opencv人脸数据收集

项目中收到7000多张图片需要找到图片中最大的人脸并且裁剪出来，保存到指定的目录下，用python+opencv写的脚本代码如下：

import cv2
import os

class FaceCrop:
    #初始化函数
    def __init__(self):
        self.number = 0
        self.face_image_dir = "F:/temp/origin"
        self.face_image_path = "F:/temp/result1"
        self.face_detector=cv2.CascadeClassifier(
            'haarcascade_frontalface_default.xml'
        )
        list = os.listdir(self.face_image_dir)
        for i in range(0, len(list)):
            print(list[i])
            path = os.path.join(self.face_image_dir, list[i])
            img = cv2.imread(path)
            myImg, max_face = self.face_collect(img)
            if (max_face[0] < 0) or (max_face[1] < 0):
                continue
            # if max_face == [-80, -80, 160, 300]:
            #     continue
            print(max_face)
            self.face_save(myImg, max_face)

    #找出面积最大的矩形
    def max_matrix(self, matrixs):
        max_matrix=[0,0,0,0]
        for [x,y,w,h] in matrixs:
            if w*h >= max_matrix[2]*max_matrix[3]:
                max_matrix=[x,y,w,h]
        max_matrix[0] = max_matrix[0] - 80
        max_matrix[1] = max_matrix[1] - 80
        max_matrix[2] = max_matrix[2] + 160
        max_matrix[3] = max_matrix[3] + 300
        return max_matrix

    # 给图片绘制矩形
    # def plot_rectangle(self, img, matrix):
    #     cv2.rectangle(img, (matrix[0], matrix[1]), (matrix[0] + matrix[2], matrix[1] + matrix[3]),
    #                   (255, 0, 0), 2)

    # 人脸检测，标记并返回检测到的最大的人脸，用于人脸识别
    def face_collect(self, img):
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = self.face_detector.detectMultiScale(gray, 1.2, 8)
        max_face = self.max_matrix(faces)
        # self.plot_rectangle(img, max_face)
        return img, max_face

    # 保存人脸照片
    def face_save(self, img, max_face):
        # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        if max_face[2] > 30:
            self.number = self.number + 1
            print(self.number)
            print(
                self.face_image_path + "/" + str(self.number) + ".jpg"
            )
            cv2.imwrite(
                self.face_image_path + '/' + str(self.number) + '.jpg',
                img[max_face[1]:max_face[1] + max_face[3], max_face[0]:max_face[0] + max_face[2]]
            )
        return

if __name__=="__main__":
    f=FaceCrop()