def updateEntryTimeTable(entry):
bracList = []
if entry['type'] == 'dir':
if entry['recursive']:
bracs = common.locate('*.brac', entry['path'])
elif not entry['recursive']:
bracs = glob.glob(os.path.join(entry['path'], '*.brac'))
for f in bracs:
if f not in bracList:
bracList.append(f)
elif entry['type'] == 'file':
if entry['path'][-4:].lower() == 'brac' and entry['path'] not in bracList:
bracList.append(entry['path'])
entry['bracList'] = []
for b in bracList:
timetbl = getBracTimeTable(b)
entry['bracList'].append({
'path': b,
'mtime': os.path.getmtime(b),
'timetable': timetbl,
})
if entry['type'] == 'dir':
entry['subdirs'] = {}
subdirs = common.subdirs(entry['path'])
for sd in subdirs:
entry['subdirs'][sd] = os.path.getmtime(sd)
entry['mtime'] = os.path.getmtime(entry['path'])
def updateEntryTimeTable(entry):
bracList = []
if entry['type'] == 'dir':
if entry['recursive']:
bracs = common.locate('*.brac', entry['path'])
elif not entry['recursive']:
bracs = glob.glob(os.path.join(entry['path'], '*.brac'))
for f in bracs:
if f not in bracList:
bracList.append(f)
elif entry['type'] == 'file':
if entry['path'][-4:].lower(
) == 'brac' and entry['path'] not in bracList:
bracList.append(entry['path'])
entry['bracList'] = []
for b in bracList:
timetbl = getBracTimeTable(b)
entry['bracList'].append({
'path': b,
'mtime': os.path.getmtime(b),
'timetable': timetbl,
})
if entry['type'] == 'dir':
entry['subdirs'] = {}
subdirs = common.subdirs(entry['path'])
for sd in subdirs:
entry['subdirs'][sd] = os.path.getmtime(sd)
entry['mtime'] = os.path.getmtime(entry['path'])
def register_inheritors(self, property, root_path, base_type, lifestyle_type = "UNKNOWN", include_base = False):
if (lifestyle_type == "UNKNOWN"): lifestyle_type = self.default_lifestyle_type
self.assert_valid_lifestyle_type(lifestyle_type)
all_classes = []
for module_path in common.locate("*.py", root=root_path, recursive=False):
module = reflection.get_module_from_path(module_path)
classes = reflection.get_classes_for_module(module)
for cls in classes:
should_include = include_base and cls.__name__ == base_type.__name__
should_include = should_include or (cls.__bases__ is (list, tuple) and base_type.__name__ in [klass.__name__ for klass in cls.__bases__])
if should_include:
all_classes.append(cls)
component_definition = "indirect", lifestyle_type, all_classes, None, None
def register_inheritors(self, property, root_path, base_type, lifestyle_type = "UNKNOWN", include_base = False):
if (lifestyle_type == "UNKNOWN"): lifestyle_type = self.default_lifestyle_type
self.assert_valid_lifestyle_type(lifestyle_type)
all_classes = []
for module_path in common.locate("*.py", root=root_path, recursive=False):
try:
module = reflection.get_module_from_path(module_path)
except UnicodeDecodeError, err:
print err
raise
classes = reflection.get_classes_for_module(module)
for cls in classes:
should_include = include_base and cls.__name__ == base_type.__name__
should_include = should_include or (isinstance(cls.__bases__, (list, tuple)) and base_type.__name__ in [klass.__name__.replace("__init__.","") for klass in cls.__bases__])
if should_include:
def register_files(self, property, root_path, pattern, lifestyle_type = "UNKNOWN"):
if (lifestyle_type == "UNKNOWN"): lifestyle_type = self.default_lifestyle_type
self.assert_valid_lifestyle_type(lifestyle_type)
all_classes = []
for module_path in common.locate(pattern, root=root_path):
module = reflection.get_module_from_path(module_path)
class_name = common.camel_case(module.__name__)
cls = reflection.get_class_for_module(module, class_name)
if cls == None:
raise AttributeError("The class %s could not be found in file %s. Please make sure that the class has the same name as the file, but Camel Cased." % (class_name, module_path))
all_classes.append(cls)
component_definition = "indirect", lifestyle_type, all_classes, None, None
self.components[property] = component_definition
print('idx {}'.format(idx))
idx += 1
img = tf.io.read_file(p + '/img.png')
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize(img, [width, height])
raw_img = img
img = img / 255.0
result = detect_model.predict(np.array([img]))
mask = create_mask(result)
mask = tf.keras.preprocessing.image.array_to_img(mask)
mask = np.asarray(mask)
img = np.asarray(img)
plate_image = locate(img, mask)
plate_chars = recognition_model.predict(np.array([plate_image]))
plate = []
for cs in plate_chars:
plate.append(index_to_char[np.argmax(cs)])
real_plate = pathlib.Path(p).name
predict_plate = ''.join(plate)
if predict_plate != real_plate:
print('wrong real plate is {}, predict plate is {}'.format(
real_plate, predict_plate))
error_count += 1
raw_img = np.asarray(raw_img)
plate_image = locate(raw_img, mask)
tf.io.write_file('./dataset/error/' + real_plate + '/plate.jpeg',
