OpenPose 的 Python 模块提供了 Python API，可以用于构建 OpenPose 类(class)，其输入是 numpy array 格式的图像，并得到 numpy array 格式的 Pose 位置估计.
该 Python API 还提供了直接从网络(network)得到的 heatmaps 作为输入，并提取出 pose 关键点(需要安装 Caffe Python接口).
现阶段，Python API 仅支持 Body 姿态估计，Hands 和 Face 将来会支持.
1. Python API 模块及安装
OpenPose Python 模块兼容 Python2 和 Python3. 需要 python-dev, Numpy 和 OpenCV(图片加载)
# Python 3 (default and recommended)
sudo apt-get install python3-dev
sudo pip3 install numpy opencv-python
# Python 2
sudo apt-get install python-dev
sudo pip install numpy opencv-pythonOpenPose的 Python API，需要在 CMake GUI 中设置 BUILD_PYTHON.
OpenPose 模块 - openpose.py：
在 build中运行 make install 安装命令，则 OpenPose 库安装路径为 /usr/local/python.
"""
OpenPose 库 Python 接口
"""
import numpy as np
import ctypes as ct
import cv2
import os
from sys import platform
dir_path = os.path.dirname(os.path.realpath(__file__))
# dir_path = 'OPENPOSE_ROOT/build/python/openpose/'
class OpenPose(object):
"""
Ctypes linkage
"""
if platform == "linux" or platform == "linux2":
_libop= np.ctypeslib.load_library(
'_openpose', dir_path+'/_openpose.so')
elif platform == "darwin":
_libop= np.ctypeslib.load_library(
'_openpose', dir_path+'/_openpose.dylib')
elif platform == "win32":
try:
_libop= np.ctypeslib.load_library(
'_openpose', dir_path+'/Release/_openpose.dll')
except OSError as e:
_libop= np.ctypeslib.load_library(
'_openpose', dir_path+'/Debug/_openpose.dll')
_libop.newOP.argtypes = [ct.c_int, ct.c_char_p, ct.c_char_p,
ct.c_char_p, ct.c_float, ct.c_float,
ct.c_int, ct.c_float, ct.c_int,
ct.c_bool, ct.c_char_p]
_libop.newOP.restype = ct.c_void_p
_libop.delOP.argtypes = [ct.c_void_p]
_libop.delOP.restype = None
_libop.forward.argtypes = [
ct.c_void_p,
np.ctypeslib.ndpointer(dtype=np.uint8),
ct.c_size_t,
ct.c_size_t,
np.ctypeslib.ndpointer(dtype=np.int32),
np.ctypeslib.ndpointer(dtype=np.uint8),
ct.c_bool]
_libop.forward.restype = None
_libop.getOutputs.argtypes = [
ct.c_void_p,
np.ctypeslib.ndpointer(dtype=np.float32)]
_libop.getOutputs.restype = None
_libop.poseFromHeatmap.argtypes = [
ct.c_void_p,
np.ctypeslib.ndpointer(dtype=np.uint8),
ct.c_size_t,
ct.c_size_t,
np.ctypeslib.ndpointer(dtype=np.uint8),
np.ctypeslib.ndpointer(dtype=np.float32),
np.ctypeslib.ndpointer(dtype=np.int32),
np.ctypeslib.ndpointer(dtype=np.float32)]
_libop.poseFromHeatmap.restype = None
def encode(self, string):
return ct.c_char_p(string.encode('utf-8'))
def __init__(self, params):
"""
OpenPose Constructor: 初始化 OpenPose 对象
参数：
----------
params : 需要的参数，dict 格式.
返回值：
-------
outs: OpenPose object
"""
self.op = self._libop.newOP(
params["logging_level"],
self.encode(params["output_resolution"]),
self.encode(params["net_resolution"]),
self.encode(params["model_pose"]),
params["alpha_pose"],
params["scale_gap"],
params["scale_number"],
params["render_threshold"],
params["num_gpu_start"],
params["disable_blending"],
self.encode(params["default_model_folder"])
)
def __del__(self):
"""
OpenPose Destructor: 销毁 OpenPose 对象
"""
self._libop.delOP(self.op)
def forward(self, image, display = False):
"""
Forward: 输入图像，输出人体 2D 姿态，以及可视化图像结果.
