前一段时间简单测试了一下 OpenPose 提供的 Python API - Github 项目 - OpenPose Python API - AIUAI，最近发现新近版本的 OpenPose v1.5.0 更新了 Python API 的例示 - openpose/examples/tutorial_api_python/. 这里再稍微测试下使用.
1. Python API 编译时遇到的问题解决
环境：Ubuntu16.04，CUDA9.0，Cudnn 7.1.3.
在 openpose 编译完成后，采用 Python API 调用时，遇到如下问题：
from . import pyopenpose as pyopenpose
ImportError: cannot import name pyopenpose解决方法：
[1] - 首先，在 openpose 路径找到文件：build/python/openpose/pyopenpose.cpython-35m-x86_64-linux-gnu.so；复制该文件到路径：/usr/local/lib/python3.5/dist-packages.
cd OpenPose_rootpath/build/python/openpose/
sudo cp pyopenpose.cpython-35m-x86_64-linux-gnu.so /usr/local/lib/python3.5/dist-packages/[2] - 然后，进入路径：/usr/local/lib/python3.5/dist-packages，创建软连接：
cd /usr/local/lib/python3.5/dist-packages/
sudo ln -s pyopenpose.cpython-36m-x86_64-linux-gnu.so pyopenpose确认环境变量中 LD_LIBRARY_PATH 包含 /usr/local/lib/python3.5/dist-packages.
[3] - 最后，在 Python 脚本中调用：
import pyopenpose as op
#注：
#原始脚本中是 from openpose import pyopenpose as op，需要修改为上行代码.2. whole_body_from_image
#!/usr/bin/python3
#!--*-- coding:utf-8 --*--
import sys
import cv2
import os
import matplotlib.pyplot as plt
import time
# Import Openpose (Ubuntu)
import pyopenpose as op
#
img_file = "/path/to/test.jpg"
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "/path/to/openpose/models/"
params["face"] = False
params["hand"] = True
params["num_gpu"] = 1
params["num_gpu_start"] = 1
# params["net_resolution"] = "160x160"
start = time.time()
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
imageToProcess = cv2.imread(img_file)
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
print("[INFO]timecost: ", time.time() - start)
# Display Image
print("Body keypoints: \n" + str(datum.poseKeypoints))
print("Face keypoints: \n" + str(datum.faceKeypoints))
print("Left hand keypoints: \n" + str(datum.handKeypoints[0]))
print("Right hand keypoints: \n" + str(datum.handKeypoints[1]))
plt.figure(figsize=(12, 10))
plt.imshow(datum.cvOutputData[:, :, ::-1])
plt.title("OpenPose 1.5.0 - Tutorial Python API")
plt.axis("off")
plt.show()
print("[INFO]Done.")输出如：
3. heatmaps_from_image
#!/usr/bin/python3
#!--*-- coding:utf-8 --*--
import sys
import cv2
import matplotlib.pyplot as plt
import time
# Import Openpose (Ubuntu/OSX)
import pyopenpose as op
#
img_file = "/path/to/test.jpg"
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "/path/to/openpose/models/"
params["heatmaps_add_parts"] = True
params["heatmaps_add_bkg"] = True
params["heatmaps_add_PAFs"] = True
params["heatmaps_scale"] = 2
params["num_gpu"] = 1
params["num_gpu_start"] = 1
# params["net_resolution"] = "160x160"
start = time.time()
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image
datum = op.Datum()
imageToProcess = cv2.imread(img_file)
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
# Process outputs
outputImageF = (datum.inputNetData[0].copy())[0, :, :, :] + 0.5
outputImageF = cv2.merge([outputImageF[0, :, :], outputImageF[1, :, :], outputImageF[2, :, :]])
outputImageF = (outputImageF * 255.).astype(dtype='uint8')
heatmaps = datum.poseHeatMaps.copy()
heatmaps = (heatmaps).astype(dtype='uint8')
print("[INFO]timecost: ", time.time() - start)
# Display Image
counter = 0
while 1:
num_maps = heatmaps.shape[0]
heatmap = heatmaps[counter, :, :].copy()
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
combined = cv2.addWeighted(outputImageF, 0.5, heatmap, 0.5, 0)
plt.figure(figsize=(12, 10))
