test_image = cv2.imread（INPUT_FOLDER + TEST_IMAGE）
street_lanes = find_street_lanes（test_image）

def find_street_lanes(image):grayscale_image = grayscale(image)blur_image = blur(grayscale_image)canny_image = canny(blur_image)roi_image = roi(canny_image)hough_lines_image = hough_lines(roi_image, 0.9, np.pi/180, 100, 100, 50)final_image = combine_images(hough_lines_image, image)

grayscale = (r + g + b) / 3
grayscale_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

blur_image = cv2.GaussianBlur(image, (3,3),0)

canny_image = cv2.Canny(image, 100, 150)

bottom_padding = 100 # Front bumper compensation
height = image.shape[0]
width = image.shape[1]
bottom_left = [0, height-bottom_padding]
bottom_right = [width, height-bottom_padding]
top_right = [width*1/3, height*1/3]
top_left = [width*2/3, height*1/3]
vertices = [np.array([bottom_left, bottom_right, top_left, top_right], dtype=np.int32)]
mask = np.zeros_like(image)
cv2.fillPoly(mask, vertices, 255)
masked_image = cv2.bitwise_and(image, mask)

  parameters = np.polyfit((x1, x2), (y1, y2), 1)slope = parameters[0]intercept = parameters[1]if slope >= 0: right_lines.append([slope, intercept])else:left_lines.append([slope, intercept])

  def merge_lines(image, lines):if len(lines) > 0:slope, intercept = np.average(lines, axis=0)y1 = image.shape[0]y2 = int(y1*(1/2))x1 = int((y1 - intercept)/slope)x2 = int((y2 - intercept)/slope)return np.array([x1, y1, x2, y2])left = merge_lines(image, left_lines)right = merge_lines(image, right_lines)

 lines_image = np.zeros((image.shape[0], image.shape[1], 3), dtype=np.uint8)lines = cv2.HoughLinesP(image, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)if lines is not None:lines = averaged_lines(image, lines)for line in lines:if line is not None:x1,y1,x2,y2 = linecv2.line(lines_image, (x1, y1), (x2, y2), (0, 0, 255), 20)return lines_image

combined_image = cv2.addWeighted(initial_image, α, image, β, λ)