Live Human Pose Estimation with OpenVINO¶
This notebook demonstrates live pose estimation with OpenVINO. We use the OpenPose model human-pose-estimation-0001 from Open Model Zoo. At the bottom of this notebook, you will see live inference results from your webcam. You can also upload a video file.
NOTE: To use the webcam, you must run this Jupyter notebook on a computer with a webcam. If you run on a server, the webcam will not work. However, you can still do inference on a video in the final step.
Imports¶
import collections
import os
import sys
import time
import cv2
import numpy as np
from IPython import display
from numpy.lib.stride_tricks import as_strided
from openvino.runtime import Core
from decoder import OpenPoseDecoder
sys.path.append("../utils")
import notebook_utils as utils
The model¶
Download the model¶
We use omz_downloader, which is a command line tool from the
openvino-dev package. omz_downloader automatically creates a
directory structure and downloads the selected model.
If you want to download another model, please change the model name and precision. Note: This will require a different pose decoder.
# directory where model will be downloaded
base_model_dir = "model"
# model name as named in Open Model Zoo
model_name = "human-pose-estimation-0001"
# selected precision (FP32, FP16, FP16-INT8)
precision = "FP16-INT8"
model_path = f"model/intel/{model_name}/{precision}/{model_name}.xml"
model_weights_path = f"model/intel/{model_name}/{precision}/{model_name}.bin"
if not os.path.exists(model_path):
download_command = f"omz_downloader " \
f"--name {model_name} " \
f"--precision {precision} " \
f"--output_dir {base_model_dir}"
! $download_command
################|| Downloading human-pose-estimation-0001 ||################
========== Downloading model/intel/human-pose-estimation-0001/FP16-INT8/human-pose-estimation-0001.xml
========== Downloading model/intel/human-pose-estimation-0001/FP16-INT8/human-pose-estimation-0001.bin
Load the model¶
Downloaded models are located in a fixed structure, which indicates vendor, model name and precision.
Only a few lines of code are required to run the model. First, we initialize the Inference Engine. Then we read the network architecture and model weights from the .bin and .xml files to compile it for the desired device.
# initialize inference engine
ie_core = Core()
# read the network and corresponding weights from file
model = ie_core.read_model(model=model_path, weights=model_weights_path)
# load the model on the CPU (you can use GPU or MYRIAD as well)
compiled_model = ie_core.compile_model(model=model, device_name="CPU")
# get input and output names of nodes
input_layer = compiled_model.input(0)
output_layers = list(compiled_model.outputs)
# get input size
height, width = list(input_layer.shape)[2:]
Input layer has the name of the input node and output layers contain names of output nodes of the network. In the case of the OpenPose Model, we have one input and two outputs: pafs and keypoints heatmap.
input_layer.any_name, [o.any_name for o in output_layers]
('data', ['Mconv7_stage2_L1', 'Mconv7_stage2_L2'])
Processing¶
OpenPoseDecoder¶
We need a decoder to transform the raw results from the neural network
into pose estimations. This magic happens inside Open Pose Decoder,
which is provided in the Open Model
Zoo
and compatible with the human-pose-estimation-0001 model.
If you choose a model other than human-pose-estimation-0001 you will
need another decoder (e.g. AssociativeEmbeddingDecoder), which is
available in the demos
section
of Open Model Zoo.
decoder = OpenPoseDecoder()
Process Results¶
A bunch of useful functions to transform results into poses.
First, we will pool the heatmap. Since pooling is not available in numpy, we use a simple method to do it directly with numpy. Then, we use non-maximum suppression to get the keypoints from the heatmap. After that, we decode poses using the decoder. Since the input image is bigger than the network outputs, we need to multiply all pose coordinates by a scaling factor.
# 2d pooling in numpy (from: htt11ps://stackoverflow.com/a/54966908/1624463)
def pool2d(A, kernel_size, stride, padding, pool_mode="max"):
"""
2D Pooling
Parameters:
A: input 2D array
kernel_size: int, the size of the window
stride: int, the stride of the window
padding: int, implicit zero paddings on both sides of the input
pool_mode: string, 'max' or 'avg'
"""
# Padding
A = np.pad(A, padding, mode="constant")
# Window view of A
output_shape = (
(A.shape[0] - kernel_size) // stride + 1,
(A.shape[1] - kernel_size) // stride + 1,
)
kernel_size = (kernel_size, kernel_size)
A_w = as_strided(
A,
shape=output_shape + kernel_size,
strides=(stride * A.strides[0], stride * A.strides[1]) + A.strides
)
A_w = A_w.reshape(-1, *kernel_size)
# Return the result of pooling
if pool_mode == "max":
return A_w.max(axis=(1, 2)).reshape(output_shape)
elif pool_mode == "avg":
return A_w.mean(axis=(1, 2)).reshape(output_shape)
# non maximum suppression
def heatmap_nms(heatmaps, pooled_heatmaps):
return heatmaps * (heatmaps == pooled_heatmaps)
# get poses from results
def process_results(img, pafs, heatmaps):
# this processing comes from
# https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/common/python/models/open_pose.py
pooled_heatmaps = np.array(
[[pool2d(h, kernel_size=3, stride=1, padding=1, pool_mode="max") for h in heatmaps[0]]]
)
nms_heatmaps = heatmap_nms(heatmaps, pooled_heatmaps)
# decode poses
poses, scores = decoder(heatmaps, nms_heatmaps, pafs)
output_shape = list(compiled_model.output(index=0).partial_shape)
output_scale = img.shape[1] / output_shape[3].get_length(), img.shape[0] / output_shape[2].get_length()
# multiply coordinates by scaling factor
poses[:, :, :2] *= output_scale
return poses, scores
Draw Pose Overlays¶
Draw pose overlays on the image to visualize estimated poses. Joints are drawn as circles and limbs are drawn as lines. The code is based on the Human Pose Estimation Demo from Open Model Zoo.
