def main():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = args.json_file
list = os.listdir(json_file)
for i in range(0, len(list)):
path = os.path.join(json_file, list[i])
if os.path.isfile(path):
data = json.load(open(path))
img = utils.img_b64_to_arr(data['imageData'])
lbl, lbl_names = utils.labelme_shapes_to_label(
img.shape, data['shapes'])
captions = [
'%d: %s' % (l, name) for l, name in enumerate(lbl_names)
]
lbl_viz = utils.draw_label(lbl, img, captions)
out_dir = osp.basename(list[i]).replace('.', '_')
out_dir = osp.join(osp.dirname(list[i]), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(
osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in lbl_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=lbl_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
def Json_to_dataset(self):
count = os.listdir(self.JsonjpgChange)
for i in range(0, len(count)):
path = os.path.join(self.Jsonjpg7, count[i])
if os.path.isfile(path) and path.endswith('json'):
data = json.load(open(path))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in data['shapes']:
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# label_values must be dense
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
captions = ['{}: {}'.format(lv, ln)
for ln, lv in label_name_to_value.items()]
lbl_viz = draw.draw_label(lbl, img, captions)
out_dir = osp.basename(count[i]).replace('.', '_')
out_dir = osp.join(osp.dirname(count[i]), out_dir)
out_dir = osp.join(self.outputChange, out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
self.lineEdit_do_jsontodataset.setText('Json To Dataset Complete!')
def main():
logger.warning('This script is aimed to demonstrate how to convert the'
'JSON file to a single image dataset, and not to handle'
'multiple JSON files to generate a real-use dataset.')
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_file), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
logger.warning('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
logger.info('Saved to: {}'.format(out_dir))
def save_label_from_json(json_file, out_dir):
data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = osp.join(osp.dirname(json_file), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
def main():
logger.warning("This script is aimed to demonstrate how to convert the "
"JSON file to a single image dataset.")
logger.warning("It won't handle multiple JSON files to generate a "
"real-use dataset.")
parser = argparse.ArgumentParser()
parser.add_argument("json_file")
parser.add_argument("-o", "--out", default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace(".", "_")
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
data = json.load(open(json_file))
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"])
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {"_background_": 0}
for shape in sorted(data["shapes"], key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(img.shape, data["shapes"],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(label=lbl,
img=imgviz.asgray(img),
label_names=label_names,
loc="rb")
PIL.Image.fromarray(img).save(osp.join(out_dir, "img.png"))
utils.lblsave(osp.join(out_dir, "label.png"), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png"))
with open(osp.join(out_dir, "label_names.txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
logger.info("Saved to: {}".format(out_dir))
def main():
logger.warning('This script is aimed to demonstrate how to convert the'
'JSON file to a single image dataset, and not to handle'
'multiple JSON files to generate a real-use dataset.')
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_file), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
logger.warning('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
logger.info('Saved to: {}'.format(out_dir))
def main():
print('This script is aimed to demonstrate how to convert the'
'JSON file to a single image dataset, and not to handle'
'multiple JSON files to generate a real-use dataset.')
path_file_name = glob.glob('*.json')
file_num = len(path_file_name)
print('INFO:There are ' + str(file_num) + ' json files')
file_name = [i for i in range(file_num)]
for i in range(file_num):
file_name[i] = path_file_name[i].split('\\')[-1]
print('INFO:' + file_name[i] + ' is dealt')
data = json.load(open(path_file_name[i]))
imageData = data.get('imageData')
out_dir = osp.basename(path_file_name[i]).replace('.', '_')
out_dir = osp.join(osp.dirname(path_file_name[i]), out_dir)
if not os.path.exists(out_dir):
os.mkdir(out_dir)
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file),
data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(label=lbl,
img=imgviz.asgray(img),
label_names=label_names,
loc='rb')
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
print('INFO:Saved to: {}'.format('images/segmentation/test/' +
file_name[i]))
print('INFO:finished!')
