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
lane-detect-pi.py 文件源码
python
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