Human pose estimation models update

demos/README.md

@@ -14,6 +14,7 @@ The Open Model Zoo includes the following demos:
 - [Formula Recognition Python* Demo](./python_demos/formula_recognition_demo/README.md) - The demo demonstrates how to run Im2latex formula recognition models and recognize latex formulas.
 - [Gaze Estimation C++ Demo](./gaze_estimation_demo/README.md) - Face detection followed by gaze estimation, head pose estimation and facial landmarks regression.
 - [Human Pose Estimation C++ Demo](./human_pose_estimation_demo/README.md) - Human pose estimation demo.
+- [Human Pose Estimation Python\* Demo](./python_demos/human_pose_estimation_demo/README.md) - Human pose estimation demo.
 - [Image Inpainting Python Demo](./python_demos/image_inpainting_demo/README.md) - Demo application for GMCNN inpainting network.
 - [Image Retrieval Python* Demo](./python_demos/image_retrieval_demo/README.md) - The demo demonstrates how to run Image Retrieval models using OpenVINO™.
 - [Image Segmentation C++ Demo](./segmentation_demo/README.md) - Inference of image segmentation networks like FCN8 (the demo supports only images as inputs).
@@ -69,7 +70,10 @@ The table below shows the correlation between models, demos, and supported plugi
 | person-vehicle-bike-detection-crossroad-0078     | [Crossroad Camera Demo](./crossroad_camera_demo/README.md)                            | Supported | Supported | Supported   | Supported       |
 | person-vehicle-bike-detection-crossroad-1016 | [Crossroad Camera Demo](./crossroad_camera_demo/README.md)                         | Supported |           |             |                 |
 | person-vehicle-bike-detection-crossroad-yolov3-1020 | [Object Detection for YOLO V3 Python\* Demo](./python_demos/object_detection_demo_yolov3_async/README.md) | Supported |           |             |                 |
-| human-pose-estimation-0001                       | [Human Pose Estimation Demo](./human_pose_estimation_demo/README.md)                  | Supported | Supported | Supported   | Supported       |
+| human-pose-estimation-0001                       | [Human Pose Estimation Demo](./human_pose_estimation_demo/README.md)<br>[Human Pose Estimation Python\* Demo](./python_demos/human_pose_estimation_demo/README.md) | Supported | Supported | Supported   | Supported       |
+| human-pose-estimation-0002                       | [Human Pose Estimation Python\* Demo](./python_demos/human_pose_estimation_demo/README.md) | Supported | Supported |             |                 |
+| human-pose-estimation-0003                       | [Human Pose Estimation Python\* Demo](./python_demos/human_pose_estimation_demo/README.md) | Supported | Supported |             |                 |
+| human-pose-estimation-0004                       | [Human Pose Estimation Python\* Demo](./python_demos/human_pose_estimation_demo/README.md) | Supported | Supported |             |                 |
 | image-retrieval-0001                             | [Image Retrieval Python\* Demo](./python_demos/image_retrieval_demo/README.md)         | Supported | Supported | Supported   | Supported       |
 | semantic-segmentation-adas-0001                  | [Image Segmentation Demo](./segmentation_demo/README.md)                              | Supported | Supported |             | Supported       |
 | instance-segmentation-security-0010              | [Instance Segmentation Demo](./python_demos/instance_segmentation_demo/README.md) | Supported |           |             | Supported       |

demos/python_demos/human_pose_estimation_demo/README.md

@@ -0,0 +1,118 @@
+# Human Pose Estimation Python\* Demo
+
+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.
+
+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.
+
+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.
+
+## How It Works
+
+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.
+
+> **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).
+
+## Running
+
+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.
+```
+
+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
+```
+
+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/).
+
+> **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).
+
+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).
+
+## Demo Output
+
+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.
+
+## 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)