广西宏泰成建设集团网站,做门户网站那个系统好,微信小程序saas平台,网站建设网址写在前面 工作中遇到#xff0c;简单整理博文内容涉及基于 OpenCV 和 Dlib头部姿态评估的简单Demo理解不足小伙伴帮忙指正 庐山烟雨浙江潮#xff0c;未到千般恨不消。到得还来别无事#xff0c;庐山烟雨浙江潮。 ----《庐山烟雨浙江潮》苏轼 https://github.com/LIRUILONGS…写在前面 工作中遇到简单整理博文内容涉及基于 OpenCV 和 Dlib头部姿态评估的简单Demo理解不足小伙伴帮忙指正 庐山烟雨浙江潮未到千般恨不消。到得还来别无事庐山烟雨浙江潮。 ----《庐山烟雨浙江潮》苏轼 https://github.com/LIRUILONGS/Head-posture-detection-dlib-opencv-.git
实验项目以上传只需 git 克隆安装需要的 pytohn 包就可以开始使用了但是需要说明的是 Dlib 的基于 HOG特征和SVM分类器的人脸检测器很一般很多脸都检测不到实际情况中可以考虑使用深度学习模型来做关键点检测然后评估姿态。可以查看文章末尾大佬的开源项目
实现效果
Demo原图特征点标记后姿态标记姿态对应的YawPitchRoll 度数
步骤
三个主要步骤
人脸检测
人脸检测引入人脸检测器 dlib.get_frontal_face_detector() 以检测包含人脸的图片多个人脸会选择面积最大的人脸。
dlib.get_frontal_face_detector() 是 dlib 库中的一个函数用于获取一个基于HOG特征和SVM分类器的人脸检测器。该函数返回一个可以用于检测图像中人脸的对象。
具体来说HOGHistogram of Oriented Gradients梯度方向直方图是一种常用于图像识别中的特征描述子SVMSupport Vector Machine支持向量机是一种常用的分类器。将HOG特征与SVM分类器结合起来可以得到一个有效的人脸检测器。
在使用 dlib.get_frontal_face_detector()函数时只需将待检测的图像作为参数传入即可得到一个用于检测人脸的对象。一个Demo
import dlib
import cv2# 读取图像
img cv2.imread(image.jpg)# 获取人脸检测器
detector dlib.get_frontal_face_detector()# 在图像中检测人脸
faces detector(img)# 输出检测到的人脸数
print(检测到的人脸数为, len(faces))
面部特征点检测
面部特征点检测利用预训练模型 shape_predictor_68_face_landmarks.dat 以人脸图像为输入输出68个人脸特征点。
shape_predictor_68_face_landmarks.dat 是基于 dlib 库中的人脸特征点检测模型该模型使用了基于 HOG 特征和 SVM 分类器的人脸检测器来检测图像中的人脸并使用回归算法来预测人脸的 68 个关键点位置。这些关键点包括眼睛、鼻子、嘴巴等部位可以用于进行人脸识别、表情识别、姿态估计等应用。
这个模型文件可以在dlib的官方网站上下载。在使用它之前需要安装dlib库并将模型文件加载到程序中。
predictor dlib.shape_predictor(r.\shape_predictor_68_face_landmarks.dat)姿势估计
姿势估计。在获得 68 个面部特征点后选择部分特征点通过 PnP算法计算姿势 Yaw、Pitch、Roll 度数 (success, rotation_vector, translation_vector) cv2.solvePnP(model_points, image_points, camera_matrix,dist_coeffs, flagscv2.SOLVEPNP_ITERATIVE)Yaw、Pitch、Roll 是用于描述物体或相机在三维空间中的旋转角度的术语常用于姿态估计和姿态控制中。
Yaw(左右)绕垂直于物体或相机的轴旋转的角度也称为偏航角。通常以 z 轴为轴进行旋转正值表示逆时针旋转负值表示顺时针旋转。Pitch(上下)绕物体或相机的横轴旋转的角度也称为俯仰角。通常以 x 轴为轴进行旋转正值表示向上旋转负值表示向下旋转。Roll(弯曲)绕物体或相机的纵轴旋转的角度也称为翻滚角。通常以 y 轴为轴进行旋转正值表示向右旋转负值表示向左旋转。
这三个角度通常以欧拉角的形式表示可以用于描述物体或相机的姿态信息。在计算机视觉中常用于人脸识别、动作捕捉、机器人控制等应用场景。
完整 Demo 代码
#!/usr/bin/env python
# -*- encoding: utf-8 -*-File : face_ypr_demo.py
Time : 2023/06/05 21:32:45
Author : Li Ruilong
Version : 1.0
Contact : liruilongergmail.com
Desc : 根据68个人脸关键点获取人头部姿态评估
# here put the import libimport cv2
import numpy as np
import dlib
import math
import uuid# 头部姿态检测dlibopencvdetector dlib.get_frontal_face_detector()
predictor dlib.shape_predictor(r.\shape_predictor_68_face_landmarks.dat)
POINTS_NUM_LANDMARK 68# shape_predictor_68_face_landmarks.dat 是一个预训练的人脸关键点检测模型可以用于识别人脸的68个关键点如眼睛、鼻子、嘴巴等。这个模型可以被用于人脸识别、人脸表情分析、面部姿势估计等领域。
# 它是由dlib库提供的可以在Python中使用。如果你想使用它可以在dlib的官方网站上下载。# 获取最大的人脸
def _largest_face(dets):Time : 2023/06/05 21:30:37Author : liruilongergmail.comVersion : 1.0Desc : 从一个由 dlib 库检测到的人脸框列表中找到最大的人脸框并返回该框在列表中的索如果只有一个人脸直接返回Args:dets 一个由 dlib.rectangle 类型的对象组成的列表每个对象表示一个人脸框Returns:人脸索引# 如果列表长度为1则直接返回if len(dets) 1:return 0# 计算每个人脸框的面积face_areas [(det.right() - det.left()) * (det.bottom() - det.top()) for det in dets]import heapq# 找到面积最大的人脸框的索引largest_area face_areas[0]largest_index 0for index in range(1, len(dets)):if face_areas[index] largest_area:largest_index indexlargest_area face_areas[index]# 打印最大人脸框的索引和总人脸数print(largest_face index is {} in {} faces.format(largest_index, len(dets)))return largest_indexdef get_image_points_from_landmark_shape(landmark_shape):Time : 2023/06/05 22:30:02Author : liruilongergmail.comVersion : 1.0Desc : 从dlib的检测结果抽取姿态估计需要的点坐标Args:landmark_shape: 