2022年4月26日第一次提交

README.md

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+# CarLaneDetection
+Miracle行车记录仪的车道线检测部分的Python代码
+
+需要安装[OpenCV](http://opencv.org)，建议从源码安装

src/SimpleLaneDetector.py

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+import cv2
+import numpy as np
+from moviepy.editor import VideoFileClip
+
+def filter_only_yellow_white(img):
+    hlsColorspacedImage=cv2.cvtColor(img,cv2.COLOR_RGB2HLS)
+    yellowLower=np.array([10,0,90])
+    yellowUpper=np.array([50,255,255])
+    yellowMask=cv2.inRange(hlsColorspacedImage,yellowLower,yellowUpper)
+    whiteLower=np.array([0,190,10])
+    whiteUpper=np.array([255,255,255])
+    whiteMask=cv2.inRange(hlsColorspacedImage,whiteLower,whiteUpper)
+    mask=cv2.bitwise_or(yellowMask,whiteMask)
+    lineImg=cv2.bitwise_and(img,img,mask=mask)
+    return lineImg
+
+def crop_region_of_interest(img):
+    shapes = np.array([[(0, img.shape[0]), (int(img.shape[1]*0.45), int(img.shape[0]*0.645)), (int(img.shape[1]*0.55), int(img.shape[0]*0.645)), (img.shape[1], img.shape[0])]])
+    filledPolygon=np.zeros_like(img)
+    if len(img.shape) > 2:
+        channel_count = img.shape[2]
+        color_to_filter = (255,) * channel_count
+    else:
+        color_to_filter = 255
+    #print(shapes)
+    cv2.fillPoly(filledPolygon, shapes, color_to_filter)
+    masked_img = cv2.bitwise_and(img, filledPolygon)
+    return masked_img
+
+def detect_edges(img):
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    #edges = cv2.Canny(gray, 50, 120)
+    return gray
+
+def draw_lanes(img,lines, color=[255, 0, 0], thickness=2):
+    rightSlope=[]
+    rightIntercept=[]
+    leftSlope=[]
+    leftIntercept=[]
+    for line in lines:
+        for x1, y1, x2, y2 in line:
+            yDiff=y1-y2
+            xDiff=x1-x2
+            slope=yDiff/xDiff
+            yIntecept=y2-(slope*x2)
+            if slope>0.3 and x1>500:
+                rightSlope.append(slope)
+                rightIntercept.append(yIntecept)
+            elif slope<-0.3 and x1<600:
+                leftSlope.append(slope)
+                leftIntercept.append(yIntecept)
+    if len(leftSlope)==0: return
+    if len(rightSlope)==0: return
+    leftAvgSlope=sum(leftSlope)/len(leftSlope)
+    leftAvgIntercept=sum(leftIntercept)/len(leftIntercept)
+    rightAvgSlope=sum(rightSlope)/len(rightSlope)
+    rightAvgIntercept=sum(rightIntercept)/len(rightIntercept)
+    leftLineX1=int((0.65*img.shape[0]-leftAvgIntercept)/leftAvgSlope)
+    leftLineX2=int((img.shape[0]-leftAvgIntercept)/leftAvgSlope)
+    rightLineX1=int((0.65*img.shape[0]-rightAvgIntercept)/rightAvgSlope)
+    rightLineX2=int((img.shape[0]-rightAvgIntercept)/rightAvgSlope)
+    cv2.line(img,(leftLineX1,int(0.645*img.shape[0])),(leftLineX2,img.shape[0]),color,thickness)
+    cv2.line(img,(rightLineX1,int(0.645*img.shape[0])),(rightLineX2,img.shape[0]),color,thickness)
+
+def detect_lane(img):
+    colorFilteredImage=filter_only_yellow_white(img)
+    regionOfInterest=crop_region_of_interest(colorFilteredImage)
+    edgesOnly=detect_edges(regionOfInterest)
+    lines = cv2.HoughLinesP(edgesOnly, 1, np.pi / 180, 10, np.array([]), minLineLength=20,maxLineGap=100)
+    draw_lanes(img, lines)
+    return img
+
+output = '../resources/video_1_sol.mp4'
+clip = VideoFileClip("../resources/video_1.mp4")
+out_clip = clip.fl_image(detect_lane)
+out_clip.write_videofile(output, audio=False)