plt.imshow(combined[:, :, ::-1])
plt.title("OpenPose 1.5.0 - Tutorial Python API")
plt.axis("off")
plt.show()
print("[INFO]Done.")输出如：
4. keypoints_from_heatmaps
#!/usr/bin/python3
#!--*-- coding:utf-8 --*--
import sys
import cv2
import matplotlib.pyplot as plt
import time
# Import Openpose (Ubuntu)
import pyopenpose as op
#
img_file = "/path/to/test.jpg"
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "/path/to/openpose/models/"
params["heatmaps_add_parts"] = True
params["heatmaps_add_bkg"] = True
params["heatmaps_add_PAFs"] = True
params["heatmaps_scale"] = 3
params["upsampling_ratio"] = 1
params["body"] = 1
start = time.time()
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Process Image and get heatmap
datum = op.Datum()
imageToProcess = cv2.imread(img_file)
datum.cvInputData = imageToProcess
opWrapper.emplaceAndPop([datum])
poseHeatMaps = datum.poseHeatMaps.copy()
opWrapper.stop()
print("[INFO]timecost: ", time.time() - start)
# Heatmap to keypoints
params["body"] = 2 # Disable OP Network
params["upsampling_ratio"] = 0 # * Unset this variable
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Pass Heatmap and Run OP
datum = op.Datum()
datum.cvInputData = imageToProcess
datum.poseNetOutput = poseHeatMaps
opWrapper.emplaceAndPop([datum])
# Display Image
print("Body keypoints: \n" + str(datum.poseKeypoints))
plt.figure(figsize=(12, 10))
plt.imshow(datum.cvOutputData[:, :, ::-1])
plt.title("OpenPose 1.5.0 - Tutorial Python API")
plt.axis("off")
plt.show()
print("[INFO]Done.")输出如：
5. face_from_image
#!/usr/bin/python3
#!--*-- coding:utf-8 --*--
import sys
import cv2
import matplotlib.pyplot as plt
import time
# Import Openpose (Ubuntu)
import pyopenpose as op
#
img_file = "/path/to/test.jpg"
# Custom Params (refer to include/openpose/flags.hpp for more parameters)
params = dict()
params["model_folder"] = "/path/to/openpose/models/"
params["face"] = True
params["face_detector"] = 2
params["body"] = 0
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
# Read image and face rectangle locations
imageToProcess = cv2.imread(img_file)
faceRectangles = [
op.Rectangle(330.119385, 277.532715, 48.717274, 48.717274),
op.Rectangle(24.036991, 267.918793, 65.175171, 65.175171),
op.Rectangle(151.803436, 32.477852, 108.295761, 108.295761),
]
# Create new datum
datum = op.Datum()
datum.cvInputData = imageToProcess
datum.faceRectangles = faceRectangles
# Process and display image
opWrapper.emplaceAndPop([datum])
print("Face keypoints: \n" + str(datum.faceKeypoints))
plt.figure(figsize=(12, 10))
plt.imshow(datum.cvOutputData[:, :, ::-1])
plt.title("OpenPose 1.5.0 - Tutorial Python API")
plt.axis("off")
plt.show()
print("[INFO]Done.")hand_from_image 的实现类似.
6. 可选参数
OpenPose Python API 可以采用如下方式配置参数：
params = dict()
params["model_folder"] = "/path/to/openpose/models/"
params["face"] = False
params["hand"] = True
params["num_gpu"] = 1
params["num_gpu_start"] = 1
params["net_resolution"] = "160x160"
# Starting OpenPose
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()其中，params 的可选参数可参考 include/openpose/flags.hpp.
具体如下(可以 ctrl+F 查询相关参数)：
#ifndef OPENPOSE_FLAGS_HPP
#define OPENPOSE_FLAGS_HPP
// Note: This class is not included within the basic OpenPose `headers.hpp` and must be explicitly included. In
// addition, Google Flags library must also be linked to the resulting binary or library. OpenPose library does
// not force to use Google Flags, but the OpenPose examples do so.