def json2label(json_folder):
json_list = glob.glob(os.path.join(json_folder, '*.json'))
annotations_dir = os.path.join(json_folder, "annotations")
josn2labels_dir = os.path.join(json_folder, "josn2labels")
if not os.path.exists(annotations_dir):
os.makedirs(annotations_dir)
if not os.path.exists(josn2labels_dir):
os.makedirs(josn2labels_dir)
for json_file in json_list:
temp_name = json_file.split(".")[0].split("/")[-1]
out_dir = os.path.join(josn2labels_dir, temp_name)
if not osp.exists(out_dir):
os.mkdir(out_dir)
data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_file),
data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0, '1': 1, '2': 2, '3': 3}
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
PIL.Image.fromarray(img).save(
osp.join(out_dir, '%s_img.png' % (temp_name)))
utils.lblsave(osp.join(out_dir, '%s.png' % (temp_name)), lbl)
utils.lblsave(osp.join(annotations_dir, '%s.png' % (temp_name)), lbl)
PIL.Image.fromarray(lbl_viz).save(
osp.join(out_dir, '%s_label_viz.png' % (temp_name)))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
def main():
logger.warning('This script is aimed to demonstrate how to convert the'
'JSON file to a single image dataset, and not to handle'
'multiple JSON files to generate a real-use dataset.')
# parser = argparse.ArgumentParser()
# parser.add_argument('json_file')
# parser.add_argument('-o', '--out', default=None)
# args = parser.parse_args()
json_files = glob.glob(r'C:\Users\Zeran\Desktop\loudi\*.json')
for json_file in json_files:
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
# reload(sys)
# sys.setdefaultencoding('utf8')
f = open(json_file, encoding='utf-8')
text = f.read()
# text = text.decode("gbk").encode("utf-8")
data = json.loads(text)
# data = f.read().decode(encoding='gbk').encode(encoding='utf-8')
# data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_file),
data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
# if label_name in label_name_to_value:
# label_value = label_name_to_value[label_name]
# else:
# label_value = len(label_name_to_value)
label_name_to_value[label_name] = 255
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
saved_name = os.path.splitext(os.path.basename(json_file))[0] + '.png'
utils.lblsave(
osp.join('D:\\coslight\\0304_beforetolabel\\label\\', saved_name),
lbl)
def label_convert(json_file_path, png_label_path, class_names):
if not os.path.isdir(json_file_path):
raise ValueError('Input path does not exist!\n')
os.makedirs(png_label_path, exist_ok=True)
# all the json annotation file list
json_files = glob.glob(os.path.join(json_file_path, '*.json'))
# form a dict of class_name to label value
label_name_to_value = {}
for i, class_name in enumerate(class_names):
label_name_to_value[class_name] = i
# count class item number
class_count = OrderedDict([(item, 0) for item in class_names])
pbar = tqdm(total=len(json_files), desc='Label converting')
for i, json_file in enumerate(json_files):
data = json.load(open(json_file))
# get image info
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"].replace('\\', '/'))
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
# convert json labels to numpy label array
# and save to png
label_array, _ = utils.shapes_to_label(
img.shape, data["shapes"], label_name_to_value
)
# count object class for statistic
label_list = list(np.unique(label_array))
for label in label_list:
class_name = class_names[label]
class_count[class_name] = class_count[class_name] + 1
utils.lblsave(os.path.join(png_label_path, os.path.basename(json_file)+".png"), label_array)
pbar.update(1)
pbar.close()
# show item number statistic
print('Image number for each class:')
for (class_name, number) in class_count.items():
if class_name == 'background':
continue
print('%s: %d' % (class_name, number))
print('total number of converted images: ', len(json_files))
def json2img(json_file, out, index):
if out is None:
out_dir = osp.basename(json_file).replace(".", "_")
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = out
mkdir(out_dir)
data = json.load(open(json_file))
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"])
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {"_background_": 0}
