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Add script to convert dataturks json to yolov3 format
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Asif
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13
Dataturks-to-YOLOv3/README.md
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13
Dataturks-to-YOLOv3/README.md
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# Convert Dataturks json to YOLO format
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The ```convert.py``` script downloads the images and converts the annotations from the Dataturks json file to YOLO format.
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## Usage
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1. Clone or download this repository.
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2. Create folders for saving images and YOLO config files.
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3. Execute:
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```
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python3 convert.py -d <path/to/dataturks.json> -i <directory/to/save/downloaded/images/> -y <directory/to/save/yolo/config/files/>
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```
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Note: ```-v``` flag can be used for detailed output.
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## Output
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1. Downloaded images along with their annotations in the specified directory.
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2. YOLO config files ```train.txt, obj.names, obj.data, yolov3.cfg``` saved in the specified directory.
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165
Dataturks-to-YOLOv3/convert.py
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165
Dataturks-to-YOLOv3/convert.py
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import os
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import argparse
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import json
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import requests
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def download_image(image_url, image_dir):
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'''Downloads image from image_url to image_dir if the image doesn't exist.'''
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file_name = image_url.split('/')[-1]
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file_path = os.path.join(image_dir, file_name)
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if os.path.exists(file_path):
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verbose("[INFO]%s exists, skipping download" % file_name, v_flag)
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return file_path
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response = requests.get(image_url)
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if response.status_code == 200:
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with open(file_path, 'wb') as file:
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file.write(response.content)
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verbose("[INFO]Downloaded %s" % file_name, v_flag)
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return file_path
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else:
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print('[WARNING]Unable to download image, skipping...')
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return False
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def generate_annotation(label, data):
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'''Generate annotation from the json file.'''
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image_width = data['imageWidth']
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image_height = data['imageHeight']
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#if four coordinates of the bounding box is given
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if len(data['points']) == 4:
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xmin = image_width * min(data['points'][0][0], data['points'][1][0], data['points'][2][0], data['points'][3][0])
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ymin = image_height * min(data['points'][0][1], data['points'][1][1], data['points'][2][1], data['points'][3][1])
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xmax = image_width * max(data['points'][0][0], data['points'][1][0], data['points'][2][0], data['points'][3][0])
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ymax = image_height * max(data['points'][0][1], data['points'][1][1], data['points'][2][1], data['points'][3][1])
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#if diagonal coordinates given
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else:
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xmin = int(data['points'][0]['x'] * image_width)
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ymin = int(data['points'][0]['y'] * image_height)
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xmax = int(data['points'][1]['x'] * image_width)
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ymax = int(data['points'][1]['y'] * image_height)
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#calculating coodinate ratios as required for training yolo
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x_center = ((xmax + xmin) / 2.0) / image_width
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y_center = ((ymax + ymin) / 2.0) / image_height
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width = (xmax - xmin) / image_width
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height = (ymax - ymin) / image_height
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return ("%.6f %.6f %.6f %.6f\n"% (x_center, y_center, width, height))
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def convert_to_yolo_annotation():
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classes = []
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train_txt = []
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with open(dataturks_json_path, 'r') as file:
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lines = file.readlines()
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for line in lines:
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data = json.loads(line)
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if data['annotation'] == None:
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continue
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file_path = download_image(data['content'], image_dir)
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if not file_path:
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continue
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annotation = ''
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for item in data['annotation']:
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if item['label'] == None:
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continue
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labels = item['label']
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if not isinstance(labels, list):
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labels = [labels]
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for label in labels:
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if label not in classes:
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classes.append(label)
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annotation = annotation + str(classes.index(label)) + ' ' + generate_annotation(label, item)
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train_txt.append(str(os.path.abspath(file_path)) + '\n')
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annotation_file = '.'.join(file_path.split('.')[:-1]) + '.txt'
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with open(annotation_file, 'w') as f:
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f.write(annotation)
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verbose("[INFO]%s file generated." % annotation_file, v_flag)
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with open(os.path.join(yolo_dir, 'train.txt'), 'w') as file:
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file.writelines(train_txt)
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verbose("[INFO]train.txt file generated.", v_flag)
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return classes
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def generate_yolo_cfg_files(classes):
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with open(os.path.join(yolo_dir, 'obj.names'), 'w') as file:
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for item in classes:
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file.write(item + '\n')
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verbose("[INFO]obj.names file generated.", v_flag)
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with open(os.path.join(yolo_dir, 'obj.data'), 'w') as file:
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file.write('classes = %s\ntrain = %s\nnames = %s\nbackup = %s' %
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(str(len(classes)),
