Python cv2.CV_32FC1属性代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import CV_32FC1 [as 别名]
def compute_stereo_rectification_maps(stereo_rig, im_size, size_factor):
    new_size = (int(im_size[1] * size_factor), int(im_size[0] * size_factor))
    rotation1, rotation2, pose1, pose2 = \
        cv2.stereoRectify(cameraMatrix1=stereo_rig.cameras[0].intrinsics.intrinsic_mat,
                          distCoeffs1=stereo_rig.cameras[0].intrinsics.distortion_coeffs,
                          cameraMatrix2=stereo_rig.cameras[1].intrinsics.intrinsic_mat,
                          distCoeffs2=stereo_rig.cameras[1].intrinsics.distortion_coeffs,
                          imageSize=(im_size[1], im_size[0]),
                          R=stereo_rig.cameras[1].extrinsics.rotation,
                          T=stereo_rig.cameras[1].extrinsics.translation,
                          flags=cv2.CALIB_ZERO_DISPARITY,
                          newImageSize=new_size
                          )[0:4]
    map1x, map1y = cv2.initUndistortRectifyMap(stereo_rig.cameras[0].intrinsics.intrinsic_mat,
                                               stereo_rig.cameras[0].intrinsics.distortion_coeffs,
                                               rotation1, pose1, new_size, cv2.CV_32FC1)
    map2x, map2y = cv2.initUndistortRectifyMap(stereo_rig.cameras[1].intrinsics.intrinsic_mat,
                                               stereo_rig.cameras[1].intrinsics.distortion_coeffs,
                                               rotation2, pose2, new_size, cv2.CV_32FC1)
    return map1x, map1y, map2x, map2y 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import CV_32FC1 [as 别名]
def optical_distortion(
    img, k=0, dx=0, dy=0, interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_REFLECT_101, value=None
):
    """Barrel / pincushion distortion. Unconventional augment.

    Reference:
        |  https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion
        |  https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
        |  https://stackoverflow.com/questions/2477774/correcting-fisheye-distortion-programmatically
        |  http://www.coldvision.io/2017/03/02/advanced-lane-finding-using-opencv/
    """
    height, width = img.shape[:2]

    fx = width
    fy = height

    cx = width * 0.5 + dx
    cy = height * 0.5 + dy

    camera_matrix = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)

    distortion = np.array([k, k, 0, 0, 0], dtype=np.float32)
    map1, map2 = cv2.initUndistortRectifyMap(camera_matrix, distortion, None, None, (width, height), cv2.CV_32FC1)
    img = cv2.remap(img, map1, map2, interpolation=interpolation, borderMode=border_mode, borderValue=value)
    return img 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import CV_32FC1 [as 别名]
def __call__(self, img, mask=None):
        if random.random() < self.prob:
            height, width, channel = img.shape

            if 0:
                img = img.copy()
                for x in range(0, width, 10):
                    cv2.line(img, (x, 0), (x, height), (1, 1, 1), 1)
                for y in range(0, height, 10):
                    cv2.line(img, (0, y), (width, y), (1, 1, 1), 1)

            k = random.uniform(-self.distort_limit, self.distort_limit) * 0.00001
            dx = random.uniform(-self.shift_limit, self.shift_limit) * width
            dy = random.uniform(-self.shift_limit, self.shift_limit) * height

            #  map_x, map_y =
            # cv2.initUndistortRectifyMap(intrinsics, dist_coeffs, None, None, (width,height),cv2.CV_32FC1)
            # https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion
            # https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
            x, y = np.mgrid[0:width:1, 0:height:1]
            x = x.astype(np.float32) - width/2 - dx
            y = y.astype(np.float32) - height/2 - dy
            theta = np.arctan2(y, x)
            d = (x*x + y*y)**0.5
            r = d*(1+k*d*d)
            map_x = r*np.cos(theta) + width/2 + dx
            map_y = r*np.sin(theta) + height/2 + dy

            img = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
            if mask is not None:
                mask = cv2.remap(mask, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
        return img, mask 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import CV_32FC1 [as 别名]
def init_undistort():
    #cv2.initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) -> map1, map2
    frame_size=(640,480)
    map1, map2=cv2.initUndistortRectifyMap(mtx, dist, None, newcameramtx, frame_size, cv2.CV_32FC1)
    return map1, map2
   
# this is a faster undistort_crop that only does remapping. Requires call to init_undistort first to
# to create the map1 and map2 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import CV_32FC1 [as 别名]
def distort1(img, k=0, dx=0, dy=0):
    """"
    ## unconverntional augmnet ################################################################################3
    ## https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion

