Python cv2.getOptimalNewCameraMatrix方法代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getOptimalNewCameraMatrix [as 别名]
def live_undistort(camera, camera_matrix, distortion_coefficients):
    """ Using a given calibration matrix, display the distorted, undistorted, and cropped frame"""
    scaled_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(
        camera_matrix, distortion_coefficients, camera.size, 1, camera.size
    )
    while True:
        ret, frame = camera.cap.read()
        assert ret
        distorted_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        undistorted_frame = cv2.undistort(
            distorted_frame, camera_matrix, distortion_coefficients, None, scaled_camera_matrix,
        )
        roi_x, roi_y, roi_w, roi_h = roi
        cropped_frame = undistorted_frame[roi_y : roi_y + roi_h, roi_x : roi_x + roi_w]
        cv2.imshow("distorted %s" % (distorted_frame.shape,), distorted_frame)
        cv2.imshow("undistorted %s" % (undistorted_frame.shape,), undistorted_frame)
        cv2.imshow("cropped %s" % (cropped_frame.shape,), cropped_frame)
        cv2.waitKey(10) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getOptimalNewCameraMatrix [as 别名]
def _undistort_image(self, image):
        if self._camera_matrix is None or self._distortion_coefficients is None:
            import warnings
            warnings.warn("Undistortion has no effect because <camera_matrix>/<distortion_coefficients> is None!")
            return image

        h, w = image.shape[:2]
        new_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(self._camera_matrix,
                                                               self._distortion_coefficients, (w, h),
                                                               1,
                                                               (w, h))
        undistorted = cv2.undistort(image, self._camera_matrix, self._distortion_coefficients, None,
                                    new_camera_matrix)
        return undistorted 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getOptimalNewCameraMatrix [as 别名]
def main():

  basler_undistorted = pickle.load(open("basler_pickle.p", "rb"))

  #objects = []
  #with (open("./basler_pickle.p", "rb")) as openfile:
  #    while True:
  #        try:
  #            basler_undistorted.append(pickle.load(openfile))
  #        except EOFError:
  #            break
    
  image_width  = 1280
  image_height = 1024

  #print(basler_undistorted)

  cam_mtx = basler_undistorted["mtx"]
  cam_dist = basler_undistorted["dist"]
  rvecs = basler_undistorted["rvecs"]
  tvecs = basler_undistorted["tvecs"]
  #imageSize = image_height * image_width
  imageSize = ( image_height, image_width )
  
  #getOptimal...Mtx(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) -> retval, validPixROI
  # Doesn't take Rect of validPixROI, contrary to the cpp method
  new_cam_mtx, valid_roi = cv.getOptimalNewCameraMatrix(cam_mtx, cam_dist, imageSize, 1, imageSize, 1) 
  
  # getOptimalNewCameraMatrix() possibly not working like in cpp
  #map1, map2 = cv.initUndistortRectifyMap(cam_mtx, cam_dist, np.eye(3), new_cam_mtx, imageSize, cv.CV_16SC2);
  map1, map2 = cv.initUndistortRectifyMap(cam_mtx, cam_dist, np.eye(3), cam_mtx, imageSize, cv.CV_16SC2);
  
  # map1 and map2 can be used together with cv.remap() for efficient real-time undistortion
  # Only need to be calculated once.
  maps = { "map1": map1, "map2": map2 }
  pickle.dump( maps, open("maps.p", "wb")) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getOptimalNewCameraMatrix [as 别名]
def undistort(img, dist, mtx): #undistorstion routine
	h,  w = img.shape[:2]
	newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))
	dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
	x,y,w,h = roi
	dst = dst[y:y+h, x:x+w]
	return dst

#testing undistort function 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getOptimalNewCameraMatrix [as 别名]
def get_view_matrix(self, alpha):
        """
        Returns camera matrix for handling image and coordinates distortion and undistortion. Based on alpha,
        up to all pixels of the distorted image can be visible in the undistorted image.

        :param alpha: Free scaling parameter between 0 (when all the pixels in the undistorted image are valid) and 1
                      (when all the source image pixels are retained in the undistorted image). For convenience for -1
                      returns custom camera matrix self.Kundistortion and None returns self.K.
        :type alpha: float or None
        :return: camera matrix for a view defined by alpha
        :rtype: array, shape=(3, 3)
        """
        if alpha == -1:
            Kundistortion = self.Kundistortion
        elif alpha is None:
            Kundistortion = self.K
        elif self.calibration_type == 'opencv':
            Kundistortion, _ = cv2.getOptimalNewCameraMatrix(self.K, self.opencv_dist_coeff, tuple(self.size_px), alpha)
        elif self.calibration_type == 'opencv_fisheye':
            Kundistortion = cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(self.K, self.opencv_dist_coeff,
                                                                                   tuple(self.size_px), self.R,
                                                                                   balance=alpha)
        else:
            # TODO
            assert False, 'not implemented'
        return Kundistortion 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import getOptimalNewCameraMatrix [as 别名]
def apply(self, img, crop = True):
		h,  w = img.shape[:2]
		newcameramtx, roi=cv2.getOptimalNewCameraMatrix(self.mtx, self.dist,(w,h),1,(w,h))

		# undistort
		dst = cv2.undistort(img, self.mtx, self.dist, None, newcameramtx)

		# crop the image
		if crop is not True:
			return dst

		x,y,w,h = roi
		dst = dst[y:y+h, x:x+w]

		return dst