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str(os.path.join(os.path.abspath(yolo_dir), 'train.txt')),
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str(os.path.join(os.path.abspath(yolo_dir), 'obj.names')),
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str(os.path.join(os.path.abspath(yolo_dir), 'backup/'))
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)
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)
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verbose("[INFO]obj.data file generated.", v_flag)
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n_classes = len(classes)
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n_filters = (n_classes + 5) * 3
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with open(os.path.join(yolo_dir, 'yolov3.cfg'), 'w') as file:
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with open('yolov3.cfg.template') as template:
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lines = template.readlines()
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for i in range(len(lines)):
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lines[i] = lines[i].replace('#FILTER#', str(n_filters))
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lines[i] = lines[i].replace('#CLASS#', str(n_classes))
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file.writelines(lines)
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verbose("[INFO]yolov3.cfg file generated.", v_flag)
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def main():
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if not os.path.isdir(image_dir):
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print('[ERROR]The directory %s does not exist' % os.path.abspath(image_dir))
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return
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if not os.path.exists(dataturks_json_path):
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print('[ERROR]The specified json file does not exitst')
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return
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if not os.path.isdir(yolo_dir):
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print('[ERROR]The directory %s does not exist' % os.path.abspath(yolo_dir))
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return
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classes = convert_to_yolo_annotation()
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generate_yolo_cfg_files(classes)
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def arg_parser():
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parser = argparse.ArgumentParser(description = 'Converts Dataturks JSON format to yolo-darknet format.')
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parser.add_argument('-v', help = 'Verbose output.', action = 'store_true')
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parser.add_argument('-d', '--dataturks_json_path', required = True, help = 'Path to the Dataturks JSON file.')
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parser.add_argument('-i', '--image_dir', required = True, help = 'Path to the directory where the images with annotations will be stored.')
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parser.add_argument('-y', '--yolo_dir', required = True, help = 'Path to the directory where the files for training YOLO will be stored.')
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return parser.parse_args()
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def verbose(message, v_flag):
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if v_flag == True:
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print(message)
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if __name__ == '__main__':
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args = arg_parser()
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global dataturks_json_path
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global image_dir
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global yolo_dir
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global v_flag
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dataturks_json_path = args.dataturks_json_path
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image_dir = args.image_dir
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yolo_dir = args.yolo_dir
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v_flag = args.v
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main()
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789
Dataturks-to-YOLOv3/yolov3.cfg.template
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789
Dataturks-to-YOLOv3/yolov3.cfg.template
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@ -0,0 +1,789 @@
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[net]
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# Testing
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batch=1
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subdivisions=1
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# Training
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# batch=64
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# subdivisions=16
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width=416
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height=416
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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burn_in=1000
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max_batches = 500200
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policy=steps
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steps=400000,450000
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scales=.1,.1
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[convolutional]
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batch_normalize=1
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filters=32
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size=3
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stride=1
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pad=1
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activation=leaky
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=32
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=64
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=64
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=64
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=128
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=128
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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# Downsample
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=2
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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|
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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|
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[shortcut]
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from=-3
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activation=linear
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[convolutional]
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batch_normalize=1
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filters=256
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size=1
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stride=1
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pad=1
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activation=leaky
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|
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[convolutional]
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batch_normalize=1
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filters=512
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size=3
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stride=1
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pad=1
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activation=leaky
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[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
# Downsample
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=2
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[shortcut]
|
||||
from=-3
|
||||
activation=linear
|
||||
|
||||
######################
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=1024
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=#FILTER#
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 6,7,8
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=#CLASS#
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 61
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=512
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=#FILTER#
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=#CLASS#
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 36
|
||||
|
||||
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
filters=256
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=#FILTER#
|
||||
activation=linear
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
|
||||
classes=#CLASS#
|
||||
num=9
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
Loading…
Reference in New Issue
Block a user