A example of OpenCV dnn framework working on a bare Raspberry Pi with TensorFlow models.
Papers
https://arxiv.org/abs/1611.10012
Training set: COCO
Size: 300x300
Frame rate V1 : 3.19 FPS (RPi 4)
Frame rate V1_0.75: 4.98 FPS (RPi 4)
Frame rate V2 : 2.02 FPS (RPi 4)
Frame rate V2 Lite: 3.86 FPS (RPi 4)
Special made for a bare Raspberry Pi see: https://qengineering.eu/deep-learning-with-opencv-on-raspberry-pi-4.html
To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/MobileNet_SSD_OpenCV_TensorFlow/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md
Your MyDir folder must now look like this:
Traffic.jpg
COCO_labels.txt
frozen_inference_graph_V1.pb (download this file from: https://drive.google.com/open?id=1sDn1guYV6oj-AeYuC-riGRh4kv9XBTMz )
frozen_inference_graph_V2.pb (download this file from: https://drive.google.com/open?id=1EU6tVcDNLNwv-pbJUXL7wYUchFHxr5fw )
ssd_mobilenet_v1_coco_2017_11_17.pbtxt
ssd_mobilenet_v2_coco_2018_03_29.pbtxt
TestOpenCV_TensorFlow.cpb
MobileNetV1.cpp (can be use for V2 version also)
Run TestOpenCV_Caffe.cpb with Code::Blocks. Remember, you also need a working OpenCV 4 on your Raspberry.
