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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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class YoutubeVOSDataset(BaseDataset): |
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def __init__(self, image_dir, anno, meta): |
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self.image_root = image_dir |
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self.anno_root = anno |
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self.meta_file = meta |
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video_dirs = [] |
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with open(self.meta_file) as f: |
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records = json.load(f) |
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records = records["videos"] |
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for video_id in records: |
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video_dirs.append(video_id) |
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self.records = records |
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self.data = video_dirs |
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self.size = (512,512) |
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self.clip_size = (224,224) |
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self.dynamic = 1 |
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def __len__(self): |
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return 40000 |
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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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video_id = list(self.records.keys())[idx] |
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objects_id = np.random.choice( list(self.records[video_id]["objects"].keys()) ) |
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frames = self.records[video_id]["objects"][objects_id]["frames"] |
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min_interval = len(frames) // 10 |
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start_frame_index = np.random.randint(low=0, high=len(frames) - min_interval) |
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end_frame_index = start_frame_index + np.random.randint(min_interval, len(frames) - start_frame_index ) |
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end_frame_index = min(end_frame_index, len(frames) - 1) |
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ref_image_name = frames[start_frame_index] |
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tar_image_name = frames[end_frame_index] |
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ref_image_path = os.path.join(self.image_root, video_id, ref_image_name) + '.jpg' |
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tar_image_path = os.path.join(self.image_root, video_id, tar_image_name) + '.jpg' |
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ref_mask_path = ref_image_path.replace('JPEGImages','Annotations').replace('.jpg', '.png') |
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tar_mask_path = tar_image_path.replace('JPEGImages','Annotations').replace('.jpg', '.png') |
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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 = Image.open(ref_mask_path ).convert('P') |
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ref_mask= np.array(ref_mask) |
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ref_mask = ref_mask == int(objects_id) |
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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 == int(objects_id) |
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item_with_collage = self.process_pairs(ref_image, ref_mask, tar_image, tar_mask) |
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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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