树莓派小车，求怎么循迹？

刚开始是打算用红外循迹，通过不同颜色反射的红外线不同来循迹，但是问商家说漫反射光电开关不能用，有考虑过用opencv

循迹是指通过识别地面上的标记或颜色来控制小车移动方向的技术。对于树莓派小车的循迹，你可以尝试以下几种方法：

使用红外线传感器：你可以安装红外线传感器在小车底部，通过检测地面上红外线的反射程度，来判断小车应该往哪个方向移动。你可以通过编程来控制小车根据传感器的检测结果进行运动。
使用摄像头和图像处理：你可以配备一台摄像头，然后使用OpenCV库进行图像处理，通过识别地面上的标记或颜色来判断小车的移动方向。你可以编写算法来分析摄像头捕捉到的图像，在图像中找到颜色标记或轮廓，然后根据识别结果控制小车的运动。

另外，在树莓派小车的循迹过程中，你可以使用Python编程语言进行开发。Python具有简单易用的语法和强大的图像处理和机器视觉库，适合实现循迹功能。

关于具体的实现细节和代码示例，你可以参考一些开源项目、教程或资料，例如GitHub上的树莓派小车项目和相关论坛中的讨论。同时，你也可以参考树莓派官方文档以及OpenCV文档，了解更多有关使用树莓派和OpenCV进行循迹的方法和代码示例。

这有个类似的问题, 你可以参考下: https://ask.csdn.net/questions/7780799
你也可以参考下这篇文章：opencv的图片处理：缩小尺寸为原图的一半【自己练习存档，没有参考价值，多看其他大神代码，谢谢】
除此之外, 这篇博客: 用opencv的人脸识别来分辨明星似乎不太灵光中的 二、从素材准备到预测 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:

下载图片
男女明星各一组，每组2人，每人各9张。图片找小冰要的，度娘也一样。
图片规整化
训练时要求图片尺寸统一，而下载来的图片大大小小，所以要统一到同一尺寸. 用简单粗暴的方法，查出所有图片的最小尺寸，然后将所有照片调到此尺寸。不能变形，不足的方向补黑边。

def unifyImg(strSource):
    #read all images and get the smallest size
    x_min = 9999
    y_min = 9999
    img_Bingbing = list()
    img_file_list = os.listdir(strSource)
    for i in range(0,len(img_file_list)):
        strFileName = os.path.join(strSource,img_file_list[i]) 
        if os.path.isfile(strFileName):
            img = cv2.imread(strFileName)
            if img is None:
                print('Failed when read file %s'%strFileName)
                continue
            
            # record size
            if img.shape[0] < y_min:
                y_min = img.shape[0]
            if img.shape[1] < x_min:
                x_min = img.shape[1]
                
            #store img and its name
            img_Bingbing.append([img,img_file_list[i]])

    #Resize images  
    frontFaceCascade = cv2.CascadeClassifier(r'.\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
    profileFaceCascade = cv2.CascadeClassifier(r'.\Lib\site-packages\cv2\data\haarcascade_profileface.xml')
    
    for i in range(0,len(img_Bingbing)):
        img = img_Bingbing[i][0]

        #check if face is identified in the image
        frontFaces = frontFaceCascade.detectMultiScale(img)
        profileFaces = profileFaceCascade.detectMultiScale(img)
        if len(frontFaces) == 0:
            if len(profileFaces) == 0:
                print("no face detected on img: %s, deleted."%img_Bingbing[i][1])
                continue
            else:
                x = profileFaces[0][0]
                y = profileFaces[0][1]
                w = profileFaces[0][2]
                h = profileFaces[0][3]
        else:
            x = frontFaces[0][0]
            y = frontFaces[0][1]
            w = frontFaces[0][2]
            h = frontFaces[0][3]
        
        img_new = img[y:y+h,x:x+w]
        cv2.imwrite(strSource + "\\Face_only\\" + img_Bingbing[i][1],img_new)

        # 98% make sure the output image size is smaller than the min_x or min_y
        rate_x = x_min / img.shape[1] * 0.98
        rate_y = y_min / img.shape[0] * 0.98 
        rate = min(rate_x, rate_y)
        
        img_new = cv2.resize(img, None, fx = rate, fy = rate)
        if len(img_new.shape) == 2:
            # grey
            img_template = np.zeros((y_min, x_min),np.uint8) 
        else:
            img_template = np.zeros((y_min, x_min, 3),np.uint8) 
        
        h = img_new.shape[0]
        w = img_new.shape[1]
          
        img_template[0:h,0:w] = img_new
        
        cv2.imwrite(strSource + "\\Unified\\" + img_Bingbing[i][1],img_template)

strSource = r'C:\Waley\Personal\Learning\Python\Image\TryFaceIdentify'
unifyImg(strSource)

挑一部分统一好尺寸的图片整合进一个列表，用来训练

src = list()
lables = list()
# LiYiFeng
for i in range(0,6):
    img = cv2.imread(strSource + "\\unified\\LYF_" + str(i+1) + ".jpg",cv2.IMREAD_GRAYSCALE)
    if img is None:
        print("Fail to read images")
        sys.exit(0)
    else:
        src.append(img)
        lables.append(0)
#YiYangQianXi
for i in range(0,6):
    img = cv2.imread(strSource + "\\unified\\YYQX_" + str(i+1) + ".jpg",cv2.IMREAD_GRAYSCALE)
    if img is None:
        print("Fail to read images")
        sys.exit(0)
    else:
        src.append(img)
        lables.append(1)              
names = {"0":"LiYiFeng","1":"YiYangQianXi"}

