├── README.md
└── ann2mask.py


/README.md:
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1 | # annotation2mask
2 | Converting coco-like annotation json files to png masks
3 | 
4 | This repo is read-only and the main code is moved to https://github.com/pooya-mohammadi/deep_utils/blob/master/deep_utils/utils/coco_utils/main.py
5 | 


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/ann2mask.py:
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 1 | """
 2 | Converting json coco file segmentations to masks for Unet and other methods.
 3 | """
 4 | # pip install deep_utils
 5 | from deep_utils import remove_create
 6 | import os
 7 | import cv2
 8 | import json
 9 | from tqdm import tqdm
10 | import numpy as np
11 | from collections import defaultdict
12 | 
13 | 
14 | def get_image_id(json_dict, img_name):
15 |     images = json_dict['images']
16 |     for d in images:
17 |         if d['file_name'] == img_name:
18 |             return d['id']
19 | 
20 | 
21 | def get_annotation(json_dict, id_):
22 |     annotations = json_dict['annotations']
23 |     annotations_list = list()
24 |     for ann in annotations:
25 |         if ann['image_id'] == id_:
26 |             annotations_list.append(ann)
27 |     return annotations_list
28 | 
29 | 
30 | def main(json_path, image_path, mask_path):
31 |     remove_create(mask_path)
32 |     with open(json_path, 'r', encoding='utf-8') as f:
33 |         json_dict = json.load(f)
34 |     # get the annotations
35 |     annotations_point = json_dict['annotations']
36 |     # make a dictionary from annotations with image_ids as keys for each of use.
37 |     # img_annotation_dict = {ann['image_id']: ann for ann in annotations_point}
38 |     img_annotation_dict = defaultdict(list)
39 |     for ann in annotations_point:
40 |         img_annotation_dict[ann['image_id']].append(ann)
41 |     img_annotation_dict = {1: [{segmentations: ....} , {segmentations: ....}, ... ]}
42 |     # get the images' information
43 |     image_file = json_dict['images'] # [{img_name: ... , width, id:... } , {}, ... ]
44 |     # make a dictionary of images' information for ease of use.
45 |     img_dict = {img_file['id']: img_file for img_file in image_file}
46 |     for img_id, img_info in tqdm(img_dict.items(), total=len(img_dict)):
47 |         mask = np.zeros((img_info['height'], img_info['width']), dtype=np.uint8)
48 |         annotations = img_annotation_dict[img_id]
49 | 	for ann in annotations:
50 | 	    segmentations = ann['segmentation']
51 | 	    cat_id = ann['category_id']
52 | 	    for seg in segmentations:
53 | 		# getting the points
54 | 		xs = seg[0::2]
55 | 		ys = seg[1::2]
56 | 		pts = np.array([[x, y] for x, y in zip(xs, ys)], dtype=np.int32)
57 | 		pts = pts.reshape((-1, 1, 2))
58 | 
59 | 		# draw the points on the mask image.
60 | 		try:
61 | 		    mask = cv2.fillPoly(mask, [pts], cat_id)
62 | 		except Exception as e:
63 | 		    print(f"[ERROR] error for {img_info['file_name']}, len={len(pts)}")
64 |         cv2.imwrite(os.path.join(mask_path, img_info['file_name']), mask)
65 |         print(f'[INFO] img_id: {img_id} is done!')
66 | 
67 | 
68 | if __name__ == '__main__':
69 |     main('cloth_fc.json', "cloth_fc", "cloth_fc_masks")
70 | 
71 | 


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