// GFlags: DEFINE_bool, _int32, _int64, _uint64, _double, _string
#include <gflags/gflags.h>
// Allow Google Flags in Ubuntu 14
#ifndef GFLAGS_GFLAGS_H_
namespace gflags = google;
#endif
// See all the available parameter options withe the `--help` flag. E.g., `build/examples/openpose/openpose.bin --help`
// Note: This command will show you flags for other unnecessary 3rdparty files. Check only the flags for the OpenPose
// executable. E.g., for `openpose.bin`, look for `Flags from examples/openpose/openpose.cpp:`.
// Debugging/Other
DEFINE_int32(logging_level, 3, "The logging level. Integer in the range [0, 255]. 0 will output any log() message, while"
" 255 will not output any. Current OpenPose library messages are in the range 0-4: 1 for"
" low priority messages and 4 for important ones.");
DEFINE_bool(disable_multi_thread, false, "It would slightly reduce the frame rate in order to highly reduce the lag. Mainly useful"
" for 1) Cases where it is needed a low latency (e.g., webcam in real-time scenarios with"
" low-range GPU devices); and 2) Debugging OpenPose when it is crashing to locate the"
" error.");
DEFINE_int32(profile_speed, 1000, "If PROFILER_ENABLED was set in CMake or Makefile.config files, OpenPose will show some"
" runtime statistics at this frame number.");
#ifndef OPENPOSE_FLAGS_DISABLE_POSE
#ifndef OPENPOSE_FLAGS_DISABLE_PRODUCER
// Producer
DEFINE_int32(camera, -1, "The camera index for cv::VideoCapture. Integer in the range [0, 9]. Select a negative"
" number (by default), to auto-detect and open the first available camera.");
DEFINE_string(camera_resolution, "-1x-1", "Set the camera resolution (either `--camera` or `--flir_camera`). `-1x-1` will use the"
" default 1280x720 for `--camera`, or the maximum flir camera resolution available for"
" `--flir_camera`");
DEFINE_string(video, "", "Use a video file instead of the camera. Use `examples/media/video.avi` for our default"
" example video.");
DEFINE_string(image_dir, "", "Process a directory of images. Use `examples/media/` for our default example folder with 20"
" images. Read all standard formats (jpg, png, bmp, etc.).");
DEFINE_bool(flir_camera, false, "Whether to use FLIR (Point-Grey) stereo camera.");
DEFINE_int32(flir_camera_index, -1, "Select -1 (default) to run on all detected flir cameras at once. Otherwise, select the flir"
" camera index to run, where 0 corresponds to the detected flir camera with the lowest"
" serial number, and `n` to the `n`-th lowest serial number camera.");
DEFINE_string(ip_camera, "", "String with the IP camera URL. It supports protocols like RTSP and HTTP.");
DEFINE_uint64(frame_first, 0, "Start on desired frame number. Indexes are 0-based, i.e., the first frame has index 0.");
DEFINE_uint64(frame_step, 1, "Step or gap between processed frames. E.g., `--frame_step 5` would read and process frames"
" 0, 5, 10, etc..");
DEFINE_uint64(frame_last, -1, "Finish on desired frame number. Select -1 to disable. Indexes are 0-based, e.g., if set to"
" 10, it will process 11 frames (0-10).");
DEFINE_bool(frame_flip, false, "Flip/mirror each frame (e.g., for real time webcam demonstrations).");
DEFINE_int32(frame_rotate, 0, "Rotate each frame, 4 possible values: 0, 90, 180, 270.");
DEFINE_bool(frames_repeat, false, "Repeat frames when finished.");
DEFINE_bool(process_real_time, false, "Enable to keep the original source frame rate (e.g., for video). If the processing time is"
" too long, it will skip frames. If it is too fast, it will slow it down.");
DEFINE_string(camera_parameter_path, "models/cameraParameters/flir/", "String with the folder where the camera parameters are located. If there"
" is only 1 XML file (for single video, webcam, or images from the same camera), you must"
" specify the whole XML file path (ending in .xml).");
DEFINE_bool(frame_undistort, false, "If false (default), it will not undistort the image, if true, it will undistortionate them"
" based on the camera parameters found in `camera_parameter_path`");
