Openpose驾驶员危险驾驶检测（抽烟打电话）

其他 2020-02-07 14:30:18 阅读次数: 0

目录

序

目的：最近在做基于图像的驾驶员疲劳检测系统项目，其中行为检测包含驾驶员打电话和抽烟的危险行为，基于图像用之前的Dlib68特征点和opencv肤色和动态追踪图像处理已经远远达不到理想标准，我们需要获取脸部和手部关键点信息；为了精确识别打电话和抽烟或者是喝水等动作，这里引入了机器视觉中一经典开源模型——Openpose（人体姿态识别模型）。
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Openpose简介

参考：Github开源人体姿态识别项目OpenPose中文文档
在这里插入图片描述
OpenPose优点：将人体、人手、人脸的landmarks三元归一

正文

（1）配置Openpose环境

Windows10 + python3.7 + anaconda3 + jupyter5.6.0
可以按步骤配置，也可以直接下载训练好的模型

  1. 下载开源项目：https://github.com/CMU-Perceptual-Computing-Lab/openpose
    在这里插入图片描述

  2. 解压，进入..\openpose-master\models目录，双击运行getModels.bat
    在这里插入图片描述
    等待加载模型pose_iter_584000.caffemodel：
    在这里插入图片描述

    等待时间较长，如中途中断，重新双击运行getModels.bat即可，
    完整加载模型大小：100M，
    保存位置：..\openpose-master\models\pose\body_25\pose_iter_584000.caffemodel
    在这里插入图片描述

跑了一天完全加载5个模型如图，openpose总大小800M,编译真的太心寒了，这里快速下载入口。
OpenPose 人体姿态模型下载路径：

BODY25:http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/body_25/pose_iter_584000.caffemodel
COCO: http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel
MPI: http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel

（2）主要模型介绍

  1. 目录
    openpose分别检测：人脸、人手、人姿态（又分为三种数据集，mpi为较小的数据集）
    在这里插入图片描述
  2. 一个简单地demo：Windows10 + python3.7 + anaconda3 + jupyter5.6.0
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import os


# Load a Caffe Model

if not os.path.isdir('model'):
    os.mkdir("model")    

protoFile = "D:/myworkspace/JupyterNotebook/openpose/openpose-master/models/pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt"
weightsFile = "D:/myworkspace/JupyterNotebook/openpose/openpose-master/models/pose/mpi/pose_iter_160000.caffemodel"


# Specify number of points in the model 
nPoints = 15
POSE_PAIRS = [[0,1], [1,2], [2,3], [3,4], [1,5], [5,6], [6,7], [1,14], [14,8], [8,9], [9,10], [14,11], [11,12], [12,13] ]
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)


# Read Image
im = cv2.imread("D:/myworkspace/JupyterNotebook/openpose/tf-pose-estimation-master/images/apink1.jpg")
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
inWidth = im.shape[1]
inHeight = im.shape[0]


# Convert image to blob
netInputSize = (368, 368)
inpBlob = cv2.dnn.blobFromImage(im, 1.0 / 255, netInputSize, (0, 0, 0), swapRB=True, crop=False)
net.setInput(inpBlob)


# Run Inference (forward pass)
output = net.forward()

# Display probability maps
plt.figure(figsize=(20,10))
plt.title('Probability Maps of Keypoints')
for i in range(nPoints):
    probMap = output[0, i, :, :]
    displayMap = cv2.resize(probMap, (inWidth, inHeight), cv2.INTER_LINEAR)
    plt.subplot(3, 5, i+1); plt.axis('off'); plt.imshow(displayMap, cmap='jet')


# Extract points

# X and Y Scale
scaleX = float(inWidth) / output.shape[3]
scaleY = float(inHeight) / output.shape[2]

# Empty list to store the detected keypoints
points = []

# Confidence treshold 
threshold = 0.1

for i in range(nPoints):
    # Obtain probability map
    probMap = output[0, i, :, :]
    
    # Find global maxima of the probMap.
    minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
    
    # Scale the point to fit on the original image
    x = scaleX * point[0]
    y = scaleY * point[1]

    if prob > threshold : 
        # Add the point to the list if the probability is greater than the threshold
        points.append((int(x), int(y)))
    else :
        points.append(None)


