基于PaddleHub的AI人脸侦测：不再用手打灰机（附代码）

项目实现：

用摄像头做人脸识别

判定头部角度，以此来进行游戏控制

所有代码和相关文件可在github中自取：

github: planegame_head_control

效果展示请见B站：

</p> <p>基于PaddleHub的AI人脸侦测：不再用手打灰机（附代码）</p> <p>

一、项目背景

随着AI技术的发展，越来越多的人脸技术被应用到了我们生活中的方方面面，刷脸支付、刷脸闸机通行、酒店人脸比对等场景都是人脸技术的应用，对人们的生活已经产生了巨大的影响。

而在这些技术当中，人脸关键点检测是最重要的基石之一，它是诸如自动人脸识别、表情分析、三维人脸重建及三维动画等其它人脸相关问题的前提和突破口。

PaddleHub 近期发布了人脸关键点检测模型face_landmark_localization，地址：

PaddleHub: face_landmark_localization

该模型转换自 face-landmark ，支持同一张图中的多个人脸检测。它可以识别人脸中的68个关键点，地址：

github: face-landmark

二、依赖环境

pip install paddlehub
pip install pygame
pip install opencv-python

三、关键程序

头部运动检测部分程序

import cv2
import numpy as np
import paddlehub as hub
from paddlehub.common.logger import logger
import time
import math
import osclass HeadPostEstimation(object):"""头部姿态识别"""NOD_ACTION = 1SHAKE_ACTION = 2def __init__(self, face_detector=None):self.module = hub.Module(name="face_landmark_localization", face_detector_module=face_detector)# 头部3D关键点坐标self.model_points = np.array([[6.825897, 6.760612, 4.402142],[1.330353, 7.122144, 6.903745],[-1.330353, 7.122144, 6.903745],[-6.825897, 6.760612, 4.402142],[5.311432, 5.485328, 3.987654],[1.789930, 5.393625, 4.413414],[-1.789930, 5.393625, 4.413414],[-5.311432, 5.485328, 3.987654],[2.005628, 1.409845, 6.165652],[-2.005628, 1.409845, 6.165652],[2.774015, -2.080775, 5.048531],[-2.774015, -2.080775, 5.048531],[0.000000, -3.116408, 6.097667],[0.000000, -7.415691, 4.070434],[-7.308957, 0.913869, 0.000000],[7.308957, 0.913869, 0.000000],[0.746313,0.348381,6.263227],[0.000000,0.000000,6.763430],[-0.746313,0.348381,6.263227],], dtype='float')# 点头动作index是0， 摇头动作index是1# 当连续30帧上下点头动作幅度超过15度时，认为发生了点头动作# 当连续30帧上下点头动作幅度超过45度时，认为发生了摇头动作，由于摇头动作较为敏感，故所需幅度更大self._index_action = {0:'nod', 1:'shake'}self._frame_window_size = 15self._pose_threshold = {0: 15/180 * math.pi,1: 45/180 * math.pi}# 头部3D投影点self.reprojectsrc = np.float32([[10.0, 10.0, 10.0],[10.0, 10.0, -10.0], [10.0, -10.0, -10.0],[10.0, -10.0, 10.0], [-10.0, 10.0, 10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, -10.0],[-10.0, -10.0, 10.0]])# 头部3D投影点连线self.line_pairs = [[0, 1], [1, 2], [2, 3], [3, 0],[4, 5], [5, 6], [6, 7], [7, 4],[0, 4], [1, 5], [2, 6], [3, 7]]@propertydef frame_window_size(self):return self._frame_window_size@frame_window_size.setterdef frame_window_size(self, value):assert isinstance(value, int)self._frame_window_size = value@propertydef pose_threshold(self):return self._pose_threshold@pose_threshold.setterdef pose_threshold(self, dict_value):assert list(dict_value.keys()) == [0,1,2]self._pose_threshold = dict_valuedef get_face_landmark(self, image):"""预测人脸的68个关键点坐标images(ndarray): 单张图片的像素数据"""try:# 选择GPU运行，use_gpu=True，并且在运行整个教程代码之前设置CUDA_VISIBLE_DEVICES环境变量res = self.module.keypoint_detection(images=[image], use_gpu=False)return True, res[0]['data'][0]except Exception as e:logger.error("Get face landmark localization failed! Exception: %s " % e)return False, Nonedef get_image_points_from_landmark(self, face_landmark):"""从face_landmark_localization的检测结果抽取姿态估计需要的点坐标"""image_points = np.array([face_landmark[17], face_landmark[21], face_landmark[22], face_landmark[26], face_landmark[36], face_landmark[39], face_landmark[42], face_landmark[45], face_landmark[31], face_landmark[35],face_landmark[48], face_landmark[54],face_landmark[57], face_landmark[8],face_landmark[14], face_landmark[2], face_landmark[32], face_landmark[33],face_landmark[34], ], dtype='float')return image_pointsdef get_lips_distance(self,face_landmark):"""从face_landmark_localization的检测结果中查看上下嘴唇的距离"""lips_points = np.array([face_landmark[52], face_landmark[58]], dtype='float')head_points = np.array([face_landmark[25], face_landmark[8]], dtype='float')lips_distance = np.sum(np.square(lips_points[0] - lips_points[1]))head_distance = np.sum(np.square(head_points[0] - head_points[1]))relative_distance = lips_distance / head_distancereturn relative_distancedef caculate_pose_vector(self, image_points):"""获取旋转向量和平移向量"""# 相机视角center = (self.img_size[1]/2, self.img_size[0]/2) # 目前相机视角的中心点，即画面的长/2，宽/2focal_length = center[0] / np.tan(60/ 2 * np.pi / 180)camera_matrix = np.array([[focal_length, 0, center[0]],[0, focal_length, center[1]],[0, 0, 1]],dtype = "float")# 假设没有畸变dist_coeffs = np.zeros((4,1))success, rotation_vector, translation_vector= cv2.solvePnP(self.model_points, image_points,camera_matrix, dist_coeffs)reprojectdst, _ = cv2.projectPoints(self.reprojectsrc, rotation_vector, translation_vector, camera_matrix, dist_coeffs)return success, rotation_vector, translation_vector, camera_matrix, dist_coeffs, reprojectdstdef caculate_euler_angle(self, rotation_vector, translation_vector):"""将旋转向量转换为欧拉角"""rvec_matrix = cv2.Rodrigues(rotation_vector)[0]proj_matrix = np.hstack((rvec_matrix, translation_vector))euler_angles = cv2.decomposeProjectionMatrix(proj_matrix)[6]pitch, yaw, roll = [math.radians(_) for _ in euler_angles]return pitch, yaw, rolldef classify_pose_in_euler_angles(self, video, poses=3):"""根据欧拉角分类头部姿态(点头nod/摇头shake)video 表示不断产生图片的生成器pose=1 表示识别点头动作pose=2 表示识别摇头动作pose=3 表示识别点头和摇头动作"""frames_euler = []self.nod_time = self.totate_time = self.shake_time = time.time()self.action_time = 0index_action ={0:[self.NOD_ACTION], 1:[self.SHAKE_ACTION]}for index, img in enumerate(video(), start=1):self.img_size = img.shapesuccess, face_landmark = self.get_face_landmark(img)for i, action in enumerate(index_action):if i == 0:index_action[action].append((20, int(self.img_size[0]/2 + 110)))elif i == 1:index_action[action].append((120, int(self.img_size[0]/2 + 110)))if not success:logger.info("Get face landmark localization failed! Please check your image!")continueimage_points = self.get_image_points_from_landmark(face_landmark)success, rotation_vector, translation_vector, camera_matrix, dist_coeffs, reprojectdst = self.caculate_pose_vector(image_points)if not success:logger.info("Get rotation and translation vectors failed!")continue# 计算嘴唇距离，如果张嘴，显示"open"distance = self.get_lips_distance(face_landmark)if distance > 0.045:cv2.putText(img, "open", (20, int(self.img_size[0] / 2 + 90)),cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)# 画出投影正方体alpha=0.3if not hasattr(self, 'before'):self.before = reprojectdstelse:reprojectdst = alpha * self.before + (1-alpha)* reprojectdstreprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))for start, end in self.line_pairs:cv2.line(img, reprojectdst[start], reprojectdst[end], (0, 0, 255))# 计算头部欧拉角pitch, yaw, roll = self.caculate_euler_angle(rotation_vector, translation_vector)cv2.putText(img, "pitch: " + "{:7.2f}".format(pitch), (20, int(self.img_size[0]/2 -10)), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)cv2.putText(img, "yaw: " + "{:7.2f}".format(yaw), (20, int(self.img_size[0]/2 + 30) ), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)cv2.putText(img, "roll: " + "{:7.2f}".format(roll), (20, int(self.img_size[0]/2 +70)), cv2.FONT_HERSHEY_SIMPLEX,0.75, (0, 0, 255), thickness=2)for index, action in enumerate(index_action):cv2.putText(img, "{}".format(self._index_action[action]), index_action[action][1], cv2.FONT_HERSHEY_SIMPLEX, 