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Human pose estimation models update
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demos/python_demos/human_pose_estimation_demo/README.md
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| # Human Pose Estimation Python\* Demo | ||
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| This demo showcases the work of multi-person 2D pose estimation algorithms. The task is to predict a pose: body skeleton, which consists of a predefined set of keypoints and connections between them, for every person in an input image/video. | ||
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| Demo application supports inference in both sync and async modes. Please refer to [Optimization Guide](https://docs.openvinotoolkit.org/latest/_docs_optimization_guide_dldt_optimization_guide.html) and [Object Detection SSD, Async API performance showcase](../../object_detection_demo_ssd_async/README.md) demo for more information about Async API and its use. | ||
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| Other demo objectives are: | ||
| * Video as input support via OpenCV\* | ||
| * Visualization of the resulting poses | ||
| * Demonstration of the Async API in action. For this, the demo features two modes toggled by the **Tab** key: | ||
| - "User specified" mode, where you can set the number of Infer Requests, throughput streams and threads. | ||
| Inference, starting new requests and displaying the results of completed requests are all performed asynchronously. | ||
| The purpose of this mode is to get the higher FPS by fully utilizing all available devices. | ||
| - "Min latency" mode, which uses only one Infer Request. The purpose of this mode is to get the lowest latency. | ||
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| ## How It Works | ||
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| On the start-up, the application reads command-line parameters and loads a network to the Inference | ||
| Engine. Upon getting a frame from the OpenCV VideoCapture, it performs inference and displays the results. | ||
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| > **NOTE**: By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work | ||
| with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your | ||
| model using the Model Optimizer tool with `--reverse_input_channels` argument specified. For more information about | ||
| the argument, refer to **When to Reverse Input Channels** section of | ||
| [Converting a Model Using General Conversion Parameters](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html). | ||
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| ## Running | ||
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| Running the application with the `-h` option yields the following usage message: | ||
| ``` | ||
| python3 human_pose_estimation.py -h | ||
| ``` | ||
| The command yields the following usage message: | ||
| ``` | ||
| usage: human_pose_estimation.py [-h] -i INPUT -m MODEL -at {ae,openpose} | ||
| [--tsize TSIZE] [-t PROB_THRESHOLD] [-r] | ||
| [-d DEVICE] [-nireq NUM_INFER_REQUESTS] | ||
| [-nstreams NUM_STREAMS] | ||
| [-nthreads NUM_THREADS] [-loop LOOP] | ||
| [-no_show] [-u UTILIZATION_MONITORS] | ||
| Options: | ||
| -h, --help Show this help message and exit. | ||
| -i INPUT, --input INPUT | ||
| Required. Path to an image, video file or a numeric | ||
| camera ID. | ||
| -m MODEL, --model MODEL | ||
| Required. Path to an .xml file with a trained model. | ||
| -at {ae,openpose}, --architecture_type {ae,openpose} | ||
| Required. Type of the network, either "ae" for | ||
| Associative Embedding or "openpose" for OpenPose. | ||
| --tsize TSIZE Optional. Target input size. This demo implements | ||
| image pre-processing pipeline that is common to human | ||
| pose estimation approaches. Image is resize first to | ||
| some target size and then the network is reshaped to | ||
| fit the input image shape. By default target image | ||
| size is determined based on the input shape from IR. | ||
| Alternatively it can be manually set via this | ||
| parameter. Note that for OpenPose-like nets image is | ||
| resized to a predefined height, which is the target | ||
| size in this case. For Associative Embedding-like nets | ||
| target size is the length of a short image side. | ||
| -t PROB_THRESHOLD, --prob_threshold PROB_THRESHOLD | ||
| Optional. Probability threshold for poses filtering. | ||
| -r, --raw_output_message | ||
| Optional. Output inference results raw values showing. | ||
| -d DEVICE, --device DEVICE | ||
| Optional. Specify the target device to infer on; CPU, | ||
| GPU, FPGA, HDDL or MYRIAD is acceptable. The sample | ||
| will look for a suitable plugin for device specified. | ||
| Default value is CPU. | ||
| -nireq NUM_INFER_REQUESTS, --num_infer_requests NUM_INFER_REQUESTS | ||
| Optional. Number of infer requests | ||
| -nstreams NUM_STREAMS, --num_streams NUM_STREAMS | ||
| Optional. Number of streams to use for inference on | ||
| the CPU or/and GPU in throughput mode (for HETERO and | ||
| MULTI device cases use format | ||
| <device1>:<nstreams1>,<device2>:<nstreams2> or just | ||
| <nstreams>) | ||
| -nthreads NUM_THREADS, --num_threads NUM_THREADS | ||
| Optional. Number of threads to use for inference on | ||
| CPU (including HETERO cases) | ||
| -loop LOOP, --loop LOOP | ||
| Optional. Number of times to repeat the input. | ||
| -no_show, --no_show Optional. Don't show output | ||
| -u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS | ||
| Optional. List of monitors to show initially. | ||
| ``` | ||
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| Running the application with the empty list of options yields the short usage message and an error message. | ||
| You can use the following command to do inference on CPU with a pre-trained human pose estimation model: | ||
| ``` | ||
| python3 human_pose_estimation.py -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/hpe.xml -d CPU | ||
| ``` | ||
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| To run the demo, you can use public or pre-trained models. You can download the pre-trained models with the OpenVINO | ||
| [Model Downloader](../../../tools/downloader/README.md) or from | ||
| [https://download.01.org/opencv/](https://download.01.org/opencv/). | ||
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| > **NOTE**: Before running the demo with a trained model, make sure the model is converted to the Inference Engine | ||
| format (\*.xml + \*.bin) using the | ||
| [Model Optimizer tool](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). | ||
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| The only GUI knob is to use **Tab** to switch between the synchronized execution ("Min latency" mode) | ||
| and the asynchronous mode configured with provided command-line parameters ("User specified" mode). | ||
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| ## Demo Output | ||
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| The demo uses OpenCV to display the resulting frame with estimated poses. | ||
| The demo reports | ||
| * **FPS**: average rate of video frame processing (frames per second) | ||
| * **Latency**: average time required to process one frame (from reading the frame to displaying the results) | ||
| You can use both of these metrics to measure application-level performance. | ||
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| ## See Also | ||
| * [Using Open Model Zoo demos](../../README.md) | ||
| * [Model Optimizer](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | ||
| * [Model Downloader](../../../tools/downloader/README.md) |
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