head_pose_picture

This sample provides reference for you to learn the Ascend AI Software Stack and cannot be used for commercial purposes.

This sample works with CANN 3.0.0 and later versions, and supports Atlas 200 DK and Atlas 300.

WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

Function: tracks multiple pedestrians in a scene with the mot_v2.om model.

Input: a source image

Output: the result image

Prerequisites

Before deploying this sample, ensure that:

• The environment has been set up by referring to Environment Preparation and Dependency Installation.
• The development environment and operating environment of the corresponding product have been set up.

Software Preparation

• Make sure you log in to the operating environment (HwHiAiUser)

  ssh [email protected]
  

  NOTE

  Replace xxx.xxx.xxx.xxx with the IP address of the operating environment. The IP address of Atlas 200 DK is 192.168.1.2 when it is connected over the USB port, and that of Atlas 300 is the corresponding public network IP address.

1. Obtain the source package.

cd $HOME
git clone https://github.com/Ascend/samples.git

2. Obtain the Offline Model (om) or Convert pb to om in Step 3.

Ensure you are in the project directory (head_pose_picture/) and run one of the following commands in the table to obtain the pedestrian tracking model used in the application.

cd $HOME/samples/python/contrib/head_pose_picture/

Model	How to Obtain
WHENet_b2_a1_modified.om	wget -nc --no-check-certificate 'https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/head_pose_picture/moWHENet_b2_a1_modifiedt_v2.om' -O model/moWHENet_b2_a1_modifiedt_v2.om
yolo_model.om	wget -nc --no-check-certificate 'https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/head_pose_picture/yolo_model.om' -O model/yolo_model.om
WHENet_b2_a1_modified.pb	wget -nc --no-check-certificate 'https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/head_pose_picture/WHENet_b2_a1_modified.pb' -O model/WHENet_b2_a1_modified.pb
yolo_model.pb	wget -nc --no-check-certificate 'https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/head_pose_picture/yolo_model.pb' -O model/yolo_model.pb

NOTE

• WHENet_b2_a1_modified.om and yolo_model.om offline models you can use out-of-the-box without model conversion. If you use this then you can skip the next step on model conversion.
• WHENet_b2_a1_modified.pb and yolo_model.pb pb models for those that want to configure the model conversion process.

3. (OPTIONAL) Convert the original pb model to a DaVinci offline model.

Note: Ensure that the environment variables have been configured in Environment Preparation and Dependency Installation.

1. Set the LD_LIBRARY_PATH environment variable.

   The LD_LIBRARY_PATH environment variable conflicts with the sample when Ascend Tensor Compiler (ATC) is used. Therefore, you need to set this environment variable separately in the command line to facilitate modification.

   export LD_LIBRARY_PATH=${install_path}/compiler/lib64
   

For CANN 3.0.0 and later:

2. Go to the project directory (head_pose_picture) and run the model conversion command to convert the model:

   cd $HOME/samples/python/contrib/head_pose_picture/
   atc --framework=3 --model=model/yolo_model.pb --input_shape="input_1:1,416,416,3" --input_format=NHWC --output=model/yolo_model --output_type=FP32 --soc_version=Ascend310
   atc --framework=3 --model=model/WHENet_b2_a1_modified.pb --input_shape="input_1:1,224,224,3" --input_format=NHWC --output=model/WHENet_b2_a1_modified --output_type=FP32 --soc_version=Ascend310
   

Sample Running

• Download Sample Image

  cd $HOME/samples/python/contrib/head_pose_picture/
  wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/head_pose_picture/test.jpg -O data/test.jpg
  

• Test Sample Image

  cd $HOME/samples/python/contrib/head_pose_picture/src
  python3 main.py