参数：
----------
image : ndarray 格式的彩色图像.
display :Boole 布尔值，如果为 True，则返回带有人体关键点的可视化图像.
返回值：
-------
array: ndarray of human 2D poses [People * BodyPart * XYConfidence]
displayImage : image for visualization
"""
shape = image.shape
displayImage = np.zeros(shape=(image.shape),dtype=np.uint8)
size = np.zeros(shape=(3),dtype=np.int32)
self._libop.forward(self.op,
image,
shape[0],
shape[1],
size,
displayImage,
display)
array = np.zeros(shape=(size),dtype=np.float32)
self._libop.getOutputs(self.op, array)
if display:
return array, displayImage
return array
def poseFromHM(self, image, hm, ratios=[1]):
"""
Pose From Heatmap: 输入图像，和计算的 heatmaps，以及 scales，计算人体姿态.
参数：
----------
image : ndarray 格式的彩色图像.
hm : heatmap of type ndarray with heatmaps and part affinity fields
ratios : 多尺度multiple scales融合时的缩放比例 scaling ration.
返回值：
-------
array: ndarray of human 2D poses [People * BodyPart * XYConfidence]
displayImage : image for visualization
"""
if len(ratios) != len(hm):
raise Exception("Ratio shape mismatch")
# Find largest
hm_combine = np.zeros(shape=(len(hm),
hm[0].shape[1],
hm[0].shape[2],
hm[0].shape[3]),dtype=np.float32)
i=0
for h in hm:
hm_combine[i,:,0:h.shape[2],0:h.shape[3]] = h
i+=1
hm = hm_combine
ratios = np.array(ratios,dtype=np.float32)
shape = image.shape
displayImage = np.zeros(shape=(image.shape),dtype=np.uint8)
size = np.zeros(shape=(4),dtype=np.int32)
size[0] = hm.shape[0]
size[1] = hm.shape[1]
size[2] = hm.shape[2]
size[3] = hm.shape[3]
self._libop.poseFromHeatmap(self.op,
image,
shape[0],
shape[1],
displayImage,
hm,
size,
ratios)
array = np.zeros(shape=(size[0],size[1],size[2]),
dtype=np.float32)
self._libop.getOutputs(self.op, array)
return array, displayImage
@staticmethod
def process_frames(frame, boxsize = 368, scales = [1]):
base_net_res = None
imagesForNet = []
imagesOrig = []
for idx, scale in enumerate(scales):
# 计算网络分辨率
# Calculate net resolution (width, height)
if idx == 0:
net_res = (16 * int((boxsize * frame.shape[1] / float(frame.shape[0]) / 16) + 0.5), boxsize)
base_net_res = net_res
else:
net_res = (int(min(base_net_res[0], max(1, int((base_net_res[0] * scale)+0.5)/16*16))),
int(min(base_net_res[1], max(1, int((base_net_res[1] * scale)+0.5)/16*16))))
input_res = [frame.shape[1], frame.shape[0]]
scale_factor = min((net_res[0] - 1) / float(input_res[0] - 1), (net_res[1] - 1) / float(input_res[1] - 1))
warp_matrix = np.array([[scale_factor,0,0],
[0,scale_factor,0]])
if scale_factor != 1:
imageForNet = cv2.warpAffine(
frame,
warp_matrix,
net_res,
flags=(cv2.INTER_AREA if scale_factor < 1. else cv2.INTER_CUBIC),
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0,0,0))
else:
imageForNet = frame.copy()
imageOrig = imageForNet.copy()
imageForNet = imageForNet.astype(float)
imageForNet = imageForNet/256. - 0.5
imageForNet = np.transpose(imageForNet, (2,0,1))
imagesForNet.append(imageForNet)
imagesOrig.append(imageOrig)
return imagesForNet, imagesOrig
@staticmethod
def draw_all(imageForNet, heatmaps, currIndex, div=4., norm=False):
