Awesome
origami-dnn
OpenCV DNN project building on origami to run Tensorflow/Caffe/Darknet networks for image/video real time analysis.
New YoloV6
To quickly get started, run one of the following command:
# Yolo v6n
lein yolo.v6cam networks.yolo:yolov6n:1.0
# Yolo v6s
lein yolo.v6cam networks.yolo:yolov6s:1.0
# Yolo v6t
lein yolo.v6cam networks.yolo:yolov6t:1.0
# Yolo v6l
lein yolo.v6cam networks.yolo:yolov6l:1.0
Run origami-dnn on a file
To run detection on a pretrained network, read the image, and call the network detection:
(-> input
(imread)
(mobilenet/find-objects net opts)
(d/blue-boxes! labels)
(imwrite output))
Run origami-dnn on a stream from camera
(ns origami-dnn.demo.ssdnet.cam
(:require [origami-dnn.net.mobilenet :refer [find-objects]]
[origami-dnn.core :as origami-dnn]
[origami-dnn.draw :as d]
[opencv4.utils :refer [resize-by simple-cam-window]]))
(defn -main [& args]
(let [ [net opts labels] (origami-dnn/read-net-from-repo "networks.tensorflow:tf-ssdmobilenet:1.0.0") ]
(simple-cam-window
(read-string (slurp "cam_config.edn"))
(fn [buffer]
(-> buffer
(find-objects net opts)
(d/red-boxes! labels))))))
| Alias | Format | Network | Network Origami ID | DataSet | Type | Example |
|---|---|---|---|---|---|---|
| mobilenet.cam | caffe | mobilenet | networks.caffe:mobilenet:1.0.0 | Run mobilenet on a webcam stream | ||
| mobilenet.videotofile | caffe | mobilenet | networks.caffe:mobilenet:1.0.0 | Run mobilenet on a video file, and store it as a file | ||
| mobilenet.videotoscreen | caffe | mobilenet | networks.caffe:mobilenet:1.0.0 | Run mobilenet on a video file, and display the file in a window | ||
| mobilenet.one | caffe | mobilenet | networks.caffe:mobilenet:1.0.0 | Run mobilenet on one image and save the picture as a file | ||
| yolo.cam | darknet | Yolo | networks.yolo:yolov2-tiny:1.0.0 | Run yolo on a webcam stream | ||
| yolo.one | darknet | Yolo | networks.yolo:yolov2-tiny:1.0.0 | Run yolo (tiny) on a picture | ||
| yolo.v2 | darknet | Yolo | networks.yolo:yolov2:1.0.0 | Run yolo v2 on a picture | ||
| yolo.v2tiny | darknet | Yolo | networks.yolo:yolov2-tiny:1.0.0 | Run yolo v2 tiny on a picture | ||
| yolo.v3 | darknet | Yolo | networks.yolo:yolov3:1.0.0 | Run yolo v3 on a picture | ||
| yolo.v3tiny | darknet | Yolo | networks.yolo:yolov3-tiny:1.0.0 | Run yolo v3 tiny on a picture | ||
| yolo.v4 | darknet | Yolo | networks.yolo:yolov4:1.0.0 | Run yolo v4 on a picture | ||
| yolo.v6 | darknet | Yolo | networks.yolo:yolov6n:1.0 | Run yolo v6 on a picture | ||
| yolo.v6cam | darknet | Yolo | networks.yolo:yolov6n:1.0 | Run yolo v6 on a cam | ||
| yolo.videotoscreen | darknet | Yolo | networks.yolo:yolov2-tiny:1.0.0 | Run yolo on a video file, and display the file in a window | ||
| convnet.gender | caffe | ConvNet | networks.caffe:convnet-gender:1.0.0 | classification | Run convnet on a picture, determine male or female | |
| convnet.age | caffe | ConvNet | networks.caffe:convnet-age:1.0.0 | classification | Run cnet on a picture, determine age | |
| marcel | caffe | MobileNet | networks.caffe:mobilenet:1.0.0 | detection | Run detection using mobilet on video and display | |
| marcel2 | caffe | MobileNet | networks.caffe:mobilenet:1.0.0 | detection | Run detection using mobilenet on video and save to file | |
| bvlc | caffe | AlexNet | networks.caffe:bvlc_alexnet:1.0.0 | classification | Run object classification using bvlc | |
| places365 | caffe | networks.caffe:places365:1.0.0 | classification | Run object classification using places365 | ||
| resnet | caffe | ResNet | networks.caffe:resnet:1.0.0 | classification | Run object classfication using Resnet | |
| cifar | darknet | networks.darknet:cifar-custom:1.0.0 | classification | Classification using a custom Trained Darknet Model based on cifar | ||
| enet | darknet | Enet | networks.darknet:enet-coco:1.0.0 | Run detection with enet | ||
| openimages | darknet | network.darknet:yolo-openimages:1.0.0 | Run detection with Yolo v3 Trained on OpenImages | |||
| flowers | caffe | networks.caffe:flowers:1.0.0 | Flower detection based on trained oxford102 | |||
| tensorflow.mobilenet | tensorflow | MobileNet | networks.tensorflow:tf-ssdmobilenet:1.0.0 | Coco | Detection | On an image |
Marcel le chat
This is a sample output generated on a macbook.
lein run -m origami-dnn.demo.mobilenet.catvideotofile resources/vids/Marcel.m4v
# or
lein run -m origami-dnn.demo.marcel.marcel
or another one ...
Video courtesy of Marcel le chat.
Testing networks using the clj command
Create a deps.edn with the following content:
{:mvn/repos
{"vendredi" {:url "https://repository.hellonico.info/repository/hellonico/"}}
:deps
{ origami-dnn {:mvn/version "0.1.16"}}}
and run one of the namespaces like shown below:
# Run age detection on a cam
clj -m origami-dnn.demo.agecam
# Run Yolo on a cam
clj -m origami-dnn.demo.yolo.cam
# Run YoloV6 on a cam
clj -m origami-dnn.demo.yolo.v6cam
or start a repl and do the same:
# clj
(require '[origami-dnn.demo.agecam :as agecam])
(agecam/-main)