#endif // OPENPOSE_FLAGS_DISABLE_PRODUCER
// OpenPose
DEFINE_string(model_folder, "models/", "Folder path (absolute or relative) where the models (pose, face, ...) are located.");
DEFINE_string(prototxt_path, "", "The combination `--model_folder` + `--prototxt_path` represents the whole path to the"
" prototxt file. If empty, it will use the default OpenPose ProtoTxt file.");
DEFINE_string(caffemodel_path, "", "The combination `--model_folder` + `--caffemodel_path` represents the whole path to the"
" caffemodel file. If empty, it will use the default OpenPose CaffeModel file.");
DEFINE_string(output_resolution, "-1x-1", "The image resolution (display and output). Use \"-1x-1\" to force the program to use the"
" input image resolution.");
DEFINE_int32(num_gpu, -1, "The number of GPU devices to use. If negative, it will use all the available GPUs in your"
" machine.");
DEFINE_int32(num_gpu_start, 0, "GPU device start number.");
DEFINE_int32(keypoint_scale, 0, "Scaling of the (x,y) coordinates of the final pose data array, i.e., the scale of the (x,y)"
" coordinates that will be saved with the `write_json` & `write_keypoint` flags."
" Select `0` to scale it to the original source resolution; `1`to scale it to the net output"
" size (set with `net_resolution`); `2` to scale it to the final output size (set with"
" `resolution`); `3` to scale it in the range [0,1], where (0,0) would be the top-left"
" corner of the image, and (1,1) the bottom-right one; and 4 for range [-1,1], where"
" (-1,-1) would be the top-left corner of the image, and (1,1) the bottom-right one. Non"
" related with `scale_number` and `scale_gap`.");
DEFINE_int32(number_people_max, -1, "This parameter will limit the maximum number of people detected, by keeping the people with"
" top scores. The score is based in person area over the image, body part score, as well as"
" joint score (between each pair of connected body parts). Useful if you know the exact"
" number of people in the scene, so it can remove false positives (if all the people have"
" been detected. However, it might also include false negatives by removing very small or"
" highly occluded people. -1 will keep them all.");
DEFINE_bool(maximize_positives, false, "It reduces the thresholds to accept a person candidate. It highly increases both false and"
" true positives. I.e., it maximizes average recall but could harm average precision.");
DEFINE_double(fps_max, -1., "Maximum processing frame rate. By default (-1), OpenPose will process frames as fast as"
" possible. Example usage: If OpenPose is displaying images too quickly, this can reduce"
" the speed so the user can analyze better each frame from the GUI.");
// OpenPose Body Pose
DEFINE_int32(body, 1, "Select 0 to disable body keypoint detection (e.g., for faster but less accurate face"
" keypoint detection, custom hand detector, etc.), 1 (default) for body keypoint"
" estimation, and 2 to disable its internal body pose estimation network but still"
" still run the greedy association parsing algorithm");
DEFINE_string(model_pose, "BODY_25", "Model to be used. E.g., `COCO` (18 keypoints), `MPI` (15 keypoints, ~10% faster), "
"`MPI_4_layers` (15 keypoints, even faster but less accurate).");
DEFINE_string(net_resolution, "-1x368", "Multiples of 16. If it is increased, the accuracy potentially increases. If it is"
" decreased, the speed increases. For maximum speed-accuracy balance, it should keep the"
" closest aspect ratio possible to the images or videos to be processed. Using `-1` in"
" any of the dimensions, OP will choose the optimal aspect ratio depending on the user's"
" input value. E.g., the default `-1x368` is equivalent to `656x368` in 16:9 resolutions,"
" e.g., full HD (1980x1080) and HD (1280x720) resolutions.");
DEFINE_int32(scale_number, 1, "Number of scales to average.");
DEFINE_double(scale_gap, 0.25, "Scale gap between scales. No effect unless scale_number > 1. Initial scale is always 1."
