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#from moviepy.editor import VideoFileClip import matplotlib.pyplot as plt import matplotlib.image as mplimg import numpy as np import cv2 blur_ksize = 5 # Gaussian blur kernel size canny_lthreshold = 50 # Canny edge detection low threshold canny_hthreshold = 150 # Canny edge detection high threshold # Hough transform parameters rho = 1 theta = np.pi / 180 threshold = 15 min_line_length = 40 max_line_gap = 20 def roi_mask(img, vertices): mask = np.zeros_like(img) #defining a 3 channel or 1 channel color to fill the mask with depending on the input image if len(img.shape) > 2: channel_count = img.shape[2] # i.e. 3 or 4 depending on your image mask_color = (255,) * channel_count else: mask_color = 255 cv2.fillPoly(mask, vertices, mask_color) masked_img = cv2.bitwise_and(img, mask) return masked_img def draw_roi(img, vertices): cv2.polylines(img, vertices, True, [255, 0, 0], thickness=2) def draw_lines(img, lines, color=[255, 0, 0], thickness=2): for line in lines: for x1, y1, x2, y2 in line: cv2.line(img, (x1, y1), (x2, y2), color, thickness) def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap): lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap) line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) # draw_lines(line_img, lines) draw_lanes(line_img, lines) return line_img def draw_lanes(img, lines, color=[255, 0, 0], thickness=8): left_lines, right_lines = [], [] for line in lines: for x1, y1, x2, y2 in line: k = (y2 - y1) / (x2 - x1) if k < 0: left_lines.append(line) else: right_lines.append(line) if (len(left_lines) <= 0 or len(right_lines) <= 0): return img clean_lines(left_lines, 0.1) clean_lines(right_lines, 0.1) left_points = [(x1, y1) for line in left_lines for x1,y1,x2,y2 in line] left_points = left_points + [(x2, y2) for line in left_lines for x1,y1,x2,y2 in line] right_points = [(x1, y1) for line in right_lines for x1,y1,x2,y2 in line] right_points = right_points + [(x2, y2) for line in right_lines for x1,y1,x2,y2 in line] left_vtx = calc_lane_vertices(left_points, 325, img.shape[0]) right_vtx = calc_lane_vertices(right_points, 325, img.shape[0]) cv2.line(img, left_vtx[0], left_vtx[1], color, thickness) cv2.line(img, right_vtx[0], right_vtx[1], color, thickness) def clean_lines(lines, threshold): slope = [(y2 - y1) / (x2 - x1) for line in lines for x1, y1, x2, y2 in line] while len(lines) > 0: mean = np.mean(slope) diff = [abs(s - mean) for s in slope] idx = np.argmax(diff) if diff[idx] > threshold: slope.pop(idx) lines.pop(idx) else: break def calc_lane_vertices(point_list, ymin, ymax): x = [p[0] for p in point_list] y = [p[1] for p in point_list] fit = np.polyfit(y, x, 1) fit_fn = np.poly1d(fit) xmin = int(fit_fn(ymin)) xmax = int(fit_fn(ymax)) return [(xmin, ymin), (xmax, ymax)] def process_an_image(img): roi_vtx = np.array([[(0, img.shape[0]), (460, 325), (520, 325), (img.shape[1], img.shape[0])]]) gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) blur_gray = cv2.GaussianBlur(gray, (blur_ksize, blur_ksize), 0, 0) edges = cv2.Canny(blur_gray, canny_lthreshold, canny_hthreshold) roi_edges = roi_mask(edges, roi_vtx) line_img = hough_lines(roi_edges, rho, theta, threshold, min_line_length, max_line_gap) res_img = cv2.addWeighted(img, 0.8, line_img, 1, 0) plt.figure() plt.imshow(img) plt.savefig('images/lane_original.png', bbox_inches='tight') plt.figure() plt.imshow(gray, cmap='gray') plt.savefig('images/gray.png', bbox_inches='tight') plt.figure() plt.imshow(blur_gray, cmap='gray') plt.savefig('images/blur_gray.png', bbox_inches='tight') plt.figure() plt.imshow(edges, cmap='gray') plt.savefig('images/edges.png', bbox_inches='tight') plt.figure() plt.imshow(roi_edges, cmap='gray') plt.savefig('images/roi_edges.png', bbox_inches='tight') plt.figure() plt.imshow(line_img, cmap='gray') plt.savefig('images/line_img.png', bbox_inches='tight') plt.figure() plt.imshow(res_img) plt.savefig('images/res_img.png', bbox_inches='tight') plt.show() return res_img img = mplimg.imread("lane.jpg") process_an_image(img) #output = 'video_2_sol.mp4' #clip = VideoFileClip("video_2.mp4") #out_clip = clip.fl_image(process_an_image) #out_clip.write_videofile(output, audio=False)