def _check_image_height_and_width(imageData, imageHeight, imageWidth):
img_arr = utils.img_b64_to_arr(imageData)
if imageHeight is not None and img_arr.shape[0] != imageHeight:
imageHeight = img_arr.shape[0]
if imageWidth is not None and img_arr.shape[1] != imageWidth:
imageWidth = img_arr.shape[1]
return imageHeight, imageWidth
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 extract_heatmaps_info(json_data, label_name_to_value=LABEL_NAME_TO_VALUE):
"""
Given labelme json data, construct a heatmaps info
:param json_data: dict, labelme data from a json file
:param label_name_to_value: dict
:return: dict, heatmaps info
"""
img = img_b64_to_arr(json_data['imageData'])
info = {
'img_height': img.shape[0],
'img_width': img.shape[1],
'cwh_list': [],
}
for shape in json_data['shapes']:
lbl = shape['label']
if lbl in label_name_to_value:
points = shape['points']
if len(points) > 3:
c_x, c_y, w, h = points_to_cwh(points)
info['cwh_list'].append({
'label': lbl,
'center': (c_x, c_y),
'width': w,
'height': h,
})
return info
def main():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
args = parser.parse_args()
json_file = args.json_file
data = json.load(open(json_file)) # 加载json文件
img = utils.img_b64_to_arr(data['imageData']) # 解析原图片数据
dense(img, data['shapes'])
#src=cv2.imread('json/bird.jpg')
#cv2.imshow('input_image',img)
#lbl, lbl_names = utils.labelme_shapes_to_label(img.shape, data['shapes']) # 解析'shapes'中的字段信息，解析出每个对象的mask与对应的label lbl存储 mask，lbl_names 存储对应的label
# lal 像素取值 0、1、2 其中0对应背景，1对应第一个对象，2对应第二个对象
# 使用该方法取出每个对象的mask mask=[] mask.append((lbl==1).astype(np.uint8)) # 解析出像素值为1的对象，对应第一个对象 mask 为0、1组成的（0为背景，1为对象）
# lbl_names ['background','cat_1','cat_2']
#captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]
#lbl_viz = utils.draw_label(lbl, img, captions)
a = 1
cv2.waitKey(0)
cv2.destroyAllWindows()
def get_raw_data(self, key, save_processed_image=False):
"""
Args:
key (int): key
Returns:
ret_dict
"""
assert isinstance(key, int)
json_file = self.json_path_list[key]
data = json.load(open(json_file))
imageData = data["imageData"]
raw_img = utils.img_b64_to_arr(imageData) # img: H x W x C
seg_mask, _ = utils.shapes_to_label(raw_img.shape, data["shapes"],
self.label_name_to_value) # HxW
seg_mask = torch.tensor(seg_mask, dtype=torch.uint8)
seg_mask = self.label_unifier(seg_mask)
loss_mask = torch.ones_like(seg_mask)
raw_img = Image.fromarray(raw_img)
return {
'image': raw_img,
'seg_mask': seg_mask,
'loss_mask': loss_mask,
'valid_label_idx': self.valid_label_idx,
}
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 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 singleFile(filePath):
if os.path.isfile(filePath) and filePath.endswith('.json'):
data = json.load(open(filePath))
img = lUtils.img_b64_to_arr(data['imageData'])
lbl, lbl_names = lUtils.labelme_shapes_to_label(
img.shape, data['shapes'])
# lbl[lbl>0] = 255
# print(lbl)
captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]
lbl_viz = lUtils.draw_label(lbl, img, captions)
out_dir = osp.basename(filePath).replace('.', '_')
out_dir = osp.join(osp.dirname(filePath), out_dir)
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.png'))
print(np.max(lbl))
lbl = np.array(lbl, dtype=np.uint8)
lbl[lbl > 0] = 255
PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label_255.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 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 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():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
args = parser.parse_args()
json_file = args.json_file
data = json.load(open(json_file)) # 加载json文件
img = utils.img_b64_to_arr(data['imageData']) # 解析原图片数据
lbl, lbl_names = utils.labelme_shapes_to_label(
img.shape, data['shapes']
) # 解析'shapes'中的字段信息，解析出每个对象的mask与对应的label lbl存储 mask，lbl_names 存储对应的label
# lal 像素取值 0、1、2 其中0对应背景，1对应第一个对象，2对应第二个对象
# 使用该方法取出每个对象的mask mask=[] mask.append((lbl==1).astype(np.uint8)) # 解析出像素值为1的对象，对应第一个对象 mask 为0、1组成的（0为背景，1为对象）
# lbl_names ['background','cat_1','cat_2']
captions = ['%d: %s' % (l, name) for l, name in enumerate(lbl_names)]
lbl_viz = utils.draw_label(lbl, img, captions)
#画左边图
plt.subplot(121)
plt.imshow(img)
#画右边的对比图
plt.subplot(122)
plt.imshow(lbl_viz)
plt.show()
def json2dataset(json_dirpath):
json_file = json_dirpath
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)
return lbl
def main():
'''
batch convert json to signle channel labels
src_path: the file path for json file
target_path: the file path for labels out
