Spaces:
Sleeping
Sleeping
| 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 YoutubeVISDataset(BaseDataset): | |
| def __init__(self, image_dir, anno, meta): | |
| self.image_root = image_dir | |
| self.anno_root = anno | |
| self.meta_file = meta | |
| video_dirs = [] | |
| with open(self.meta_file) as f: | |
| records = json.load(f) | |
| records = records["videos"] | |
| for video_id in records: | |
| video_dirs.append(video_id) | |
| self.records = records | |
| self.data = video_dirs | |
| self.size = (512,512) | |
| self.clip_size = (224,224) | |
| self.dynamic = 1 | |
| def __len__(self): | |
| return 40000 | |
| 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): | |
| video_id = list(self.records.keys())[idx] | |
| objects_id = np.random.choice( list(self.records[video_id]["objects"].keys()) ) | |
| frames = self.records[video_id]["objects"][objects_id]["frames"] | |
| # 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, video_id, ref_image_name) + '.jpg' | |
| tar_image_path = os.path.join(self.image_root, video_id, tar_image_name) + '.jpg' | |
| ref_mask_path = ref_image_path.replace('JPEGImages','Annotations').replace('.jpg', '.png') | |
| tar_mask_path = tar_image_path.replace('JPEGImages','Annotations').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 = Image.open(ref_mask_path ).convert('P') | |
| ref_mask= np.array(ref_mask) | |
| ref_mask = ref_mask == int(objects_id) | |
| tar_mask = Image.open(tar_mask_path ).convert('P') | |
| tar_mask= np.array(tar_mask) | |
| tar_mask = tar_mask == int(objects_id) | |
| 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 | |