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general_json2yolo.py
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import os, argparse
from os import listdir
from os.path import isfile, join
import json
import cv2
import pandas as pd
from PIL import Image
from utils import *
# Convert INFOLKS JSON file into YOLO-format labels ----------------------------
def convert_infolks_json(name, files, img_path):
# Create folders
path = make_dirs()
# Import json
data = []
for file in glob.glob(files):
with open(file) as f:
jdata = json.load(f)
jdata['json_file'] = file
data.append(jdata)
# Write images and shapes
name = path + os.sep + name
file_id, file_name, wh, cat = [], [], [], []
for x in tqdm(data, desc='Files and Shapes'):
f = glob.glob(img_path + Path(x['json_file']).stem + '.*')[0]
file_name.append(f)
wh.append(exif_size(Image.open(f))) # (width, height)
cat.extend(a['classTitle'].lower() for a in x['output']['objects']) # categories
# filename
with open(name + '.txt', 'a') as file:
file.write('%s\n' % f)
# Write *.names file
names = sorted(np.unique(cat))
# names.pop(names.index('Missing product')) # remove
with open(name + '.names', 'a') as file:
[file.write('%s\n' % a) for a in names]
# Write labels file
for i, x in enumerate(tqdm(data, desc='Annotations')):
label_name = Path(file_name[i]).stem + '.txt'
with open(path + '/labels/' + label_name, 'a') as file:
for a in x['output']['objects']:
# if a['classTitle'] == 'Missing product':
# continue # skip
category_id = names.index(a['classTitle'].lower())
# The INFOLKS bounding box format is [x-min, y-min, x-max, y-max]
box = np.array(a['points']['exterior'], dtype=np.float32).ravel()
box[[0, 2]] /= wh[i][0] # normalize x by width
box[[1, 3]] /= wh[i][1] # normalize y by height
box = [box[[0, 2]].mean(), box[[1, 3]].mean(), box[2] - box[0], box[3] - box[1]] # xywh
if (box[2] > 0.) and (box[3] > 0.): # if w > 0 and h > 0
file.write('%g %.6f %.6f %.6f %.6f\n' % (category_id, *box))
# Split data into train, test, and validate files
split_files(name, file_name)
write_data_data(name + '.data', nc=len(names))
print('Done. Output saved to %s' % (os.getcwd() + os.sep + path))
# Convert vott JSON file into YOLO-format labels -------------------------------
def convert_vott_json(name, files, img_path):
# Create folders
path = make_dirs()
name = path + os.sep + name
# Import json
data = []
for file in glob.glob(files):
with open(file) as f:
jdata = json.load(f)
jdata['json_file'] = file
data.append(jdata)
# Get all categories
file_name, wh, cat = [], [], []
for i, x in enumerate(tqdm(data, desc='Files and Shapes')):
try:
cat.extend(a['tags'][0] for a in x['regions']) # categories
except:
pass
# Write *.names file
names = sorted(pd.unique(cat))
with open(name + '.names', 'a') as file:
[file.write('%s\n' % a) for a in names]
# Write labels file
n1, n2 = 0, 0
missing_images = []
for i, x in enumerate(tqdm(data, desc='Annotations')):
f = glob.glob(img_path + x['asset']['name'] + '.jpg')
if len(f):
f = f[0]
file_name.append(f)
wh = exif_size(Image.open(f)) # (width, height)
n1 += 1
if (len(f) > 0) and (wh[0] > 0) and (wh[1] > 0):
n2 += 1
# append filename to list
with open(name + '.txt', 'a') as file:
file.write('%s\n' % f)
# write labelsfile
label_name = Path(f).stem + '.txt'
with open(path + '/labels/' + label_name, 'a') as file:
for a in x['regions']:
category_id = names.index(a['tags'][0])
# The INFOLKS bounding box format is [x-min, y-min, x-max, y-max]
box = a['boundingBox']
box = np.array([box['left'], box['top'], box['width'], box['height']]).ravel()