for shape in sorted(data["shapes"], key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(img.shape, data["shapes"],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(label=lbl,
img=imgviz.asgray(img),
label_names=label_names,
loc="rb")
img_name = str(index) + '.jpg'
out_dir_raw = out_dir + '/raw'
out_dir_label = out_dir + '/label'
mkdir(out_dir_raw)
mkdir(out_dir_label)
PIL.Image.fromarray(img).save(osp.join(out_dir_raw, img_name)) #保存图片 1.jpg
utils.lblsave(osp.join(out_dir_label, img_name.replace('.jpg', '.png')),
lbl) #保存标签 1.png
#PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png"))
with open(osp.join(out_dir, "label_names.txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
logger.info("Saved to: {}".format(out_dir))
def generate_datasets(list, i):
global json_file, json_out, NAME_LABEL_MAP
path = os.path.join(json_file, list[i])
filename = list[i][:-5] # .json
if os.path.isfile(path):
data = json.load(open(path))
img = utils.image.img_b64_to_arr(data['imageData'])
lbl, lbl_names = utils.shape.labelme_shapes_to_label(
img.shape, data['shapes']) # labelme_shapes_to_label
# modify labels according to NAME_LABEL_MAP
lbl_tmp = copy.copy(lbl)
for key_name in lbl_names:
old_lbl_val = lbl_names[key_name]
new_lbl_val = NAME_LABEL_MAP[key_name]
lbl_tmp[lbl == old_lbl_val] = new_lbl_val
lbl_names_tmp = {}
for key_name in lbl_names:
lbl_names_tmp[key_name] = NAME_LABEL_MAP[key_name]
# Assign the new label to lbl and lbl_names dict
lbl = np.array(lbl_tmp, dtype=np.int8)
lbl_names = lbl_names_tmp
captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]
lbl_viz = utils.draw.draw_label(lbl, img, captions)
out_dir = osp.basename(list[i]).replace('.', '_')
out_dir = json_out + '/' + osp.join(osp.dirname(list[i]), out_dir)
print(out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(
osp.join(out_dir, '{}.png'.format(filename)))
utils.lblsave(osp.join(out_dir, '{}_gt.png'.format(filename)), lbl)
PIL.Image.fromarray(lbl_viz).save(
osp.join(out_dir, '{}_viz.png'.format(filename)))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in lbl_names:
f.write(lbl_name + '\n')
info = dict(label_names=lbl_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
def save_image_and_label(image, lbl, output_dir, label_names):
"""
save image and label to output_dir
:param image: image
:param lbl: label
:param output_dir: output directory
:param label_names: label names
:return:
"""
PIL.Image.fromarray(image).save(osp.join(output_dir, 'img.png'))
utils.lblsave(osp.join(output_dir, 'label.png'), lbl)
lbl_viz = imgviz.label2rgb(lbl, imgviz.asgray(image), label_names=label_names, loc="rb")
PIL.Image.fromarray(lbl_viz).save(osp.join(output_dir, 'label_viz.png'))
with open(osp.join(output_dir, 'label_names.txt'), 'w', encoding="utf8") as label_f:
for lbl_name in label_names:
label_f.write(lbl_name + '\n')
print(f"Saved to: {output_dir}")
def writeimages(img, lbl, depth_map, jsonFile, sufix, out_images_dir,
out_labels_dir, out_labels_raw_dir, debug_dir,
label_name_to_value, args):
#Plot visualization if in debug mode
if args.debug:
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
PIL.Image.fromarray(lbl_viz).save(
osp.join(
debug_dir,
jsonFile.replace('./', '').replace('../', '').replace(
'/', '_').replace('.json',
'-' + sufix + '-label_viz.png')))
jsonFileName = jsonFile[jsonFile.rfind('/') + 1:]
#Save image
if out_images_dir is not None:
PIL.Image.fromarray(img).save(
osp.join(out_images_dir, jsonFileName.replace('.json', '.jpg')))
#Save labels
utils.lblsave(
osp.join(out_labels_dir,
jsonFileName.replace('.json', '-' + sufix + '-label.png')),
lbl)
#Save labels with raw annotation
PIL.Image.fromarray(lbl.astype(dtype=np.uint8)).save(
osp.join(out_labels_raw_dir,
jsonFileName.replace('.json',
'-' + sufix + '-label_raw.png')))
if args.depth:
#Save depth image
# PIL.Image.fromarray(depth_map.astype(dtype=np.uint8)).save(
# osp.join(out_images_dir, jsonFile.replace('.json','-depth.png'))
# )
PIL.Image.fromarray(depth_map.astype(dtype=np.uint8)).save(
osp.join(out_images_dir,
jsonFileName.replace('.json',
'-' + sufix + '-depth.png')))
def voc_to_mask(img_path, contours, png_dir, mask_dir):
filename = osp.basename(img_path).replace(
'jpg', 'png') # get the last filename of the path
