快速建企业网站,昆明学习网站建设,深圳全网推广公司,wordpress开发主题使用YOLOv8#xff08;You Only Look Once#xff09;和OpenCV实现车道线和车辆检测#xff0c;目标是创建一个可以检测道路上的车道并识别车辆的系统#xff0c;并估计它们与摄像头的距离。该项目结合了计算机视觉技术和深度学习物体检测。
1、系统主要功能
车道检测You Only Look Once和OpenCV实现车道线和车辆检测目标是创建一个可以检测道路上的车道并识别车辆的系统并估计它们与摄像头的距离。该项目结合了计算机视觉技术和深度学习物体检测。
1、系统主要功能
车道检测使用边缘检测和霍夫线变换检测道路车道。汽车检测使用 YOLOv8 模型识别汽车并在汽车周围绘制边界框。距离估计使用边界框大小计算检测到的汽车与摄像头的距离。
2、环境要求
OpenCV用于图像处理和车道检测。Ultralytics YOLOv8用于车辆检测。NumPy用于数组操作。
pip install opencv-python-headless numpy ultralyticsopencv-python 和 opencv-python-headless 区别是 OpenCV 的 Python 包主要区别在于是否包含 GUI 相关的功能。
opencv-python
包含 GUI 功能支持窗口显示、鼠标事件等图形界面操作。依赖需要 GUI 库如 GTK、Qt支持。适用场景适用于需要显示图像或与用户交互的环境如桌面应用。
opencv-python-headless
不包含 GUI 功能去除了窗口显示和用户交互功能。依赖无需 GUI 库适合无图形界面的环境。适用场景适用于服务器或无图形界面的环境如远程服务器、Docker 容器。
选择建议
如果需要显示图像或与用户交互选择 opencv-python。如果仅需图像处理且无图形界面需求选择 opencv-python-headless。
3、代码
import cv2
import numpy as np
import math
import time
from ultralytics import YOLO # YOLOv8 module# Function to mask out the region of interest
def region_of_interest(img, vertices):mask np.zeros_like(img)match_mask_color 255cv2.fillPoly(mask, vertices, match_mask_color)masked_image cv2.bitwise_and(img, mask)return masked_image# Function to draw the filled polygon between the lane lines
def draw_lane_lines(img, left_line, right_line, color[0, 255, 0], thickness10):line_img np.zeros_like(img)poly_pts np.array([[(left_line[0], left_line[1]),(left_line[2], left_line[3]),(right_line[2], right_line[3]),(right_line[0], right_line[1])]], dtypenp.int32)# Fill the polygon between the linescv2.fillPoly(line_img, poly_pts, color)# Overlay the polygon onto the original imageimg cv2.addWeighted(img, 0.8, line_img, 0.5, 0.0)return img# The lane detection pipeline
def pipeline(image):height image.shape[0]width image.shape[1]region_of_interest_vertices [(0, height),(width / 2, height / 2),(width, height),]# Convert to grayscale and apply Canny edge detectiongray_image cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)cannyed_image cv2.Canny(gray_image, 100, 200)# Mask out the region of interestcropped_image region_of_interest(cannyed_image,np.array([region_of_interest_vertices], np.int32))# Perform Hough Line Transformation to detect lineslines cv2.HoughLinesP(cropped_image,rho6,thetanp.pi / 60,threshold160,linesnp.array([]),minLineLength40,maxLineGap25)# Separating left and right lines based on slopeleft_line_x []left_line_y []right_line_x []right_line_y []if lines is None:return imagefor line in lines:for x1, y1, x2, y2 in line:slope (y2 - y1) / (x2 - x1) if (x2 - x1) ! 0 else 0if math.fabs(slope) 0.5: # Ignore nearly horizontal linescontinueif slope 0: # Left laneleft_line_x.extend([x1, x2])left_line_y.extend([y1, y2])else: # Right laneright_line_x.extend([x1, x2])right_line_y.extend([y1, y2])# Fit a linear polynomial to the left and right linesmin_y int(image.shape[0] * (3 / 5)) # Slightly below the middle of the imagemax_y image.shape[0] # Bottom of the imageif left_line_x and left_line_y:poly_left np.poly1d(np.polyfit(left_line_y, left_line_x, deg1))left_x_start int(poly_left(max_y))left_x_end int(poly_left(min_y))else:left_x_start, left_x_end 0, 0 # Defaults if no lines detectedif right_line_x and right_line_y:poly_right np.poly1d(np.polyfit(right_line_y, right_line_x, deg1))right_x_start int(poly_right(max_y))right_x_end int(poly_right(min_y))else:right_x_start, right_x_end 0, 0 # Defaults if no lines detected# Create the filled polygon between the left and right lane lineslane_image draw_lane_lines(image,[left_x_start, max_y, left_x_end, min_y],[right_x_start, max_y, right_x_end, min_y])return lane_image# Function to estimate distance based on bounding box size
def estimate_distance(bbox_width, bbox_height):# For simplicity, assume the distance is inversely proportional to the box size# This is a basic estimation, you may use camera calibration for more accuracyfocal_length 1000 # Example focal length, modify based on camera setupknown_width 2.0 # Approximate width of the car (in meters)distance (known_width * focal_length) / bbox_width # Basic distance estimationreturn distance# Main function to read and process video with YOLOv8
