twiceDetect4linux.py

import os
import sys
from sys import platform
os.environ['HDF5_DISABLE_VERSION_CHECK'] = '2'
import cv2
import numpy as np

# import pyopenpose:
# platform这句也可以不写,因为自己的电脑已经确定是windows系统的了

# 获取当前该文件所在文件夹的绝对路径
dir_path = os.path.dirname(os.path.realpath(__file__))
try:
    # Windows Import
    if platform == "win32":
        # Change these variables to point to the correct folder (Release/x64 etc.)
        sys.path.append(dir_path + '/../../python/openpose/Release')
        os.environ['PATH'] = os.environ['PATH'] + ';' + dir_path + '/../../x64/Release;' + dir_path + '/../../bin;'
        import pyopenpose as op
    else:
        # Change these variables to point to the correct folder (Release/x64 etc.)
        sys.path.append('openpose/build/python')
        # If you run `make install` (default path is `/usr/local/python` for Ubuntu), you can also access the
        # OpenPose/python module from there. This will install OpenPose and the python library at your desired
        # installation path. Ensure that this is in your python path in order to use it. sys.path.append(
        # '/usr/local/python')
        from openpose import pyopenpose as op
except ImportError as e:
    print(
        'Error: OpenPose library could not be found. Did you enable `BUILD_PYTHON` in CMake and have this Python '
        'script in the right folder?')
    raise e
# 模型导入
params = dict()
# 这里的models用的是我们当前工程下的models文件夹
params['model_folder'] = 'openpose/models'  # 模型地址

# params[
# "net_resolution"] = '1280x720'  # 分辨率,需要是16的倍数，降低这个参数可以以准确率为代价显著提升处理速度。
params["net_resolution"] = '320x240'
params["number_people_max"] = 1

params["body"] = 1  # 0禁用身体检测，1启用

params["disable_blending"] = False  # 如果为True，只显示骨骼关键点，背景为黑

params["model_pose"] = "BODY_25"  # 参数设置"BODY_25“表示使用25点的检测模式，CUDA
# 版本中最快最准的模式。此外设置"COCO"使用18
# 点的检测模式，设置"MPI"使用15点的检测模式，最不精确，但在CPU上最快。设置"MPI_4_layers"使用15点的检测模式，甚至比上一种更快，但不够准确。

params["keypoint_scale"] = 0  # 最终姿态数据数组(x,y)坐标的缩放，即(x,y)的缩放。"将以' write_json '和'
# write_keypoint '标记保存的坐标。"选择“0”将其缩放到原始源分辨率;' 1 '将其缩放到净输出" "大小(用'
# net_resolution '设置);' 2 '将其缩放到最终的输出大小(设置为" " '分辨率');' 3 '将其缩放到[0,1]的范围内，其中(
# 0,0)将是图像左上角的“”角，(1,1)是右下角的“”角;4表示范围[-1,1]，其中" "(-1，-1)是图像的左上角，(1,
# 1)是右下角。与“scale_number”和“scale_gap”相关的非。


def detect(imgPath, isShow=False):
    # 启动pyopenpose:
    opWrapper = op.WrapperPython()
    opWrapper.configure(params)
    opWrapper.start()
    # 传入一张图片
    datum = op.Datum()

    imageToProcess = cv2.imread(imgPath, cv2.IMREAD_ANYCOLOR)

    datum.cvInputData = imageToProcess
    opWrapper.emplaceAndPop(op.VectorDatum([datum]))
    pose = datum.poseKeypoints

    # print(hand)
    if isShow:
        try:
            # print(type(pose))
            # print(datum.cvOutputData)
            # print("Body keypoints: \n" + str(pose))
            print(pose.shape)
            for jg in pose:
                print('pose[4]:' + str(jg[4]))
                print("pose[3]:" + str(jg[3]))
                print('pose[6]:' + str(jg[6]))
                print('pose[7]:' + str(jg[7]))
                print()
            # print(pose[0][4][0])
        except AttributeError:
            print("没有检测出人体")
        with open('pose.csv', 'w') as file:
            for b1 in pose:
                np.savetxt(file, b1, delimiter=",")
        # b,g,r = cv2.split(datum.cvOutputData)#分别提取B、G、R通道
        # img= cv2.merge([r,g,b])	#重新组合为R、G、B
        # plt.imshow(img)
        # plt.axis('off')
        # plt.show()
        cv2.namedWindow('opStudy', 0)

        cv2.resizeWindow("opStudy", 600, 600)
        cv2.moveWindow("opStudy", 100, 100)
        cv2.imshow("opStudy", datum.cvOutputData)
        cv2.waitKey(0)
    return pose, datum.cvOutputData