" If you want to change the initial scale, you actually want to multiply the"
" `net_resolution` by your desired initial scale.");
// OpenPose Body Pose Heatmaps and Part Candidates
DEFINE_bool(heatmaps_add_parts, false, "If true, it will fill op::Datum::poseHeatMaps array with the body part heatmaps, and"
" analogously face & hand heatmaps to op::Datum::faceHeatMaps & op::Datum::handHeatMaps."
" If more than one `add_heatmaps_X` flag is enabled, it will place then in sequential"
" memory order: body parts + bkg + PAFs. It will follow the order on"
" POSE_BODY_PART_MAPPING in `src/openpose/pose/poseParameters.cpp`. Program speed will"
" considerably decrease. Not required for OpenPose, enable it only if you intend to"
" explicitly use this information later.");
DEFINE_bool(heatmaps_add_bkg, false, "Same functionality as `add_heatmaps_parts`, but adding the heatmap corresponding to"
" background.");
DEFINE_bool(heatmaps_add_PAFs, false, "Same functionality as `add_heatmaps_parts`, but adding the PAFs.");
DEFINE_int32(heatmaps_scale, 2, "Set 0 to scale op::Datum::poseHeatMaps in the range [-1,1], 1 for [0,1]; 2 for integer"
" rounded [0,255]; and 3 for no scaling.");
DEFINE_bool(part_candidates, false, "Also enable `write_json` in order to save this information. If true, it will fill the"
" op::Datum::poseCandidates array with the body part candidates. Candidates refer to all"
" the detected body parts, before being assembled into people. Note that the number of"
" candidates is equal or higher than the number of final body parts (i.e., after being"
" assembled into people). The empty body parts are filled with 0s. Program speed will"
" slightly decrease. Not required for OpenPose, enable it only if you intend to explicitly"
" use this information.");
DEFINE_double(upsampling_ratio, 0., "Upsampling ratio between the `net_resolution` and the output net results. A value less"
" or equal than 0 (default) will use the network default value (recommended).");
// OpenPose Face
DEFINE_bool(face, false, "Enables face keypoint detection. It will share some parameters from the body pose, e.g."
" `model_folder`. Note that this will considerable slow down the performance and increse"
" the required GPU memory. In addition, the greater number of people on the image, the"
" slower OpenPose will be.");
DEFINE_int32(face_detector, 0, "Kind of face rectangle detector. Select 0 (default) to select OpenPose body detector (most"
" accurate one and fastest one if body is enabled), 1 to select OpenCV face detector (not"
" implemented for hands), 2 to indicate that it will be provided by the user, or 3 to"
" also apply hand tracking (only for hand). Hand tracking might improve hand keypoint"
" detection for webcam (if the frame rate is high enough, i.e., >7 FPS per GPU) and video."
" This is not person ID tracking, it simply looks for hands in positions at which hands were"
" located in previous frames, but it does not guarantee the same person ID among frames.");
DEFINE_string(face_net_resolution, "368x368", "Multiples of 16 and squared. Analogous to `net_resolution` but applied to the face keypoint"
" detector. 320x320 usually works fine while giving a substantial speed up when multiple"
" faces on the image.");
// OpenPose Hand
DEFINE_bool(hand, false, "Enables hand keypoint detection. It will share some parameters from the body pose, e.g."
" `model_folder`. Analogously to `--face`, it will also slow down the performance, increase"
" the required GPU memory and its speed depends on the number of people.");
DEFINE_int32(hand_detector, 0, "Kind of hand rectangle detector. Analogous to `--face_detector`.");
DEFINE_string(hand_net_resolution, "368x368", "Multiples of 16 and squared. Analogous to `net_resolution` but applied to the hand keypoint"
" detector.");
DEFINE_int32(hand_scale_number, 1, "Analogous to `scale_number` but applied to the hand keypoint detector. Our best results"
" were found with `hand_scale_number` = 6 and `hand_scale_range` = 0.4.");
DEFINE_double(hand_scale_range, 0.4, "Analogous purpose than `scale_gap` but applied to the hand keypoint detector. Total range"
" between smallest and biggest scale. The scales will be centered in ratio 1. E.g., if"
" scaleRange = 0.4 and scalesNumber = 2, then there will be 2 scales, 0.8 and 1.2.");
// OpenPose 3-D Reconstruction
DEFINE_bool(3d, false, "Running OpenPose 3-D reconstruction demo: 1) Reading from a stereo camera system."
