From 36ffc2cf67ef46e07298e82fe38391e646016fd9 Mon Sep 17 00:00:00 2001 From: shengzhanpeng <3299915477@qq.com> Date: Wed, 24 Dec 2025 20:01:09 +0800 Subject: [PATCH 1/3] 554455 --- .../Autonomous_vehicle_simulation_obstacle_avoidance.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/Driving_Car/Autonomous_vehicle_simulation_obstacle_avoidance.py b/src/Driving_Car/Autonomous_vehicle_simulation_obstacle_avoidance.py index cab284fd06..422ce61069 100644 --- a/src/Driving_Car/Autonomous_vehicle_simulation_obstacle_avoidance.py +++ b/src/Driving_Car/Autonomous_vehicle_simulation_obstacle_avoidance.py @@ -142,7 +142,7 @@ def detect_obstacle(): if len(front_points) == 0: return False - # 计算每个点的水平角度（相对于车辆前进方向，单位：度） + # 计算每个点的水平角（相对于车辆前进方向，单位：度） # 车辆前进方向为x轴正方向，y轴正方向为左，负方向为右 angles = np.degrees(np.arctan2(front_points[:, 1], front_points[:, 0])) # 过滤角度在[-阈值, +阈值]范围内的点（前方左右各OBSTACLE_ANGLE_THRESHOLD度） From 0b54e4c602635763365b56ed4d17a5ed8b57265a Mon Sep 17 00:00:00 2001 From: shengzhanpeng <3299915477@qq.com> Date: Fri, 26 Dec 2025 20:01:52 +0800 Subject: [PATCH 2/3] 554455 --- ...anned_vehicle_lane_detection_simulation.py | 175 ++++++++++++++++++ 1 file changed, 175 insertions(+) create mode 100644 src/Driving_Car/Unmanned_vehicle_lane_detection_simulation.py diff --git a/src/Driving_Car/Unmanned_vehicle_lane_detection_simulation.py b/src/Driving_Car/Unmanned_vehicle_lane_detection_simulation.py new file mode 100644 index 0000000000..26e4147867 --- /dev/null +++ b/src/Driving_Car/Unmanned_vehicle_lane_detection_simulation.py @@ -0,0 +1,175 @@ +import cv2 +import numpy as np +import matplotlib.pyplot as plt + +def grayscale(img): + """图像灰度化""" + return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + +def gaussian_blur(img, kernel_size): + """高斯模糊去噪""" + return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0) + +def canny_edge_detection(img, low_threshold, high_threshold): + """Canny边缘检测""" + return cv2.Canny(img, low_threshold, high_threshold) + +def region_of_interest(img, vertices): + """提取感兴趣区域（只保留道路区域，过滤背景）""" + # 创建掩码 + mask = np.zeros_like(img) + # 填充掩码的感兴趣区域 + cv2.fillPoly(mask, vertices, 255) + # 与原图像进行按位与操作 + masked_img = cv2.bitwise_and(img, mask) + return masked_img + +def draw_lines(img, lines, color=(0, 255, 0), thickness=5): + """绘制检测到的车道线（拟合左右车道线后绘制）""" + left_x = [] + left_y = [] + right_x = [] + right_y = [] + + if lines is None: + return img + + for line in lines: + for x1, y1, x2, y2 in line: + # 计算斜率 + slope = (y2 - y1) / (x2 - x1) if (x2 - x1) != 0 else 0 + # 过滤斜率过小的直线（非车道线） + if abs(slope) < 0.5: + continue + # 区分左车道（斜率为负）和右车道（斜率为正） + if slope < 0: + left_x.extend([x1, x2]) + left_y.extend([y1, y2]) + else: + right_x.extend([x1, x2]) + right_y.extend([y1, y2]) + + # 拟合左车道线 + if left_x and left_y: + left_fit = np.polyfit(left_y, left_x, 1) + left_func = np.poly1d(left_fit) + # 定义车道线的上下边界（y坐标） + y_min = int(img.shape[0] * 0.6) # 车道线上边界 + y_max = img.shape[0] # 车道线下边界 + # 计算对应x坐标 + x_left_min = int(left_func(y_min)) + x_left_max = int(left_func(y_max)) + cv2.line(img, (x_left_min, y_min), (x_left_max, y_max), color, thickness) + + # 拟合右车道线 + if right_x and right_y: + right_fit = np.polyfit(right_y, right_x, 1) + right_func = np.poly1d(right_fit) + y_min = int(img.shape[0] * 0.6) + y_max = img.shape[0] + x_right_min = int(right_func(y_min)) + x_right_max = int(right_func(y_max)) + cv2.line(img, (x_right_min, y_min), (x_right_max, y_max), color, thickness) + + return img + +def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap): + """霍夫变换检测直线""" + 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) + return line_img + +def weighted_img(img, initial_img, α=0.8, β=1., γ=0.): + """将检测到的车道线与原图像融合""" + return cv2.addWeighted(initial_img, α, img, β, γ) + +def lane_detection_pipeline(image): + """车道检测完整流水线""" + # 1. 