所有的位置点Returns:voidif landmark_shape.num_parts ! POINTS_NUM_LANDMARK:print(ERROR:landmark_shape.num_parts-{}.format(landmark_shape.num_parts))return -1, None# 2D image points. If you change the image, you need to change vectorimage_points np.array([(landmark_shape.part(17).x, landmark_shape.part(17).y), # 17 left brow left corner(landmark_shape.part(21).x, landmark_shape.part(21).y), # 21 left brow right corner(landmark_shape.part(22).x, landmark_shape.part(22).y), # 22 right brow left corner(landmark_shape.part(26).x, landmark_shape.part(26).y), # 26 right brow right corner(landmark_shape.part(36).x, landmark_shape.part(36).y), # 36 left eye left corner(landmark_shape.part(39).x, landmark_shape.part(39).y), # 39 left eye right corner(landmark_shape.part(42).x, landmark_shape.part(42).y), # 42 right eye left corner(landmark_shape.part(45).x, landmark_shape.part(45).y), # 45 right eye right corner(landmark_shape.part(31).x, landmark_shape.part(31).y), # 31 nose left corner(landmark_shape.part(35).x, landmark_shape.part(35).y), # 35 nose right corner(landmark_shape.part(48).x, landmark_shape.part(48).y), # 48 mouth left corner(landmark_shape.part(54).x, landmark_shape.part(54).y), # 54 mouth right corner(landmark_shape.part(57).x, landmark_shape.part(57).y), # 57 mouth central bottom corner(landmark_shape.part(8).x, landmark_shape.part(8).y), # 8 chin corner], dtypedouble)return 0, image_pointsdef get_image_points(img):Time : 2023/06/05 22:30:43Author : liruilongergmail.comVersion : 1.0Desc : 用dlib检测关键点返回姿态估计需要的几个点坐标Args:Returns:voidgray cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 图片调整为灰色dets detector(img, 0)if 0 len(dets):print(ERROR: found no face)return -1, Nonelargest_index _largest_face(dets)face_rectangle dets[largest_index]landmark_shape predictor(img, face_rectangle)draw im.copy()cv2.circle(draw, (landmark_shape.part(0).x, landmark_shape.part(0).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(1).x, landmark_shape.part(1).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(2).x, landmark_shape.part(2).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(3).x, landmark_shape.part(3).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(4).x, landmark_shape.part(4).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(5).x, landmark_shape.part(5).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(6).x, landmark_shape.part(6).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(7).x, landmark_shape.part(7).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(8).x, landmark_shape.part(8).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(9).x, landmark_shape.part(9).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(10).x, landmark_shape.part(10).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(11).x, landmark_shape.part(11).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(12).x, landmark_shape.part(12).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(13).x, landmark_shape.part(13).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(14).x, landmark_shape.part(14).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(15).x, landmark_shape.part(15).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(16).x, landmark_shape.part(16).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(17).x, landmark_shape.part(17).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(18).x, landmark_shape.part(18).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(19).x, landmark_shape.part(19).