This repository is a fork of the primary repository with minor modifications to run on Raspian Buster and Ubuntu 18+ with OpenCV. The Open Horizon service yolo utilizes this repository to build Docker containers. Please refer to the Dockerfile for details.
OpenYOLO is an open source object detection and classification library written in C with bindings in Python. The library analyzes images and video streams to identify objects and classify them according to a dictionary of eighty (80) entities.
Outside of use in building the yolo service, run make in the top-level directory to build the darknet/ directory as well as test each version (tiny-v2,tiny-v3,v2,v3); for example (edited for length):
% make
make -C darknet
... lots of lines deleted ...
export \
DARKNET_WEIGHTS=/Volumes/dcmartin/GIT/openyolo/yolov2-tiny-voc.weights \
DARKNET_CONFIG=/Volumes/dcmartin/GIT/openyolo/darknet/cfg/yolov2-tiny-voc.cfg \
DARKNET_DATA=/Volumes/dcmartin/GIT/openyolo/darknet/cfg/voc.data \
&& \
./example/test-yolo.sh
layer filters size input output
0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BFLOPs
1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16
2 conv 32 3 x 3 / 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BFLOPs
3 max 2 x 2 / 2 208 x 208 x 32 -> 104 x 104 x 32
4 conv 64 3 x 3 / 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BFLOPs
5 max 2 x 2 / 2 104 x 104 x 64 -> 52 x 52 x 64
6 conv 128 3 x 3 / 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BFLOPs
7 max 2 x 2 / 2 52 x 52 x 128 -> 26 x 26 x 128
8 conv 256 3 x 3 / 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BFLOPs
9 max 2 x 2 / 2 26 x 26 x 256 -> 13 x 13 x 256
10 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
11 max 2 x 2 / 1 13 x 13 x 512 -> 13 x 13 x 512
12 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
13 conv 1024 3 x 3 / 1 13 x 13 x1024 -> 13 x 13 x1024 3.190 BFLOPs
14 conv 125 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 125 0.043 BFLOPs
15 detection
mask_scale: Using default '1.000000'
Loading weights from /Volumes/dcmartin/GIT/openyolo/yolov2-tiny-voc.weights...Done!
/Volumes/dcmartin/GIT/openyolo/data/horses.jpg: Predicted in 0.865594 seconds.
cow: 75%
OpenYOLO includes the Python script detector.py to run the YOLO algorithm on a specified image; options may be specified for a variety of needs:
The Python script takes three arguments (n.b. two and three are optional):
<JPEG file>- JPEG image file to process<config>- may betiny-v2,tiny-v3,v2, orv3; default:tiny-v2(optional)#.##- minimum value for a positive match; range: [0.0,1.0]; default:0.25(optional)
After successfully building the darknet executable use the Python script detector.py to detect entities in the original image; for example:
% ./example/detector.py example/horses.jpg | tee example/horses.json | jq '.'
layer filters size input output
0 conv 16 3 x 3 / 1 416 x 416 x 3 -> 416 x 416 x 16 0.150 BFLOPs
1 max 2 x 2 / 2 416 x 416 x 16 -> 208 x 208 x 16
2 conv 32 3 x 3 / 1 208 x 208 x 16 -> 208 x 208 x 32 0.399 BFLOPs
3 max 2 x 2 / 2 208 x 208 x 32 -> 104 x 104 x 32
4 conv 64 3 x 3 / 1 104 x 104 x 32 -> 104 x 104 x 64 0.399 BFLOPs
5 max 2 x 2 / 2 104 x 104 x 64 -> 52 x 52 x 64
6 conv 128 3 x 3 / 1 52 x 52 x 64 -> 52 x 52 x 128 0.399 BFLOPs
7 max 2 x 2 / 2 52 x 52 x 128 -> 26 x 26 x 128
8 conv 256 3 x 3 / 1 26 x 26 x 128 -> 26 x 26 x 256 0.399 BFLOPs
9 max 2 x 2 / 2 26 x 26 x 256 -> 13 x 13 x 256
10 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
11 max 2 x 2 / 1 13 x 13 x 512 -> 13 x 13 x 512
12 conv 1024 3 x 3 / 1 13 x 13 x 512 -> 13 x 13 x1024 1.595 BFLOPs
13 conv 256 1 x 1 / 1 13 x 13 x1024 -> 13 x 13 x 256 0.089 BFLOPs
14 conv 512 3 x 3 / 1 13 x 13 x 256 -> 13 x 13 x 512 0.399 BFLOPs
15 conv 255 1 x 1 / 1 13 x 13 x 512 -> 13 x 13 x 255 0.044 BFLOPs
16 yolo
17 route 13
18 conv 128 1 x 1 / 1 13 x 13 x 256 -> 13 x 13 x 128 0.011 BFLOPs
19 upsample 2x 13 x 13 x 128 -> 26 x 26 x 128
20 route 19 8
21 conv 256 3 x 3 / 1 26 x 26 x 384 -> 26 x 26 x 256 1.196 BFLOPs
22 conv 255 1 x 1 / 1 26 x 26 x 256 -> 26 x 26 x 255 0.088 BFLOPs
23 yolo
Loading weights from yolov3-tiny.weights...Done!
{
"file":"data/horses.jpg",
"meta":"coco.data",
"net":"cfg/yolov3-tiny.cfg",
"results":[
{
"center":{
"x":515,
"y":279
},
"confidence":55.9509813785553,
"entity":"cow",
"height":143,
"id":"0",
"width":180
},
{
"center":{
"x":85,
"y":245
},
"confidence":55.62857985496521,
"entity":"horse",
"height":140,
"id":"1",
"width":184
},
{
"center":{
"x":515,
"y":282
},
"confidence":54.17007803916931,
"entity":"cow",
"height":95,
"id":"2",
"width":78
}
],
"threshold":0.5,
"weights":"yolov3-tiny.weights"
}
Annotates the original image using the yoloanno.sh script. The script requires ImageMagick and jq software; to install on Debian LINUX:
sudo apt update -qq -y && sudo apt install -qq -y imagemagick jq
Use the the shell script to annotate the image with the expectation of both <example>.jpg and <example>.json exist; for example:
% ./yoloanno.sh example/horses
example/horses-yolo.jpg
The annotated image is stored using the <example>-yolo.json name.
Releases are based on Semantic Versioning, and use the format
of MAJOR.MINOR.PATCH. In a nutshell, the version will be incremented
based on the following:
MAJOR: Incompatible or major changes.MINOR: Backwards-compatible new features and enhancements.PATCH: Backwards-compatible bugfixes and package updates.
David C Martin ([email protected])