92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
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 SaliencyDataset(BaseDataset):
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def __init__(self, MSRA_root, TR_root, TE_root, HFlickr_root):
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image_mask_dict = {}
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# ====== MSRA-10k ======
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file_lst = os.listdir(MSRA_root)
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image_lst = [MSRA_root+i for i in file_lst if '.jpg' in i]
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for i in image_lst:
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mask_path = i.replace('.jpg','.png')
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image_mask_dict[i] = mask_path
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# ===== DUT-TR ========
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file_lst = os.listdir(TR_root)
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image_lst = [TR_root+i for i in file_lst if '.jpg' in i]
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for i in image_lst:
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mask_path = i.replace('.jpg','.png').replace('DUTS-TR-Image','DUTS-TR-Mask')
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image_mask_dict[i] = mask_path
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# ===== DUT-TE ========
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file_lst = os.listdir(TE_root)
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image_lst = [TE_root+i for i in file_lst if '.jpg' in i]
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for i in image_lst:
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mask_path = i.replace('.jpg','.png').replace('DUTS-TE-Image','DUTS-TE-Mask')
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image_mask_dict[i] = mask_path
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# ===== HFlickr =======
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file_lst = os.listdir(HFlickr_root)
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mask_list = [HFlickr_root+i for i in file_lst if '.png' in i]
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for i in file_lst:
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image_name = i.split('_')[0] +'.jpg'
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image_path = HFlickr_root.replace('masks', 'real_images') + image_name
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mask_path = HFlickr_root + i
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image_mask_dict[image_path] = mask_path
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self.image_mask_dict = image_mask_dict
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self.data = list(self.image_mask_dict.keys() )
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self.size = (512,512)
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self.clip_size = (224,224)
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self.dynamic = 0
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def __len__(self):
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return 20000
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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 or w > W:
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pass_flag = False
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elif mode == 'min':
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if h < H or 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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# ==== get pairs =====
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image_path = self.data[idx]
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mask_path = self.image_mask_dict[image_path]
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instances_mask = cv2.imread(mask_path)
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if len(instances_mask.shape) == 3:
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instances_mask = instances_mask[:,:,0]
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instances_mask = (instances_mask > 128).astype(np.uint8)
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# ======================
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ref_image = cv2.imread(image_path)
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ref_image = cv2.cvtColor(ref_image.copy(), cv2.COLOR_BGR2RGB)
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tar_image = ref_image
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ref_mask = instances_mask
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tar_mask = instances_mask
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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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