README.rst

Cascading Networks

Face Detection and Facial Landmarks

face_detection_and_landmarks.sh demonstrates face detection and facial landmarking on one video file source.

This is done by running a face detection pipeline (infer + postprocessing), cropping and scaling all detected faces, and sending them into the 2nd network of facial landmarking. All resulting detections and landmarks are then aggregated and drawn on the original frame. The two networks are running using one Hailo-8 device with two hailonet elements.

Options

./face_detection_and_landmarks.sh [OPTIONS] [-i INPUT_PATH]

• -i --input is an optional flag, a path to the video/camera displayed.
• --print-gst-launch prints the ready gst-launch command without running it
• --show-fps optional - enables printing FPS on screen
• --max-camera-resolution The maximun input resolution from camera as an input

Run

cd $TAPPAS_WORKSPACE/apps/h8/gstreamer/general/cascading_networks
./face_detection_and_landmarks.sh

The output should look like:

Models

• lightface_slim in resolution of 320X240X3 - https://github.com/hailo-ai/hailo_model_zoo/blob/master/hailo_model_zoo/cfg/networks/lightface_slim.yaml
• tddfa_mobilenet_v1 in resolution of 120X120X3 - https://github.com/hailo-ai/hailo_model_zoo/blob/master/hailo_model_zoo/cfg/networks/tddfa_mobilenet_v1.yaml

Method of Operation

This app is based on our cascaded networks pipeline template

Object Detection And Pose Estimation

object_detection_and_pose_estimation.sh demonstrates object detection and pose estimation on one video file source.

This is done by running an object detection pipeline, cropping and scaling each detected person, and sending them into a 2nd network of pose estimation. All resulting detections and landmarks are then aggregated and drawn on the original frame. The two networks are running using one Hailo-8 device with two hailonet elements.

Options

./object_detection_and_pose_estimation.sh [OPTIONS] [-i INPUT_PATH]

• -i --input is an optional flag, a path to the video/camera displayed.
• --print-gst-launch prints the ready gst-launch command without running it
• --show-fps optional - enables printing FPS on screen
• --max-camera-resolution The maximun input resolution from camera as an input

Run

cd $TAPPAS_WORKSPACE/apps/h8/gstreamer/general/cascading_networks
./object_detection_and_pose_estimation.sh

The output should look like:

Model

Joined together:

• 'yolov5m_wo_spp_60p' - https://github.com/hailo-ai/hailo_model_zoo/blob/master/hailo_model_zoo/cfg/networks/yolov5m_wo_spp_60p.yaml
• 'mspn_regnetx_800mf' - https://github.com/hailo-ai/hailo_model_zoo/blob/master/hailo_model_zoo/cfg/networks/mspn_regnetx_800mf.yaml

Method of Operation

This app is based on our cascaded networks pipeline template