face_capture.py 文件源码

def __init__(self, matric_num):
        WHITE = [255, 255, 255]

        face_cascade = cv2.CascadeClassifier('Haar/haarcascade_frontalcatface.xml')
        eye_cascade = cv2.CascadeClassifier('Haar/haarcascade_eye.xml')

        ID = NameFind.AddName(matric_num)
        Count = 0
        cap = cv2.VideoCapture(0)  # Camera object
        self.__trainer__ = None

        if not os.path.exists('dataSet'):
            os.makedirs('dataSet')

        while True:
            ret, img = cap.read()
            gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # Convert the Camera to grayScale
            faces = face_cascade.detectMultiScale(gray, 1.3, 5)  # Detect the faces and store the positions
            for (x, y, w, h) in faces:  # Frames  LOCATION X, Y  WIDTH, HEIGHT
                FaceImage = gray[y - int(h / 2): y + int(h * 1.5),
                            x - int(x / 2): x + int(w * 1.5)]  # The Face is isolated and cropped
                Img = (NameFind.DetectEyes(FaceImage))
                cv2.putText(gray, "FACE DETECTED", (x + (w / 2), y - 5), cv2.FONT_HERSHEY_DUPLEX, .4, WHITE)
                if Img is not None:
                    frame = Img  # Show the detected faces
                else:
                    frame = gray[y: y + h, x: x + w]
                cv2.imwrite("dataSet/" + matric_num.replace('/', '') + "." + str(ID) + "." + str(Count) + ".jpg", frame)
                Count = Count + 1
                # cv2.waitKey(300)
                cv2.imshow("CAPTURED PHOTO", frame)  # show the captured image
            cv2.imshow('Face Recognition System Capture Faces', gray)  # Show the video
            if Count == 150:
                Trainer()
                break
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
        print 'FACE CAPTURE FOR THE SUBJECT IS COMPLETE'
        cap.release()
        cv2.destroyAllWindows()