colors = ((255, 0, 0), (255, 0, 255), (170, 0, 255), (255, 0, 85), (255, 0, 170), (85, 255, 0),
(255, 170, 0), (0, 255, 0), (255, 255, 0), (0, 255, 85), (170, 255, 0), (0, 85, 255),
(0, 255, 170), (0, 0, 255), (0, 255, 255), (85, 0, 255), (0, 170, 255))
default_skeleton = ((15, 13), (13, 11), (16, 14), (14, 12), (11, 12), (5, 11), (6, 12), (5, 6), (5, 7),
(6, 8), (7, 9), (8, 10), (1, 2), (0, 1), (0, 2), (1, 3), (2, 4), (3, 5), (4, 6))
def draw_poses(img, poses, point_score_threshold, skeleton=default_skeleton):
if poses.size == 0:
return img
img_limbs = np.copy(img)
for pose in poses:
points = pose[:, :2].astype(np.int32)
points_scores = pose[:, 2]
# Draw joints.
for i, (p, v) in enumerate(zip(points, points_scores)):
if v > point_score_threshold:
cv2.circle(img, tuple(p), 1, colors[i], 2)
# Draw limbs.
for i, j in skeleton:
if points_scores[i] > point_score_threshold and points_scores[j] > point_score_threshold:
cv2.line(img_limbs, tuple(points[i]), tuple(points[j]), color=colors[j], thickness=4)
cv2.addWeighted(img, 0.4, img_limbs, 0.6, 0, dst=img)
return img
Main Processing Function¶
Run pose estimation on the specified source. Either a webcam or a video file.
# main processing function to run pose estimation
def run_pose_estimation(source=0, flip=False, use_popup=False, skip_first_frames=0):
pafs_output_key = compiled_model.output("Mconv7_stage2_L1")
heatmaps_output_key = compiled_model.output("Mconv7_stage2_L2")
player = None
try:
# create video player to play with target fps
player = utils.VideoPlayer(source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
# start capturing
player.start()
if use_popup:
title = "Press ESC to Exit"
cv2.namedWindow(title, cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)
processing_times = collections.deque()
while True:
# grab the frame
frame = player.next()
if frame is None:
print("Source ended")
break
# if frame larger than full HD, reduce size to improve the performance
scale = 1280 / max(frame.shape)
if scale < 1:
frame = cv2.resize(frame, None, fx=scale, fy=scale, interpolation=cv2.INTER_AREA)
# resize image and change dims to fit neural network input
# (see https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/human-pose-estimation-0001)
input_img = cv2.resize(frame, (width, height), interpolation=cv2.INTER_AREA)
# create batch of images (size = 1)
input_img = input_img.transpose((2,0,1))[np.newaxis, ...]
# measure processing time
start_time = time.time()
# get results
results = compiled_model([input_img])
stop_time = time.time()
pafs = results[pafs_output_key]
heatmaps = results[heatmaps_output_key]
# get poses from network results
poses, scores = process_results(frame, pafs, heatmaps)
# draw poses on a frame
frame = draw_poses(frame, poses, 0.1)
processing_times.append(stop_time - start_time)
# use processing times from last 200 frames
if len(processing_times) > 200:
processing_times.popleft()
_, f_width = frame.shape[:2]
# mean processing time [ms]
processing_time = np.mean(processing_times) * 1000
fps = 1000 / processing_time
cv2.putText(frame, f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)", (20, 40),
cv2.FONT_HERSHEY_COMPLEX, f_width / 1000, (0, 0, 255), 1, cv2.LINE_AA)
# use this workaround if there is flickering
if use_popup:
cv2.imshow(title, frame)
key = cv2.waitKey(1)
# escape = 27
if key == 27:
break
else:
# encode numpy array to jpg
_, encoded_img = cv2.imencode(".jpg", frame, params=[cv2.IMWRITE_JPEG_QUALITY, 90])
# create IPython image
i = display.Image(data=encoded_img)
# display the image in this notebook
display.clear_output(wait=True)
display.display(i)
# ctrl-c
except KeyboardInterrupt:
print("Interrupted")
# any different error
except RuntimeError as e:
print(e)
finally:
if player is not None:
# stop capturing
player.stop()
if use_popup:
cv2.destroyAllWindows()
Run¶
Run Live Pose Estimation¶
Run using a webcam as the video input. By default, the primary webcam is
set with source=0. If you have multiple webcams, each one will be
assigned a consecutive number starting at 0. Set flip=True when
using a front-facing camera. Some web browsers, especially Mozilla
Firefox, may cause flickering. If you experience flickering, set
use_popup=True.
Note: To use this notebook with a webcam, you need to run the notebook on a computer with a webcam. If you run the notebook on a server (e.g. Binder), the webcam will not work.
Note: Popup mode may not work if you run this notebook on a remote computer (e.g. Binder).
run_pose_estimation(source=0, flip=True, use_popup=False)
Cannot open camera 0
Run Pose Estimation on a Video File¶
If you don’t have a webcam, you can still run this demo with a video file. Any format supported by OpenCV will work. You can skip first N frames to fast forward video.
video_file = "https://github.com/intel-iot-devkit/sample-videos/blob/master/store-aisle-detection.mp4?raw=true"
run_pose_estimation(video_file, flip=False, use_popup=False, skip_first_frames=500)
Source ended