# Display Points & Skeleton

imPoints = im.copy()
imSkeleton = im.copy()
# Draw points
for i, p in enumerate(points):
    cv2.circle(imPoints, p, 8, (255, 255,0), thickness=-1, lineType=cv2.FILLED)
    cv2.putText(imPoints, "{}".format(i), p, cv2.FONT_HERSHEY_SIMPLEX, 1, (255,0,0), 2, lineType=cv2.LINE_AA)

# Draw skeleton
for pair in POSE_PAIRS:
    partA = pair[0]
    partB = pair[1]

    if points[partA] and points[partB]:
        cv2.line(imSkeleton, points[partA], points[partB], (255, 255,0), 2)
        cv2.circle(imSkeleton, points[partA], 8, (255, 0, 0), thickness=-1, lineType=cv2.FILLED)

plt.figure(figsize=(20,10))
plt.subplot(121); plt.axis('off'); plt.imshow(imPoints);
#plt.title('Displaying Points')
plt.subplot(122); plt.axis('off'); plt.imshow(imSkeleton);
#plt.title('Displaying Skeleton')
plt.show()
  1. 运行效果：
    标出了15个人体姿态特征点
    在这里插入图片描述在这里插入图片描述
    mpi15个关键点参考说明：
    在这里插入图片描述

（3）主要模型参考图

	#   Body25: 25 points
    #   COCO:   18 points
    #   MPI:    15 points

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左：BODY_25， 右：COCO
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Face
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Hand
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（4）人体姿态各模型检测

#!/usr/bin/python3
#!--*-- coding: utf-8 --*--
from __future__ import division
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import os


class general_pose_model(object):
    def __init__(self, modelpath, mode="BODY25"):
        # 指定采用的模型
        #   Body25: 25 points
        #   COCO:   18 points
        #   MPI:    15 points
        self.inWidth = 368
        self.inHeight = 368
        self.threshold = 0.1
        if mode == "BODY25":
            self.pose_net = self.general_body25_model(modelpath)
        elif mode == "COCO":
            self.pose_net = self.general_coco_model(modelpath)
        elif mode == "MPI":
            self.pose_net = self.get_mpi_model(modelpath)


    def get_mpi_model(self, modelpath):
        self.points_name = { 
            "Head": 0, "Neck": 1, 
            "RShoulder": 2, "RElbow": 3, "RWrist": 4,
            "LShoulder": 5, "LElbow": 6, "LWrist": 
            7, "RHip": 8, "RKnee": 9, "RAnkle": 10, 
            "LHip": 11, "LKnee": 12, "LAnkle": 13, 
            "Chest": 14, "Background": 15 }
        self.num_points = 15
        self.point_pairs = [[0, 1], [1, 2], [2, 3], 
                            [3, 4], [1, 5], [5, 6], 
                            [6, 7], [1, 14],[14, 8], 
                            [8, 9], [9, 10], [14, 11], 
                            [11, 12], [12, 13]
                            ]
        prototxt = os.path.join(
            modelpath,
            "pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt")
        caffemodel = os.path.join(
            modelpath, 
            "pose/mpi/pose_iter_160000.caffemodel")
        mpi_model = cv2.dnn.readNetFromCaffe(prototxt, caffemodel)

        return mpi_model


    def general_coco_model(self, modelpath):
        self.points_name = {
            "Nose": 0, "Neck": 1, 
            "RShoulder": 2, "RElbow": 3, "RWrist": 4,
            "LShoulder": 5, "LElbow": 6, "LWrist": 7, 
            "RHip": 8, "RKnee": 9, "RAnkle": 10, 
            "LHip": 11, "LKnee": 12, "LAnkle": 13, 
            "REye": 14, "LEye": 15, 
            "REar": 16, "LEar": 17, 
            "Background": 18}
        self.num_points = 18
        self.point_pairs = [[1, 0], [1, 2], [1, 5], 
                            [2, 3], [3, 4], [5, 6], 
                            [6, 7], [1, 8], [8, 9],
                            [9, 10], [1, 11], [11, 12], 
                            [12, 13], [0, 14], [0, 15], 
                            [14, 16], [15, 17]]
        prototxt   = os.path.join(
            modelpath, 
            "pose/coco/pose_deploy_linevec.prototxt")
        caffemodel = os.path.join(
            modelpath, 
            "pose/coco/pose_iter_440000.caffemodel")
        coco_model = cv2.dnn.readNetFromCaffe(prototxt, caffemodel)

