OpenCV based DNN Object Detection Library for Openframeworks
ofxOpenCvDnnObjectDetection is an addon for openframeworks(version.0.11.0).
I created this addon for beginers or learner who are going to try object detection with Yolo, SSD and R-CNN on OF. Latest oF includes opencv(version4) as a default opencv library, so you can run dnn object detection with ofxOpenCv.
See Examples for more details
Detection example for single image.
Realtime Detection example for webcam video.
My annotation software to export a yolo format. It enables you to annotate image, video, camera. I’m preparing the manual to use it…. not yet.
git clone https://github.com/TetsuakiBaba/ofxOpenCvDnnObjectDetection.git
sh getWeights.sh
Openframeworks(version.0.11.0) includes opencv-4.0.1 for ofxOpenCv addon. But you can get faster inference time by using local-compiled opencv library. Here is my inference time result on each dnn object detection.
Now, I’ve done an inference test on M1 mac with arm64 compiled opencv2.framework. If you would like to try oF with M1 native, please check the below link.
CPU Inference Time on MacBook Pro (13-inch Mid 2018) https://browser.geekbench.com/v5/cpu/1184669 | opencv.a (opencv-4.0.1, oF bundle) | opencv2.framework (opencv-4.2.0, local compiled) |
---|---|---|
yolov3 (416x416,coco) | 760 [ms] | 307 [ms] |
yolov3-tiny (416x416,coco) | 62 [ms] | 27 [ms] |
yolov2-tiny (416x416,coco) | 61 [ms] | 28 [ms] |
MobileNet-SSD (300x300, voc) | 40 [ms] | 26 [ms] |
SSD (300x300, coco) | 768 [ms] | 310 [ms] |
CPU Inference Time on mac mini (M1 2020) https://browser.geekbench.com/v5/cpu/5044574 | opencv.a (opencv-4.0.1, oF bundle) | opencv2.framework (opencv-4.5, intel macbook pro(2018) compiled) | opencv2.frameworks (Arm64 M1 compiled) |
---|---|---|---|
yolov3 (416x416,coco) | 412 [ms] | 413 [ms] | 320 [ms] |
yolov3-tiny (416x416,coco) | 39 [ms] | 39 [ms] | 30 [ms] |
yolov2-tiny (416x416,coco) | 37 [ms] | 38 [ms] | 29 [ms] |
MobileNet-SSD (300x300, voc) | — [ms] | — [ms] | — [ms] |
SSD (300x300, coco) | — [ms] | — [ms] | — [ms] |