def add_region(self,
x,
y,
w,
h,
label_id: int,
confidence: float = 0.0,
region_tensor: Gst.Structure = None,
normalized: bool = False) -> RegionOfInterest:
if normalized:
x = int(x * self.video_info().width)
y = int(y * self.video_info().height)
w = int(w * self.video_info().width)
h = int(h * self.video_info().height)
if not self.__is_bounded(x, y, w, h):
x_init, y_init, w_init, h_init = x, y, w, h
x, y, w, h = self.__clip(x, y, w, h)
Gst.debug(
"ROI coordinates [x, y, w, h] are out of image borders and will be clipped: [{}, {}, {}, {}] -> "
"[{}, {}, {}, {}]".format(x_init, y_init, w_init, h_init, x, y,
w, h))
label = self.__get_label_by_label_id(region_tensor, label_id)
video_roi_meta = GstVideo.buffer_add_video_region_of_interest_meta(
self.__buffer, label, x, y, w, h)
if not region_tensor:
region_tensor = Gst.Structure.new_empty("detection")
else:
region_tensor.set_name(
"detection") # make sure we're about to add detection Tensor
region_tensor.set_value('label_id', label_id)
region_tensor.set_value('confidence', confidence)
region_tensor.set_value('x_min', x / self.video_info().width)
region_tensor.set_value('x_max', (x + w) / self.video_info().width)
region_tensor.set_value('y_min', y / self.video_info().height)
region_tensor.set_value('y_max', (y + h) / self.video_info().height)
self.__regions.append(
RegionOfInterest(
ctypes.cast(
hash(video_roi_meta),
ctypes.POINTER(VideoRegionOfInterestMeta)).contents))
self.__regions[-1].add_tensor(tensor=region_tensor)
return self.__regions[-1]
def add_region(self,
x,
y,
w,
h,
label: str = "",
confidence: float = 0.0,
normalized: bool = False) -> RegionOfInterest:
if normalized:
x = int(x * self.video_info().width)
y = int(y * self.video_info().height)
w = int(w * self.video_info().width)
h = int(h * self.video_info().height)
if not self.__is_bounded(x, y, w, h):
x_init, y_init, w_init, h_init = x, y, w, h
x, y, w, h = self.__clip(x, y, w, h)
warn(
"ROI coordinates [x, y, w, h] are out of image borders and will be clipped: [{}, {}, {}, {}] -> "
"[{}, {}, {}, {}]".format(x_init, y_init, w_init, h_init, x, y,
w, h),
stacklevel=2)
video_roi_meta = GstVideo.buffer_add_video_region_of_interest_meta(
self.__buffer, label, x, y, w, h)
roi = RegionOfInterest(
ctypes.cast(hash(video_roi_meta),
ctypes.POINTER(VideoRegionOfInterestMeta)).contents)
tensor = roi.add_tensor("detection")
tensor['confidence'] = float(confidence)
tensor['x_min'] = float(x / self.video_info().width)
tensor['x_max'] = float((x + w) / self.video_info().width)
tensor['y_min'] = float(y / self.video_info().height)
tensor['y_max'] = float((y + h) / self.video_info().height)
return roi