# print(filename)
with open(img_path, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
# print('img shape:', img.shape)
# print(np.max(img))
label_name_to_value = {'_background_': 0, 'bone': 1}
lbl = utils.shapes_to_label(img.shape, contours, label_name_to_value)
# print('mask shape:', lbl.shape)
# print(np.max(lbl))
PIL.Image.fromarray(img).save(osp.join(png_dir, filename))
utils.lblsave(osp.join(mask_dir, filename), lbl)
def save_label_info(img, name, dir_label, dir_info, dir_vis, shape_):
label_name_to_value = {'_background_': 0, "1": 1, "2": 2, "3": 3}
# for shape in shapes:
# label_name = shape['label']
# if label_name in label_name_to_value:
# label_value = label_name_to_value[label_name]
# else:
# label_value = len(label_name_to_value)
# label_name_to_value[label_name] = label_value
# print(label_name_to_value.items())
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
# print(ln,lv)
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, shape_,
label_name_to_value) # 'instance'
# print(ins)
captions = [
'{}: {}'.format(lv, ln) for ln, lv in label_name_to_value.items()
]
lbl_viz = utils.draw_label(lbl, img, captions)
# ins_viz=utils.draw_label(lbl, img, captions)
# PIL.Image.fromarray(img).save('img.png')
# PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.png'))
utils.lblsave(dir_label + name + '.png', lbl)
PIL.Image.fromarray(lbl_viz).save(dir_vis + name + '.png')
# with open('label_names.txt', 'w') as f:
# for lbl_name in label_names:
# f.write(lbl_name + '\n')
# warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(dir_info + name + '.yaml', 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
def labelmejson_to_dataset(json_path):
json_paths = sorted([
os.path.join(json_path, fname) for fname in os.listdir(json_path)
if fname.endswith(".json")
])
os.makedirs("converted_dataset\\img", exist_ok=True)
os.makedirs("converted_dataset\\gt", exist_ok=True)
for i in range(len(json_paths)):
data = json.load(open(json_paths[i]))
imageData = data.get("imageData")
# ignore "json_file" is not defined warning
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file),
data["imagePath"])
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {"_background_": 0}
for shape in sorted(data["shapes"], key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(img.shape, data["shapes"],
label_name_to_value)
img = img[:, :, :3]
PIL.Image.fromarray(img).save(
os.path.join("converted_dataset\\img", "{}.jpg".format(i)))
utils.lblsave(
os.path.join("converted_dataset\\gt", "{}.png".format(i)), lbl)
# for shape in sorted(data["shapes"], key=lambda x: x["label"]):
# label_name = shape["label"]
# if label_name in label_name_to_value:
# label_value = label_name_to_value[label_name]
# else:
# label_value = len(label_name_to_value)
# label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(
img.shape, data["shapes"], label_name_to_value
)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(
label=lbl, img=imgviz.asgray(img), label_names=label_names, loc="rb"
)
_, name, _ = json_file.replace('.', '_').split('_')
PIL.Image.fromarray(img).save(osp.join(SAVE_DIR, "img_" + name + ".png"))
utils.lblsave(osp.join(SAVE_DIR, "label_" + name + ".png"), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(SAVE_DIR, "viz_" + name + ".png"))
with open(osp.join(SAVE_DIR, "label_names.txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# label_values must be dense
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(),
key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
PIL.Image.fromarray(img).save(
osp.join(jpgs_path, count[i].split(".")[0] + '.jpg'))
new = np.zeros([np.shape(img)[0], np.shape(img)[1]])
for name in label_names:
index_json = label_names.index(name)
index_all = classes.index(name)
new = new + index_all * (np.array(lbl) == index_json)
utils.lblsave(osp.join(pngs_path, count[i].split(".")[0] + '.png'),
new)
print('Saved ' + count[i].split(".")[0] + '.jpg and ' +
count[i].split(".")[0] + '.png')
def json_to_dataset(json_file: str, out_dir: str, rename: bool):
"""
JSON文件转换为DataSet格式文件夹
@param {str} json_file - 要处理的JSON文件
@param {str} out_dir - 输出目录
@param {bool} rename - 遇到输出目录存在是否修改文件名
"""
_json_path = osp.split(json_file)[0]
