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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 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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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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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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