人脸识别图片眨眼生成器，虚拟相机过人脸软件， 秒解人脸识别软件

下载地址：https://www.pan38.com/dow/share.php?code=JCnzE 提取密码：1133

这个系统包含三个主要模块：人脸检测与特征点识别、虚拟相机实现和主程序入口。代码使用了dlib库进行人脸检测和特征点识别，可以检测眼睛位置并添加眨眼效果。虚拟相机模块可以将处理后的视频流输出到虚拟摄像头设备。

import cv2
import numpy as np
import dlib
from PIL import Image, ImageDraw
import random
import time

class FaceProcessor:
def init(self):
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

def detect_faces(self, image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = self.detector(gray)
    return faces

def get_landmarks(self, image, face):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    landmarks = self.predictor(gray, face)
    return [(landmarks.part(i).x, landmarks.part(i).y) for i in range(68)]

def add_blink_effect(self, image, landmarks):
    pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
    draw = ImageDraw.Draw(pil_image)

    left_eye = landmarks[36:42]
    right_eye = landmarks[42:48]

    # Draw semi-closed eyes
    for eye in [left_eye, right_eye]:
        eye_top = (eye[1][0], eye[1][1] + (eye[5][1] - eye[1][1])/3)
        eye_bottom = (eye[4][0], eye[4][1] - (eye[4][1] - eye[2][1])/3)
        draw.line([eye_top, eye_bottom], fill=(0,0,0), width=3)

    return cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)

、、、import pyfakewebcam
import numpy as np
import time
from face_utils import FaceProcessor

class VirtualCamera:
def init(self, width=640, height=480):
self.width = width
self.height = height
self.camera = pyfakewebcam.FakeWebcam('/dev/video2', width, height)
self.processor = FaceProcessor()
self.frame_count = 0

def process_frame(self, frame):
    self.frame_count += 1
    faces = self.processor.detect_faces(frame)

    if len(faces) > 0:
        landmarks = self.processor.get_landmarks(frame, faces[0])

        # Every 30 frames, add a blink effect
        if self.frame_count % 30 == 0:
            frame = self.processor.add_blink_effect(frame, landmarks)

    return frame

def stream(self, source=0):
    cap = cv2.VideoCapture(source)

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

        frame = cv2.resize(frame, (self.width, self.height))
        processed_frame = self.process_frame(frame)

        # Convert BGR to RGB
        rgb_frame = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB)
        self.camera.schedule_frame(rgb_frame)

        time.sleep(1/30)

    cap.release()

face_utils import FaceProcessor
from virtual_camera import VirtualCamera
import cv2
import argparse

def main():
parser = argparse.ArgumentParser(description="人脸处理系统")
parser.add_argument('--image', help='处理单张图片')
parser.add_argument('--video', help='处理视频文件')
parser.add_argument('--camera', action='https://developer.aliyun.com/store_true', help='使用摄像头')
parser.add_argument('--virtual', action='https://developer.aliyun.com/store_true', help='启用虚拟摄像头')

args = parser.parse_args()
processor = FaceProcessor()

if args.image:
    image = cv2.imread(args.image)
    faces = processor.detect_faces(image)

    if len(faces) > 0:
        landmarks = processor.get_landmarks(image, faces[0])
        result = processor.add_blink_effect(image, landmarks)
        cv2.imshow("Result", result)
        cv2.waitKey(0)

elif args.video or args.camera:
    source = 0 if args.camera else args.video
    cap = cv2.VideoCapture(source)

    if args.virtual:
        vcam = VirtualCamera()

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

        faces = processor.detect_faces(frame)

        if len(faces) > 0:
            landmarks = processor.get_landmarks(frame, faces[0])
            frame = processor.add_blink_effect(frame, landmarks)

        cv2.imshow("Face Processing", frame)

        if args.virtual:
            vcam.process_frame(frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if name == "main":
main()