" 2) Performing 3-D reconstruction from the multiple views. 3) Displaying 3-D reconstruction"
" results. Note that it will only display 1 person. If multiple people is present, it will"
" fail.");
DEFINE_int32(3d_min_views, -1, "Minimum number of views required to reconstruct each keypoint. By default (-1), it will"
" require all the cameras to see the keypoint in order to reconstruct it.");
DEFINE_int32(3d_views, -1, "Complementary option for `--image_dir` or `--video`. OpenPose will read as many images per"
" iteration, allowing tasks such as stereo camera processing (`--3d`). Note that"
" `--camera_parameter_path` must be set. OpenPose must find as many `xml` files in the"
" parameter folder as this number indicates.");
// Extra algorithms
DEFINE_bool(identification, false, "Experimental, not available yet. Whether to enable people identification across frames.");
DEFINE_int32(tracking, -1, "Experimental, not available yet. Whether to enable people tracking across frames. The"
" value indicates the number of frames where tracking is run between each OpenPose keypoint"
" detection. Select -1 (default) to disable it or 0 to run simultaneously OpenPose keypoint"
" detector and tracking for potentially higher accurary than only OpenPose.");
DEFINE_int32(ik_threads, 0, "Experimental, not available yet. Whether to enable inverse kinematics (IK) from 3-D"
" keypoints to obtain 3-D joint angles. By default (0 threads), it is disabled. Increasing"
" the number of threads will increase the speed but also the global system latency.");
// OpenPose Rendering
DEFINE_int32(part_to_show, 0, "Prediction channel to visualize (default: 0). 0 for all the body parts, 1-18 for each body"
" part heat map, 19 for the background heat map, 20 for all the body part heat maps"
" together, 21 for all the PAFs, 22-40 for each body part pair PAF.");
DEFINE_bool(disable_blending, false, "If enabled, it will render the results (keypoint skeletons or heatmaps) on a black"
" background, instead of being rendered into the original image. Related: `part_to_show`,"
" `alpha_pose`, and `alpha_pose`.");
// OpenPose Rendering Pose
DEFINE_double(render_threshold, 0.05, "Only estimated keypoints whose score confidences are higher than this threshold will be"
" rendered. Generally, a high threshold (> 0.5) will only render very clear body parts;"
" while small thresholds (~0.1) will also output guessed and occluded keypoints, but also"
" more false positives (i.e., wrong detections).");
DEFINE_int32(render_pose, -1, "Set to 0 for no rendering, 1 for CPU rendering (slightly faster), and 2 for GPU rendering"
" (slower but greater functionality, e.g., `alpha_X` flags). If -1, it will pick CPU if"
" CPU_ONLY is enabled, or GPU if CUDA is enabled. If rendering is enabled, it will render"
" both `outputData` and `cvOutputData` with the original image and desired body part to be"
" shown (i.e., keypoints, heat maps or PAFs).");
DEFINE_double(alpha_pose, 0.6, "Blending factor (range 0-1) for the body part rendering. 1 will show it completely, 0 will"
" hide it. Only valid for GPU rendering.");
DEFINE_double(alpha_heatmap, 0.7, "Blending factor (range 0-1) between heatmap and original frame. 1 will only show the"
" heatmap, 0 will only show the frame. Only valid for GPU rendering.");
// OpenPose Rendering Face
DEFINE_double(face_render_threshold, 0.4, "Analogous to `render_threshold`, but applied to the face keypoints.");
DEFINE_int32(face_render, -1, "Analogous to `render_pose` but applied to the face. Extra option: -1 to use the same"
" configuration that `render_pose` is using.");
DEFINE_double(face_alpha_pose, 0.6, "Analogous to `alpha_pose` but applied to face.");
DEFINE_double(face_alpha_heatmap, 0.7, "Analogous to `alpha_heatmap` but applied to face.");
// OpenPose Rendering Hand
DEFINE_double(hand_render_threshold, 0.2, "Analogous to `render_threshold`, but applied to the hand keypoints.");
DEFINE_int32(hand_render, -1, "Analogous to `render_pose` but applied to the hand. Extra option: -1 to use the same"
" configuration that `render_pose` is using.");
