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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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from pycocotools import mask as mask_utils |
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class UVOValDataset(BaseDataset): |
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def __init__(self, image_dir, video_json, image_json): |
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json_path = video_json |
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with open(json_path, 'r') as fcc_file: |
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data = json.load(fcc_file) |
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image_json_path = image_json |
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with open(image_json_path , 'r') as image_file: |
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video_dict = json.load(image_file) |
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self.image_root = image_dir |
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self.data = data['annotations'] |
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self.video_dict = video_dict |
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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 8000 |
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def __getitem__(self, idx): |
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while(1): |
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idx = np.random.randint(0, len(self.data)-1) |
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try: |
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item = self.get_sample(idx) |
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return item |
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except: |
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idx = np.random.randint(0, len(self.data)-1) |
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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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ins_anno = self.data[idx] |
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video_id = str(ins_anno['video_id']) |
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video_names = self.video_dict[video_id] |
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masks = ins_anno['segmentations'] |
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frames = video_names |
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min_interval = len(frames) // 5 |
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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, ref_image_name) |
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tar_image_path = os.path.join(self.image_root, tar_image_name) |
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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 = mask_utils.decode(masks[start_frame_index]) |
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tar_mask = mask_utils.decode(masks[end_frame_index]) |
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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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