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(20).x, landmark_shape.part(20).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(21).x, landmark_shape.part(21).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(22).x, landmark_shape.part(22).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(23).x, landmark_shape.part(23).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(24).x, landmark_shape.part(24).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(25).x, landmark_shape.part(25).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(26).x, landmark_shape.part(26).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(27).x, landmark_shape.part(27).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(28).x, landmark_shape.part(28).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(29).x, landmark_shape.part(29).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(30).x, landmark_shape.part(30).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(31).x, landmark_shape.part(31).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(32).x, landmark_shape.part(32).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(33).x, landmark_shape.part(33).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(34).x, landmark_shape.part(34).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(35).x, landmark_shape.part(35).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(36).x, landmark_shape.part(36).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(37).x, landmark_shape.part(37).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(38).x, landmark_shape.part(38).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(39).x, landmark_shape.part(39).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(40).x, landmark_shape.part(40).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(41).x, landmark_shape.part(41).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(42).x, landmark_shape.part(42).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(43).x, landmark_shape.part(43).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(44).x, landmark_shape.part(44).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(45).x, landmark_shape.part(45).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(46).x, landmark_shape.part(46).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(47).x, landmark_shape.part(47).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(48).x, landmark_shape.part(48).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(49).x, landmark_shape.part(49).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(50).x, landmark_shape.part(50).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(51).x, landmark_shape.part(51).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(52).x, landmark_shape.part(52).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(53).x, landmark_shape.part(53).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(54).x, landmark_shape.part(54).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(55).x, landmark_shape.part(55).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(56).x, landmark_shape.part(56).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(57).x, landmark_shape.part(57).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(58).x, landmark_shape.part(58).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(59).x, landmark_shape.part(59).