        return coco_model


    def general_body25_model(self, modelpath):
        self.num_points = 25
        self.point_pairs = [[1, 0], [1, 2], [1, 5], 
                            [2, 3], [3, 4], [5, 6], 
                            [6, 7], [0, 15], [15, 17], 
                            [0, 16], [16, 18], [1, 8],
                            [8, 9], [9, 10], [10, 11], 
                            [11, 22], [22, 23], [11, 24],
                            [8, 12], [12, 13], [13, 14], 
                            [14, 19], [19, 20], [14, 21]]
        prototxt   = os.path.join(
            modelpath, 
            "pose/body_25/pose_deploy.prototxt")
        caffemodel = os.path.join(
            modelpath, 
            "pose/body_25/pose_iter_584000.caffemodel")
        coco_model = cv2.dnn.readNetFromCaffe(prototxt, caffemodel)

        return coco_model


    def predict(self, imgfile):
        img_cv2 = cv2.imread(imgfile)
        img_height, img_width, _ = img_cv2.shape
        inpBlob = cv2.dnn.blobFromImage(img_cv2, 
                                        1.0 / 255, 
                                        (self.inWidth, self.inHeight),
                                        (0, 0, 0), 
                                        swapRB=False, 
                                        crop=False)
        self.pose_net.setInput(inpBlob)
        self.pose_net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
        self.pose_net.setPreferableTarget(cv2.dnn.DNN_TARGET_OPENCL)

        output = self.pose_net.forward()

        H = output.shape[2]
        W = output.shape[3]
        print(output.shape)

        # vis heatmaps
        self.vis_heatmaps(img_file, output)

        #
        points = []
        for idx in range(self.num_points):
            probMap = output[0, idx, :, :] # confidence map.

            # Find global maxima of the probMap.
            minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)

            # Scale the point to fit on the original image
            x = (img_width * point[0]) / W
            y = (img_height * point[1]) / H

            if prob > self.threshold:
                points.append((int(x), int(y)))
            else:
                points.append(None)

        return points


    def vis_heatmaps(self, imgfile, net_outputs):
        img_cv2 = cv2.imread(imgfile)
        plt.figure(figsize=[10, 10])

        for pdx in range(self.num_points):
            probMap = net_outputs[0, pdx, :, :]
            probMap = cv2.resize(
                probMap, 
                (img_cv2.shape[1], img_cv2.shape[0])
            )
            plt.subplot(5, 5, pdx+1)
            plt.imshow(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
            plt.imshow(probMap, alpha=0.6)
            plt.colorbar()
            plt.axis("off")
        plt.show()


    def vis_pose(self, imgfile, points):
        img_cv2 = cv2.imread(imgfile)
        img_cv2_copy = np.copy(img_cv2)
        for idx in range(len(points)):
            if points[idx]:
                cv2.circle(img_cv2_copy, 
                           points[idx], 
                           8, 
                           (0, 255, 255), 
                           thickness=-1,
                           lineType=cv2.FILLED)
                cv2.putText(img_cv2_copy, 
                            "{}".format(idx), 
                            points[idx], 
                            cv2.FONT_HERSHEY_SIMPLEX,
                            1, 
                            (0, 0, 255), 
                            2, 
                            lineType=cv2.LINE_AA)

        # Draw Skeleton
        for pair in self.point_pairs:
            partA = pair[0]
            partB = pair[1]

            if points[partA] and points[partB]:
                cv2.line(img_cv2, 
                         points[partA], 
                         points[partB], 
                         (0, 255, 255), 3)
                cv2.circle(img_cv2, 
                           points[partA], 
                           8, 
                           (0, 0, 255), 
                           thickness=-1, 
                           lineType=cv2.FILLED)

        plt.figure(figsize=[10, 10])
        plt.subplot(1, 2, 1)
        plt.imshow(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
        plt.axis("off")
        plt.subplot(1, 2, 2)
        plt.imshow(cv2.cvtColor(img_cv2_copy, cv2.COLOR_BGR2RGB))
        plt.axis("off")
        plt.show()


if __name__ == '__main__':
    print("[INFO]Pose estimation.")

