def popUp(self, text=None, move=True, flags=None, group_id=None):
if self._fit_to_content["row"]:
self.labelList.setMinimumHeight(
self.labelList.sizeHintForRow(0) * self.labelList.count() + 2)
if self._fit_to_content["column"]:
self.labelList.setMinimumWidth(
self.labelList.sizeHintForColumn(0) + 2)
# if text is None, the previous label in self.edit is kept
if text is None:
text = self.edit.text()
if flags:
self.setFlags(flags)
else:
self.resetFlags(text)
self.edit.setText(text)
self.edit.setSelection(0, len(text))
if group_id is None:
self.edit_group_id.clear()
else:
self.edit_group_id.setText(str(group_id))
items = self.labelList.findItems(text, QtCore.Qt.MatchFixedString)
if items:
if len(items) != 1:
logger.warning("Label list has duplicate '{}'".format(text))
self.labelList.setCurrentItem(items[0])
row = self.labelList.row(items[0])
self.edit.completer().setCurrentRow(row)
self.edit.setFocus(QtCore.Qt.PopupFocusReason)
if move:
self.move(QtGui.QCursor.pos())
if self.exec_():
return self.edit.text(), self.getFlags(), self.getGroupId()
else:
return None, None, None
def main():
""" 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.'
)
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('--output_dir', default=None)
args = parser.parse_args()
json_file = args.json_file
if args.output_dir is None:
out_dir = osp.basename(json_file).replace('.', '_')
out_dir = osp.join(osp.dirname(json_file), out_dir)
else:
out_dir = args.output_dir
if not osp.exists(out_dir):
os.mkdir(out_dir)
(data, img) = get_data_and_image(json_file)
(label_names, lbl) = get_label_names(data, img)
save_image_and_label(img, lbl, out_dir, label_names)
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 readLstFile(self, path_in):
"""Reads the .lst file and generates corresponding iterator.
Parameters
----------
path_in: string
Returns
-------
item iterator that contains information in .lst file
"""
with open(path_in) as fin:
while True:
self.checkAborted()
line = fin.readline()
if not line:
break
line = [i.strip() for i in line.strip().split('\t')]
line_len = len(line)
# check the data format of .lst file
if line_len < 3:
logger.warning(
'lst should have at least has three parts, but only has {} parts for {}}'
.format(line_len, line))
continue
try:
item = [int(line[0])
] + [line[-1]] + [float(i) for i in line[1:-1]]
except Exception as e:
logger.error(
'Parsing lst met error for {}, detail: {}'.format(
line, e))
continue
yield item
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():
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 popUp(self, text=None, move=True):
if self._fit_to_content['row']:
self.labelList.setMinimumHeight(
self.labelList.sizeHintForRow(0) * self.labelList.count() + 2
)
if self._fit_to_content['column']:
self.labelList.setMinimumWidth(
self.labelList.sizeHintForColumn(0) + 2
)
# if text is None, the previous label in self.edit is kept
if text is None:
text = self.edit.text()
self.edit.setText(text)
self.edit.setSelection(0, len(text))
items = self.labelList.findItems(text, QtCore.Qt.MatchFixedString)
if items:
if len(items) != 1:
logger.warning("Label list has duplicate '{}'".format(text))
self.labelList.setCurrentItem(items[0])
row = self.labelList.row(items[0])
self.edit.completer().setCurrentRow(row)
self.edit.setFocus(QtCore.Qt.PopupFocusReason)
if move:
self.move(QtGui.QCursor.pos())
return self.edit.text() if self.exec_() else None
def invertDict(in_dict):
inverted_dict = {}
for key in in_dict:
val = in_dict[key]
if val in inverted_dict:
logger.warning('Overwriting key {} with value: {}, previous value: {}'.format(val, key, inverted_dict[val]))
inverted_dict[val] = key
return inverted_dict
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 isValidFormat(self, dataset_folder_or_file):
if not os.path.isfile(dataset_folder_or_file):
logger.warning('Dataset file {} does not exist'.format(dataset_folder_or_file))
return False
try:
with open(dataset_folder_or_file, 'r') as f:
data = json.load(f)
return True
except Exception as e:
logger.warning('Error during parsing of json file {}: {}'.format(dataset_folder_or_file, e))
return False
def getContext(self, gpus=None):
if gpus is None or gpus == '':
return [mx.cpu()]
ctx = [mx.gpu(int(i)) for i in gpus.split(',') if i.strip()]
try:
tmp = mx.nd.array([1, 2, 3], ctx=ctx[0])
except mx.MXNetError as e:
ctx = [mx.cpu()]
logger.error(traceback.format_exc())
logger.warning('Unable to use GPU. Using CPU instead')
logger.debug('Use context: {}'.format(ctx))
return ctx
