def test_match_template():
image1 = toolbox.imread(IMAGE_PATH)
image2 = toolbox.imread(IMAGE_PATH)
ret = toolbox.match_template_with_object(image1, image2)
assert ret["ok"]
ret = toolbox.match_template_with_path(IMAGE_PATH, image2)
assert ret["ok"]
def contain_image(self,
image_path: str = None,
image_object: np.ndarray = None,
threshold: float = None,
*args,
**kwargs):
assert image_path or image_object, 'should fill image_path or image_object'
if not threshold:
threshold = 0.99
if image_path:
logger.debug(f'found image path, use it first: {image_path}')
assert os.path.isfile(
image_path), f'image {image_path} not existed'
image_object = toolbox.imread(image_path)
image_object = toolbox.turn_grey(image_object)
# TODO use client or itself..?
fi = FindIt(engine=['template'])
fi_template_name = 'default'
fi.load_template(fi_template_name, pic_object=image_object)
with toolbox.video_capture(self.video.path) as cap:
target_id = self.pick(*args, **kwargs)[0]
frame = toolbox.get_frame(cap, target_id)
frame = toolbox.turn_grey(frame)
result = fi.find(str(target_id), target_pic_object=frame)
find_result = result['data'][fi_template_name]['TemplateEngine']
position = find_result['target_point']
sim = find_result['target_sim']
logger.debug(f'position: {position}, sim: {sim}')
return sim > threshold
def contain_image(self,
image_path: str = None,
image_object: np.ndarray = None,
*args,
**kwargs) -> typing.Dict:
assert image_path or image_object, "should fill image_path or image_object"
if image_path:
logger.debug(f"found image path, use it first: {image_path}")
assert os.path.isfile(
image_path), f"image {image_path} not existed"
image_object = toolbox.imread(image_path)
image_object = toolbox.turn_grey(image_object)
# TODO use client or itself..?
fi = FindIt(engine=["template"])
fi_template_name = "default"
fi.load_template(fi_template_name, pic_object=image_object)
target_id = self.pick(*args, **kwargs)[0]
operator = self.video.get_operator()
frame = operator.get_frame_by_id(target_id)
result = fi.find(str(target_id), target_pic_object=frame.data)
return result["data"][fi_template_name]["TemplateEngine"]
def test_keep_data():
cl = SVMClassifier()
cl.load_model(MODEL_PATH)
stable, _ = cutter_res.get_range()
classify_result = cl.classify(VIDEO_PATH, stable, keep_data=True)
# todo findit bug here
image_object = toolbox.imread(IMAGE_PATH)[0:20, 0:20]
assert classify_result.data[0].contain_image(image_object=image_object)
def predict(self, pic_path: str) -> str:
"""
predict a single picture
:param pic_path:
:return:
"""
pic_object = toolbox.imread(pic_path)
return self.predict_with_object(pic_object)
def predict(self, pic_path: str, *args, **kwargs) -> str:
"""
predict a single picture
:param pic_path:
:return:
"""
pic_object = toolbox.imread(pic_path)
# fake VideoFrame for apply_hook
fake_frame = VideoFrame(0, 0.0, pic_object)
fake_frame = self._apply_hook(fake_frame, *args, **kwargs)
return self.predict_with_object(fake_frame.data)
def test_compress():
image = toolbox.imread(IMAGE_PATH)
frame = toolbox.compress_frame(image, target_size=(100, 100))
assert frame.shape == (100, 100)
def test_turn_lbp_desc():
image = toolbox.imread(IMAGE_PATH)
toolbox.turn_lbp_desc(image)
def test_turn_binary():
image = toolbox.imread(IMAGE_PATH)
toolbox.turn_binary(image)
def test_turn_blur():
image = toolbox.imread(IMAGE_PATH)
grey = toolbox.turn_grey(image)
toolbox.turn_blur(grey)
def read_from_path(data: typing.List[pathlib.Path], *_, **__):
return (toolbox.imread(each.as_posix()) for each in data)