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(60).x, landmark_shape.part(60).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(61).x, landmark_shape.part(61).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(62).x, landmark_shape.part(62).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(63).x, landmark_shape.part(63).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(64).x, landmark_shape.part(64).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(65).x, landmark_shape.part(65).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(66).x, landmark_shape.part(66).y), 2, (0, 255, 0), -1)cv2.circle(draw, (landmark_shape.part(67).x, landmark_shape.part(67).y), 2, (0, 255, 0), -1)# 部分关键点特殊标记cv2.circle(draw, (landmark_shape.part(17).x, landmark_shape.part(17).y), 2, (0, 165, 255),-1) # 17 left brow left cornercv2.circle(draw, (landmark_shape.part(21).x, landmark_shape.part(21).y), 2, (0, 165, 255),-1) # 21 left brow right cornercv2.circle(draw, (landmark_shape.part(22).x, landmark_shape.part(22).y), 2, (0, 165, 255),-1) # 22 right brow left cornercv2.circle(draw, (landmark_shape.part(26).x, landmark_shape.part(26).y), 2, (0, 165, 255),-1) # 26 right brow right cornercv2.circle(draw, (landmark_shape.part(36).x, landmark_shape.part(36).y), 2, (0, 165, 255),-1) # 36 left eye left cornercv2.circle(draw, (landmark_shape.part(39).x, landmark_shape.part(39).y), 2, (0, 165, 255),-1) # 39 left eye right cornercv2.circle(draw, (landmark_shape.part(42).x, landmark_shape.part(42).y), 2, (0, 165, 255),-1) # 42 right eye left cornercv2.circle(draw, (landmark_shape.part(45).x, landmark_shape.part(45).y), 2, (0, 165, 255),-1) # 45 right eye right cornercv2.circle(draw, (landmark_shape.part(31).x, landmark_shape.part(31).y), 2, (0, 165, 255),-1) # 31 nose left cornercv2.circle(draw, (landmark_shape.part(35).x, landmark_shape.part(35).y), 2, (0, 165, 255),-1) # 35 nose right cornercv2.circle(draw, (landmark_shape.part(48).x, landmark_shape.part(48).y), 2, (0, 165, 255),-1) # 48 mouth left cornercv2.circle(draw, (landmark_shape.part(54).x, landmark_shape.part(54).y), 2, (0, 165, 255),-1) # 54 mouth right cornercv2.circle(draw, (landmark_shape.part(57).x, landmark_shape.part(57).y), 2, (0, 165, 255),-1) # 57 mouth central bottom cornercv2.circle(draw, (landmark_shape.part(8).x, landmark_shape.part(8).y), 2, (0, 165, 255), -1)# 保存关键点标记后的图片cv2.imwrite(new_ KeyPointDetection.jpg, draw)return get_image_points_from_landmark_shape(landmark_shape)def get_pose_estimation(img_size, image_points):Time : 2023/06/05 22:31:31Author : liruilongergmail.comVersion : 1.0Desc : 获取旋转向量和平移向量Args:Returns:void# 3D model points.model_points np.array([(6.825897, 6.760612, 4.402142), # 33 left brow left corner(1.330353, 7.122144, 6.903745), # 29 left brow right corner(-1.330353, 7.122144, 6.903745), # 34 right brow left corner(-6.825897, 6.760612, 4.402142), # 38 right brow right corner(5.311432, 5.485328, 3.987654), # 13 left eye left corner(1.789930, 5.393625, 4.413414), # 17 left eye right corner(-1.789930, 5.393625, 4.413414), # 25 right eye left corner(-5.311432, 5.485328, 3.987654), # 21 right eye right corner(2.005628, 1.409845, 6.165652), # 55 nose left corner(-2.005628, 1.409845, 6.165652), # 49 nose right corner(2.774015, -2.080775, 5.048531), # 43 mouth left corner(-2.774015, -2.080775, 5.048531), # 39 mouth right corner(0.000000, -3.116408, 6.097667), # 45 mouth central bottom corner(0.000000, -7.415691, 