_dir_name = osp.split(json_file)[1].replace(".", "_")
if out_dir == '':
_out_dir = osp.join(_json_path, _dir_name)
else:
_out_dir = osp.join(out_dir, _dir_name)
if not osp.exists(_out_dir):
os.makedirs(_out_dir)
elif rename:
# 遇到文件存在的情况，重命名
_index = 1
while osp.exists(_out_dir):
_out_dir = osp.join(_json_path,
'%s%d%s' % (_dir_name[0:-5], _index, '_json'))
_index += 1
os.makedirs(_out_dir)
data = json.load(open(json_file))
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"])
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {"_background_": 0}
# 修正json_to_dataset的bug, 按形状名进行排序，将hole放在最后面
data["shapes"].sort(
key=lambda x: x["label"] if x["label"] != "hole" else "zzzzzz")
for shape in sorted(data["shapes"], key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(img.shape, data["shapes"],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(label=lbl,
img=imgviz.asgray(img),
label_names=label_names,
loc="rb")
PIL.Image.fromarray(img).save(osp.join(_out_dir, "img.png"))
utils.lblsave(osp.join(_out_dir, "label.png"), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(_out_dir, "label_viz.png"))
with open(osp.join(_out_dir, "label_names.txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
logger.info("Saved to: {}".format(_out_dir))
def main():
count = os.listdir("./BEFORE_DATASET/")
index = 0
for i in range(0, len(count)):
path = os.path.join("./BEFORE_DATASET", count[i])
if os.path.isfile(path) and path.endswith('json'):
data = json.load(open(path))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(path),
data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in data['shapes']:
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# label_values must be dense
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(),
key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
captions = [
'{}: {}'.format(lv, ln)
for ln, lv in label_name_to_value.items()
]
lbl_viz = utils.draw_label(lbl, img, captions)
if not os.path.exists("DATASET"):
os.mkdir("DATASET")
label_path = "DATASET/mask"
if not os.path.exists(label_path):
os.mkdir(label_path)
img_path = "DATASET/imgs"
if not os.path.exists(img_path):
os.mkdir(img_path)
yaml_path = "DATASET/yaml"
if not os.path.exists(yaml_path):
os.mkdir(yaml_path)
label_viz_path = "DATASET/label_viz"
if not os.path.exists(label_viz_path):
os.mkdir(label_viz_path)
PIL.Image.fromarray(img).save(
osp.join(img_path,
str(index) + '.jpg'))
utils.lblsave(osp.join(label_path, str(index) + '.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(
osp.join(label_viz_path,
str(index) + '.png'))
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(yaml_path, str(index) + '.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
index = index + 1
print('Saved : %s' % str(index))
def main(JSON_DIR, SAVE_DIR):
# read .json file list
_, _, jsons = next(os.walk(JSON_DIR))
jsons = [s for s in jsons if ".json" in s]
# take the label_names.txt
with open(osp.join(JSON_DIR, "label_names.txt"), "r") as f:
cnt = 0
label_name_to_value = {}
for line in f:
label_name_to_value[line.rstrip('\n')] = cnt
cnt += 1
for json_file in jsons:
# read json
data = json.load(open(JSON_DIR + json_file))
# read image
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(JSON_DIR, data["imagePath"])
img = np.asarray(PIL.Image.open(imagePath))
else:
img = utils.img_b64_to_arr(imageData)
with open(osp.join(JSON_DIR, "label_names.txt"), "r") as f:
cnt = 0
label_name_to_value = {}
for line in f:
label_name_to_value[line.rstrip('\n')] = cnt
cnt += 1
# make a label data
lbl, _ = utils.shapes_to_label(img.shape, data["shapes"],
label_name_to_value)
# make a viz data
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(label=lbl,
img=imgviz.asgray(img),
label_names=label_names,
loc="rb")
# save dataset
_, name, _ = json_file.replace('.', '_').split('_')
PIL.Image.fromarray(img).save(
osp.join(SAVE_DIR, "img_" + name + ".png"))
utils.lblsave(osp.join(SAVE_DIR, "label_" + name + ".png"), lbl)
PIL.Image.fromarray(lbl_viz).save(
osp.join(SAVE_DIR, "viz_" + name + ".png"))
with open(osp.join(SAVE_DIR, "label_names.txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
def main():
#logger.warning('This script is aimed to demonstrate how to convert the'