def process_video():# Load the YOLOv8 modelmodel YOLO(weights/yolov8n.pt)# 或者加载官方模型# model YOLO(yolov8n.pt) # load an official model# Open the video filecap cv2.VideoCapture(video/video.mp4)# Check if video opened successfullyif not cap.isOpened():print(Error: Unable to open video file.)return# Set the desired frame ratetarget_fps 30frame_time 1.0 / target_fps # Time per frame to maintain 30fps# Resize to 720p (1280x720)cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)# Loop through each framewhile cap.isOpened():ret, frame cap.read()if not ret:break# Resize frame to 720presized_frame cv2.resize(frame, (1280, 720))# Run the lane detection pipelinelane_frame pipeline(resized_frame)# Run YOLOv8 to detect cars in the current frameresults model(resized_frame)# Process the detections from YOLOv8for result in results:boxes result.boxesfor box in boxes:x1, y1, x2, y2 map(int, box.xyxy[0]) # Bounding box coordinatesconf box.conf[0] # Confidence scorecls int(box.cls[0]) # Class ID# Only draw bounding boxes for cars with confidence 0.5if model.names[cls] car and conf 0.5:label f{model.names[cls]} {conf:.2f}# Draw the bounding boxcv2.rectangle(lane_frame, (x1, y1), (x2, y2), (0, 255, 255), 2)cv2.putText(lane_frame, label, (x1, y1 - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)# Estimate the distance of the carbbox_width x2 - x1bbox_height y2 - y1distance estimate_distance(bbox_width, bbox_height)# Display the estimated distancedistance_label fDistance: {distance:.2f}mcv2.putText(lane_frame, distance_label, (x1, y2 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)# Display the resulting frame with both lane detection and car detectioncv2.imshow(Lane and Car Detection, lane_frame)# Limit the frame rate to 30fpstime.sleep(frame_time)# Break the loop when q is pressedif cv2.waitKey(1) 0xFF ord(q):break# Release video capture and close windowscap.release()cv2.destroyAllWindows()# Run the video processing function
process_video()4、工作原理
4.1 车道线检测 Pipeline
车道线检测包括一下几个步骤:
Step 1: 屏蔽感兴趣区域ROI 只处理图像的下半部分车道线通常是可见的。
def region_of_interest(img, vertices):mask np.zeros_like(img)match_mask_color 255cv2.fillPoly(mask, vertices, match_mask_color)masked_image cv2.bitwise_and(img, mask)return masked_imageStep 2: 使用Canny进行边缘检测 将图像转换为灰度并应用Canny边缘检测来突出显示边缘。
gray_image cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
cannyed_image cv2.Canny(gray_image, 100, 200)Step 3: 霍夫线变换 霍夫线变换用于检测当前车道的线段。
lines cv2.HoughLinesP(cropped_image,rho6,thetanp.pi / 60,threshold160,linesnp.array([]),minLineLength40,maxLineGap25
)4.2 使用YOLOv8进行车辆检测
Step 1: 加载YOLOv8模型 我们使用预训练的YOLOv8模型来检测每一帧中的汽车或者使用官方提供的模型。
from ultralytics import YOLO
model YOLO(weights/yolov8n.pt)
# model YOLO(yolov8n.pt) #官方提供的模型Step 2: 绘制边界框 对于每一辆检测到的汽车绘制边界框并显示类名汽车和置信度分数。
for box in boxes:x1, y1, x2, y2 map(int, box.xyxy[0])conf box.conf[0]if model.names[cls] car and conf 0.5:label f{model.names[cls]} {conf:.2f}cv2.rectangle(lane_frame, (x1, y1), (x2, y2), (0, 255, 255), 2)cv2.putText(lane_frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 255), 2)Step 3:. 距离估计 根据边界框的大小估计到每辆检测到的汽车的距离。
def estimate_distance(bbox_width, bbox_height):focal_length 1000 # Example focal lengthknown_width 2.0 # Approximate width of a car (in meters)distance (known_width * focal_length) / bbox_widthreturn distanceStep 4:. 视频处理 Pipeline 将车道检测、车辆检测和距离估计结合到一个实时视频处理pipeline中。
while cap.isOpened():ret, frame cap.read()if not ret:breaklane_frame pipeline(resized_frame)results model(resized_frame)for result in results:# Draw bounding boxes and estimate distancecv2.imshow(Lane and Car Detection, lane_frame)if cv2.waitKey(1) 0xFF ord(q):break5、结果 项目源码地址: https://github.com/CityIsBetter/Lane_Detection