4.070434) # 6 chin corner])# Camera internalsfocal_length img_size[1]center (img_size[1] / 2, img_size[0] / 2)camera_matrix np.array([[focal_length, 0, center[0]],[0, focal_length, center[1]],[0, 0, 1]], dtypedouble)dist_coeffs np.array([7.0834633684407095e-002, 6.9140193737175351e-002, 0.0, 0.0, -1.3073460323689292e000],dtypedouble) # Assuming no lens distortion(success, rotation_vector, translation_vector) cv2.solvePnP(model_points, image_points, camera_matrix,dist_coeffs, flagscv2.SOLVEPNP_ITERATIVE)# print(Rotation Vector:\n {}.format(rotation_vector))# print(Translation Vector:\n {}.format(translation_vector))return success, rotation_vector, translation_vector, camera_matrix, dist_coeffsdef draw_annotation_box(image, rotation_vector, translation_vector, camera_matrix, dist_coeefs, color(0, 255, 0),line_width2):Time : 2023/06/05 22:09:14Author : liruilongergmail.comVersion : 1.0Desc : 标记一个人脸朝向的3D框Args:Returns:voidDraw a 3D box as annotation of posepoint_3d []rear_size 10rear_depth 0point_3d.append((-rear_size, -rear_size, rear_depth))point_3d.append((-rear_size, rear_size, rear_depth))point_3d.append((rear_size, rear_size, rear_depth))point_3d.append((rear_size, -rear_size, rear_depth))point_3d.append((-rear_size, -rear_size, rear_depth))front_size 10# 高度front_depth 10point_3d.append((-front_size, -front_size, front_depth))point_3d.append((-front_size, front_size, front_depth))point_3d.append((front_size, front_size, front_depth))point_3d.append((front_size, -front_size, front_depth))point_3d.append((-front_size, -front_size, front_depth))point_3d np.array(point_3d, dtypenp.float32).reshape(-1, 3)# Map to 2d image points(point_2d, _) cv2.projectPoints(point_3d,rotation_vector,translation_vector,camera_matrix,dist_coeefs)point_2d np.int32(point_2d.reshape(-1, 2))# Draw all the linescv2.polylines(image, [point_2d], True, color, line_width, cv2.LINE_AA)cv2.line(image, tuple(point_2d[1]), tuple(point_2d[6]), color, line_width, cv2.LINE_AA)cv2.line(image, tuple(point_2d[2]), tuple(point_2d[7]), color, line_width, cv2.LINE_AA)cv2.line(image, tuple(point_2d[3]), tuple(point_2d[8]), color, line_width, cv2.LINE_AA)# 从旋转向量转换为欧拉角
def get_euler_angle(rotation_vector):Time : 2023/06/05 22:31:52Author : liruilongergmail.comVersion : 1.0Desc : 从旋转向量转换为欧拉角Args:Returns:void# calculate rotation anglestheta cv2.norm(rotation_vector, cv2.NORM_L2)# transformed to quaterniondw math.cos(theta / 2)x math.sin(theta / 2) * rotation_vector[0][0] / thetay math.sin(theta / 2) * rotation_vector[1][0] / thetaz math.sin(theta / 2) * rotation_vector[2][0] / thetaysqr y * y# pitch (x-axis rotation)t0 2.0 * (w * x y * z)t1 1.0 - 2.0 * (x * x ysqr)# print(t0:{}, t1:{}.format(t0, t1))pitch math.atan2(t0, t1)# yaw (y-axis rotation)t2 2.0 * (w * y - z * x)if t2 1.0:t2 1.0if t2 -1.0:t2 -1.0yaw math.asin(t2)# roll (z-axis rotation)t3 2.0 * (w * z x * y)t4 1.0 - 2.0 * (ysqr z * z)roll math.atan2(t3, t4)print(pitch:{}, yaw:{}, roll:{}.format(pitch, yaw, roll))# 单位转换将弧度转换为度pitch_degree int((pitch / math.pi) * 180)yaw_degree int((yaw / math.pi) * 180)roll_degree int((roll / math.pi) * 180)return 0, pitch, yaw, roll, pitch_degree, yaw_degree, roll_degreedef get_pose_estimation_in_euler_angle(landmark_shape, im_szie):try:ret, image_points get_image_points_from_landmark_shape(landmark_shape)if ret ! 