OpenVINO を使用したリアルタイムの 3D 人間の姿勢推定¶
この Jupyter ノートブックはオンラインで起動でき、ブラウザーのウィンドウで対話型環境を開きます。ローカルにインストールすることもできます。次のオプションのいずれかを選択します。
このノートブックは、ウェブカメラ経由で OpenVINO を使用したリアルタイムの 3D 人間の姿勢推定を示します。Open Model Zoo の human-pose-estimation-3d-0001 モデルを使用します。このノートブックの最後では、ウェブカメラからのリアルタイムの推論結果が表示されます (利用可能な場合)。あるいは、ビデオファイルをアップロードしてアルゴリズムをテストすることもできます。Jupyter 拡張機能が適切にインストールされていること、および ``README.md`` で提案されているように JupyterLab を使用してデモを実行していることを確認してください。
注: ウェブカメラを使用するには、ウェブカメラを備えたコンピューター上でこの Jupyter ノートブックを実行する必要があります。リモートサーバー上で実行すると、ウェブカメラは機能しなくなります。ただし、最終ステップではビデオに対して推論を行うことができます。このデモでは、WebGL と統合された ``Three.js`` の Python インターフェイスを利用して、モデル推論からのデータを処理します。これらの結果は処理されノートブックに表示されます。
結果が正しく表示されることを確認するには、次のオペレーティング・システムのいずれかの推奨ブラウザーでコードを実行します。
- Linux*: Ubuntu*
- Windows*: Chrome
- macOS*: Safari
目次¶
必要条件¶
最新の Jupyter ノートブックのリリース (2.4.1) を使用すると、``pythreejs`` 拡張子が正しく表示されない場合があります。したがって、代わりに Jupyter Lab を使用することをお勧めします。
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DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
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インポート¶
import collections
import sys
import time
from pathlib import Path
import cv2
import ipywidgets as widgets
import numpy as np
from IPython.display import clear_output, display
import openvino as ov
sys.path.append("../utils")
import notebook_utils as utils
sys.path.append("./engine")
import engine.engine3js as engine
from engine.parse_poses import parse_poses
モデル¶
モデルのダウンロード¶
ここでは、openvino-dev パッケージのコマンドライン・ツールである omz_downloader を使用します。 omz_downloader ディレクトリー構造が自動的に作成され、選択したモデルがダウンロードされます。
# directory where model will be downloaded
base_model_dir = "model"
# model name as named in Open Model Zoo
model_name = "human-pose-estimation-3d-0001"
# selected precision (FP32, FP16)
precision = "FP32"
BASE_MODEL_NAME = f"{base_model_dir}/public/{model_name}/{model_name}"
model_path = Path(BASE_MODEL_NAME).with_suffix(".pth")
onnx_path = Path(BASE_MODEL_NAME).with_suffix(".onnx")
ir_model_path = f"model/public/{model_name}/{precision}/{model_name}.xml"
model_weights_path = f"model/public/{model_name}/{precision}/{model_name}.bin"
if not model_path.exists():
download_command = (
f"omz_downloader " f"--name {model_name} " f"--output_dir {base_model_dir}"
)
! $download_command
################|| Downloading human-pose-estimation-3d-0001 ||################
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========== Unpacking model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.tar.gz
モデルを OpenVINO IR 形式に変換¶
選択されたモデルはパブリック・ディレクトリーから取得されます。つまり、OpenVINO 中間表現 (OpenVINO IR) に変換する必要があります。omz_converter を使用して、ONNX 形式モデルを OpenVINO IR 形式に変換します。
if not onnx_path.exists():
convert_command = (
f"omz_converter "
f"--name {model_name} "
f"--precisions {precision} "
f"--download_dir {base_model_dir} "
f"--output_dir {base_model_dir}"
)
! $convert_command
========== Converting human-pose-estimation-3d-0001 to ONNX
Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/model_zoo/internal_scripts/pytorch_to_onnx.py --model-path=model/public/human-pose-estimation-3d-0001 --model-name=PoseEstimationWithMobileNet --model-param=is_convertible_by_mo=True --import-module=model --weights=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.pth --input-shape=1,3,256,448 --input-names=data --output-names=features,heatmaps,pafs --output-file=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx
ONNX check passed successfully.
