def sliding_window_conv_detect_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
img = mpimg.imread(test_img_path)
warped_binary = rgb_to_warped_binary(img, cam_intrinsic, cam_distortion)
result = LaneDetector.sliding_window_conv_detect(
warped_binary,
sliding_conv_config["width"],
sliding_conv_config["height"],
sliding_conv_config["margin"],
return_debug_img=True)
left_fit = result.l_fit
right_fit = result.r_fit
# Generate x and y values for plotting
ploty = np.linspace(0, warped_binary.shape[0] - 1, warped_binary.shape[0])
left_fitx = left_fit[0] * ploty**2 + left_fit[1] * ploty + left_fit[2]
right_fitx = right_fit[0] * ploty**2 + right_fit[1] * ploty + right_fit[2]
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20, 10))
f.suptitle(test_img_path.split("/")[-1], fontsize=30)
axes1.imshow(img)
axes1.set_title('Original', fontsize=15)
axes2.imshow(result.debug_image, cmap="gray")
axes2.set_title('Detected', fontsize=15)
axes2.plot(left_fitx, ploty, color='yellow')
axes2.plot(right_fitx, ploty, color='yellow')
axes2.set_xlim(0, img.shape[1])
axes2.set_ylim(img.shape[0], 0)
plt.show()
def sobel_hsl_warp_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
img = mpimg.imread(test_img_path)
undistorted = undistort_image(img, cam_intrinsic, cam_distortion)
gray_img = cv2.cvtColor(undistorted, cv2.COLOR_RGB2GRAY)
sobel_mask_img = sobel_filter(
gray_img,
abs_x_thresh=thresh_config["abs_sobel_x_thresh"],
magnitude_thresh=thresh_config["gradient_magnitude_thresh"],
direction_thresh=thresh_config["gradient_direction_thresh"]).astype(
np.uint8)
hls_mask_img = hls_filter(
undistorted,
s_thresh=thresh_config["s_channel_thresh"]).astype(np.uint8)
threshed_result = np.dstack(
(np.zeros_like(gray_img), sobel_mask_img, hls_mask_img)) * 255
warp_matrix = cv2.getPerspectiveTransform(warp_config["src_rect"],
warp_config["dst_rect"])
combined_result = warp_image(threshed_result, warp_matrix)
f, (axes1, axes2, axes3) = plt.subplots(1, 3, figsize=(20, 10))
axes1.imshow(img)
axes1.set_title('Original', fontsize=15)
axes2.imshow(threshed_result)
axes2.set_title('Filter By Sobel & S-Channel', fontsize=15)
axes3.imshow(combined_result)
axes3.set_title("Soble & S-Channel & Warp", fontsize=15)
plt.show()
def rgb_to_warped_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
img = mpimg.imread(test_img_path)
warped_binary = rgb_to_warped_binary(img, cam_intrinsic, cam_distortion)
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20, 10))
axes1.imshow(img)
axes1.set_title('Original', fontsize=15)
axes2.imshow(warped_binary, cmap="gray")
axes2.set_title('Warped Binary', fontsize=15)
plt.show()
def hls_filter_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
img = mpimg.imread(test_img_path)
undistorted = undistort_image(img, cam_intrinsic, cam_distortion)
mask_img = hls_filter(undistorted,
s_thresh=thresh_config["s_channel_thresh"]).astype(
np.uint8)
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20, 10))
axes1.imshow(img)
axes1.set_title('Original', fontsize=15)
axes2.imshow(mask_img, cmap="gray")
axes2.set_title('Filter By S-Channel', fontsize=15)
plt.show()
def undistort_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
calib_test_image = mpimg.imread(test_img_path)
undistorted_calib_test_image = undistort_image(calib_test_image,
cam_intrinsic,
cam_distortion)
# image_name = test_img_path.split("/")[-1]
# name_without_suffix = image_name.split(".")[0]
# mpimg.imsave(name_without_suffix + "_undistorted.png", undistorted_calib_test_image, format="png")
# mpimg.imsave(name_without_suffix + ".png", calib_test_image, format="png")
# Visualize undistortion
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20, 10))
axes1.imshow(calib_test_image)
axes1.set_title('Original', fontsize=15)
axes2.imshow(undistorted_calib_test_image)
axes2.set_title('Undistorted', fontsize=15)
plt.show()
def warp_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
img = mpimg.imread(test_img_path)
undistorted_img = undistort_image(img, cam_intrinsic, cam_distortion)
warp_matrix = cv2.getPerspectiveTransform(warp_config["src_rect"],
warp_config["dst_rect"])
warped_img = warp_image(undistorted_img, warp_matrix)
