本次專案是採用keras深度學習框架，訓練人臉識別模型，採用opencv實時檢測與識別周圍的人臉，本文主要講解如何採集訓練集，本次程式碼在人臉識別的程式碼上添加了將識別的照片依次寫入指定資料夾，並命名(000-499).jpg和顯示識別的第N張照片的文字顯示。

程式碼如下：

# -*- coding:utf-8 -*-
import cv2

def CatchPicture(window_name, catch_pic_num, path_name):
    cv2.namedWindow(window_name)
    cap = cv2.VideoCapture(0)
    cascade_path = "haarcascade_frontalface_default.xml"
    num = 0

    while True:
        ok, frame = cap.read()
        if not ok:
            break

        frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        cascade = cv2.CascadeClassifier(cascade_path)
        #識別出人臉數量
        facerect = cascade.detectMultiScale(frame_gray, scaleFactor=1.2, minNeighbors=3, minSize=(12, 12))

        if len(facerect) > 0:
            print('face detected')
            color = (255, 255, 255)  # 白
            for rect in facerect:
                x, y, w, h = rect
                img_name = '%s/%d.jpg' % (path_name, num)
                image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
                cv2.imwrite(img_name, image)  # 照片寫入到檔案
                num += 1
                if num > (catch_pic_num):  # 如果超過指定最大儲存數量退出迴圈
                    break

                # 畫出矩形框
                cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 2)
                # 顯示當前捕捉到了多少人臉圖片了
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(frame, 'num:%d' % (num), (x + 30, y + 30), font, 1, (0, 255, 0), 2)
         # 超過指定最大儲存數量結束程式
        if num > (catch_pic_num): break
        # 顯示影象
        cv2.imshow(window_name, frame)
        c = cv2.waitKey(15)
        if c & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    CatchPicture("擷取人臉",500,'./data/boss')