Implementing Object detection on Android with NCNN about opendatacam-mobile

[tdurand]

Leaving this issue as documentation , also dropping this link here which discuss some of the existing neural network inference framework out there for mobile devices, maybe good to add to the future documentation: https://qengineering.eu/deep-learning-software-for-raspberry-pi-and-alternatives.html

[tdurand]

In order to avoid Licensing the mobile app at GPLv3 ( by using https://github.com/cmdbug/YOLOv5_NCNN ) , need to rework the YOLO implementation by using:

• The example using OpenCV here: https://github.com/Tencent/ncnn/blob/master/examples/yolov4.cpp
• And combine it with the android implementation of yolov5 : https://github.com/nihui/ncnn-android-yolov5/blob/master/app/src/main/jni/yolov5ncnn_jni.cpp

Which are BSD licenced.. compatible with MIT

[tdurand]

Some more notes on this:

• Switched to a very optimized inference size which should boost FPS performance without degrating much perfs, as the Aspect ratio is 16/9, input in yolo is now set to 320 x 192 (needs to be multiple of 32) ( got the idea here ultralytics/yolov5#1127 (comment) )

• CameraX API notes: refactored it using code from : https://github.com/android/camera-samples/tree/main/CameraXBasic , https://github.com/android/camera-samples/tree/main/CameraXTfLite

• NCNN upgrade done also