calibrator.py 文件源码

def downsample_and_detect(self, img):
        """
        Downsample the input image to approximately VGA resolution and detect the
        calibration target corners in the full-size image.

        Combines these apparently orthogonal duties as an optimization. Checkerboard
        detection is too expensive on large images, so it's better to do detection on
        the smaller display image and scale the corners back up to the correct size.

        Returns (scrib, corners, downsampled_corners, board, (x_scale, y_scale)).
        """
        # Scale the input image down to ~VGA size
        height = img.shape[0]
        width = img.shape[1]
        scale = math.sqrt( (width*height) / (640.*480.) )
        if scale > 1.0:
            scrib = cv2.resize(img, (int(width / scale), int(height / scale)))
        else:
            scrib = img
        # Due to rounding, actual horizontal/vertical scaling may differ slightly
        x_scale = float(width) / scrib.shape[1]
        y_scale = float(height) / scrib.shape[0]

        if self.pattern == Patterns.Chessboard:
            # Detect checkerboard
            (ok, downsampled_corners, board) = self.get_corners(scrib, refine = True)

            # Scale corners back to full size image
            corners = None
            if ok:
                if scale > 1.0:
                    # Refine up-scaled corners in the original full-res image
                    # TODO Does this really make a difference in practice?
                    corners_unrefined = downsampled_corners.copy()
                    corners_unrefined[:, :, 0] *= x_scale
                    corners_unrefined[:, :, 1] *= y_scale
                    radius = int(math.ceil(scale))
                    if len(img.shape) == 3 and img.shape[2] == 3:
                        mono = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                    else:
                        mono = img
                    cv2.cornerSubPix(mono, corners_unrefined, (radius,radius), (-1,-1),
                                                  ( cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1 ))
                    corners = corners_unrefined
                else:
                    corners = downsampled_corners
        else:
            # Circle grid detection is fast even on large images
            (ok, corners, board) = self.get_corners(img)
            # Scale corners to downsampled image for display
            downsampled_corners = None
            if ok:
                if scale > 1.0:
                    downsampled_corners = corners.copy()
                    downsampled_corners[:,:,0] /= x_scale
                    downsampled_corners[:,:,1] /= y_scale
                else:
                    downsampled_corners = corners

        return (scrib, corners, downsampled_corners, board, (x_scale, y_scale))