def isValidFormat(self, dataset_folder_or_file):
if not os.path.isfile(dataset_folder_or_file):
logger.warning('Dataset file {} does not exist'.format(
dataset_folder_or_file))
return False
file_dir = os.path.dirname(dataset_folder_or_file)
file_name = os.path.basename(dataset_folder_or_file)
base = os.path.splitext(file_name)[0]
idx_file = os.path.join(file_dir, base + '.idx')
if not os.path.isfile(idx_file):
logger.warning('Idx file {} does not exist'.format(idx_file))
return False
return True
def isValidFormat(self, dataset_folder_or_file):
root_folder = dataset_folder_or_file
if self.all_image_sets:
if not os.path.isdir(dataset_folder_or_file):
logger.warning('Dataset folder {} does not exist'.format(
dataset_folder_or_file))
return False
else:
root_folder = self._getRootFolderFromFile(dataset_folder_or_file)
if not os.path.isfile(dataset_folder_or_file):
logger.warning('Dataset file {} does not exist'.format(
dataset_folder_or_file))
return False
annotations_dir = os.path.join(root_folder,
FormatVoc._directories['annotations'])
if not os.path.isdir(annotations_dir):
logger.warning(
'Annotations folder {} does not exist'.format(annotations_dir))
return False
images_dir = os.path.join(root_folder,
FormatVoc._directories['images'])
if not os.path.isdir(images_dir):
logger.warning(
'Images folder {} does not exist'.format(images_dir))
return False
return True
def main():
logger.warning("This script is aimed to demonstrate how to convert "
"JSON files to image dataset from a dir.")
parser = argparse.ArgumentParser()
parser.add_argument("path")
parser.add_argument("-o", "--out", default=None)
parser.add_argument("-r", "--rename", default='N')
args = parser.parse_args()
_path = args.path
_out_dir = ''
if args.out is not None:
_out_dir = osp.realpath(args.out)
# 执行转换
path_to_dataset(_path, _out_dir, args.rename == 'Y')
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_true") # 정답지
parser.add_argument("json_file_target") # 검증이 필요한 파일
parser.add_argument("-o", "--out", default=None) # 저장 경로
args = parser.parse_args()
json_file_true = args.json_file_true # 정답 json
json_file_target = args.json_file_target # 임의 json
true_json_folder_path = r"D:\2020\DS\Project\2020-11-02-labelme\labelme-master\labelme\cli\validataion_example\true_label"
target_json_folder_path = r"D:\2020\DS\Project\2020-11-02-labelme\labelme-master\labelme\cli\validataion_example\user_label"
true_json_list = glob.glob(os.path.join(true_json_folder_path, "*.json"))
# target_json_list = glob.glob(os.path.join(target_json_folder_path, "*.json"))
not_matched_files = []
for true_json in true_json_list:
target_json = osp.join(target_json_folder_path,
osp.basename(true_json))
if not osp.exists(target_json):
result_list.append([
len(result_list),
osp.basename(true_json), "None", "None", "None", "None",
"None", "None", "None"
])
not_matched_files.append(target_json)
else:
validate_json_file(true_json, target_json, args.out)
save_result_csv(osp.join(args.out, "result_total.csv"))
print(result_list)
def popUp(self, text=None, move=True):
if self._fit_to_content['row']:
self.labelList.setMinimumHeight(
self.labelList.sizeHintForRow(0) * self.labelList.count() + 2)
if self._fit_to_content['column']:
self.labelList.setMinimumWidth(
self.labelList.sizeHintForColumn(0) + 2)
# if text is None, the previous label in self.edit is kept
if text is None:
text = self.edit.text()
self.edit.setText(text)
self.edit.setSelection(0, len(text))
items = self.labelList.findItems(text, QtCore.Qt.MatchFixedString)
if items:
if len(items) != 1:
logger.warning("Label list has duplicate '{}'".format(text))
self.labelList.setCurrentItem(items[0])
row = self.labelList.row(items[0])
self.edit.completer().setCurrentRow(row)
self.edit.setFocus(QtCore.Qt.PopupFocusReason)
if move:
self.move(QtGui.QCursor.pos())
return self.edit.text() if self.exec_() else None
def importToIntermediate(self, rec_file, output_folder):
# Labels
all_labels = []
input_folder = os.path.dirname(self.input_folder_or_file)
label_file = os.path.join(input_folder,
FormatImageRecord._files['labels'])
if os.path.isfile(label_file):
logger.debug('Load labels from file {}'.format(label_file))
for i, line in enumerate(open(label_file).readlines()):
all_labels.append(line)
else:
logger.warning('No label file found at {}'.format(label_file))
self.thread.update.emit(_('Loading image record file ...'), 10, -1)
self.checkAborted()
file_pos = 0
file_size = os.path.getsize(rec_file)
logger.debug(
'Start loading of image record file {} with size of {} bytes'.