0:print(get_image_points failed)return -1, None, None, Noneret, rotation_vector, translation_vector, camera_matrix, dist_coeffs get_pose_estimation(im_szie,image_points)if ret ! True:print(get_pose_estimation failed)return -1, None, None, Noneret, pitch, yaw, roll get_euler_angle(rotation_vector)if ret ! 0:print(get_euler_angle failed)return -1, None, None, Noneeuler_angle_str Pitch:{}, Yaw:{}, Roll:{}.format(pitch, yaw, roll)print(euler_angle_str)return 0, pitch, yaw, rollexcept Exception as e:print(get_pose_estimation_in_euler_angle exception:{}.format(e))return -1, None, None, Nonedef build_img_text_marge(img_, text, height):Time : 2023/06/01 05:29:09Author : liruilongergmail.comVersion : 1.0Desc : 生成文字图片拼接到 img 对象Args:Returns:voidimport cv2from PIL import Image, ImageDraw, ImageFont# 定义图片大小和背景颜色width img_.shape[1]background_color (255, 255, 255)# 定义字体、字号和颜色font_path arial.ttffont_size 26font_color (0, 0, 0)# 创建空白图片image Image.new(RGB, (width, height), background_color)# 创建画笔draw ImageDraw.Draw(image)# 加载字体font ImageFont.truetype(font_path, font_size)# 写入文字text_width, text_height draw.textsize(text, font)text_x (width - text_width) // 2text_y (height - text_height) // 2draw.text((text_x, text_y), text, fontfont, fillfont_color)# 将Pillow图片转换为OpenCV图片image_cv cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)montage_size (width, img_.shape[0])import imutilsmontages imutils.build_montages([img_, image_cv], montage_size, (1, 2))# 保存图片return montages[0]if __name__ __main__:from imutils import paths# for imagePath in paths.list_images(W:\\python_code\\deepface\\huge_1.jpg):for imagePath in range(1):print(f处理的图片路径为 {imagePath})# Read Imageim cv2.imread(image.jpg)size im.shape# 对图像进行缩放的操作if size[0] 700:h size[0] / 3w size[1] / 3# 如果图像的高度大于700就将其高度和宽度分别缩小为原来的1/3然后使用双三次插值的方法进行缩放。最后返回缩放后的图像的大小。im cv2.resize(im, (int(w), int(h)), interpolationcv2.INTER_CUBIC)size im.shape# 获取坐标点 ret, image_points get_image_points(im)if ret ! 0:print(get_image_points failed)continueret, rotation_vector, translation_vector, camera_matrix, dist_coeffs get_pose_estimation(size, image_points)if ret ! True:print(get_pose_estimation failed)continuedraw_annotation_box(im, rotation_vector, translation_vector, camera_matrix, dist_coeffs)cv2.imwrite(new_ draw_annotation_box.jpg, im)ret, pitch, yaw, roll, pitch_degree, yaw_degree, roll_degree get_euler_angle(rotation_vector)draw im.copy()# Yaw:if yaw_degree 0:output_yaw left : str(abs(yaw_degree)) degreeselif yaw_degree 0:output_yaw right : str(abs(yaw_degree)) degreeselse:output_yaw No left or rightprint(output_yaw)# Pitch:if pitch_degree 0:output_pitch dow : str(abs(pitch_degree)) degreeselif pitch_degree 0:output_pitch up : str(abs(pitch_degree)) degreeselse:output_pitch No downwards or upwardsprint(output_pitch)# Roll:if roll_degree 0:output_roll bends to the right: str(abs(roll_degree)) degreeselif roll_degree 0:output_roll bends to the left: str(abs(roll_degree)) degreeselse:output_roll No bend right or left.print(output_roll)# Initial status:if abs(yaw) 0.00001 and abs(pitch) 0.00001 and abs(roll) 0.00001:cv2.putText(draw, Initial ststus, (20, 40), cv2.FONT_HERSHEY_SIMPLEX, .5, (0, 255, 0))print(Initial ststus)# 姿态检测完的数据写在对应的照片imgss build_img_text_marge(im, output_yaw \n output_pitch \n output_roll, 200)cv2.imwrite(new_ str(uuid.uuid4()).replace(-, ) .jpg, imgss)
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© 文中涉及参考链接内容版权归原作者所有如有侵权请告知这是一个开源项目如果你认可它不要吝啬星星哦 https://blog.csdn.net/zhang2gongzi/article/details/124520896
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