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Example Go application using go-darknet and REST
This is an example Go server application (in terms of REST) which uses go-darknet.
Run
Navigate to example folder:
cd $GOPATH/github.com/LdDl/go-darknet/example/rest_example
Download dataset (sample of image, coco.names, yolov3.cfg, yolov3.weights).
./download_data_v3.sh
Note: you don't need coco.data file anymore, because script below does insert coco.names into 'names' filed in yolov3.cfg file (so AlexeyAB's fork can deal with it properly) So last rows in yolov3.cfg file will look like:
......
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
names = coco.names # this is path to coco.names file
Build and run program
go build main.go && ./main --configFile=yolov3.cfg --weightsFile=yolov3.weights --port 8090
After server started check if REST-requests works. We provide cURL-based example
curl -F 'image=@sample.jpg' 'http://localhost:8090/detect_objects'
Servers response should be something like this:
{
"net_time": "43.269289ms",
"overall_time": "43.551604ms",
"num_detections": 44,
"detections": [
{
"class_id": 7,
"class_name": "truck",
"probability": 49.51231,
"start_point": {
"x": 0,
"y": 136
},
"end_point": {
"x": 85,
"y": 311
}
},
{
"class_id": 2,
"class_name": "car",
"probability": 36.36933,
"start_point": {
"x": 95,
"y": 152
},
"end_point": {
"x": 186,
"y": 283
}
},
{
"class_id": 7,
"class_name": "truck",
"probability": 48.417683,
"start_point": {
"x": 95,
"y": 152
},
"end_point": {
"x": 186,
"y": 283
}
},
{
"class_id": 7,
"class_name": "truck",
"probability": 45.652023,
"start_point": {
"x": 694,
"y": 178
},
"end_point": {
"x": 798,
"y": 310
}
},
{
"class_id": 2,
"class_name": "car",
"probability": 76.8402,
"start_point": {
"x": 1,
"y": 145
},
"end_point": {
"x": 84,
"y": 324
}
},
{
"class_id": 7,
"class_name": "truck",
"probability": 25.592052,
"start_point": {
"x": 107,
"y": 89
},
"end_point": {
"x": 215,
"y": 263
}
},
{
"class_id": 2,
"class_name": "car",
"probability": 99.87823,
"start_point": {
"x": 511,
"y": 185
},
"end_point": {
"x": 748,
"y": 328
}
},
{
"class_id": 2,
"class_name": "car",
"probability": 99.819336,
"start_point": {
"x": 261,
"y": 189
},
"end_point": {
"x": 427,
"y": 322
}
},
{
"class_id": 2,
"class_name": "car",
"probability": 99.64055,
"start_point": {
"x": 426,
"y": 197
},
"end_point": {
"x": 539,
"y": 311
}
},
{
"class_id": 2,
"class_name": "car",
"probability": 74.56263,
"start_point": {
"x": 692,
"y": 186
},
"end_point": {
"x": 796,
"y": 316
}
},
{
"class_id": 2,
"class_name": "car",
"probability": 72.79756,
"start_point": {
"x": 388,
"y": 206
},
"end_point": {
"x": 437,
"y": 276
}
},
{
"class_id": 1,
"class_name": "bicycle",
"probability": 72.27595,
"start_point": {
"x": 178,
"y": 270
},
"end_point": {
"x": 268,
"y": 406
}
},
{
"class_id": 0,
"class_name": "person",
"probability": 97.30075,
"start_point": {
"x": 143,
"y": 135
},
"end_point": {
"x": 268,
"y": 343
}
}
]
}