box[[0, 2]] /= wh[0] # normalize x by width
box[[1, 3]] /= wh[1] # normalize y by height
box = [box[0] + box[2] / 2, box[1] + box[3] / 2, box[2], box[3]] # xywh
if (box[2] > 0.) and (box[3] > 0.): # if w > 0 and h > 0
file.write('%g %.6f %.6f %.6f %.6f\n' % (category_id, *box))
else:
missing_images.append(x['asset']['name'])
print('Attempted %g json imports, found %g images, imported %g annotations successfully' % (i, n1, n2))
if len(missing_images):
print('WARNING, missing images:', missing_images)
# Split data into train, test, and validate files
split_files(name, file_name)
print('Done. Output saved to %s' % (os.getcwd() + os.sep + path))
# Convert ath JSON file into YOLO-format labels --------------------------------
def convert_ath_json(json_dir): # dir contains json annotations and images
# Create folders
dir = make_dirs() # output directory
jsons = []
for dirpath, dirnames, filenames in os.walk(json_dir):
for filename in [f for f in filenames if f.lower().endswith('.json')]:
jsons.append(os.path.join(dirpath, filename))
# Import json
n1, n2, n3 = 0, 0, 0
missing_images, file_name = [], []
for json_file in sorted(jsons):
with open(json_file) as f:
data = json.load(f)
# # Get classes
# try:
# classes = list(data['_via_attributes']['region']['class']['options'].values()) # classes
# except:
# classes = list(data['_via_attributes']['region']['Class']['options'].values()) # classes
# # Write *.names file
# names = pd.unique(classes) # preserves sort order
# with open(dir + 'data.names', 'w') as f:
# [f.write('%s\n' % a) for a in names]
# Write labels file
for i, x in enumerate(tqdm(data['_via_img_metadata'].values(), desc='Processing %s' % json_file)):
image_file = str(Path(json_file).parent / x['filename'])
f = glob.glob(image_file) # image file
if len(f):
f = f[0]
file_name.append(f)
wh = exif_size(Image.open(f)) # (width, height)
n1 += 1 # all images
if len(f) > 0 and wh[0] > 0 and wh[1] > 0:
label_file = dir + 'labels/' + Path(f).stem + '.txt'
nlabels = 0
try:
with open(label_file, 'a') as file: # write labelsfile
for a in x['regions']:
# try:
# category_id = int(a['region_attributes']['class'])
# except:
# category_id = int(a['region_attributes']['Class'])
category_id = 0 # single-class
# bounding box format is [x-min, y-min, x-max, y-max]
box = a['shape_attributes']
box = np.array([box['x'], box['y'], box['width'], box['height']],
dtype=np.float32).ravel()
box[[0, 2]] /= wh[0] # normalize x by width
box[[1, 3]] /= wh[1] # normalize y by height
box = [box[0] + box[2] / 2, box[1] + box[3] / 2, box[2],
box[3]] # xywh (left-top to center x-y)
if box[2] > 0. and box[3] > 0.: # if w > 0 and h > 0
file.write('%g %.6f %.6f %.6f %.6f\n' % (category_id, *box))
n3 += 1
nlabels += 1
if nlabels == 0: # remove non-labelled images from dataset
os.system('rm %s' % label_file)
# print('no labels for %s' % f)
continue # next file
# write image
img_size = 4096 # resize to maximum
img = cv2.imread(f) # BGR
assert img is not None, 'Image Not Found ' + f
r = img_size / max(img.shape) # size ratio
if r < 1: # downsize if necessary
h, w, _ = img.shape
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA)
ifile = dir + 'images/' + Path(f).name
if cv2.imwrite(ifile, img): # if success append image to list
with open(dir + 'data.txt', 'a') as file:
file.write('%s\n' % ifile)
n2 += 1 # correct images
except:
os.system('rm %s' % label_file)
print('problem with %s' % f)
else:
missing_images.append(image_file)
nm = len(missing_images) # number missing
print('\nFound %g JSONs with %g labels over %g images. Found %g images, labelled %g images successfully' %
