | |
| # 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 = mask_utils.decode(masks[start_frame_index]) | |
| tar_mask = mask_utils.decode(masks[end_frame_index]) | |
| 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 | |