========== Converting human-pose-estimation-3d-0001 to IR (FP32)
Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=model/public/human-pose-estimation-3d-0001/FP32 --model_name=human-pose-estimation-3d-0001 --input=data '--mean_values=data[128.0,128.0,128.0]' '--scale_values=data[255.0,255.0,255.0]' --output=features,heatmaps,pafs --input_model=model/public/human-pose-estimation-3d-0001/human-pose-estimation-3d-0001.onnx '--layout=data(NCHW)' '--input_shape=[1, 3, 256, 448]' --compress_to_fp16=False
[ INFO ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/406-3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.xml
[ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.workspace/scm/ov-notebook/notebooks/406-3D-pose-estimation-webcam/model/public/human-pose-estimation-3d-0001/FP32/human-pose-estimation-3d-0001.bin
推論デバイスの選択¶
OpenVINO を使用して推論を実行するためにドロップダウン・リストからデバイスを選択します。
core = ov.Core()
device = widgets.Dropdown(
options=core.available_devices + ["AUTO"],
value='AUTO',
description='Device:',
disabled=False,
)
device
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
モデルのロード¶
変換されたモデルは、ベンダー、モデル名、精度を示す固定構造に配置されます。
まず、OpenVINO ランタイムを初期化します。次に、.bin および .xml ファイルからネットワーク・アーキテクチャーとモデルの重みを読み取り、目的のデバイス用にコンパイルします。次に、コンパイルされたモデルを推論するため推論要求が作成されます。
# initialize OpenVINO
core = ov.Core()
# read the network and corresponding weights from file
model = core.read_model(model=ir_model_path, weights=model_weights_path)
# load the model on the specified device
compiled_model = core.compile_model(model=model, device_name=device.value)
infer_request = compiled_model.create_infer_request()
input_tensor_name = model.inputs[0].get_any_name()
# get input and output names of nodes
input_layer = compiled_model.input(0)
output_layers = list(compiled_model.outputs)
モデルの入力は入力画像からのデータであり、出力はヒートマップ、PAF (パーツ・アフィニティー・フィールド)、および特徴です。
input_layer.any_name, [o.any_name for o in output_layers]
('data', ['features', 'heatmaps', 'pafs'])
処理¶
モデル推論¶
ビデオファイルまたはリアルタイムのウェブカメラからキャプチャーされたフレームは、3D モデルの入力として使用されます。これは、出力ヒートマップ、PAF (パーツ・アフィニティー・フィールド)、および特徴を取得する方法です。
def model_infer(scaled_img, stride):
"""
Run model inference on the input image
Parameters:
scaled_img: resized image according to the input size of the model
stride: int, the stride of the window
"""
# Remove excess space from the picture
img = scaled_img[
0 : scaled_img.shape[0] - (scaled_img.shape[0] % stride),
0 : scaled_img.shape[1] - (scaled_img.shape[1] % stride),
]
img = np.transpose(img, (2, 0, 1))[
None,
]
infer_request.infer({input_tensor_name: img})
# A set of three inference results is obtained
results = {
name: infer_request.get_tensor(name).data[:]
for name in {"features", "heatmaps", "pafs"}
}
# Get the results
results = (results["features"][0], results["heatmaps"][0], results["pafs"][0])
return results
2D ポーズ・オーバーレイの描画¶
推論結果を取得した後、結果の画像に人体の構造を描画できるように、関節の接続を事前に定義する必要があります。関節は円として描画され、手足は線で描画されます。このコードは、Open Model Zoo の 3D Human Pose Estimation Demo をベースにしています。
# 3D edge index array
body_edges = np.array(
[
[0, 1],
[0, 9], [9, 10], [10, 11], # neck - r_shoulder - r_elbow - r_wrist
[0, 3], [3, 4], [4, 5], # neck - l_shoulder - l_elbow - l_wrist
[1, 15], [15, 16], # nose - l_eye - l_ear
[1, 17], [17, 18], # nose - r_eye - r_ear
[0, 6], [6, 7], [7, 8], # neck - l_hip - l_knee - l_ankle
[0, 12], [12, 13], [13, 14], # neck - r_hip - r_knee - r_ankle
]
)
body_edges_2d = np.array(
[
[0, 1], # neck - nose
[1, 16], [16, 18], # nose - l_eye - l_ear
[1, 15], [15, 17], # nose - r_eye - r_ear
[0, 3], [3, 4], [4, 5], # neck - l_shoulder - l_elbow - l_wrist
[0, 9], [9, 10], [10, 11], # neck - r_shoulder - r_elbow - r_wrist
[0, 6], [6, 7], [7, 8], # neck - l_hip - l_knee - l_ankle
[0, 12], [12, 13], [13, 14], # neck - r_hip - r_knee - r_ankle
]
)
def draw_poses(frame, poses_2d, scaled_img, use_popup):
"""
Draw 2D pose overlays on the image to visualize estimated poses.