DEFINE_double(hand_alpha_pose, 0.6, "Analogous to `alpha_pose` but applied to hand.");
DEFINE_double(hand_alpha_heatmap, 0.7, "Analogous to `alpha_heatmap` but applied to hand.");
#ifndef OPENPOSE_FLAGS_DISABLE_DISPLAY
// Display
DEFINE_bool(fullscreen, false, "Run in full-screen mode (press f during runtime to toggle).");
DEFINE_bool(no_gui_verbose, false, "Do not write text on output images on GUI (e.g., number of current frame and people). It"
" does not affect the pose rendering.");
DEFINE_int32(display, -1, "Display mode: -1 for automatic selection; 0 for no display (useful if there is no X server"
" and/or to slightly speed up the processing if visual output is not required); 2 for 2-D"
" display; 3 for 3-D display (if `--3d` enabled); and 1 for both 2-D and 3-D display.");
#endif // OPENPOSE_FLAGS_DISABLE_DISPLAY
// Command Line Interface Verbose
DEFINE_double(cli_verbose, -1.f, "If -1, it will be disabled (default). If it is a positive integer number, it will print on"
" the command line every `verbose` frames. If number in the range (0,1), it will print the"
" progress every `verbose` times the total of frames.");
// Result Saving
DEFINE_string(write_images, "", "Directory to write rendered frames in `write_images_format` image format.");
DEFINE_string(write_images_format, "png", "File extension and format for `write_images`, e.g., png, jpg or bmp. Check the OpenCV"
" function cv::imwrite for all compatible extensions.");
DEFINE_string(write_video, "", "Full file path to write rendered frames in motion JPEG video format. It might fail if the"
" final path does not finish in `.avi`. It internally uses cv::VideoWriter. Flag"
" `write_video_fps` controls FPS. Alternatively, the video extension can be `.mp4`,"
" resulting in a file with a much smaller size and allowing `--write_video_with_audio`."
" However, that would require: 1) Ubuntu or Mac system, 2) FFmpeg library installed"
" (`sudo apt-get install ffmpeg`), 3) the creation temporarily of a folder with the same"
" file path than the final video (without the extension) to storage the intermediate frames"
" that will later be used to generate the final MP4 video.");
DEFINE_double(write_video_fps, -1., "Frame rate for the recorded video. By default, it will try to get the input frames producer"
" frame rate (e.g., input video or webcam frame rate). If the input frames producer does not"
" have a set FPS (e.g., image_dir or webcam if OpenCV not compiled with its support), set"
" this value accordingly (e.g., to the frame rate displayed by the OpenPose GUI).");
DEFINE_bool(write_video_with_audio, false, "If the input is video and the output is so too, it will save the video with audio. It"
" requires the output video file path finishing in `.mp4` format (see `write_video` for"
" details).");
DEFINE_string(write_video_3d, "", "Analogous to `--write_video`, but applied to the 3D output.");
DEFINE_string(write_video_adam, "", "Experimental, not available yet. Analogous to `--write_video`, but applied to Adam model.");
DEFINE_string(write_json, "", "Directory to write OpenPose output in JSON format. It includes body, hand, and face pose"
" keypoints (2-D and 3-D), as well as pose candidates (if `--part_candidates` enabled).");
DEFINE_string(write_coco_json, "", "Full file path to write people pose data with JSON COCO validation format. If foot, face,"
" hands, etc. JSON is also desired (`--write_coco_json_variants`), they are saved with"
" different file name suffix.");
DEFINE_int32(write_coco_json_variants, 1, "Add 1 for body, add 2 for foot, 4 for face, and/or 8 for hands. Use 0 to use all the"
" possible candidates. E.g., 7 would mean body+foot+face COCO JSON.");
DEFINE_int32(write_coco_json_variant, 0, "Currently, this option is experimental and only makes effect on car JSON generation. It"
" selects the COCO variant for cocoJsonSaver.");
DEFINE_string(write_heatmaps, "", "Directory to write body pose heatmaps in PNG format. At least 1 `add_heatmaps_X` flag"
" must be enabled.");
DEFINE_string(write_heatmaps_format, "png", "File extension and format for `write_heatmaps`, analogous to `write_images_format`."