    img_file = "D:/myworkspace/JupyterNotebook/openpose/tf-pose-estimation-master/images/apink1.jpg"
    #
    start = time.time()
    modelpath = "D:/myworkspace/JupyterNotebook/openpose/openpose-master/models/"
    # pose_model = general_pose_model(modelpath, mode="BODY25")# 可以三选一
    # pose_model = general_pose_model(modelpath, mode="COCO")
    pose_model = general_pose_model(modelpath, mode="MPI")
    print("[INFO]Model loads time: ", time.time() - start)

    start = time.time()
    res_points = pose_model.predict(img_file)
    print("[INFO]Model predicts time: ", time.time() - start)
    pose_model.vis_pose(img_file, res_points)

在这里插入图片描述
由图示我们可以发现，

  • 接电话 可以计算右手到右耳的距离（左手倒左耳的距离）来进行判断，即点4到点16的距离（点7到点17的距离）。

  • 抽烟 可以用双手到鼻的距离来进行判断，即点4,7到点0的距离。
    在这里插入图片描述

多人姿态估计参考：https://www.aiuai.cn/aifarm946.html

（5）手部检测

模型快速下载：
/models/hand/pose_deploy.prototxt
/models/hand/pose_iter_102000.caffemodel

# 参考：https://blog.csdn.net/zziahgf/article/details/90706693
# 作者：AIHGF
#!/usr/bin/python3
#!--*-- coding: utf-8 --*--
from __future__ import division
import os
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt


class general_pose_model(object):
    def __init__(self, modelpath):
        self.num_points = 22
        self.point_pairs = [[0,1],[1,2],[2,3],[3,4],
                            [0,5],[5,6],[6,7],[7,8],
                            [0,9],[9,10],[10,11],[11,12],
                            [0,13],[13,14],[14,15],[15,16],
                            [0,17],[17,18],[18,19],[19,20]]
        # self.inWidth = 368
        self.inHeight = 368
        self.threshold = 0.1
        self.hand_net = self.get_hand_model(modelpath)


    def get_hand_model(self, modelpath):

        prototxt   = os.path.join(modelpath, "hand/pose_deploy.prototxt")
        caffemodel = os.path.join(modelpath, "hand/pose_iter_102000.caffemodel")
        hand_model = cv2.dnn.readNetFromCaffe(prototxt, caffemodel)

        return hand_model


    def predict(self, imgfile):
        img_cv2 = cv2.imread(imgfile)
        img_height, img_width, _ = img_cv2.shape
        aspect_ratio = img_width / img_height

        inWidth = int(((aspect_ratio * self.inHeight) * 8) // 8)
        inpBlob = cv2.dnn.blobFromImage(img_cv2, 1.0 / 255, (inWidth, self.inHeight), (0, 0, 0), swapRB=False, crop=False)

        self.hand_net.setInput(inpBlob)

        output = self.hand_net.forward()

        # vis heatmaps
        self.vis_heatmaps(imgfile, output)

        #
        points = []
        for idx in range(self.num_points):
            probMap = output[0, idx, :, :] # confidence map.
            probMap = cv2.resize(probMap, (img_width, img_height))