netDecreaseFactor = float(imageForNet.shape[0]) / float(heatmaps.shape[2]) # 8
resized_heatmaps = np.zeros(shape=(heatmaps.shape[0],
heatmaps.shape[1],
imageForNet.shape[0],
imageForNet.shape[1]))
num_maps = heatmaps.shape[1]
combined = None
for i in range(0, num_maps):
heatmap = heatmaps[0,i,:,:]
resizedHeatmap = cv2.resize(heatmap, (0,0),
fx=netDecreaseFactor,
fy=netDecreaseFactor)
minVal, maxVal, minLoc, maxLoc = cv2.minMaxLoc(resizedHeatmap)
if i==currIndex and currIndex >=0:
resizedHeatmap = np.abs(resizedHeatmap)
resizedHeatmap = (resizedHeatmap*255.).astype(dtype='uint8')
im_color = cv2.applyColorMap(resizedHeatmap, cv2.COLORMAP_JET)
resizedHeatmap = cv2.addWeighted(imageForNet, 1, im_color, 0.3, 0)
cv2.circle(resizedHeatmap, (int(maxLoc[0]),int(maxLoc[1])), 5, (255,0,0), -1)
return resizedHeatmap
else:
resizedHeatmap = np.abs(resizedHeatmap)
if combined is None:
combined = np.copy(resizedHeatmap);
else:
if i <= num_maps-2:
combined += resizedHeatmap;
if norm:
combined = np.maximum(0, np.minimum(1, combined));
if currIndex < 0:
combined /= div
combined = (combined*255.).astype(dtype='uint8')
im_color = cv2.applyColorMap(combined, cv2.COLORMAP_JET)
combined = cv2.addWeighted(imageForNet, 0.5, im_color, 0.5, 0)
cv2.circle(combined, (int(maxLoc[0]),int(maxLoc[1])), 5, (255,0,0), -1)
return combined
if __name__ == "__main__":
params = dict()
params["logging_level"] = 3
params["output_resolution"] = "-1x-1"
params["net_resolution"] = "-1x368"
params["model_pose"] = "BODY_25"
params["alpha_pose"] = 0.6
params["scale_gap"] = 0.3
params["scale_number"] = 1
params["render_threshold"] = 0.05
params["num_gpu_start"] = 0
params["disable_blending"] = False
params["default_model_folder"] = "../../../models/"
openpose = OpenPose(params)
img = cv2.imread("test.jpg")
arr, output_image = openpose.forward(img, True)
print(arr)
while 1:
cv2.imshow("output", output_image)
cv2.waitKey(15)2. Python API 模块测试
Python API 模块的实例位于 build/examples/tutorial_api_python 路径.
运行测试：
# From command line
cd build/examples/tutorial_api_python
# Python 3 (default version)
python3 1_body_from_image.py
python3 2_whole_body_from_image.py
# python3 [any_other_example.py]
# Python 2
python2 1_body_from_image.py
python2 2_whole_body_from_image.py
# python2 [any_other_example.py]OpenPose 旧版本中的测试示例：
在 OPENPOSE_ROOT/build/examples/tutorial_api_python/1_extract_pose.py 和 OPENPOSE_ROOT//build/examples/tutorial_developer/python_1_pose_from_heatmaps.py.
有两个例子：
[1] - 1_extract_pose.py - Python API 的简单使用.
[2] - 2_pose_from_heatmaps.py - 基于 Caffe 网络从 heatmaps 构建 Body Pose.
cd build/examples/tutorial_api_python python 1_extract_pose.py
2.1. 直接利用 OpenPose 库提取人体关键点
在自定义的调用 OpenPose Python API 的脚本中，需要添加编译安装的 OpenPose 路径，其有两种方案：
方案一：
如：
import os, sys
dir_path = os.path.dirname(os.path.realpath(__file__))
sys.path.append('../../python')
# sys.path.append('OPENPOSE_ROOT/build/python')方案二：
在 build/ 中运行了 openpose 安装命令 make install
默认安装路径：/usr/local/python（Ubuntu)
也可以采用访问 OpenPose/python 的方式.
可以将 openpose 和其 python 库安装在指定路径.
