79 lines
2.2 KiB
Python
79 lines
2.2 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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from pycocotools import mask as mask_utils
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class SAMDataset(BaseDataset):
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def __init__(self, sub1, sub2, sub3, sub4):
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image_mask_dict = {}
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self.data = []
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self.register_subset(sub1)
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self.register_subset(sub2)
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self.register_subset(sub3)
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self.register_subset(sub4)
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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 register_subset(self, path):
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data = os.listdir(path)
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data = [ os.path.join(path, i) for i in data if '.json' in i]
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self.data = self.data + data
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def get_sample(self, idx):
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# ==== get pairs =====
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json_path = self.data[idx]
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image_path = json_path.replace('.json', '.jpg')
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with open(json_path, 'r') as json_file:
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data = json.load(json_file)
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annotation = data['annotations']
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valid_ids = []
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for i in range(len(annotation)):
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area = annotation[i]['area']
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if area > 100 * 100 * 5:
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valid_ids.append(i)
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chosen_id = np.random.choice(valid_ids)
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mask = mask_utils.decode(annotation[chosen_id]["segmentation"] )
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# ======================
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image = cv2.imread(image_path)
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ref_image = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB)
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tar_image = ref_image
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ref_mask = mask
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tar_mask = 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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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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