            # Find global maxima of the probMap.
            minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)

            if prob > self.threshold:
                points.append((int(point[0]), int(point[1])))
            else:
                points.append(None)

        return points


    def vis_heatmaps(self, imgfile, net_outputs):
        img_cv2 = cv2.imread(imgfile)
        plt.figure(figsize=[10, 10])

        for pdx in range(self.num_points):
            probMap = net_outputs[0, pdx, :, :]
            probMap = cv2.resize(probMap, (img_cv2.shape[1], img_cv2.shape[0]))
            plt.subplot(5, 5, pdx+1)
            plt.imshow(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
            plt.imshow(probMap, alpha=0.6)
            plt.colorbar()
            plt.axis("off")
        plt.show()


    def vis_pose(self, imgfile, points):
        img_cv2 = cv2.imread(imgfile)
        img_cv2_copy = np.copy(img_cv2)
        for idx in range(len(points)):
            if points[idx]:
                cv2.circle(img_cv2_copy, points[idx], 8, (0, 255, 255), thickness=-1,
                           lineType=cv2.FILLED)
                cv2.putText(img_cv2_copy, "{}".format(idx), points[idx], cv2.FONT_HERSHEY_SIMPLEX,
                            1, (0, 0, 255), 2, lineType=cv2.LINE_AA)

        # Draw Skeleton
        for pair in self.point_pairs:
            partA = pair[0]
            partB = pair[1]

            if points[partA] and points[partB]:
                cv2.line(img_cv2, points[partA], points[partB], (0, 255, 255), 3)
                cv2.circle(img_cv2, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)

        plt.figure(figsize=[10, 10])
        plt.subplot(1, 2, 1)
        plt.imshow(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
        plt.axis("off")
        plt.subplot(1, 2, 2)
        plt.imshow(cv2.cvtColor(img_cv2_copy, cv2.COLOR_BGR2RGB))
        plt.axis("off")
        plt.show()


if __name__ == '__main__':
    print("[INFO]Pose estimation.")

    imgs_path = "D:/myworkspace/JupyterNotebook/openpose/images"
    img_files = [os.path.join(imgs_path, img_file) for img_file in os.listdir(imgs_path)]

    #
    start = time.time()
    modelpath = "D:/myworkspace/JupyterNotebook/openpose/openpose-master/models/"
    pose_model = general_pose_model(modelpath)
    print("[INFO]Model loads time: ", time.time() - start)

    for img_file in img_files:
        start = time.time()
        res_points = pose_model.predict(img_file)
        print("[INFO]Model predicts time: ", time.time() - start)
        pose_model.vis_pose(img_file, res_points)

    print("[INFO]Done.")

在这里插入图片描述
在这里插入图片描述

（6）驾驶员危险驾驶检测

# 单人姿态估计
#!/usr/bin/python3
#!--*-- coding: utf-8 --*--
from __future__ import division
import cv2
import time
import numpy as np
import matplotlib.pyplot as plt
import os


class general_pose_model(object):
    def __init__(self, modelpath, mode="COCO"):
        #   COCO:   18 points
        self.inWidth = 368
        self.inHeight = 368
        self.threshold = 0.1
        self.pose_net = self.general_coco_model(modelpath)
        self.num_points = 22
        self.point_pairs = [[0,1],[1,2],[2,3],[3,4],
                            [0,5],[5,6],[6,7],[7,8],
                            [0,9],[9,10],[10,11],[11,12],
                            [0,13],[13,14],[14,15],[15,16],
                            [0,17],[17,18],[18,19],[19,20]]
        self.hand_net = self.get_hand_model(modelpath)
        
      
    def get_hand_model(self, modelpath):

        prototxt   = os.path.join(modelpath, "hand/pose_deploy.prototxt")
        caffemodel = os.path.join(modelpath, "hand/pose_iter_102000.caffemodel")
        hand_model = cv2.dnn.readNetFromCaffe(prototxt, caffemodel)

        return hand_model

    def general_coco_model(self, modelpath):
        self.points_name = {
            "Nose": 0, "Neck": 1, 
            "RShoulder": 2, "RElbow": 3, "RWrist": 4,
            "LShoulder": 5, "LElbow": 6, "LWrist": 7, 
            "RHip": 8, "RKnee": 9, "RAnkle": 10, 
            "LHip": 11, "LKnee": 12, "LAnkle": 13, 
            "REye": 14, "LEye": 15, 
            "REar": 16, "LEar": 17, 
            "Background": 18}
        self.num_points = 18
        self.point_pairs = [[1, 0], [1, 2], [1, 5], 
                            [2, 3], [3, 4], [5, 6], 
                            [6, 7], [1, 8], [8, 9],
                            [9, 10], [1, 11], [11, 12], 
                            [12, 13], [0, 14], [0, 15], 
                            [14, 16], [15, 17]]
        prototxt   = os.path.join(modelpath,"pose/coco/pose_deploy_linevec.prototxt")
        caffemodel = os.path.join(modelpath,"pose/coco/pose_iter_440000.caffemodel")
        coco_model = cv2.dnn.readNetFromCaffe(prototxt, caffemodel)
        return coco_model