(len(jsons), n3, n1, n1 - nm, n2))
if len(missing_images):
print('WARNING, missing images:', missing_images)
# Write *.names file
names = ['knife'] # preserves sort order
with open(dir + 'data.names', 'w') as f:
[f.write('%s\n' % a) for a in names]
# Split data into train, test, and validate files
split_rows_simple(dir + 'data.txt')
write_data_data(dir + 'data.data', nc=1)
print('Done. Output saved to %s' % Path(dir).absolute())
def convert_coco_json(train_path, val_path, json_dir='../coco/annotations/', use_segments=False, cls91to80=False):
save_dir = make_dirs() # output directory
coco80 = coco91_to_coco80_class()
# Import json
for json_file in sorted(Path(json_dir).resolve().glob('*.json')):
fn = Path(save_dir) / 'labels' / json_file.stem.replace('instances_', '') # folder name
fn.mkdir()
with open(json_file) as f:
data = json.load(f)
# Create image dict
images = {'%g' % x['id']: x for x in data['images']}
# Write labels file
for x in tqdm(data['annotations'], desc=f'Annotations {json_file}'):
if x['iscrowd']:
continue
img = images['%g' % x['image_id']]
h, w, f = img['height'], img['width'], img['file_name']
# The COCO box format is [top left x, top left y, width, height]
box = np.array(x['bbox'], dtype=np.float64)
box[:2] += box[2:] / 2 # xy top-left corner to center
box[[0, 2]] /= w # normalize x
box[[1, 3]] /= h # normalize y
# Segments
if use_segments:
segments = [j for i in x['segmentation'] for j in i] # all segments concatenated
s = (np.array(segments).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
# Write
if box[2] > 0 and box[3] > 0: # if w > 0 and h > 0
if x['category_id'] == 1:
cls = coco80[x['category_id'] - 1] if cls91to80 else x['category_id'] - 1 # class
line = cls, *(s if use_segments else box) # cls, box or segments
with open((fn / f).with_suffix('.txt'), 'a') as file:
file.write(('%g ' * len(line)).rstrip() % line + '\n')
onlyfiles = [f for f in listdir(train_path) if isfile(join(train_path, f))]
f = open( Path(save_dir) / 'train_coco.txt', 'w')
for x in onlyfiles:
if x.find('.jpg') > 0:
f.write(train_path+'/'+x+'\n')
f.close()
onlyfiles = [f for f in listdir(val_path) if isfile(join(val_path, f))]
f = open( Path(save_dir) / 'test_coco.txt', 'w')
for x in onlyfiles:
if x.find('.jpg') > 0:
f.write(val_path+'/'+x+'\n')
f.close()
print('Labels path: ' + str(Path(save_dir) / 'labels'))
print('Data list files: ' + str(Path(save_dir) / 'train_coco.txt') + ', ' + str(Path(save_dir) / 'test_coco.txt'))
if __name__ == '__main__':
parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, description='JSON2YOLO')
'''
Command line options
'''
parser.add_argument(
'--instances_path', type=str, required=True,
help='path to instances json directory')
parser.add_argument(
'--train_path', type=str, required=True,
help='path to training data directory')
parser.add_argument(
'--val_path', type=str, required=True,
help='path to val data directory')
args = parser.parse_args()
source = 'COCO'
if source == 'COCO':
convert_coco_json(args.train_path, args.val_path, args.instances_path) # directory with *.json
elif source == 'infolks': # Infolks https://infolks.info/
convert_infolks_json(name='out',
files='../data/sm4/json/*.json',
img_path='../data/sm4/images/')
elif source == 'vott': # VoTT https://github.com/microsoft/VoTT
convert_vott_json(name='data',
files='../../Downloads/athena_day/20190715/*.json',
img_path='../../Downloads/athena_day/20190715/') # images folder
elif source == 'ath': # ath format
convert_ath_json(json_dir='../../Downloads/athena/') # images folder