(self, image, predictions)
return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET)
Create a montage showing the probability heatmaps for each one one of the
        detected objects

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask`.
Create a montage showing the probability heatmaps for each one one of the
        detected objects
Create a montage showing the probability heatmaps for each one one of the detected objects
def create_mask_montage(self, image, predictions):
        """
        Create a montage showing the probability heatmaps for each one one of the
        detected objects

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `mask`.
        """
        masks = predictions.get_field("mask")
        masks_per_dim = self.masks_per_dim
        masks = L.interpolate(
            masks.float(), scale_factor=1 / masks_per_dim
        ).byte()
        height, width = masks.shape[-2:]
        max_masks = masks_per_dim ** 2
        masks = masks[:max_masks]
        # handle case where we have less detections than max_masks
        if len(masks) < max_masks:
            masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8)
            masks_padded[: len(masks)] = masks
            masks = masks_padded
        masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
        result = torch.zeros(
            (masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8
        )
        for y in range(masks_per_dim):
            start_y = y * height
            end_y = (y + 1) * height
            for x in range(masks_per_dim):
                start_x = x * width
                end_x = (x + 1) * width
                result[start_y:end_y, start_x:end_x] = masks[y, x]
        return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET)
def create_mask_montage ( self , image , predictions ) : masks = predictions . get_field ( "mask" ) masks_per_dim = self . masks_per_dim masks = L . interpolate ( masks . float ( ) , scale_factor = 1 / masks_per_dim ) . byte ( ) height , width = masks . shape [ - 2 : ] max_masks = masks_per_dim ** 2 masks = masks [ : max_masks ] # handle case where we have less detections than max_masks if len ( masks ) < max_masks : masks_padded = torch . zeros ( max_masks , 1 , height , width , dtype = torch . uint8 ) masks_padded [ : len ( masks ) ] = masks masks = masks_padded masks = masks . reshape ( masks_per_dim , masks_per_dim , height , width ) result = torch . zeros ( ( masks_per_dim * height , masks_per_dim * width ) , dtype = torch . uint8 ) for y in range ( masks_per_dim ) : start_y = y * height end_y = ( y + 1 ) * height for x in range ( masks_per_dim ) : start_x = x * width end_x = ( x + 1 ) * width result [ start_y : end_y , start_x : end_x ] = masks [ y , x ] return cv2 . applyColorMap ( result . numpy ( ) , cv2 . COLORMAP_JET )