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)
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)
return result['data'][fi_template_name]['TemplateEngine']
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 = cv2.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 contain_image(self,
*,
image_path: str = None,
image_object: np.ndarray = None,
**kwargs) -> typing.Dict[str, typing.Any]:
assert image_path or (
image_object is not None), "should fill image_path or image_object"
if image_path:
logger.debug(f"found image path, use it first: {image_path}")
return toolbox.match_template_with_path(image_path, self.data,
**kwargs)
image_object = toolbox.turn_grey(image_object)
return toolbox.match_template_with_object(image_object, self.data,
**kwargs)
def do(self, frame: VideoFrame, *_, **__) -> typing.Optional[VideoFrame]:
super().do(frame, *_, **__)
# you can get frame_id and frame data here
# and use them to custom your own function
# add your code here
# ...
# for example, i want to turn grey, and save size of each frames
frame.data = toolbox.turn_grey(frame.data)
self.result[frame.frame_id] = frame.data.shape
# if you are going to change the origin frame
# just return the changed frame
# and set 'overwrite' to 'True' when you are calling __init__
return frame
def do(
self, frame_id: int, frame: np.ndarray, *_, **__
) -> typing.Optional[np.ndarray]:
super().do(frame_id, frame, *_, **__)
return toolbox.turn_grey(frame)
def test_turn_grey():
image = toolbox.imread(IMAGE_PATH)
toolbox.turn_grey(image)
def test_turn_blur():
image = toolbox.imread(IMAGE_PATH)
grey = toolbox.turn_grey(image)
toolbox.turn_blur(grey)
def init(cls, cap: cv2.VideoCapture, frame: np.ndarray) -> "VideoFrame":
frame_id = toolbox.get_current_frame_id(cap)
timestamp = toolbox.get_current_frame_time(cap)
grey = toolbox.turn_grey(frame)
logger.debug(f"new a frame: {frame_id}({timestamp})")
return VideoFrame(frame_id, timestamp, grey)
def init(cls, cap: cv2.VideoCapture, frame: np.ndarray) -> "VideoFrame":
frame_id = toolbox.get_current_frame_id(cap)
timestamp = toolbox.get_current_frame_time(cap)
grey = toolbox.turn_grey(frame)
return VideoFrame(frame_id, timestamp, grey)
def do(self, frame: VideoFrame, *_, **__) -> typing.Optional[VideoFrame]:
super().do(frame, *_, **__)
frame.data = toolbox.turn_grey(frame.data)
return frame
