This document mainly describes how to compile executable files from application source code.

$ sudo apt update
$ sudo apt install libopencv-dev python3-opencv

$ mkdir{workspace} && cd {workspace}
$ git clone https://github.com/khadas/aml_npu_app

$ cd {workspace}/aml_npu_app
$ ls
DDK_6.3.2  DDK_6.3.2.3  DDK_6.3.2.5  DDK_6.3.3.4  DDK_6.4.0.3  DDK_6.4.3  detect_library  LICENSE  NN_SLT

• DDK_xxx - Different versions of library source code
• detect_library - Application layer source code. detect_library/model_code link to the latest version of DDK
• NN_SLT - Separate source code directory for DnCnn model (no longer maintained)
• LICENSE - LICENSE file

$ cd {workspace}/aml_npu_app/detect_library
$ ls
densenet_ctc_demo_picture  inception  model_code  retinaface_demo_x11_usb  sample_demo_fb  sample_demo_x11  source_code  yolo_demo_fb_mipi  yolo_demo_fb_usb
yolo_demo_x11_mipi  yolo_demo_x11_usb  yolov7_tiny_demo_x11_usb  yolov8n_demo_x11_usb

• model_code - Load model and complete pre and post processing.
• source_code - Provide interfaces for demo to call model_code.
• Other folders - Load input and visualize the results.

Enter the directory of the library model_code, which is the directory of the DDK.

$ cd {workspace}/aml_npu_app/detect_library/model_code
$ ls
densenet_ctc  detect_mtcnn  detect_retinaface  detect_yoloface  detect_yolo_v2  detect_yolo_v3  detect_yolo_v3_tiny  detect_yolo_v4  detect_yolo_v7_tiny
detect_yolov8n  facenet

Except for detect_mtcnn, each directory represents a different model, and each model will be compiled into a library.

• densenet_ctc - Character recognition, used to identify characters. The input is only picture.
• detect_retinaface - Detect faces and the keypoints of faces.
• detect_yoloface - yoloface model, used to detect faces without keypoint.
• detect_yolo_v2 - yolov2 model, for object detection.
• detect_yolo_v3 - yolov3 model, for object detection.
• detect_yolo_v3_tiny - yolov3-tiny model, for object detection.
• detect_yolo_v4 - yolov4 model, used to detect faces.
• detect_yolo_v7_tiny - yolov7_tiny model, for object detection.
• detect_yolov8n - yolov8n model, for object detection. The latest yolo model.
• facenet - Has been deprecated.

Take detect_yolo_v3 as an example to illustrate the structure of each directory.

$ cd {workspace}/aml_npu_app/detect_library/model_code/detect_yolo_v3
$ ls
build_vx.sh  include  Makefile  makefile.linux  vnn_yolov3.c  yolo_v3.c  yolov3_process.c

Main content description:

• build_vx.sh - Compile script.
• include - The corresponding header files and all definitions will be placed in this directory.
• Makefile - Makefile file.
• makefile.linux - Make environment configuration file.
• vnn_yolov3.c - SDK The converted model processing file is mainly used to interface with the nb file.
• yolo_v3.c - Specify the called nb file, and define all the interfaces of the model call.
• yolov3_process.c - Mainly defines the pre-processing and post-processing of the model.

Enter the directory of the library source_code.

$ cd {workspace}/aml_npu_app/detect_library/model_code
$ ls
build_vx.sh  detect.c  detect_log.c  include  Makefile  makefile.linux

Main content description:

• build_vx.sh - Compile script.
• detect.c - Provide interface for detect and call model_code.
• detect_log.c - About log level.
• include - The corresponding header files and all definitions will be placed in this directory.
• Makefile - Makefile file.
• makefile.linux - Make environment configuration file.