训练生成混淆矩阵遇AttributeError：'builtin_function_or_method'无index属性

错误根源 #

你的代码中这一行存在语法错误：

names = names.tolist

tolist()是NumPy数组的方法，必须加括号调用才能将NumPy数组转换为Python列表。如果不加括号，names会被赋值为这个方法本身（而非方法执行后的列表），后续调用names.index(name)时，自然会因为方法对象没有index属性而报错。

修正后的代码 #

def load_face_dataset(inputPath, net, minConfidence=0.5,
    minSamples=15):
    # grab the paths to all images in our input directory, extract
    # the name of the person (i.e., class label) from the directory
    # structure, and count the number of example images we have per
    # face
    imagePaths = list(paths.list_images(inputPath))
    names = [p.split(os.path.sep)[-2] for p in imagePaths]
    (names, counts) = np.unique(names, return_counts=True)
    names = names.tolist()  # 此处添加括号调用tolist方法
    # initialize lists to store our extracted faces and associated
    # labels
    faces = []
    labels = []
    # loop for the image paths
    for imagePath in imagePaths:
        # load the image from disk and extract the name of the person
        # from the subdirectory structure
        image = cv2.imread(imagePath)
        name = imagePath.split(os.path.sep)[-2]
        # only process images that have a sufficient number of
        # examples belonging to the class
        if counts[names.index(name)] < minSamples:
            continue
        # perform face detection
        boxes = detect_faces(net, image, minConfidence)
        # loop over the bounding boxes
        for (startX, startY, endX, endY) in boxes:
            try:
                # extract the face ROI, resize it, and convert it to
                # grayscale
                faceROI = image[startY:endY, startX:endX]
                faceROI = cv2.resize(faceROI, (47, 62))
                faceROI = cv2.cvtColor(faceROI, cv2.COLOR_BGR2GRAY)
                # update our faces and labels lists
                faces.append(faceROI)
                labels.append(name)
            except:
                continue
    # convert our faces and labels lists to NumPy arrays
    faces = np.array(faces)
    labels = np.array(labels)
    # return a 2-tuple of the faces and labels
    return (faces, labels)

补充说明 #

• 修正后，names会被正确转换为Python列表，names.index(name)就能正常查找元素索引，进而通过counts[索引]获取对应类别的样本数量。
• 调试时可在关键步骤打印变量类型（比如print(type(names))），快速定位这类方法调用遗漏括号的问题。

内容的提问来源于stack exchange，提问作者Bernardino Neil Brian