Joints are drawn as circles and limbs are drawn as lines.
:param frame: the input image
:param poses_2d: array of human joint pairs
"""
for pose in poses_2d:
pose = np.array(pose[0:-1]).reshape((-1, 3)).transpose()
was_found = pose[2] > 0
pose[0], pose[1] = (
pose[0] * frame.shape[1] / scaled_img.shape[1],
pose[1] * frame.shape[0] / scaled_img.shape[0],
)
# Draw joints.
for edge in body_edges_2d:
if was_found[edge[0]] and was_found[edge[1]]:
cv2.line(
frame,
tuple(pose[0:2, edge[0]].astype(np.int32)),
tuple(pose[0:2, edge[1]].astype(np.int32)),
(255, 255, 0),
4,
cv2.LINE_AA,
)
# Draw limbs.
for kpt_id in range(pose.shape[1]):
if pose[2, kpt_id] != -1:
cv2.circle(
frame,
tuple(pose[0:2, kpt_id].astype(np.int32)),
3,
(0, 255, 255),
-1,
cv2.LINE_AA,
)
return frame
メイン処理関数¶
指定されたソースで 3D 姿勢推定を実行します。ウェブカメラのフィードまたはビデオファイルのどちらかである可能性があります。
def run_pose_estimation(source=0, flip=False, use_popup=False, skip_frames=0):
"""
2D image as input, using OpenVINO as inference backend,
get joints 3D coordinates, and draw 3D human skeleton in the scene
:param source: The webcam number to feed the video stream with primary webcam set to "0", or the video path.
:param flip: To be used by VideoPlayer function for flipping capture image.
:param use_popup: False for showing encoded frames over this notebook, True for creating a popup window.
:param skip_frames: Number of frames to skip at the beginning of the video.
"""
focal_length = -1 # default
stride = 8
player = None
skeleton_set = None
try:
# create video player to play with target fps video_path
# get the frame from camera
# You can skip first N frames to fast forward video. change 'skip_first_frames'
player = utils.VideoPlayer(source, flip=flip, fps=30, skip_first_frames=skip_frames)
# start capturing
player.start()
input_image = player.next()
# set the window size
resize_scale = 450 / input_image.shape[1]
windows_width = int(input_image.shape[1] * resize_scale)
windows_height = int(input_image.shape[0] * resize_scale)
# use visualization library
engine3D = engine.Engine3js(grid=True, axis=True, view_width=windows_width, view_height=windows_height)
if use_popup:
# display the 3D human pose in this notebook, and origin frame in popup window
display(engine3D.renderer)
title = "Press ESC to Exit"
cv2.namedWindow(title, cv2.WINDOW_KEEPRATIO | cv2.WINDOW_AUTOSIZE)
else:
# set the 2D image box, show both human pose and image in the notebook
imgbox = widgets.Image(
format="jpg", height=windows_height, width=windows_width
)
display(widgets.HBox([engine3D.renderer, imgbox]))
skeleton = engine.Skeleton(body_edges=body_edges)
processing_times = collections.deque()
while True:
# grab the frame
frame = player.next()
if frame is None:
print("Source ended")
break
# resize image and change dims to fit neural network input
# (see https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/human-pose-estimation-3d-0001)
scaled_img = cv2.resize(frame, dsize=(model.inputs[0].shape[3], model.inputs[0].shape[2]))