    def predict(self, imgfile):
        img_cv2 = cv2.imread(imgfile)
        img_height, img_width, _ = img_cv2.shape
        inpBlob = cv2.dnn.blobFromImage(img_cv2, 
                                        1.0 / 255, 
                                        (self.inWidth, self.inHeight),
                                        (0, 0, 0), 
                                        swapRB=False, 
                                        crop=False)
        self.pose_net.setInput(inpBlob)
        self.pose_net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
        self.pose_net.setPreferableTarget(cv2.dnn.DNN_TARGET_OPENCL)

        output = self.pose_net.forward()

        H = output.shape[2]
        W = output.shape[3]
        print(output.shape)

        #
        points = []
        for idx in range(self.num_points):
            probMap = output[0, idx, :, :] # confidence map.

            # Find global maxima of the probMap.
            minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)

            # Scale the point to fit on the original image
            x = (img_width * point[0]) / W
            y = (img_height * point[1]) / H

            if prob > self.threshold:
                points.append((int(x), int(y)))
            else:
                points.append(None)

        return points
    
    def predict_hand(self, imgfile):
        img_cv2 = cv2.imread(imgfile)
        img_height, img_width, _ = img_cv2.shape
        aspect_ratio = img_width / img_height

        inWidth = int(((aspect_ratio * self.inHeight) * 8) // 8)
        inpBlob = cv2.dnn.blobFromImage(img_cv2, 1.0 / 255, (inWidth, self.inHeight), (0, 0, 0), swapRB=False, crop=False)

        self.hand_net.setInput(inpBlob)

        output = self.hand_net.forward()

        #
        points = []
        for idx in range(self.num_points):
            probMap = output[0, idx, :, :] # confidence map.
            probMap = cv2.resize(probMap, (img_width, img_height))

            # Find global maxima of the probMap.
            minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)

            if prob > self.threshold:
                points.append((int(point[0]), int(point[1])))
            else:
                points.append(None)

        return points
    
    def vis_pose(self, imgfile, points):
        img_cv2 = cv2.imread(imgfile)
        img_cv2_copy = np.copy(img_cv2)
        for idx in range(len(points)):
            if points[idx]:
                cv2.circle(img_cv2_copy, points[idx], 8, (0, 255, 255), thickness=-1,
                           lineType=cv2.FILLED)
                cv2.putText(img_cv2_copy, "{}".format(idx), points[idx], cv2.FONT_HERSHEY_SIMPLEX,
                            1, (0, 0, 255), 2, lineType=cv2.LINE_AA)

        # Draw Skeleton
        for pair in self.point_pairs:
            partA = pair[0]
            partB = pair[1]

            if points[partA] and points[partB]:
                cv2.line(img_cv2, points[partA], points[partB], (0, 255, 255), 3)
                cv2.circle(img_cv2, points[partA], 8, (0, 0, 255), thickness=-1, lineType=cv2.FILLED)

        plt.figure(figsize=[10, 10])
        plt.subplot(1, 2, 1)
        plt.imshow(cv2.cvtColor(img_cv2, cv2.COLOR_BGR2RGB))
        plt.axis("off")
        plt.subplot(1, 2, 2)
        plt.imshow(cv2.cvtColor(img_cv2_copy, cv2.COLOR_BGR2RGB))
        plt.axis("off")
        plt.show()


if __name__ == '__main__':
    print("[INFO]Pose estimation.")

