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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 MVImageNetDataset(BaseDataset): |
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def __init__(self, txt, image_dir): |
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with open(txt,"r") as f: |
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data = f.read().split('\n')[:-1] |
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self.image_dir = image_dir |
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self.data = data |
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self.size = (512,512) |
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self.clip_size = (224,224) |
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self.dynamic = 2 |
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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_alpha_mask(self, mask_path): |
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image = cv2.imread( mask_path, cv2.IMREAD_UNCHANGED) |
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mask = (image[:,:,-1] > 128).astype(np.uint8) |
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return mask |
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def get_sample(self, idx): |
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object_dir = self.data[idx].replace('MVDir/', self.image_dir) |
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frames = os.listdir(object_dir) |
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frames = [ i for i in frames if '.png' in i] |
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min_interval = len(frames) // 8 |
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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_mask_name = frames[start_frame_index] |
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tar_mask_name = frames[end_frame_index] |
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ref_image_name = ref_mask_name.split('_')[0] + '.jpg' |
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tar_image_name = tar_mask_name.split('_')[0] + '.jpg' |
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ref_mask_path = os.path.join(object_dir, ref_mask_name) |
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tar_mask_path = os.path.join(object_dir, tar_mask_name) |
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ref_image_path = os.path.join(object_dir, ref_image_name) |
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tar_image_path = os.path.join(object_dir, tar_image_name) |
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ref_image = cv2.imread(ref_image_path).astype(np.uint8) |
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ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) |
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tar_image = cv2.imread(tar_image_path).astype(np.uint8) |
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tar_image = cv2.cvtColor(tar_image, cv2.COLOR_BGR2RGB) |
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ref_mask = self.get_alpha_mask(ref_mask_path) |
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tar_mask = self.get_alpha_mask(tar_mask_path) |
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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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