# 'JSON file to a single image dataset, and not to handle'
# 'multiple JSON files to generate a real-use dataset.')
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
# set label names file path
parser.add_argument('-n', '--names', default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
label_name_to_value = {'_background_': 0}
# add label names to dict
if args.names is not None:
with open(args.names, 'r') as f:
lines = f.readlines()
count = 1
for line in lines:
line = line.strip()
if line == '':
continue
label_name_to_value[line] = count
count += 1
data = json.load(open(json_file))
imageData = data.get('imageData')
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
def main():
parser = argparse.ArgumentParser()
parser.add_argument("json_file")
parser.add_argument("-o", "--out", default=None)
args = parser.parse_args()
json_file = args.json_file
if args.out is None:
out_dir = osp.basename(json_file).replace(".", "_")
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir)
data = json.load(open(json_file))
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"])
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {
"_background_": 0,
"table": 1,
"cup": 2,
"bottle": 3,
"glass": 4,
"fork": 5,
"knife": 6,
"food": 7,
"plate": 8
}
for shape in sorted(data["shapes"], key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(img.shape, data["shapes"],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(label=lbl,
img=imgviz.asgray(img),
label_names=label_names,
loc="rb")
PIL.Image.fromarray(img).save(osp.join(out_dir, "img.png"))
utils.lblsave(osp.join(out_dir, "label.png"), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png"))
with open(osp.join(out_dir, "label_names.txt"), "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
logger.info("Saved to: {}".format(out_dir))
def main():
# Only input:
# Give a folder with only .json files
label_path = r"/Users/frederikrogalski/Documents/Privates/Programieren/python/trainseg/data/trainseg/Masks/"
list_path = os.listdir(label_path)
for i in range(0, len(list_path)):
logger.warning('This script is aimed to demonstrate how to convert the'
'JSON file to a single image dataset, and not to handle'
'multiple JSON files to generate a real-use dataset.')
parser = argparse.ArgumentParser()
parser.add_argument('--json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = label_path + list_path[i]
print(list_path[i])
if args.out is None:
out_dir = osp.basename(json_file).replace('.', '_') # Return file name
out_dir = osp.join(osp.dirname(json_file), out_dir) # Combine directory and file name into one path
else:
out_dir = args.out
if not osp.exists(out_dir):
os.mkdir(out_dir) # Used to create directories in digital permission mode
data = json.load(open(json_file))
imageData = data.get('imageData')
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data['imagePath']) # os.path.dirname returns the file path
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(
img.shape, data['shapes'], label_name_to_value
)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = imgviz.label2rgb(
label=lbl, img=imgviz.asgray(img), label_names=label_names, loc='rb'
)
PIL.Image.fromarray(img).save(osp.join(out_dir, "Images", f"Image{i+148}.png"))
utils.lblsave(osp.join(out_dir, "Masks", f"Mask{i+148}.png"), lbl)
#PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, f"Mask{i+148}.png"))
#with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
# for lbl_name in label_names:
# f.write(lbl_name + '\n')
logger.info('Saved to: {}'.format(out_dir))
def main():
warnings.warn("This script is aimed to demonstrate how to convert the\n"
"JSON file to a single image dataset, and not to handle\n"
"multiple JSON files to generate a real-use dataset.")
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
parser.add_argument('-o', '--out', default=None)
args = parser.parse_args()
json_file = args.json_file
alist = os.listdir(json_file)
for i in range(0, len(alist)):
path = os.path.join(json_file, alist[i])
data = json.load(open(path))
out_dir = osp.basename(path).replace('.', '_')
out_dir = osp.join(osp.dirname(path), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(path), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in data['shapes']:
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# label_values must be dense
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(), key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
captions = [
'{}: {}'.format(lv, ln) for ln, lv in label_name_to_value.items()
]
lbl_viz = utils.draw_label(lbl, img, captions)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
warnings.warn('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
print('Saved to: %s' % out_dir)
def json_to_mask(json_file):
json_file = json_file
# 下面类别就是自己要分的类别数目，我的共有9类，而且下面这个字典的关键字（单引号内的）是自己在用labelme时标注的类别
# 所以我自己又加了0-9的关键字，你的改成你自己的关键字就可以了，然后冒号后面的我猜代表的是颜色的标号，这样就会出来不同的颜色。
label_name_to_value = {
'_background_': 0,
'tape': 1,
'scissors': 2,
'pen': 3,
'3': 4,
'4': 5,
'5': 6,
'6': 7,
'7': 8,
'8': 9
}
out_dir = osp.join(osp.dirname(json_file),
osp.basename(json_file).split('_')[0] + 'mask_set')
if not osp.exists(out_dir):
os.mkdir(out_dir)
count = os.listdir(json_file)
for i in count:
path = os.path.join(json_file, i)
if os.path.isfile(path):
data = json.load(open(path))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(path),
data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
for shape in data['shapes']:
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# label_values must be dense
label_values, label_names = [], []
for ln, lv in sorted(label_name_to_value.items(),
key=lambda x: x[1]):
label_values.append(lv)
label_names.append(ln)
assert label_values == list(range(len(label_values)))
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
name = osp.basename(i).replace('.', '_').split('_')
# PIL.Image.fromarray(lbl).save(osp.join(out_dir, rename+'label.png'))
utils.lblsave(osp.join(out_dir, name[0] + '_mask.png'), lbl)
print('Saved to {} mask pictures'.format(len(os.listdir(out_dir))))
return out_dir
def main():