    
    #
    start = time.time()
    
    print("[INFO]Model loads time: ", time.time() - start)

    start = time.time()
    res_points = pose_model.predict(img_file)
    print("[INFO]Model predicts time: ", time.time() - start)
    pose_model.vis_pose(img_file, res_points)
    
    print("[INFO]Pose estimation.")

    imgs_path = "D:/myworkspace/JupyterNotebook/openpose/images"
    img_files = [os.path.join(imgs_path, img_file) for img_file in os.listdir(imgs_path)]
    #
    start = time.time()
    modelpath = "D:/myworkspace/JupyterNotebook/openpose/openpose-master/models/"
    pose_model = general_pose_model(modelpath, mode="COCO")
    pose_model_hand = general_pose_model(modelpath)
    print("[INFO]Model loads time: ", time.time() - start)

    for img_file in img_files:
        start = time.time()
        res_points_hand = pose_model_hand.predict_hand(img_file)
        res_points = pose_model.predict(img_file)
        print("[INFO]Model predicts time: ", time.time() - start)
        pose_model.vis_pose(img_file, res_points)
        pose_model_hand.vis_pose(img_file, res_points_hand)
    print("[INFO]Done.")

效果是比较明显的：
在这里插入图片描述
在这里插入图片描述
由下图可知：危险驾驶判断因素——主要应该以人体姿态参考为主，手关键点检测为辅。
在这里插入图片描述 在这里插入图片描述

视频检测及判断标准待补充~

我是分界线！

其他

基于temsorflow的openpose安装
windows10 + python3.7 + anaconda3 + jupyter5.6

  • 第一步：查看本地temsorflow版本号：'1.13.1'

    python
import tensorflow as tf
tf.__version__
tf.__path__

    在这里插入图片描述

  • 第二步：下载tf-pose-estimation
    链接：https://github.com/ildoonet/tf-pose-estimation
    在这里插入图片描述

  • 第三步：根据提示配置依赖
    解压压缩包，找到requirements.txt
    在这里插入图片描述

    运行这个txt需要安装git，之前安装过的可以跳过这一节
    https://git-scm.com/downloads/
    如果不愿意安装git，可以单独安装每一个模块：
    pip install argparse或者conda install argparse

    打开控制台批量安装pip install -r requirements.txt在这里插入图片描述

    遇见问题：下载pycocotools失败，是因为和anaconda环境冲突了，用conda命令下载
    conda install pycocotools即可。在这里插入图片描述
    在这里插入图片描述
    同理，如果txt中有任何下载冲突失败的，都可以用conda install + xxxxx

  • 第四步：没错，官方还要我们用swig编译一次
    在这里插入图片描述

    ok,先来下swig,同样之前下过的可以跳过这一节
    我的swig版本是swigwin-4.0.1,下载地址是 http://www.swig.org/download.html

    具体步骤：
    解压zip，比如：D:\mydownload
    添加环境变量到path， 比如： D:\mydownload\swigwin-4.0.1
    在命令行执行： swig --help，不报错说明安装成功了。

    进入tf_pose/pafprocess目录下，打开控制台安装：

    swig -python -c++ pafprocess.i && python3 setup.py build_ext --inplace
    在这里插入图片描述

  • 第五步：运行示例
    在这里插入图片描述
    退回到tf-pose-estimation-master目录，打开控制台：
    在这里插入图片描述

    python run.py --model=mobilenet_thin --resize=432x368 --image=./images/p1.jpg

这里有一个报错：import cv2错误：ImportError: numpy.core.multiarray failed to import
在这里插入图片描述
pip安装过，但conda环境下没有安装。
在这里插入图片描述
用conda安装时，出现警告弹窗：无法定位程序输入点OPENSSL_sk_new_reserve于动态链接库
解决方法：
把Anaconda/DLLS目录下的libssl-1_1-x64 dlls文件复制到 Anaconda/Library/bin 目录下就好了，具体目录到自己的安装目录找哦。
再重新安装conda install numpy

运行依然报错：numpy，强制卸载numpy，下载新版：
在这里插入图片描述
在这里插入图片描述
重复第四步swig编译：
在这里插入图片描述
成功编译后的文件目录：
在这里插入图片描述
运行demo：

遇见问题：找不到TensorFlow：
在这里插入图片描述
是因为之前把anaconda的dll补丁替换了，需要重新下载TensorFlow；
在这里插入图片描述

cungudafa
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