if focal_length < 0: # Focal length is unknown
focal_length = np.float32(0.8 * scaled_img.shape[1])
# inference start
start_time = time.time()
# get results
inference_result = model_infer(scaled_img, stride)
# inference stop
stop_time = time.time()
processing_times.append(stop_time - start_time)
# Process the point to point coordinates of the data
poses_3d, poses_2d = parse_poses(inference_result, 1, stride, focal_length, True)
# use processing times from last 200 frames
if len(processing_times) > 200:
processing_times.popleft()
processing_time = np.mean(processing_times) * 1000
fps = 1000 / processing_time
if len(poses_3d) > 0:
# From here, you can rotate the 3D point positions using the function "draw_poses",
# or you can directly make the correct mapping below to properly display the object image on the screen
poses_3d_copy = poses_3d.copy()
x = poses_3d_copy[:, 0::4]
y = poses_3d_copy[:, 1::4]
z = poses_3d_copy[:, 2::4]
poses_3d[:, 0::4], poses_3d[:, 1::4], poses_3d[:, 2::4] = (
-z + np.ones(poses_3d[:, 2::4].shape) * 200,
-y + np.ones(poses_3d[:, 2::4].shape) * 100,
-x,
)
poses_3d = poses_3d.reshape(poses_3d.shape[0], 19, -1)[:, :, 0:3]
people = skeleton(poses_3d=poses_3d)
try:
engine3D.scene_remove(skeleton_set)
except Exception:
pass
engine3D.scene_add(people)
skeleton_set = people
# draw 2D
frame = draw_poses(frame, poses_2d, scaled_img, use_popup)
else:
try:
engine3D.scene_remove(skeleton_set)
skeleton_set = None
except Exception:
pass
cv2.putText(
frame,
f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)",
(10, 30),
cv2.FONT_HERSHEY_COMPLEX,
0.7,
(0, 0, 255),
1,
cv2.LINE_AA,
)
if use_popup:
cv2.imshow(title, frame)
key = cv2.waitKey(1)
# escape = 27, use ESC to exit
if key == 27:
break
else:
# encode numpy array to jpg
imgbox.value = cv2.imencode(
".jpg",
frame,
params=[cv2.IMWRITE_JPEG_QUALITY, 90],
)[1].tobytes()
engine3D.renderer.render(engine3D.scene, engine3D.cam)
except KeyboardInterrupt:
print("Interrupted")
except RuntimeError as e:
print(e)
finally:
clear_output()
if player is not None:
# stop capturing
player.stop()
if use_popup:
cv2.destroyAllWindows()
if skeleton_set:
engine3D.scene_remove(skeleton_set)
実行¶
ウェブカメラをビデオ入力として使用して実行します。デフォルトでは、プライマリー・ウェブカメラは source=0 に設定されます。複数のウェブカメラがある場合、0 から始まる連続した番号が割り当てられます。前面カメラを使用する場合は、flip=True を設定します。一部のウェブブラウザー、特に Mozilla Firefox ではちらつきが発生する場合があります。ちらつきが発生する場合は、use_popup=True を設定してください。
注:
- このノートブックをウェブカメラで使用するには、ウェブカメラを備えたコンピューター上でノートブックを実行する必要があります。ノートブックをサーバー (Binder など) 上で実行する場合、ウェブカメラは機能しません。
- このノートブックをリモート・コンピューター (Binder など) で実行する場合、ポップアップ・モードは機能しない可能性があります。
ウェブカメラがない場合でも、ビデオファイルを使用してこのデモを実行できます。OpenCV でサポートされている形式であればどれでも機能します。
次の方法を使用すると、左側の画像をクリックしてマウスを移動して操作できます。
USE_WEBCAM = False
cam_id = 0
video_path = "https://github.com/intel-iot-devkit/sample-videos/raw/master/face-demographics-walking.mp4"
source = cam_id if USE_WEBCAM else video_path
run_pose_estimation(source=source, flip=isinstance(source, int), use_popup=False)