#train recognizer
recognizer_eigenface.train(src,np.array(lables))
recognizer_fisher.train(src, np.array(lables))
recognizer_LBPH.train(src, np.array(lables))

识别

for i in range(7,10):
    img = cv2.imread(strSource + "\\unified\\LYF_" + str(i) + '.jpg',cv2.IMREAD_GRAYSCALE)
    print("LYF %d : "%i)
    lable, confidence = recognizer_eigenface.predict(img)
    print("predicted by Eigenface: %s, confidence is: %d"%(names[str(lable)],confidence))
    lable, confidence = recognizer_fisher.predict(img)
    print("predicted by Fisher: %s, confidence is: %d"%(names[str(lable)],confidence))
    lable, confidence = recognizer_LBPH.predict(img)
    print("predicted by LBPH: %s, confidence is: %d"%(names[str(lable)],confidence))

print("\n\n")

for i in range(7,10):
    img = cv2.imread(strSource + "\\unified\\YYQX_" + str(i) + '.jpg',cv2.IMREAD_GRAYSCALE)
    print("YYQX %d : "%i)
    lable, confidence = recognizer_eigenface.predict(img)
    print("predicted by Eigenface: %s, confidence is: %d"%(names[str(lable)],confidence))
    lable, confidence = recognizer_fisher.predict(img)
    print("predicted by Fisher: %s, confidence is: %d"%(names[str(lable)],confidence))
    lable, confidence = recognizer_LBPH.predict(img)
    print("predicted by LBPH: %s, confidence is: %d"%(names[str(lable)],confidence))

以上可以合并为一个函数，方便换人试验:

def trainAndPredict(strSource, strName1, strName2):
    # prepare training dataset
    src = list()
    lables = list()
    for i in range(0,6):
        img = cv2.imread(strSource + "\\unified\\" + strName1 + '_' + str(i+1) + ".jpg",cv2.IMREAD_GRAYSCALE)
        if img is None:
            print("Fail to read images")
            sys.exit(0)
        else:
            src.append(img)
            lables.append(0)
            
    for i in range(0,6):
        img = cv2.imread(strSource + "\\unified\\" + strName2 + '_' +  str(i+1) + ".jpg",cv2.IMREAD_GRAYSCALE)
        if img is None:
            print("Fail to read images")
            sys.exit(0)
        else:
            src.append(img)
            lables.append(1)              
    names = {"0":strName1,"1":strName2}
    #set up creator
    recognizer_eigenface = cv2.face.EigenFaceRecognizer_create()
    recognizer_fisher = cv2.face.FisherFaceRecognizer_create()
    recognizer_LBPH = cv2.face.LBPHFaceRecognizer_create()
    
    #train recognizer
    recognizer_eigenface.train(src,np.array(lables))
    recognizer_fisher.train(src, np.array(lables))
    recognizer_LBPH.train(src, np.array(lables))
    
    for i in range(7,10):
        img = cv2.imread(strSource + "\\unified\\" + strName1 + '_' +  str(i) + '.jpg',cv2.IMREAD_GRAYSCALE)
        print("%s %d : "%(strName1,i))
        lable, confidence = recognizer_eigenface.predict(img)
        print("predicted by Eigenface: %s, confidence is: %d"%(names[str(lable)],confidence))
        lable, confidence = recognizer_fisher.predict(img)
        print("predicted by Fisher: %s, confidence is: %d"%(names[str(lable)],confidence))
        lable, confidence = recognizer_LBPH.predict(img)
        print("predicted by LBPH: %s, confidence is: %d"%(names[str(lable)],confidence))
    
    print("\n\n")
    
    for i in range(7,10):
        img = cv2.imread(strSource + "\\unified\\" + strName2 + '_' +  str(i) + '.jpg',cv2.IMREAD_GRAYSCALE)
        print("%s %d : "%(strName2,i))
        lable, confidence = recognizer_eigenface.predict(img)
        print("predicted by Eigenface: %s, confidence is: %d"%(names[str(lable)],confidence))
        lable, confidence = recognizer_fisher.predict(img)
        print("predicted by Fisher: %s, confidence is: %d"%(names[str(lable)],confidence))
        lable, confidence = recognizer_LBPH.predict(img)
        print("predicted by LBPH: %s, confidence is: %d"%(names[str(lable)],confidence))

#strSource = r'C:\Waley\Personal\Learning\Python\Image\TryFaceIdentify'
#strName1 = 'LYF'
#strName2 = 'YYQX'

strSource = r'C:\Waley\Personal\Learning\Python\Image\FaceIdentify'
strName1 = 'LiBingbing'
strName2 = 'YangMi'
trainAndPredict(strSource, strName1, strName2)

结果让人意外
对于两位男明星，简直没有任何识别率。

这两位明星都不太熟悉，会不会是网站也是AI标的，标错了呢？哈哈。

换了两位女明星试验，效果似乎还行：
在这里插入图片描述

您还可以看一下英特尔老师的英特尔 OpenCV 初级认证课程课程中的高斯模糊小节, 巩固相关知识点