# warnings.warn("This script is aimed to demonstrate how to convert the\n"
# "JSON file to a single image dataset, and not to handle\n"
# "multiple JSON files to generate a real-use dataset.")
############################################### 删除的语句 ##################################
# parser = argparse.ArgumentParser()
# parser.add_argument('json_file')
# json_file = args.json_file
# parser.add_argument('-o', '--out', default=None)
# args = parser.parse_args()
############################################### end ##################################
###############################################增加的语句######################################
for json_file in json_list:
file_name = json_file.split('\\')[-1].split('.')[0]
############################################### end ##################################
############################################### 删除的语句 ##################################
# if args.out is None:
# out_dir = osp.basename(json_file).replace('.', '_')
# out_dir = osp.join(osp.dirname(json_file), out_dir)
# else:
# out_dir = args.out
# if not osp.exists(out_dir):
# os.mkdir(out_dir)
############################################### end ##################################
if not osp.exists(out_dir_img):
os.mkdir(out_dir_img)
if not osp.exists(out_dir_label):
os.mkdir(out_dir_label)
data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_file),
data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
PIL.Image.fromarray(img).save(osp.join(out_dir_img,
file_name + '.png'))
utils.lblsave(osp.join(out_dir_label, file_name + '.png'), lbl)
############################################### 删除的语句 ##################################
# for name, value in label_name_to_value.items():
# label_names[value] = name
# lbl_viz = utils.draw_label(lbl, img, label_names)
# PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir,file_name+'label_viz.png'))
# with open(osp.join(out_dir, file_name+'label_names.txt'), 'w') as f:
# for lbl_name in label_names:
# f.write(lbl_name + '\n')
# warnings.warn('info.yaml is being replaced by label_names.txt')
# info = dict(label_names=label_names)
# with open(osp.join(out_dir, file_name+'info.yaml'), 'w') as f:
# yaml.safe_dump(info, f, default_flow_style=False)
# print('Saved to: %s' % out_dir)
############################################### end ##################################
print('Saved to: %s , %s ' % (out_dir_img, out_dir_label))
def main():
# logger.warning('This script is aimed to demonstrate how to convert the'
# 'JSON file to a single image dataset, and not to handle'
# 'multiple JSON files to generate a real-use dataset.')
#
# parser = argparse.ArgumentParser()
# parser.add_argument('json_file')
# parser.add_argument('-o', '--out', default=None)
# args = parser.parse_args()
# json_file = args.json_file
#
# if args.out is None:
# out_dir = osp.basename(json_file).replace('.', '_')
# out_dir = osp.join(osp.dirname(json_file), out_dir)
# else:
# out_dir = args.out
json_dir = r"F:\pycharm_data\dataset\190423_maskrcnn_for_citie\20190418jpg\maskrcnn_datasets\1_scratch\scratch_json"
json_list = os.listdir(json_dir)
print(json_list)
for json_file in json_list:
json_file = json_dir + "\\" + json_file
out_dir = osp.basename(json_file).replace('.', '_')
print(out_dir)
base_name = out_dir
out_dir = osp.join(osp.dirname(json_file), out_dir)
print(out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_file),
data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
# lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
lbl = utils.shapes_to_label(img.shape, data['shapes'],
label_name_to_value)
# label_names = [None] * (max(label_name_to_value.values()) + 1)
label_names = [None] * (max(label_name_to_value.values()) + 1)
# for name, value in label_name_to_value.items():
# label_names[value] = name
# lbl_viz = utils.draw_label(lbl, img, label_names)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
print("0001", base_name)
print("000", out_dir)
print("0002", osp.join(out_dir, base_name + '.png'))
print("112", osp.join(out_dir, 'label_viz.png'))
print("224", osp.join(out_dir, out_dir + '.png'))
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
PIL.Image.fromarray(img).save(osp.join(out_dir, base_name + '.png'))
#masks
# utils.lblsave(osp.join(out_dir, 'label.png'), lbl) #mask
utils.lblsave(osp.join(out_dir, base_name + '_mask.png'), lbl)
#piz
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir,
