def test_image_frame():
_, image = test_images[0]
text_output = []
# Step1: Undistort image using the camera calibration
undistorted_img = _camera_calibrator.undistort(image)
# Step2: Apply perspective transformation to create a warped image
color_warped = _perspective_transformer.warp_image(undistorted_img, M)
# Step3: Use color transforms, gradients, etc., to create a thresholded binary image
binary_warped, binary_arr = ImageUtil.binary_thresholded_image(color_warped)
image_shape = binary_warped.shape
lane_detection_output = visualize_binary_thresholded_image(color_warped, binary_warped, binary_arr[0], binary_arr[1])
def process_frame(frame_img, debug=False):
""" Main method for processing each image frame of a video file """
text_output = []
# Step1: Undistort image using the camera calibration
undistorted_img = _camera_calibrator.undistort(frame_img)
# Step2: Apply perspective transformation to create a warped image
color_warped = _perspective_transformer.warp_image(undistorted_img, M)
# Step3: Use color transforms, gradients, etc., to create a thresholded binary image
binary_warped, binary_arr = ImageUtil.binary_thresholded_image(color_warped)
image_shape = binary_warped.shape
lane_detection_output = visualize_binary_thresholded_image(color_warped, binary_warped, binary_arr[0], binary_arr[1])
# Step4: Get the sliding window polynomials for the left and right line
leftx, lefty, rightx, righty = _lane.find_lane_pixels(binary_warped)
ploty = np.linspace(0, image_shape[0] - 1, image_shape[0])
left_fitx = _lane.left.polyfit_lines(leftx, lefty, image_shape)
right_fitx = _lane.right.polyfit_lines(rightx, righty, image_shape)
# Step5: Overlay the warped image to the original image
overlay_img = _lane.overlay_image(binary_warped, frame_img, Minv, left_fitx, right_fitx, ploty)
result = overlay_to_original_image(overlay_img, frame_img)
# Step6: curvature_and_vehicle_position
vehicle_position = _lane.get_vehicle_position(image_shape)
radius_of_curvature = _lane.get_radius_of_curvature()
text_output.append('Radius of curvature: {} m'.format(radius_of_curvature))
text_output.append('Vehicle: {}'.format(vehicle_position))
text_output.append('');
text_output.append('Left line curve:')
text_output.append('{:.2}, {:.2}, {:.2}'.format(_lane.left.line_fit0_queue[0], _lane.left.line_fit1_queue[0], _lane.left.line_fit2_queue[0]))
text_output.append('');
text_output.append('Right line curve:')
text_output.append('{:.2}, {:.2}, {:.2}'.format(_lane.right.line_fit0_queue[0], _lane.right.line_fit1_queue[0], _lane.right.line_fit2_queue[0]))
text_output.append('');
if _lane.left.detected == False: text_output.append('!!!Left lane not detected!!!')
if _lane.right.detected == False: text_output.append('!!!Right lane not detected!!!')
console_img = ImageUtil.image_console(result, frame_img, undistorted_img, lane_detection_output, overlay_img,
text_output)
return console_img
if show_title: ax4.set_title('Combined thresholds', fontsize=16)
# ax4.imshow(combined_binary, cmap='gray')
# plt.show()
f.canvas.draw()
data = np.fromstring(f.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(f.canvas.get_width_height()[::-1] + (3,))
return data
for image_path in glob.glob(TEST_IMAGES_PATH):
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
undistorted_img = _camera_calibrator.undistort(image)
color_warped = _perspective_transformer.warp_image(undistorted_img, M)
binary_warped, binary_arr = ImageUtil.binary_thresholded_image(color_warped)
# visualize_binary_thresholded_image(color_warped, binary_warped, binary_arr[0], binary_arr[1], True)
def visualize_peaks_in_binary_warped_image():
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
f.tight_layout(w_pad=4.0)
ax1.set_title('Warped binary image', fontsize=16)
# ax1.imshow(binary_warped, cmap='gray')
histogram = np.sum(binary_warped[binary_warped.shape[0]/2:,:], axis=0)
ax2.plot(histogram)
ax2.set_title('Histogram of lower half image', fontsize=16)
ax2.set_xlabel('Pixel positions')
ax2.set_ylabel('Counts')
# plt.show()
# visualize_peaks_in_binary_warped_image()