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prepare_coco.py
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prepare_coco.py
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import os
import json
import numpy as np
from PIL import Image
import cv2
import shutil
import argparse
def copy_file(src, dst):
shutil.copy(src, dst)
def coco2mask(coco_file_path, img_save_path):
print('coco_file_path:', coco_file_path)
print('img_save_path:', img_save_path)
with open(coco_file_path, 'r') as f:
coco_json = json.load(f)
class_mapper = dict()
for category in coco_json['categories']:
class_mapper[category['id']] = category['name']
os.makedirs(os.path.join(img_save_path,category['name']), exist_ok=True)
os.makedirs(os.path.join('data', 'Images', category['name']), exist_ok=True)
for category in coco_json['categories']:
print(category)
idx = 0
for image in coco_json['images']:
height = image['height']
width = image['width']
mask = np.zeros((height, width), dtype=np.uint8)
has_annots = False
for annotation in coco_json['annotations']:
if annotation['segmentation'] == []:
continue
if annotation['category_id'] == category['id'] and annotation['image_id'] == image['id']:
has_annots = True
seg = annotation['segmentation']
seg = np.array(seg).reshape((-1, 2)).astype(np.int32)
mask = cv2.fillPoly(mask, [seg], 128)
if has_annots:
mask_img = np.zeros((height, width, 3), dtype=np.uint8)
mask_img[:, :, 0] = mask
mask_img[:, :, 1] = mask
mask_img[:, :, 2] = mask
img_save_name = os.path.join(img_save_path, class_mapper[category['id']], "{:02}".format(idx) + '.png')
mask_img[:,:,1:] = 0
mask_img = cv2.cvtColor(mask_img, cv2.COLOR_RGB2BGR) # 将参考图像从BGR颜色空间转为RGB颜色空间
copy_file(os.path.join('auto-sam-data/',image['file_name']),
os.path.join('data', 'Images',class_mapper[category['id']], "{:02}".format(idx) + '.jpg')
)
cv2.imwrite(img_save_name, mask_img)
idx += 1
def main():
parser = argparse.ArgumentParser()
parser.add_argument('coco_path', type=str, help='path to JSON file')
parser.add_argument('output_dir', type=str, help='output directory')
args = parser.parse_args()
coco2mask(os.path.join(args.coco_path, 'result.json'), args.output_dir)
if __name__ == '__main__':
main()