format(rec_file, file_size))
record = mx.recordio.MXRecordIO(rec_file, 'r')
record.reset()
while True:
try:
self.checkAborted()
item = record.read()
if not item:
break
file_pos += len(item)
percentage = file_pos / file_size * 90
self.thread.update.emit(_('Loading image record file ...'),
10 + percentage, -1)
self.checkAborted()
header, image = mx.recordio.unpack_img(item)
img_file = os.path.join(output_folder,
'{:09d}.jpg'.format(header.id))
cv2.imwrite(img_file, image)
image_height = image.shape[0]
image_width = image.shape[1]
shapes = []
for i in range(4, len(header.label), 5):
label_idx = int(header.label[i])
bbox = header.label[i + 1:i + 5]
label_name = str(label_idx)
if label_idx < len(all_labels):
label_name = all_labels[label_idx].strip()
points = [
[
int(bbox[0] * image_width),
int(bbox[1] * image_height)
],
[
int(bbox[2] * image_width),
int(bbox[3] * image_height)
],
]
# imagerecord has only rectangle shapes
self.intermediate.addSample(img_file,
(image_height, image_width),
label_name, points,
'rectangle')
self.checkAborted()
except Exception as e:
logger.error(traceback.format_exc())
raise Exception(e)
record.close()
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 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():
logger.warning('This script is aimed to convert the '
'JSON batch to gray map of DABNet format.')
parser = argparse.ArgumentParser()
parser.add_argument('--json-list', default=None)
parser.add_argument('--label-file', default=None)
args = parser.parse_args()
# Load .json from list file
if not osp.isfile(args.json_list):
print("json_list doesn't existed!!")
return
with open(args.json_list, 'r') as f:
json_files = f.readlines()
json_files = [x.strip() for x in json_files]
# Import label file
if not osp.isfile(args.label_file):
print("label_file doesn't existed!!")
return
label_name_to_value = importLabel(args.label_file)
# main loop
for i in range(0, len(json_files)):
json_file = ''.join(json_files[i])
out_dir = json_file.split('.')[0]
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)
# check label in json is in the label file or not
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
print(label_name, " is not in the label file")
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_gray(out_dir + '.png', lbl)
PIL.Image.fromarray(lbl_viz).save(out_dir + '_viz.png')
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
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 popUp(self,
text=None,
sub_text=None,
move=True,
flags=None,
group_id=None,
mode=None,
shape=None,
eidtType='Main'):
f = mode == 'cc_rectangle' or mode == 'create_cc_region' or mode == 'cc_in_rectangle'
for item in self.cc_threshold_ui:
item.setVisible(f)
f = mode == 'text_grid'
for item in self.text_box_ui:
item.setVisible(f)
# if self._fit_to_content["row"]:
# self.labelList.setMinimumHeight(
# self.labelList.sizeHintForRow(0) * self.labelList.count() + 2
# )
# if self._fit_to_content["column"]:
# self.labelList.setMinimumWidth(
# self.labelList.sizeHintForColumn(0) + 2
# )
if eidtType == 'Main':
self.edit.setCompleter(self.completer)
self.labelList.setVisible(True)
self.sub_labelList.setVisible(False)
# if text is None, the previous label in self.edit is kept
if text is None:
text = self.edit.text()
if flags:
self.setFlags(flags)
else:
self.resetFlags(text)
self.edit.setText(text)
self.edit.setSelection(0, len(text))
if group_id is None:
self.edit_group_id.clear()
else:
self.edit_group_id.setText(str(group_id))
items = self.labelList.findItems(text, QtCore.Qt.MatchFixedString)
if items:
if len(items) != 1:
logger.warning(
"Label list has duplicate '{}'".format(text))
self.labelList.setCurrentItem(items[0])
row = self.labelList.row(items[0])
self.edit.completer().setCurrentRow(row)
self.edit.setFocus(QtCore.Qt.PopupFocusReason)
if move:
# self.move(QtGui.QCursor.pos())
self.move(
QtWidgets.QApplication.desktop().screen().rect().center() -