    ## https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
    ## https://stackoverflow.com/questions/2477774/correcting-fisheye-distortion-programmatically
    ## http://www.coldvision.io/2017/03/02/advanced-lane-finding-using-opencv/

    ## barrel\pincushion distortion
    """
    height, width = img.shape[:2]
    #  map_x, map_y =
    # cv2.initUndistortRectifyMap(intrinsics, dist_coeffs, None, None, (width,height),cv2.CV_32FC1)
    # https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion
    # https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
    k = k * 0.00001
    dx = dx * width
    dy = dy * height
    x, y = np.mgrid[0:width:1, 0:height:1]
    x = x.astype(np.float32) - width/2 - dx
    y = y.astype(np.float32) - height/2 - dy
    theta = np.arctan2(y, x)
    d = (x*x + y*y)**0.5
    r = d*(1+k*d*d)
    map_x = r*np.cos(theta) + width/2 + dx
    map_y = r*np.sin(theta) + height/2 + dy

    img = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    return img 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import CV_32FC1 [as 别名]
def __filter_candidate(greyscale_image, coord, neighborhood_size):
    window = greyscale_image[coord[0] - neighborhood_size:coord[0] + neighborhood_size + 1,
             coord[1] - neighborhood_size:coord[1] + neighborhood_size + 1]
    grad_x = cv2.Sobel(window, cv2.CV_32FC1, dx=1, dy=0, ksize=3)
    grad_y = cv2.Sobel(window, cv2.CV_32FC1, dx=0, dy=1, ksize=3)
    grad_mag = np.abs(grad_x) + np.abs(grad_y)
    grad_mag_flat = grad_mag.flatten()
    orientations_flat = (cv2.phase(grad_x, grad_y) % pi).flatten()  # phase accuracy: about 0.3 degrees
    hist = (np.histogram(orientations_flat, bins=64, range=(0, pi), weights=grad_mag_flat)[0] /
            (neighborhood_size * neighborhood_size))

    return hist, grad_mag 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import CV_32FC1 [as 别名]
def calibrate_cameras(self):
        """Calibrate cameras based on found chessboard corners."""
        criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,
                    100, 1e-5)
        flags = (cv2.CALIB_FIX_ASPECT_RATIO + cv2.CALIB_ZERO_TANGENT_DIST +
                 cv2.CALIB_SAME_FOCAL_LENGTH)
        calib = StereoCalibration()
        (calib.cam_mats["left"], calib.dist_coefs["left"],
         calib.cam_mats["right"], calib.dist_coefs["right"],
         calib.rot_mat, calib.trans_vec, calib.e_mat,
         calib.f_mat) = cv2.stereoCalibrate(self.object_points,
                                            self.image_points["left"],
                                            self.image_points["right"],
                                            self.image_size,
                                            calib.cam_mats["left"],
                                            calib.dist_coefs["left"],
                                            calib.cam_mats["right"],
                                            calib.dist_coefs["right"],
                                            calib.rot_mat,
                                            calib.trans_vec,
                                            calib.e_mat,
                                            calib.f_mat,
                                            criteria=criteria,
                                            flags=flags)[1:]
        (calib.rect_trans["left"], calib.rect_trans["right"],
         calib.proj_mats["left"], calib.proj_mats["right"],
         calib.disp_to_depth_mat, calib.valid_boxes["left"],
         calib.valid_boxes["right"]) = cv2.stereoRectify(calib.cam_mats["left"],
                                                      calib.dist_coefs["left"],
                                                      calib.cam_mats["right"],
                                                      calib.dist_coefs["right"],
                                                      self.image_size,
                                                      calib.rot_mat,
                                                      calib.trans_vec,
                                                      flags=0)
        for side in ("left", "right"):
            (calib.undistortion_map[side],
             calib.rectification_map[side]) = cv2.initUndistortRectifyMap(
                                                        calib.cam_mats[side],
                                                        calib.dist_coefs[side],
                                                        calib.rect_trans[side],
                                                        calib.proj_mats[side],
                                                        self.image_size,
                                                        cv2.CV_32FC1)
        # This is replaced because my results were always bad. Estimates are
        # taken from the OpenCV samples.
        width, height = self.image_size
        focal_length = 0.8 * width
        calib.disp_to_depth_mat = np.float32([[1, 0, 0, -0.5 * width],
                                              [0, -1, 0, 0.5 * height],
                                              [0, 0, 0, -focal_length],
                                              [0, 0, 1, 0]])
        return calib