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import cv2
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import os
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import sys
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import numpy as np
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from createfold import CreateFolder
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def CatchPICFromVideo (window_name, camera_idx, catch_pic_num, path_name, usr_name):
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#camera_idx代表摄像头编号，0即为系统默认
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#检查输入路径是否存在——不存在就创建
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CreateFolder(path_name)
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cv2.namedWindow(window_name)
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#cap = cv2.VideoCapture('./Qianyaxuan/Qianyaxuan.mp4')
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cap = cv2.VideoCapture('./Wangwenhai/Wangwenhai.mp4')
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# 设置分辨率
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#cap = cv2.VideoCapture(camera_idx)
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cap.set(3, 1920)
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cap.set(4, 1080)
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# 告诉OpenCV使用人脸识别分类器
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classfier = cv2.CascadeClassifier("./haarcascade_frontalface_alt2.xml")
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#记录已拍摄的照片数目
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num = 0
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while True:
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ok, frame = cap.read() # 读取一帧数据
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if not ok:
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break
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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#将当前桢图像转换成灰度图像
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# 人脸检测，1.2和2分别为图片缩放比例和需要检测的有效点数
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faceRects = classfier.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=2, minSize=(32, 32))
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for (x, y, w, h) in faceRects:
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#单独框出每一张人脸
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#将当前帧保存为图片
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img_name = '%s/%d.jpg' % ("./FaceData/"+usr_name, num)
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#保存灰度人脸图
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cv2.imwrite(img_name, gray[y:y+h, x:x+w])
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num += 1
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#画出矩形框的时候稍微比识别的脸大一圈
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cv2.rectangle(frame, (x - 10, y - 10), (x + w + 10, y + h + 10),(0, 255, 0),1)
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#显示当前捕捉到了多少人脸图片
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font = cv2.FONT_HERSHEY_SIMPLEX
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cv2.putText(frame, 'num:%d' % (num), (x + 30, y + 30), font, 1, (255, 0, 255), 1)
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# 超过指定最大保存数量结束程序
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cv2.imshow(window_name, frame)
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if num >= catch_pic_num:
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break
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#按键盘‘Q’中断采集
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if cv2.waitKey(5) & 0xFF == ord('q'):
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break
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# 释放摄像头并销毁所有窗口
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print("拍摄完成，训练中......")
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cap.release()
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cv2.destroyAllWindows()
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