def main():
test_images = get_input_path("test_images").glob("*.jpg")
from pathlib import Path
lane_detector = LaneDetector()
for i, test_image_path in enumerate(test_images):
test_image_path = get_input_path("test_images").joinpath("test1.jpg")
image = mpimg.imread(str(test_image_path))
plt.figure
plt.imshow(image)
warp_matrix, img = transform_image(image)
left_fitx, right_fitx, ploty, out_img = lane_detector.get_lanes(
img, draw=True)
# left_fitx, right_fitx, ploty = lane_detector.get_polynominals(out_img, leftx, lefty, rightx, righty)
fig = plt.figure()
fig.suptitle("Polylines")
plt.imshow(out_img)
plt.imsave(
str(get_output_folder_path().joinpath(
f"{test_image_path.stem}_lanes.jpg")), image)
plt.show()
break
def main():
"""
Main function it an example function
"""
test_images = get_input_path("test_images").glob("*.jpg")
for i, test_image_path in enumerate(test_images):
# loaded_img = mpimg.imread(str(test_image_path))
# test_image_path = get_input_path("output_images").joinpath("test1.jpg")
loaded_img = mpimg.imread(str(test_image_path))
binary_image = get_binary_image(loaded_img)
# show_tresholding_stages(loaded_img, gradx, grady, mag_binary, dir_binary, combined_binary)
fig, ax = plt.subplots(2)
ax[0].imshow(loaded_img)
ax[0].set_title("Loaded Image")
ax[1].imshow(binary_image, cmap="gray")
ax[1].set_title("Binary Image")
if i == 0:
plt.imsave(
str(get_output_folder_path().joinpath(
f"{test_image_path.stem}.jpg")), loaded_img)
plt.imsave(str(get_output_folder_path().joinpath(
f"{test_image_path.stem}_binary.jpg")),
binary_image,
cmap="gray")
break
plt.show()
def main():
test_images = get_input_path("test_images").glob("*.jpg")
lane_detector = LaneDetector()
for i, test_image_path in enumerate(test_images):
test_image_path = get_input_path("test_images").joinpath("test1.jpg")
image = mpimg.imread(str(test_image_path))
warp_matrix, img = transform_image(image)
leftx, lefty, rightx, righty, out_img = lane_detector.sliding_window(img)
left_fitx, right_fitx, plotpy = lane_detector.get_polynominals(out_img, leftx, lefty, rightx, righty)
polynominal_image = np.copy(out_img)
left_curverad, right_curverad = measure_curvature_real(left_fitx, right_fitx, plotpy)
print(f"Left line radius: {left_curverad} m, right lane radius: {right_curverad} m")
break
def main():
# test_main()
input_video = str(get_input_path("project_video.mp4"))
tracked_video = str(get_output_folder_path().joinpath("tracked_video.mp4"))
camera_matrix, distortion_coeff = load_camera_precalculated_coefficients()
lane_detector = LaneDetector()
clip1 = VideoFileClip(input_video)
white_clip = clip1.fl_image(
lambda image: process_image(image, lane_detector, camera_matrix, distortion_coeff)
) # NOTE: this function expects color images!!
white_clip.write_videofile(tracked_video, audio=False)
def main():
"""
Main function it an example function
"""
ret, mtx, dist = calculate_camera_coefficients()
print(f"RMS: {ret}")
print(f"Camera matrix: {mtx}")
print(f"Distortion coefficients: {dist}")
save_camera_coefficients(mtx, dist)
example_image = get_input_path("test_images").joinpath("test1.jpg")
chessboard_image = get_input_path("camera_cal").joinpath("calibration1.jpg")
img = cv2.imread(str(example_image))
undistorted = correct_camera_distortion(img, mtx, dist)
output_path_undistorted = get_output_folder_path().joinpath(f"{example_image.stem}_undistorted.jpg")
output_path_distorted = get_output_folder_path().joinpath(f"{example_image.stem}_distorted.jpg")
cv2.imwrite(str(output_path_distorted), img)
cv2.imwrite(str(output_path_undistorted), undistorted)
cv2.imshow("calibration1", undistorted)
cv2.waitKey(1000)
fig, ax = plt.subplots(2)
ax[0].plot(Polygon(src_points).get_xy()[:, 0],
Polygon(src_points).get_xy()[:, 1],
color="red")
ax[0].imshow(img)
ax[1].plot(Polygon(dest_points).get_xy()[:, 0],
Polygon(dest_points).get_xy()[:, 1],
color="red")
ax[1].imshow(warped)
return warped, M
if __name__ == "__main__":
test_images = get_input_path("test_images").glob("*.jpg")
for i, test_image_path in enumerate(test_images):
test_image_path = get_input_path("output_images").joinpath(
"test1_undistorted.jpg")
img = cv2.imread(str(test_image_path))
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
warped, M = warp_image(img_rgb, True)
fig, ax = plt.subplots(2)
ax[0].imshow(img_rgb)
ax[0].set_title("Unwarped image")
ax[1].imshow(warped)
ax[1].set_title("Warped image")