self.rect().center())
# initialize sub window
if mode == 'text_grid':
self.sub_window.initialize(pixmap=self.app.canvas.pixmap,
np_image=self.app.np_image_b,
pos=self.pos(),
rect=shape)
self.sub_window.show()
self.sub_window.move(self.sub_window.moveVal)
self.sub_window.update()
elif eidtType == 'Sub':
self.edit.setCompleter(self.sub_completer)
self.labelList.setVisible(False)
self.sub_labelList.setVisible(True)
# self.sub_labelList.item(0).text()
if sub_text is None:
sub_text = ""
self.edit.setText(sub_text)
self.edit.setSelection(0, len(sub_text))
items = self.sub_labelList.findItems(sub_text,
QtCore.Qt.MatchFixedString)
if items:
if len(items) != 1:
logger.warning(
"Label list has duplicate '{}'".format(sub_text))
self.sub_labelList.setCurrentItem(items[0])
row = self.sub_labelList.row(items[0])
self.edit.completer().setCurrentRow(row)
result_text = None
result_flag = None
result_groupid = None
if self.exec_():
result_text = self.edit.text()
result_flag = self.getFlags()
result_groupid = self.getGroupId()
if mode == 'text_grid':
self.sub_window.close()
# first is for main mode label
# second is for sub mode label
return result_text, result_flag, result_groupid, result_text
def polygons_to_mask(img_shape, polygons, shape_type=None):
logger.warning("The 'polygons_to_mask' function is deprecated, "
"use 'shape_to_mask' instead.")
return shape_to_mask(img_shape, points=polygons, shape_type=shape_type)
def main():
logger.warning('This script is aimed to remap the ADE20K '
'annotations to a customize annotation.')
parser = argparse.ArgumentParser()
parser.add_argument('--label-file', default="rtk")
parser.add_argument('--remap-table', default="rtk")
parser.add_argument('--image-list', default="training")
parser.add_argument('--save-vizImage', default=False)
parser.add_argument('--save-oriImage', default=False)
# parser.add_argument('--save-colorLabImage', default=False)
args = parser.parse_args()
# Import label file
label_file = 'labels_' + args.label_file + '.txt'
label_name_to_value = importLabel(label_file)
label_names = [None] * (max(label_name_to_value.values()) + 1)
for name, value in label_name_to_value.items():
label_names[value] = name
# print(value, " ", name)
# Import remap table
map_table = 'map_' + args.remap_table + '.txt'
mapping = importRemapTable(map_table)
# Output direction
out_folder = osp.join('annotations_' + args.label_file, args.image_list)
if not osp.exists(out_folder):
os.makedirs(out_folder, exist_ok=True)
logger.info('Saved to: {}'.format(out_folder))
# Load label image list
label_list = importLabelList(args.image_list + '.txt')
# Main loop
for idx in range(0, len(label_list)):
# load label image
with open(label_list[idx], 'rb') as f:
image_name = osp.split(label_list[idx])[1].split('.')[0]
imageData = f.read()
if not imageData:
logger.info('Lebelled Image does not existed')
break
imageData = base64.b64encode(imageData).decode('utf-8')
label_img = utils.img_b64_to_arr(imageData)
label_img = remapLabel(label_img, mapping)
utils.lblsave_gray(osp.join(out_folder, image_name + '.png'),
label_img)
# if args.save_colorLabImage:
# utils.lblsave(osp.join(out_folder, 'label_color.png'), label_img)
# load original image
if args.save_oriImage or args.save_vizImage:
image_list = label_list[idx].replace('annotations', 'images')
image_list = image_list.replace('png', 'jpg')
with open(image_list, 'rb') as f:
imageData = f.read()
if not imageData:
logger.info('Original Color Image does not existed')
args.save_oriImage = args.save_vizImage = False
imageData = base64.b64encode(imageData).decode('utf-8')
img = utils.img_b64_to_arr(imageData)
if args.save_oriImage:
PIL.Image.fromarray(img).save(
osp.join(out_folder, image_name + '.jpg'))
if args.save_vizImage:
lbl_viz = imgviz.label2rgb(label=label_img,
img=imgviz.asgray(img),
label_names=label_names,
loc='rb')
PIL.Image.fromarray(lbl_viz).save(
osp.join(out_folder, image_name + '_viz.png'))