### 改进 Python 中基于 OpenCV 的车道线检测代码 为了使车道线检测更加稳健，特别是在不同光照条件下减少误识别并正确保存输出图片，可以采取以下方法： 1. **色彩空间转换**：将图像从 RGB 转换到其他更合适的色彩空间（如 HLS 或 HSV），这样可以在一定程度上减弱光照变化带来的影响。 2. **边缘检测优化**：使用 Canny 边缘检测之前增加高斯模糊处理以平滑噪声，并调整阈值范围使其适合当前场景。 3. **感兴趣区域裁剪**：通过 `region_of_interest` 函数限定只关注车辆前方的道路部分，排除无关背景干扰。 4. **霍夫直线变换改进**：设置合理的参数组合来过滤掉不符合逻辑的直线段，比如长度太短或者角度异常的情况。 5. **结果可视化与存储**：确保每一步骤的结果都能被清晰显示出来，并且最终成果能够按照指定路径妥善存档。 以下是综合以上几点考虑之后编写的完整版本代码[^1]: ```python import cv2 import numpy as np def grayscale(img): """Applies the Grayscale transform""" return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) def canny_edge_detection(gray_img, low_threshold=50, high_threshold=150): """Performs edge detection using Canny algorithm.""" blurred_img = cv2.GaussianBlur(gray_img,(5, 5), 0) edges = cv2.Canny(blurred_img, low_threshold, high_threshold) return edges def region_of_interest(img, vertices): """ Applies an image mask. Only keeps the region of the image defined by the polygon formed from `vertices`. The rest of the image is set to black. """ mask = np.zeros_like(img) if len(img.shape) > 2: channel_count = img.shape[2] match_mask_color = (255,) * channel_count else: match_mask_color = 255 cv2.fillPoly(mask, vertices, match_mask_color) masked_image = cv2.bitwise_and(img, mask) return masked_image def draw_lines(img, lines, color=[255, 0, 0], thickness=2): line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8) for line in lines: for x1,y1,x2,y2 in line: cv2.line(line_img, (x1, y1), (x2, y2), color, thickness) img_with_lines = cv2.addWeighted(img, 0.8, line_img, 1.0, 0.0) return img_with_lines def hough_transform(edges, rho=1, theta=np.pi/180, threshold=20, min_line_len=20, max_line_gap=300): lines = cv2.HoughLinesP(edges,rho=rho,theta=theta, threshold=threshold,minLineLength=min_line_len,maxLineGap=max_line_gap) return lines # Load Image image = cv2.imread('test_image.jpg') # Convert Color Space hls = cv2.cvtColor(image,cv2.COLOR_BGR2HLS).astype(np.float32) lower_yellow = np.array([90., 70., 70.]) upper_yellow = np.array([110., 255., 255.]) yellow_mask = cv2.inRange(hls, lower_yellow, upper_yellow) white_mask = cv2.inRange(cv2.cvtColor(image,cv2.COLOR_BGR2GRAY), 200, 255.) mask = cv2.bitwise_or(white_mask, yellow_mask) # Edge Detection edges = canny_edge_detection(grayscale(image)) # Define Region Of Interest Vertices imshape = image.shape vertices = np.array([[(0, imshape[0]),(int(imshape[1]/2)-45, int(imshape[0]*0.6)), (int(imshape[1]/2)+45,int(imshape[0]*0.6)), (imshape[1]-1,imshape[0])]],dtype=np.int32 ) roi_edges = region_of_interest(edges,[vertices]) # Hough Transform For Line Segments Extraction lines = hough_transform(roi_edges) line_marked_image = draw_lines(image.copy(), lines) cv2.imshow("Result", line_marked_image ) cv2.waitKey(0) cv2.destroyAllWindows() # Save Output Images To Specified Path output_filename="processed_"+'test_image'+".jpg" cv2.imwrite(output_filename,line_marked_image) ``` 此脚本实现了完整的流程控制，包括但不限于颜色空间转化、边缘探测以及霍夫变换应用等方面的内容。它还特别注意到了如何有效地界定感兴趣的区域，并采用了适当的技术手段去提升整体性能表现水平[^2]。 ---