format:
python batch_json_to_dataset.py [src_path] [target_path]
'''
parser = argparse.ArgumentParser()
parser.add_argument('src_path')
parser.add_argument('target_path')
args = parser.parse_args()
src_path = args.src_path
target_path = args.target_path
pattern = '*.json'
for jpath in gb.glob(src_path + "/" + pattern):
json_name = jpath.split('/')[-1].split('.')[0]
data = json.load(open(jpath))
img = utils.img_b64_to_arr(data['imageData'])
lbl, lbl_names = our_labelme_shapes_to_label(img.shape, data['shapes'])
print(json_name.split('\\')[1])
print(osp.join(target_path, json_name + '.png'))
PIL.Image.fromarray(lbl).save(
osp.join(target_path,
json_name.split('\\')[1] + '.png'))
print('done...')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
args = parser.parse_args()
json_file = args.json_file
data = json.load(open(json_file))
img = utils.img_b64_to_arr(data['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
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)
plt.subplot(121)
plt.imshow(img)
plt.subplot(122)
plt.imshow(lbl_viz)
plt.show()
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 load_json(path):
'''for labelme
'''
def flat(lists):
result = []
for listx in lists:
result += listx
return result
with open(path, 'rb') as f:
data = json.load(f)
img = utils.img_b64_to_arr(data['imageData'])
# mask, labels = utils.labelme_shapes_to_label(img.shape, data['shapes'])
# print(mask.shape, labels)
# masks = [(mask == i).astype(np.uint8) for i in range(len(labels))]
# result = utils.draw_label(mask, img, labels, colormap=None)
# Image.fromarray(result).show()
blob = {
'imagePath': data['imagePath'],
'imageData': Image.fromarray(img),
'labels': [],
'points': []
}
for info in data['shapes']:
blob['labels'] += [info['label']]
blob['points'] += [flat(info['points'])]
assert len(blob['labels']) == len(blob['points']), ''
return blob
def _image(self, obj, path):
image = {}
img_x = utils.img_b64_to_arr(obj['imageData'])
image["height"] = img_x.shape[0]
image["width"] = img_x.shape[1]
image['id'] = self.img_id
image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
return image
def json_generation(self):
# image_names = [str(self.id)+'_flip_x', str(self.id)+'_flip_y', str(self.id)+'_flip_x_y']
image_names = [str(self.id) + '_flip_x_y']
if self.gaussianBlur:
image_names.append(str(self.id) + '_Gaussian')
# image_names.append(str(self.id)+'_flip_x'+'_Gaussian')
# image_names.append(str(self.id)+'_flip_y' + '_Gaussian')
image_names.append(str(self.id) + '_flip_x_y' + '_Gaussian')
if self.changeExposure:
image_names.append(str(self.id) + '_ReduceEp')
# image_names.append(str(self.id)+'_flip_x'+'_ReduceEp')
# image_names.append(str(self.id)+'_flip_y'+'_ReduceEp')
image_names.append(str(self.id) + '_flip_x_y' + '_ReduceEp')
image_names.append(str(self.id) + '_IncreaseEp')
# image_names.append(str(self.id)+'_flip_x'+'_IncreaseEp')
# image_names.append(str(self.id)+'_flip_y'+'_IncreaseEp')
image_names.append(str(self.id) + '_flip_x_y' + '_IncreaseEp')
if self.add_saltNoise:
image_names.append(str(self.id) + '_Salt')
# image_names.append(str(self.id)+'_flip_x' + '_Salt')
# image_names.append(str(self.id)+'_flip_y' + '_Salt')
image_names.append(str(self.id) + '_flip_x_y' + '_Salt')
for image_name in image_names:
with open(image_name + ".jpg", "rb") as b64:
base64_data_original = str(base64.b64encode(b64.read()))
# In pycharm:
# match_pattern=re.compile(r'b\'(.*)\'')
# base64_data=match_pattern.match(base64_data_original).group(1)
# In terminal:
base64_data = base64_data_original
with open(str(self.id) + ".json", 'r') as js:
json_data = json.load(js)
img = utils.img_b64_to_arr(json_data['imageData'])
height, width = img.shape[:2]
shapes = json_data['shapes']
for shape in shapes:
points = shape['points']
for point in points:
# match_pattern2 = re.compile(r'(.*)_x(.*)')
# match_pattern3 = re.compile(r'(.*)_y(.*)')
match_pattern4 = re.compile(r'(.*)_x_y(.*)')
if match_pattern4.match(image_name):
point[0] = width - point[0]
point[1] = height - point[1]
# elif match_pattern3.match(image_name):
# point[0] = width - point[0]
# point[1] = point[1]
# elif match_pattern2.match(image_name):
# point[0] = point[0]
# point[1] = height - point[1]
else:
point[0] = point[0]
point[1] = point[1]
json_data['imagePath'] = image_name + ".jpg"
json_data['imageData'] = None
json.dump(json_data,
open("./" + image_name + ".json", 'w'),
indent=2)
def getJsonInfo(self,file_path):
print(file_path)
with open(file_path,'r') as f:
data = json.load(f)