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20, 10))
axes1.imshow(undistorted_img)
axes1.add_patch(
Polygon(warp_config["src_rect"], closed=True, fill=False, color="red"))
axes1.set_title('undistorted image with source points drawn', fontsize=15)
axes2.imshow(warped_img)
axes2.add_patch(
Polygon(warp_config["dst_rect"], closed=True, fill=False, color="red"))
axes2.set_title('warped image with destination points drawn', fontsize=15)
plt.show()
def sobel_filter_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
img = mpimg.imread(test_img_path)
undistorted = undistort_image(img, cam_intrinsic, cam_distortion)
gray_img = cv2.cvtColor(undistorted, cv2.COLOR_RGB2GRAY)
mask_img = sobel_filter(
gray_img,
abs_x_thresh=thresh_config["abs_sobel_x_thresh"],
magnitude_thresh=thresh_config["gradient_magnitude_thresh"],
direction_thresh=thresh_config["gradient_direction_thresh"]).astype(
np.uint8)
# mask_img = sobel_filter(undistorted,
# abs_x_thresh=(20, 100)).astype(np.uint8)
# Visualize sobel based thresholding
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20, 10))
axes1.imshow(img)
axes1.set_title('Original', fontsize=15)
axes2.imshow(mask_img, cmap="gray")
axes2.set_title('Filter By Sobel', fontsize=15)
plt.show()
def __init__(self):
# load the camera parameters
camera_intrinsic, camera_distortion = load_cached_camera_parameters(
cfg.camera_param_cache_path)
# if load failed, calibrate the camera
if camera_intrinsic is None or camera_distortion is None:
# the list of calibration images
calibration_image_list = glob.glob(cfg.camera_calibration_img_dir +
'calibration*.jpg')
camera_intrinsic, camera_distortion = calibrate_camera(
calibration_image_list, 9, 6, cfg.camera_param_cache_path)
self._cam_intrinsic = camera_intrinsic
self._cam_distortion = camera_distortion
self._latest_results = LatestResults()
self._consecutive_failed_cnt = 0
self._max_allowed_failed = 5
self._use_conv = False
self._debug = True
def sliding_window_detect_test(test_img_path):
cam_intrinsic, cam_distortion = load_cached_camera_parameters(
"./camera_params.p")
img = mpimg.imread(test_img_path)
warped_binary = rgb_to_warped_binary(img, cam_intrinsic, cam_distortion)
result = LaneDetector.sliding_window_detect(
warped_binary,
sliding_window_config["height"],
sliding_window_config["margin"],
sliding_window_config["min_num_pixels"],
return_debug_img=True)
left_fit = result.l_fit
right_fit = result.r_fit
# Generate x and y values for plotting
ploty = np.linspace(0, warped_binary.shape[0] - 1, warped_binary.shape[0])
left_fitx = left_fit[0] * ploty**2 + left_fit[1] * ploty + left_fit[2]
right_fitx = right_fit[0] * ploty**2 + right_fit[1] * ploty + right_fit[2]
# Create an image to draw the detected region
warp_zero = np.zeros_like(warped_binary).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array(
[np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0, 255, 0))
# unwarp the result
unwarp_matrix_ = cv2.getPerspectiveTransform(warp_config["dst_rect"],
warp_config["src_rect"])
newwarp = warp_image(color_warp, unwarp_matrix_)
# Combine the detected result with the original image
result_img = cv2.addWeighted(img, 1, newwarp, 0.3, 0)
f, (axes1, axes2) = plt.subplots(1, 2, figsize=(20, 10))
f.suptitle(test_img_path.split("/")[-1], fontsize=30)
axes1.imshow(result_img)
axes1.set_title('Original with detected result', fontsize=15)
left_curvature = result.l_curvature
right_curvature = result.r_curvature
center_offset = result.center_offset
axes1.text(0.5,
0.95,
'left curvature : %.2fm' % (left_curvature, ),
color="yellow",
horizontalalignment='center',
transform=axes1.transAxes)
axes1.text(0.5,
0.90,
'right curvature: %.2fm' % (right_curvature, ),
color="yellow",
horizontalalignment='center',
transform=axes1.transAxes)
axes1.text(0.5,
0.85,
'center offset : %2fm' % (center_offset, ),
color="yellow",
horizontalalignment='center',
transform=axes1.transAxes)
axes2.imshow(result.debug_image)
axes2.set_title('Detected', fontsize=15)
axes2.plot(left_fitx, ploty, color='yellow')
axes2.plot(right_fitx, ploty, color='yellow')
axes2.set_xlim(0, img.shape[1])
axes2.set_ylim(img.shape[0], 0)
plt.show()