确保在 python 路径中. 如：
import sys
sys.path.append('/usr/local/python')以方案二为例：
# Ubuntu 环境
import sys
import cv2
import matplotlib.pyplot as plt
# 方案二：
sys.path.append('/usr/local/python')
# 导入 OpenPose 库
try:
from openpose import *
except:
raise Exception('Error: OpenPose library could not be found. '
'Did you enable `BUILD_PYTHON` in CMake and '
'have this Python script in the right folder?')
# OpenPose 参数
params = dict()
params["logging_level"] = 3
params["output_resolution"] = "-1x-1"
params["net_resolution"] = "-1x368"
params["model_pose"] = "BODY_25"
params["alpha_pose"] = 0.6
params["scale_gap"] = 0.3
params["scale_number"] = 1
params["render_threshold"] = 0.05
# 多 GPUs 时，GPUID 设置
params["num_gpu_start"] = 0
params["disable_blending"] = False
# 模型路径
params["default_model_folder"] = "OPENPOSE_ROOT/models/"
# 构建 OpenPose 对象，分配 GPU 显存
openpose = OpenPose(params)
img = cv2.imread("test.jpg")
# 计算输出关键点，及带有人体骨骼的图像结果
keypoints, output_image = openpose.forward(img, True)
# 打印人体关键点结果
# 如，包含图像中所有人体的关节点结果， [#people x #keypoints x 3]-维 numpy 对象
print(keypoints)
# 显示图片
plt.imshow(output_image[:,:,::-1])
plt.axis('off')
plt.show()输出结果，如：
# Keypoints
[[[2.6689908e+02 1.5720723e+02 8.7282991e-01]
[2.1294948e+02 1.8849382e+02 8.4266990e-01]
[1.7634467e+02 1.5893631e+02 7.9097664e-01]
[9.1040657e+01 1.6246671e+02 8.8326693e-01]
[8.4093750e+01 2.3556367e+02 8.0808026e-01]
[2.4605063e+02 2.1817403e+02 7.1219295e-01]
[2.3030014e+02 2.9823630e+02 9.0355945e-01]
[2.8431320e+02 2.8435046e+02 9.5072258e-01]
[1.8673714e+02 3.3133643e+02 6.4542526e-01]
[1.5715875e+02 3.3304315e+02 5.8337438e-01]
[1.6065424e+02 4.6538202e+02 8.7898481e-01]
[6.4923096e+01 5.6457629e+02 7.7615017e-01]
[2.1464272e+02 3.3305948e+02 6.8280864e-01]
[2.4602286e+02 4.4974640e+02 8.0254650e-01]
[2.7908148e+02 5.6641516e+02 7.3841733e-01]
[2.6343689e+02 1.4155238e+02 8.5776460e-01]
[2.7734644e+02 1.5720271e+02 8.8562906e-01]
[2.4253836e+02 1.3274791e+02 8.3041203e-01]
[2.8250128e+02 1.7112190e+02 9.4624631e-02]
[2.6860504e+02 6.0469604e+02 7.7051580e-01]
[2.8427838e+02 6.0294855e+02 7.2294450e-01]
[2.8258286e+02 5.7684503e+02 5.7483846e-01]
[3.7068314e+01 6.0460706e+02 7.1381354e-01]
[3.0127148e+01 5.9255536e+02 6.3077456e-01]
[6.1453045e+01 5.6467444e+02 4.6968105e-01]]]
2.2. 从 heatmaps 提取人体关键点
import sys
# 需要指定 Caffe 的编译路径
sys.path.insert(0, '/path/to/caffe/python')
import caffe
import os
os.environ["GLOG_minloglevel"] = "1"
import cv2
import numpy as np
import matplotlib.pyplot as plt
sys.path.append('/usr/local/python')
try:
from openpose import OpenPose
except:
raise Exception('Error: OpenPose library could not be found. '
'Did you enable `BUILD_PYTHON` in CMake and '
'have this Python script in the right folder?')