预处理：灰度化 + 高斯模糊 + Canny边缘检测 + gray = grayscale(image) + blur_gray = gaussian_blur(gray, kernel_size=5) + edges = canny_edge_detection(blur_gray, low_threshold=50, high_threshold=150) + + # 2. 定义感兴趣区域（多边形顶点，根据图像尺寸调整） + imshape = image.shape + vertices = np.array([[(0, imshape[0]), + (imshape[1] / 2 - 20, imshape[0] / 2 + 60), + (imshape[1] / 2 + 20, imshape[0] / 2 + 60), + (imshape[1], imshape[0])]], dtype=np.int32) + masked_edges = region_of_interest(edges, vertices) + + # 3. 霍夫变换检测直线 + rho = 1 # 霍夫空间的rho步长 + theta = np.pi / 180 # 霍夫空间的theta步长 + threshold = 15 # 检测直线的阈值 + min_line_len = 40 # 直线的最小长度 + max_line_gap = 20 # 直线之间的最大间隙 + line_img = hough_lines(masked_edges, rho, theta, threshold, min_line_len, max_line_gap) + + # 4. 融合车道线与原图像 + result = weighted_img(line_img, image) + + return result, edges, masked_edges, line_img + +# -------------------------- 主程序 -------------------------- +if __name__ == "__main__": + # 读取测试图像（可替换为自己的道路图像，建议使用车载摄像头视角的道路图） + # 若没有测试图，可使用OpenCV生成模拟道路图像 + # 生成模拟道路图像 + def create_simulation_road_image(width=800, height=600): + """生成模拟的道路图像（包含左右车道线）""" + img = np.ones((height, width, 3), dtype=np.uint8) * 255 # 白色背景 + # 绘制道路（灰色） + cv2.rectangle(img, (100, 0), (700, height), (128, 128, 128), -1) + # 绘制左车道线（白色） + cv2.line(img, (250, height), (350, height//2), (255, 255, 255), 5) + # 绘制右车道线（白色） + cv2.line(img, (550, height), (450, height//2), (255, 255, 255), 5) + return img + + # 生成模拟道路图像 + road_img = create_simulation_road_image(width=800, height=600) + + # 执行车道检测 + result, edges, masked_edges, line_img = lane_detection_pipeline(road_img) + + # 显示结果 + plt.figure(figsize=(16, 12)) + plt.subplot(2, 2, 1) + plt.imshow(cv2.cvtColor(road_img, cv2.COLOR_BGR2RGB)) + plt.title('Original Road Image') + plt.axis('off') + + plt.subplot(2, 2, 2) + plt.imshow(edges, cmap='gray') + plt.title('Canny Edges') + plt.axis('off') + + plt.subplot(2, 2, 3) + plt.imshow(masked_edges, cmap='gray') + plt.title('Masked Edges (ROI)') + plt.axis('off') + + plt.subplot(2, 2, 4) + plt.imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB)) + plt.title('Lane Detection Result') + plt.axis('off') + + plt.tight_layout() + plt.show() + + # （可选）处理视频流（模拟车载摄像头实时检测） + # 若需要处理视频，可替换为视频路径或摄像头编号（0为默认摄像头） + # cap = cv2.VideoCapture('test_video.mp4') + # while cap.isOpened(): + # ret, frame = cap.read() + # if not ret: + # break + # result, _, _, _ = lane_detection_pipeline(frame) + # cv2.imshow('Lane Detection', result) + # if cv2.waitKey(1) & 0xFF == ord('q'): + # break + # cap.release() + # cv2.destroyAllWindows() \ No newline at end of file From 136454953acd6a8d7b1f785e25f0515d1a612abe Mon Sep 17 00:00:00 2001 From: shengzhanpeng <3299915477@qq.com> Date: Sat, 27 Dec 2025 18:25:53 +0800 Subject: [PATCH 3/3] 554455 --- src/Driving_Car/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/Driving_Car/README.md b/src/Driving_Car/README.md index aea3eb6553..dc77a0dfe0 100644 --- a/src/Driving_Car/README.md +++ b/src/Driving_Car/README.md @@ -15,7 +15,7 @@ **车辆控制**: PID控制器实现精确的转向和速度控制 -**传感器模拟**: 模拟激光雷达、摄像头和超声波传感器数据 +**传感器模拟**: 模拟激光雷达摄像头和超声波传感器数据 **3D可视化**: 基于PyGame的实时场景渲染