img = utils.img_b64_to_arr(data['imageData'])
for shapes in data['shapes']:
shapes['label'] = 'chromosome'
with open(file_path,'w') as f:
json.dump(data,f)
def _image(self, obj, path):
image = {}
img_x = utils.img_b64_to_arr(obj['imageData'])
h, w = img_x.shape[:-1]
image['height'] = h
image['width'] = w
image['id'] = self.img_id
image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
return image
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 _image(self, obj, path):
image = OrderedDict()
from labelme import utils
img_x = utils.img_b64_to_arr(obj['imageData'])
h, w = img_x.shape[:-1]
image['file_name'] = os.path.basename(path).replace(".json", ".BMP")
image['height'] = h
image['width'] = w
image['id'] = self.img_id
return image
def _image(self, obj, path):
image = {}
from labelme import utils
# utils.img_b64_to_arr读取文件很耗时,若图片大小都一致，可以固定h,w
img_x = utils.img_b64_to_arr(obj['imageData'])
h, w = img_x.shape[:-1]
image['height'] = h
image['width'] = w
image['id'] = self.img_id
image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
return image
def main():
json_file = '/home/liuenli/Mask_RCNN-master--TrainOwerDatset-master/assets/total'
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)
out_dir=json_file+"/"+out_dir
if not osp.exists(out_dir):
os.mkdir(out_dir)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'img.png'))
# PIL.Image.fromarray(lbl).save()
labelpath = osp.join(out_dir, 'label.png')
# PIL.Image.fromarray(lbl).save(labelpath)
# opencvimg16 = cv2.imread(labelpath)
# opencvimg.convertTo(opencvimg6,)
lbl8u=np.zeros((lbl.shape[0],lbl.shape[1]),dtype=np.uint8)
for i in range(lbl.shape[0]):
for j in range(lbl.shape[1]):
lbl8u[i,j]=lbl[i,j]
PIL.Image.fromarray(lbl8u).save(labelpath);
# Alllabelpath="%s"
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.dump(info, f, default_flow_style=False)
fov=open(osp.join(out_dir,'info.yaml'),'w')
for key in info:
fov.writelines(key)
fov.write(':\n')
for k,v in lbl_names.items():
fov.write(' ')
fov.write(k)
fov.write(':\n')
fov.close()
print('Saved to: %s' % out_dir)
def save_image_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)
PIL.Image.fromarray(img).save(osp.join(out_dir, 'image.png'))
def _image(self, obj, path):
image = {}
from labelme import utils
img_x = utils.img_b64_to_arr(obj['imageData'])
h, w = img_x.shape[:-1]
image['height'] = h
image['width'] = w
self.img_id = int(os.path.basename(path).split(".json")[0])
image['id'] = self.img_id
# image['id'] = self.img_id
image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
return image
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 _check_image_height_and_width(imageData, imageHeight, imageWidth):
img_arr = utils.img_b64_to_arr(imageData)
if imageHeight is not None and img_arr.shape[0] != imageHeight:
logger.error(
'imageHeight does not match with imageData or imagePath, '
'so getting imageHeight from actual image.')
imageHeight = img_arr.shape[0]
if imageWidth is not None and img_arr.shape[1] != imageWidth:
logger.error(
'imageWidth does not match with imageData or imagePath, '
'so getting imageWidth from actual image.')
imageWidth = img_arr.shape[1]
return imageHeight, imageWidth
def folder_files_to_list(path):
print(path)
files = [path + f for f in listdir(path) if isfile(join(path, f))]
x = []
y = []
labels_count = []
for file in files:
with open(file) as json_file:
data = json.load(json_file)
x.append(utils.img_b64_to_arr(data['imageData']))
y.append(data['shapes'][0]['label'])
labels_count.append(len(data['shapes']))
return x, y, labels_count
def main():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
args = parser.parse_args()
json_file = args.json_file
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 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
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)
plt.subplot(121)
plt.imshow(img)
plt.subplot(122)
plt.imshow(lbl_viz)
plt.show()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('json_file')
args = parser.parse_args()
json_file = args.json_file
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)
plt.subplot(121)
plt.imshow(img)
plt.subplot(122)
plt.imshow(lbl_viz)
plt.show()
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
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 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'))
PIL.Image.fromarray(lbl).save(osp.join(out_dir, 'label.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')
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)
