OpenPose 基于OpenCV DNN 的单人姿态估计

OpenCV4.0 版本以后可以直接读取 Caffe、TensorFlow、ONNX 等模型的 API,直接采用OpenCV 的 DNN 模块即可.

这里主要测试下基于 DNN 模块和 OpenPose 模型的单人人体姿态估计的具体实现.

Github 项目 - OpenPose 关键点输出格式 - AIUAI

Github 项目 - OpenPose Python API - AIUAI

Github 项目 - OpenPose 模型与Demos - AIUAI

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

具体完整代码为:

#!/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 = "test.jpg"
    #
    start = time.time()
    modelpath = "/path/to/openpose_models/"
    pose_model = general_pose_model(modelpath, mode="BODY25")
    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)

输出如:

image

image

参考:

[1] - Github - spmallick/learnopencv/OpenPose/

[2] - Deep Learning based Human Pose Estimation using OpenCV ( C++ / Python ) - 2018.05.29

Last modification:April 9th, 2019 at 11:05 am

2 comments

  1. 不够详细,没有步骤和环境配置的信息吗?

    1. AIHGF
      @陈

      步骤和环境配置?? 博文里都是有,总不能都集中在一个里面吧,OpenCV4.X - DNN模块 Python APIs,Ubuntu - OpenCV 源码安装与测试

Leave a Comment