Python实现人脸识别

命令行输入 pip install opencv-python

      cv2(Opencv)：图像识别，摄像头调用 

https://github.com/opencv/opencv/tree/master/data/haarcascades

import cv2

# 分模块，可以用函数表示，也可以用类表示
# 统一接口, frame: 帧, int fun(int a, int b) def fun(a: int, b: int)
# 类的继承
# 类名用驼峰命名法，其他的用微软命名法


class Model:
    def __init__(self, name):
        self.name = name

    def run(self, frame: dict):
        return frame


class GetCamera(Model):  # 图片读取模块
    # 子类应该继承父类的：除了构造函数之外的所有成员函数和成员变量
    def __init__(self, name: str = 'get_camera'):
        # 构造函数
        super().__init__(name)
        self.camera = cv2.VideoCapture(0)

    def run(self, frame: dict):
        ret, img = self.camera.read()
        frame['img'] = img
        return frame


# 翻转照片
class Flip(Model):
    def __init__(self, name):
        super().__init__(name)

    def run(self, frame: dict):
        img = frame['img']
        img = cv2.flip(img, 1)
        frame['img'] = img
        return frame


class ChalkEffects(Model):
    def __init__(self, name):
        super().__init__(name)

    def run(self, frame: dict):
        img = frame['img']
        #img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 灰度化
        #img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
        #                             cv2.THRESH_BINARY, 5, 3)
        # img = cv2.bitwise_not(img)
        frame['img'] = img
        return frame


class DetectionFace(Model):
    def __init__(self, name):
        super().__init__(name)

    def run(self, frame: dict):
        img = frame['img']
        # TODO: 检测
        face_cascade = cv2.CascadeClassifier("E:Downloadhaarcascade_frontalface_default.xml")
        eye_cascade = cv2.CascadeClassifier("E:Downloadhaarcascade_eye.xml")
        # 显示图片（渲染画面）
        face = face_cascade.detectMultiScale(img,  # 输入的灰度图像
                                             scaleFactor=1.1,  # 图像缩放的比例
                                             minNeighbors=5,  # 构成目标矩形的最少相邻矩形个数
                                             minSize=(30, 30),  # 目标尺寸的最小大小
                                             flags=cv2.CASCADE_SCALE_IMAGE)
        frame['face'] = face
        eye = eye_cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=2, minSize=(7, 7))
        frame['eye'] = eye
        return frame


class DrawBbox(Model):
    def __init__(self, name):
        super().__init__(name)

    def run(self, frame: dict):
        face = frame['face']
        eye = frame['eye']
        img = frame['img']
        # TODO: 画bbox框
        # 标记位置 说明：(x,y)为绘制的边框的左上角 （x+w，y+h）为绘制的边框的右下角 （255， 0， 0）为RGB三色值 1为线条的粗细值
        for (x, y, w, h) in face:
            img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 1)
            frame['img']=img
        for (x, y, w, h) in eye:
            img = cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 1)
            frame['img'] = img
        return frame


class Show(Model):
    def __init__(self, name):
        super().__init__(name)

    def run(self, frame: dict):
        # 4. 显示图片
        img = frame['img']
        cv2.imshow('img', img)
        cv2.waitKey(1)
        return frame


if __name__ == '__main__':
    task = [
        GetCamera(),
        Flip('f'),
        ChalkEffects('ce'),
        DetectionFace('df'),
        DrawBbox('db'),
        Show('s'),
    ]
    while True:
        frame = {}
        for model in task:
            frame = model.run(frame)