https://github.com/teknoir/darknet-app
Docker image tests for Nvidia Jetson Nano
https://github.com/teknoir/darknet-app
Last synced: 7 months ago
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Docker image tests for Nvidia Jetson Nano
- Host: GitHub
- URL: https://github.com/teknoir/darknet-app
- Owner: teknoir
- Created: 2020-06-18T10:50:20.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-11-05T13:34:02.000Z (almost 5 years ago)
- Last Synced: 2025-01-30T08:43:02.427Z (8 months ago)
- Language: C++
- Size: 295 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Teknoir Darknet App C++
A small footprint c++ darknet app.
A base for building c++ apps on the teknoir platform.
## Build
Nvidia has made it virtually impossble to create a pure cloud based pipeline for the Jetson Nano.
The base image has to be built on a real Jetson Nano with matching Jetpack version.
### Build on Jentson Nano
Clone this repo and run:
```bash
./build_on_devboard.sh
```
To add yolov3, yolov3-tiny and yolov4 to the docker images:
```bash
gcloud builds submit . --config=cloudbuild.yaml --timeout=3600 --substitutions=SHORT_SHA="$(date +v%Y%m%d)-$(git describe --tags --always --dirty)-$(git diff | shasum -a256 | cut -c -6)"
```
## Build locally
```bash
cd app
mkdir build
cd build
cmake ..
make
make install
```
## Run interactively on device
```bash
sudo kubectl run darknet -ti --rm --image tekn0ir/darknet:arm64v8 --overrides='{"spec":{"imagePullSecrets":[{"name":"gcr-json-key"}],"containers":[{"name":"darknet","image":"tekn0ir/darknet:arm64v8","command":["/bin/bash"],"tty":true,"stdin":true,"imagePullPolicy":"Always","securityContext":{"privileged":true},"env":[{"name":"MQTT_SERVICE_HOST","value":"mqtt.kube-system"},{"name":"MQTT_SERVICE_PORT","value":"1883"},{"name":"MQTT_IN_0","value":"camera/images"},{"name":"MQTT_OUT_0","value":"toe/events"},{"name":"NAMES_FILE","value":"/darknet/coco.names"},{"name":"CFG_FILE","value":"/darknet/yolov3.cfg"},{"name":"WEIGHTS_FILE","value":"/darknet/yolov3.weights"}]}]}}'
teknoir_app
```
## Run locally
```bash
docker run -it --rm -p 1883:1883 -p 9001:9001 --name mqtt-broker eclipse-mosquitto
# and in another terminal window run:
docker run -it --rm -e MQTT_SERVICE_HOST= tekn0ir/darknet:arm64v8
```
To stop the example press `ctrl-c`.
## Legacy build and publish docker images
```bash
docker build -t tekn0ir/darknet:amd64 -f amd64.Dockerfile .
docker push tekn0ir/darknet:amd64
```
```bash
docker build -t tekn0ir/darknet_base:arm32v7 -f arm32v7.base.Dockerfile .
docker push tekn0ir/darknet_base:arm32v7
docker build -t tekn0ir/darknet:arm32v7 -f arm32v7.Dockerfile .
docker push tekn0ir/darknet:arm32v7
```
struct bbox_t {
unsigned int x, y, w, h; // (x,y) - top-left corner, (w, h) - width & height of bounded box
float prob; // confidence - probability that the object was found correctly
unsigned int obj_id; // class of object - from range [0, classes-1]
unsigned int track_id; // tracking id for video (0 - untracked, 1 - inf - tracked object)
unsigned int frames_counter;// counter of frames on which the object was detected
};
class Detector {
public:
Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
~Detector();
std::vector detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
std::vector detect(image_t img, float thresh = 0.2, bool use_mean = false);
static image_t load_image(std::string image_filename);
static void free_image(image_t m);
#ifdef OPENCV
std::vector detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false);
std::shared_ptr mat_to_image_resize(cv::Mat mat) const;
#endif
};