(self, image)
return result
Arguments:
            image (np.ndarray): an image as returned by OpenCV

        Returns:
            prediction (BoxList): the detected objects. Additional information
                of the detection properties can be found in the fields of
                the BoxList via `prediction.fields()`
Arguments:
            image (np.ndarray): an image as returned by OpenCV
Arguments : image ( np . ndarray ) : an image as returned by OpenCV
def run_on_opencv_image(self, image):
        """
        Arguments:
            image (np.ndarray): an image as returned by OpenCV

        Returns:
            prediction (BoxList): the detected objects. Additional information
                of the detection properties can be found in the fields of
                the BoxList via `prediction.fields()`
        """
        result = image.copy()
        predictions = self.compute_prediction(image)

        top_predictions = self.select_top_predictions(predictions)

        if self.show_mask_heatmaps:
            return self.create_mask_montage(result, top_predictions)
        # result = self.overlay_boxes(result, top_predictions)
        # if self.cfg.MODEL.MASK_ON:
        #     result = self.overlay_mask(result, top_predictions)
        # if self.cfg.MODEL.KEYPOINT_ON:
        #     result = self.overlay_keypoints(result, top_predictions)
        # result = self.overlay_class_names(result, top_predictions)
        if self.cfg.MODEL.KE_ON:
            result = self.overlay_ke(result, top_predictions)
        ext = 't'
        result = self.overlay_class_names(result, top_predictions, ext)

        return result
def run_on_opencv_image ( self , image ) : result = image . copy ( ) predictions = self . compute_prediction ( image ) top_predictions = self . select_top_predictions ( predictions ) if self . show_mask_heatmaps : return self . create_mask_montage ( result , top_predictions ) # result = self.overlay_boxes(result, top_predictions) # if self.cfg.MODEL.MASK_ON: #     result = self.overlay_mask(result, top_predictions) # if self.cfg.MODEL.KEYPOINT_ON: #     result = self.overlay_keypoints(result, top_predictions) # result = self.overlay_class_names(result, top_predictions) if self . cfg . MODEL . KE_ON : result = self . overlay_ke ( result , top_predictions ) ext = 't' result = self . overlay_class_names ( result , top_predictions , ext ) return result