0.75, (50, 50, 50), thickness=2)frames_euler.append([index, img, pitch, yaw, roll])# 转换成摄像头可显示的格式img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)final_action = Noneif len(frames_euler) > self.frame_window_size:# 比较当前头部动作欧拉角与过去的欧拉角，只有动作幅度幅度超过阈值，则判定发生相应的动作# picth值用来判断点头动作# yaw值用来判断摇头动作current = [pitch, yaw, roll]tmp = [abs(pitch), abs(yaw)]max_index = tmp.index(max(tmp))max_probability_action = index_action[max_index][0]for start_idx, start_img, p, y, r in frames_euler[0:int(self.frame_window_size/2)]:start = [p, y, r]if poses & max_probability_action and abs(start[max_index]-current[max_index]) >= self.pose_threshold[max_index]:frames_euler = []final_action = max_indexself.action_time = time.time()yield {self._index_action[max_index]: [(start_idx, start_img), (index, img)]}breakelse:# 丢弃过时的视频帧frames_euler.pop(0)# 动作判定发生则高亮显示0.5sif self.action_time !=0  and time.time() - self.action_time < 0.5:cv2.putText(img_rgb, "{}".format(self._index_action[max_index]), index_action[max_index][1], cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), thickness=2)# 本地显示预测视频框，AIStudio项目不支持显示视频框cv2.imshow('Pose Estimation', img_rgb)# 写入预测结果video_writer.write(img_rgb)class MyFaceDetector(object):"""自定义人脸检测器基于PaddleHub人脸检测模型ultra_light_fast_generic_face_detector_1mb_640，加强稳定人脸检测框"""def __init__(self):self.module = hub.Module(name="ultra_light_fast_generic_face_detector_1mb_640")self.alpha = 0.75self.start_flag =1def face_detection(self,images, use_gpu=False, visualization=False):# 使用GPU运行，use_gpu=True，并且在运行整个教程代码之前设置CUDA_VISIBLE_DEVICES环境变量result = self.module.face_detection(images=images, use_gpu=use_gpu, visualization=visualization)if not result[0]['data']:return resultface = result[0]['data'][0]if self.start_flag == 1:self.left_s = result[0]['data'][0]['left']self.right_s = result[0]['data'][0]['right']self.top_s = result[0]['data'][0]['top']self.bottom_s = result[0]['data'][0]['bottom']self.start_flag=0else:# 加权平均上一帧和当前帧人脸检测框位置，以稳定人脸检测框self.left_s = self.alpha * self.left_s +  (1-self.alpha) * face['left'] self.right_s = self.alpha * self.right_s +  (1-self.alpha) * face['right'] self.top_s = self.alpha * self.top_s +  (1-self.alpha) * face['top']self.bottom_s = self.alpha * self.bottom_s + (1-self.alpha) * face['bottom'] result[0]['data'][0]['left'] = self.left_sresult[0]['data'][0]['right'] = self.right_sresult[0]['data'][0]['top'] = self.top_sresult[0]['data'][0]['bottom'] = self.bottom_sreturn result# 定义人脸检测器
face_detector = MyFaceDetector()# 打开摄像头
capture  = cv2.VideoCapture(0)
# capture  = cv2.VideoCapture('./test_sample.mov')
fps = capture.get(cv2.CAP_PROP_FPS)
size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)),int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
# 将预测结果写成视频
video_writer = cv2.VideoWriter('result_enhancement.mp4', cv2.VideoWriter_fourcc(*'mp4v'), fps, size)def generate_image():while True:# frame_rgb即视频的一帧数据ret, frame_rgb = capture.read() # 按q键即可退出if cv2.waitKey(1) & 0xFF == ord('q'):breakif frame_rgb is None:breakframe_bgr = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2BGR)yield frame_bgrcapture.release()video_writer.release()cv2.destroyAllWindows()head_post = HeadPostEstimation(face_detector)
for res in head_post.classify_pose_in_euler_angles(video=generate_image, poses=HeadPostEstimation.NOD_ACTION | HeadPostEstimation.SHAKE_ACTION):print(list(res.keys()))

四、控制方式

直接运行 main.py 即可

头部左转：飞机往左移动

头部右转：飞机往右移动

抬头：飞机前移动

低头：飞机向后移动

张嘴：丢炸弹！

五、说明

飞机的速度，子弹的速度都可以在参数中调节

为了演示方便，把之前游戏中的速度都上调了

另外，由于摄像头的镜像关系，头部左转和右转会与左右控制相反，希望调节为反过来的可以在参数里对调一下