" For lossless compression, recommended `png` for integer `heatmaps_scale` and `float` for"
" floating values.");
DEFINE_string(write_keypoint, "", "(Deprecated, use `write_json`) Directory to write the people pose keypoint data. Set format"
" with `write_keypoint_format`.");
DEFINE_string(write_keypoint_format, "yml", "(Deprecated, use `write_json`) File extension and format for `write_keypoint`: json, xml,"
" yaml & yml. Json not available for OpenCV < 3.0, use `write_json` instead.");
// Result Saving - Extra Algorithms
DEFINE_string(write_bvh, "", "Experimental, not available yet. E.g., `~/Desktop/mocapResult.bvh`.");
// UDP Communication
DEFINE_string(udp_host, "", "Experimental, not available yet. IP for UDP communication. E.g., `192.168.0.1`.");
DEFINE_string(udp_port, "8051", "Experimental, not available yet. Port number for UDP communication.");
#endif // OPENPOSE_FLAGS_DISABLE_POSE
#endif // OPENPOSE_FLAGS_HPP
28 comments
大家运行时用的内存是多少呢？我4GB，一直死机
确认下只加载模型，不预测所需的资源
我用jetson nano B01运行opWrapper.start()语句后，nano的内存用量暴涨，很快就死机了，输入图片很小，params["net_resolution"] = "16x16"还是一样的结果。这怎么办啊
你好，请问上边列出的参数怎么用呢？是不是只能运行example_turorial里面的.py文件加上 --COCO等参数。假如我import pyopenpose以后，自定义了图片的处理代码main.py, 怎么用这些参数呢，是用argparser还是代码里的params，然后opWrapper.configure(params)呢？
py 文件里可以直接在 params 中指定相应的参数.
请问一下这个说out of memory应该怎么办啊F0312 17:41:09.703367 9296 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory
显存不足，更大显存的显卡或者减小输入图片的尺寸.
你好，为什么调用python接口，运行速度才2fps比原来慢多了呢？
相同环境吗？python 接口是比 C++ 的慢
window10 上面出现这个问题怎么解决呢
你好，我想咨询下那个黑色背景+骨架的输出图片是怎么生成的呀？（用什么命令）
您好，我想问一下在执行datum.cvInputData = imageToProcess这条语句时出现错误TypeError: (): incompatible function arguments. The following argument types are supported:1. (self: pyopenpose.Datum, arg0: op::Matrix) -> None
我也遇到同样问题,请问楼主解决了吗
应该是代码的版本不一致，函数接口发生了改变.
好的那我再看一下 非常感谢
请问这个问题解决了吗
您好，我想问下openpose编译完成后，没有找到build/python/openpose/pyopenpose.cpython-35m-x86_64-linux-gnu.so这个文件，您可以详细说下这个文件时如何产生吗？
在 cmake 配置后，会生成 build 路径，再 make -j 后即会产生该库文件.
已经解决啦，谢谢
您好，我使用了make -j 依然没有产生build/python/openpose/pyopenpose.cpython-35m-x86_64-linux-gnu.so这个文件，请问与我的opencv版本有关吗？我的opencv是2.4,我看网上有说PyOpenPose必须要3的版本
编译过程有没有出错呢？我用的是opencv4.0.1
没有出错，我用的命令行mkdir build ;cmake -DBUILD_PYTHON=ON ..;make 命令,无法运行examples/turtorial_api_python测试demo...没有生成build/python/openpose/pyopenpose.cpython-35m-x86_64-linux-gnu.so那个文件
用 cmake 界面工具先配置下，再 make 试试.
您好，我通过cmake还是没出现那个文件，请问可以加一下您的好友吗？qq:709053760
Configure 后，勾选 BUILD_PYTHON 了吗？
勾选了，不选好像生不成python文件夹
这个就很奇怪了，我测试 1.5.0 版本是没出现类似的问题
我都重新装了一遍，改好了谢谢