# 参数设置
# 单尺度
defRes = 368
scales = [1]
# # 多尺度Multi-scale
# defRes = 736
# scales = [1, 0.75, 0.5, 0.25]
class Param:
caffemodel = "OPENPOSE_ROOT/models/pose/body_25/pose_iter_584000.caffemodel"
prototxt = "OPENPOSE_ROOT/models/pose/body_25/pose_deploy.prototxt"
# 加载 OpenPose 对象和 Caffe Nets
params = dict()
params["logging_level"] = 3
params["output_resolution"] = "-1x-1"
params["net_resolution"] = "-1x"+ str(defRes)
params["model_pose"] = "BODY_25"
params["alpha_pose"] = 0.6
params["scale_gap"] = 0.25
params["scale_number"] = len(scales)
params["render_threshold"] = 0.05
params["num_gpu_start"] = 0
params["disable_blending"] = False
params["default_model_folder"] = "OPENPOSE_ROOT/models/"
openpose = OpenPose(params)
caffe.set_mode_gpu()
caffe.set_device(0)
nets = []
for scale in scales:
nets.append(caffe.Net(Param.prototxt, Param.caffemodel, caffe.TEST))
print("[INFO] Caffe Net loaded")
# 测试函数
first_run = True
def func(frame):
# Get image processed for network, and scaled image
imagesForNet, imagesOrig = OpenPose.process_frames(frame, defRes, scales)
# Reshape
global first_run
if first_run:
for i in range(0, len(scales)):
net = nets[i]
imageForNet = imagesForNet[i]
in_shape = net.blobs['image'].data.shape
in_shape = (1, 3, imageForNet.shape[1], imageForNet.shape[2])
net.blobs['image'].reshape(*in_shape)
net.reshape()
first_run = False
print("[INFO] Images Reshaped")
# Forward 计算得到 heatmaps
heatmaps = []
for i in range(0, len(scales)):
net = nets[i]
imageForNet = imagesForNet[i]
net.blobs['image'].data[0,:,:,:] = imageForNet
net.forward()
heatmaps.append(net.blobs['net_output'].data[:,:,:,:])
# Pose from HM Test
array, frame = openpose.poseFromHM(frame, heatmaps, scales)
# Draw Heatmaps instead
# hm = heatmaps[0][:,0:18,:,:];
# frame = OpenPose.draw_all(imagesOrig[0], hm, -1, 1, True)
# paf = heatmaps[0][:,20:,:,:];
# frame = OpenPose.draw_all(imagesOrig[0], paf, -1, 4, False)
return frame
img = cv2.imread('test.jpg")
frame = func(img)
plt.imshow(frame[:,:,::-1])
plt.axis('off')
plt.show()如：
2.3. Exporting Python OpenPose
如果需要将 *.py 脚本移出其原来的路径，或者，在build/examples/tutorial_api_python 路径外新建 *py 脚本.
[1] - 安装 OpenPose - 在 Ubuntu 系统中，可以通过 sudo make install 安装 OpenPose；然后，在 python 脚本中设置 OpenPose 的安装路径(默认：/usr/local/python)，然后即可在任何位置开始使用 OpenPose. 参考：build/examples/tutorial_pose/1_extract_pose.py.
[2] - 不安装 OpenPose - 为了将 OpenPose Python API Demo 放置在不同的路径，需要在 *.py 脚本中添加 sys.path.append('{OpenPose_path}/python')，其中，{OpenPose_path} 为 OpenPose 的 build 路径. 参考：build/examples/tutorial_pose/1_extract_pose.py.
可参考 2.1 和 2.2.
9 comments
openpose.py是哪里生成的
早期项目里提供的一个py脚本，现在好像是更新过了
博主你好, 请问如何才能实时处理video里面每一帧的keypoints
实时可以用一些轻量的关键点估计模型，可能会牺牲一些精度.
你好，这个性能怎么样呢？识别一张图片耗时多少呢？硬件配置是？
请问python api也要先安装vs，配置完之后再安装吗？
ubuntu 环境是没有 vs 的.
你好，请问openpose的Python API该怎么安装呢？
https://www.aiuai.cn/aifarm706.html
https://www.aiuai.cn/aifarm1155.html
这两篇里有汇总过.