'label_viz.png')) #mask
# PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, base_name+'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
# logger.warning('info.yaml is being replaced by label_names.txt')
logger.warning('info.yaml is being replaced by label_names.txt')
# info = dict(label_names=label_names)
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
logger.info('Saved to: {}'.format(out_dir))
def json2png(json_file, lab2val={'_background_': 0}):
# json_file = args.json_file
label_name_to_value = lab2val
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
out_png = osp.join(osp.dirname(json_file), 'png')
out_pngviz = osp.join(osp.dirname(json_file), 'png_viz')
out_pic = osp.join(osp.dirname(json_file), 'pic')
if not osp.exists(out_dir):
os.mkdir(out_dir)
if not osp.exists(out_png):
os.mkdir(out_png)
if not osp.exists(out_pngviz):
os.mkdir(out_pngviz)
if not osp.exists(out_pic):
os.mkdir(out_pic)
data = json.load(open(json_file))
if data['imageData']:
imageData = data['imageData']
else:
imagePath = os.path.join(os.path.dirname(json_file), data['imagePath'])
with open(imagePath, 'rb') as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
for shape in sorted(data['shapes'], key=lambda x: x['label']):
label_name = shape['label']
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl = utils.shapes_to_label(img.shape, data['shapes'], label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
lbl_viz = utils.draw_label(lbl, img, label_names)
PIL.Image.fromarray(img).save(
osp.join(out_pic,
osp.basename(json_file).replace('.json', '') + '.jpg'))
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
PIL.Image.fromarray(lbl).save(
osp.join(out_png,
osp.basename(json_file).replace('.json', '') + '.png'))
# utils.lblsave(osp.join(out_png, osp.basename(json_file).replace('.json', '')+'.png'), lbl)
utils.lblsave(osp.join(out_dir, 'label.png'), lbl)
PIL.Image.fromarray(lbl_viz).save(
osp.join(out_pngviz,
osp.basename(json_file).replace('.json', '') + '.png'))
PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, 'label_viz.png'))
with open(osp.join(out_dir, 'label_names.txt'), 'w') as f:
for lbl_name in label_names:
f.write(lbl_name + '\n')
logger.warning('info.yaml is being replaced by label_names.txt')
info = dict(label_names=label_names)
with open(osp.join(out_dir, 'info.yaml'), 'w') as f:
yaml.safe_dump(info, f, default_flow_style=False)
logger.info('Saved to: {}'.format(out_dir))
def convert_single_img(i, json_file, save_dir):
"""
将单个json文件转换为标签图像
Args:
i ([type]): [description]
json_file (str): json文件
out ([type]): [description]
"""
# parser = argparse.ArgumentParser()
# parser.add_argument("json_file")
# parser.add_argument("-o", "--out", default=None)
# args = parser.parse_args()
# json_file = json_file
if save_dir is None:
out_dir = osp.basename(json_file).replace(".", "_")
out_dir = osp.join(osp.dirname(json_file), out_dir)
if not osp.exists(save_dir):
os.mkdir(save_dir)
train_dir = osp.join(save_dir, 'train')
if not osp.exists(train_dir):
os.mkdir(train_dir)
label_dir = osp.join(save_dir, 'label')
if not osp.exists(label_dir):
os.mkdir(label_dir)
data = json.load(open(json_file))
imageData = data.get("imageData")
if not imageData:
imagePath = os.path.join(os.path.dirname(json_file), data["imagePath"])
with open(imagePath, "rb") as f:
imageData = f.read()
imageData = base64.b64encode(imageData).decode("utf-8")
img = utils.img_b64_to_arr(imageData)
label_name_to_value = {"_background_": 0}
for shape in sorted(data["shapes"], key=lambda x: x["label"]):
label_name = shape["label"]
if label_name in label_name_to_value:
label_value = label_name_to_value[label_name]
else:
label_value = len(label_name_to_value)
label_name_to_value[label_name] = label_value
lbl, _ = utils.shapes_to_label(img.shape, data["shapes"],
label_name_to_value)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
# lbl_viz = imgviz.label2rgb(label=lbl,
# img=imgviz.asgray(img),
# label_names=label_names,
# loc="rb")
PIL.Image.fromarray(img).save(osp.join(train_dir, f"{i}.png"))
utils.lblsave(osp.join(label_dir, f"{i}.png"), lbl)
# PIL.Image.fromarray(lbl_viz).save(osp.join(out_dir, "label_viz.png"))
label_name_file = osp.join(save_dir, "label_names.txt")
if not os.path.isfile(label_name_file):
with open(label_name_file, "w") as f:
for lbl_name in label_names:
f.write(lbl_name + "\n")
logger.info("Saved to: {}".format(save_dir))
