lane-detect-pi.py 文件源码

def trans_per(self, image):

        image = self.binary_extraction(image)

        self.binary_image = image

        ysize = image.shape[0]
        xsize = image.shape[1]

        # define region of interest
        left_bottom = (xsize/10, ysize)
        apex_l = (xsize/2 - 2600/(self.look_ahead**2),  ysize - self.look_ahead*275/30)
        apex_r = (xsize/2 + 2600/(self.look_ahead**2),  ysize - self.look_ahead*275/30)
        right_bottom = (xsize - xsize/10, ysize)

        # define vertices for perspective transformation
        src = np.array([[left_bottom], [apex_l], [apex_r], [right_bottom]], dtype=np.float32)
        dst = np.float32([[xsize/3,ysize],[xsize/4.5,0],[xsize-xsize/4.5,0],[xsize-xsize/3, ysize]])

        self.M = cv2.getPerspectiveTransform(src, dst)
        self.Minv = cv2.getPerspectiveTransform(dst, src)

        if len(image.shape) > 2:
            warped = cv2.warpPerspective(image, self.M, image.shape[-2:None:-1], flags=cv2.INTER_LINEAR)
        else:
            warped = cv2.warpPerspective(image, self.M, image.shape[-1:None:-1], flags=cv2.INTER_LINEAR)
        return warped

    # creat window mask for lane detecion