0.1.9

.npmignore

@@ -1,17 +1,19 @@
 node_modules
 build/
 .DS_Store
+test/yolo.weights
+test/data.modified.raw
+test/data.raw
+npm-debug.log
+*.ffm
+test/detected.mp4
 yolo.weights
 test/yolo.weights
 data
 cfg
-*.jpeg
 test/server.conf
 tiny-yolo-voc.weights
 data.raw
 *.mp4
-*.ffm
 test/data
 test/cfg
-*.ffm
-test/detected.mp4

README.md

@@ -3,6 +3,8 @@
 Darknet is [an open source neural network framework written in C and CUDA](https://github.com/pjreddie/darknet).
 This project wraps this framework in a Node.js native addon.
 
+<img src="https://lab.moovel.com/content/uploads/yolo-micro-prv.gif" width="458px" height="258px">
+
 - [darknet](http://pjreddie.com/darknet/)
 - [YOLO (You only look once)](http://pjreddie.com/darknet/yolo/)
 
@@ -20,7 +22,7 @@ Currently, the wrapper implements a demo method to run a recognition from the we
 This module requires OpenCV installed. First, you need to compile [this fork](https://github.com/OrKoN/darknet) of darknet with OpenCV support (optionally, with CUDA support):
 
 ```sh
-git clone git@github.com:OrKoN/darknet.git
+git clone https://github.com/OrKoN/darknet
 cd darknet
 make OPENCV=1 # optionally GPU=1
 make install # by default installed to /usr/local
@@ -42,7 +44,7 @@ npm install @moovel/yolo --save
 
 ## Usage
 
-Either download you own `cfg`, `data` folders and `.weight` files from the darknet project or use the ones included in `test` folder (see also [test/readme.md](test/readme.md)). You also can find there the examples from below.
+Either download your own `cfg`, `data` folders and `.weight` files from the darknet project or use the ones included in `test` folder (see also [test/readme.md](test/README.md)). You also can find there the examples from below.
 
 Detect a video from camera or a file:
 
@@ -55,14 +57,15 @@ darknet.detect({
   data: './cfg/coco.data',
   cameraIndex: 0, // optional, default: 0,
   video: "./test.mp4", // optional, forces to use the video file instead of a camera
-  thresh: 0.24,
-  hierThresh: 0.5,
-}, function(modified, original, detections) {
+  thresh: 0.24, // optional, default: 0.24
+  hierThresh: 0.5, // optional, default: 0.5
+}, function(modified, original, detections, dimensions) {
   /**
 
   modified - raw frame with detections drawn, rgb24 format
   original - raw frame, as captured by the webcam/video, rgb24 format,
   detections - array of detections
+  dimenstions - image width and height
 
   Example detections:
 
@@ -108,15 +111,15 @@ darknet.detectImage({
   weights: './yolo.weights',
   data: './cfg/coco.data',
   image: './data/dog.jpg',
-  thresh: 0.24,
-  hierThresh: 0.5,
+  thresh: 0.24, // optional, default: 0.24
+  hierThresh: 0.5, // optional, default: 0.5,
 }, function(modified, original, detections, dimensions) {
   /**
 
   modified - raw frame with detections drawn, rgb24 format
   original - raw frame, as captured by the webcam/video, rgb24 format,
   detections - array of detections
-  dimenstions - image dimensions
+  dimenstions - image width and height
 
   Example detections:
 

darknet.cc

@@ -31,7 +31,6 @@ class VideoDetectionWorker : public AsyncProgressWorkerBase<T> {
         obj->Set(Nan::New("prob").ToLocalChecked(), Nan::New<v8::Number>(result.prob));
         obj->Set(Nan::New("name").ToLocalChecked(), Nan::New<v8::String>(result.name).ToLocalChecked());
         results->Set(i, obj);
-        i++;
     }
 
     v8::Local<v8::Object> dimensions = Nan::New<v8::Object>();
@@ -84,7 +83,6 @@ class ImageDetectionWorker : public AsyncWorker {
         obj->Set(Nan::New("prob").ToLocalChecked(), Nan::New<v8::Number>(result.prob));
         obj->Set(Nan::New("name").ToLocalChecked(), Nan::New<v8::String>(result.name).ToLocalChecked());
         results->Set(i, obj);
-        i++;
     }
 
     v8::Local<v8::Object> dimensions = Nan::New<v8::Object>();

package.json

@@ -1,15 +1,28 @@
 {
   "name": "@moovel/yolo",
-  "version": "0.1.2",
+  "version": "0.1.9",
   "description": "NodeJS interface for YOLO/Darknet",
+  "keywords": [
+    "yolo",
+    "darknet",
+    "recognition"
+  ],
+  "homepage": "https://github.com/moovel/node-yolo",
+  "bugs": {
+    "url": "https://github.com/moovel/node-yolo/issues"
+  },
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/moovel/node-yolo.git"
+  },
   "main": "darknet.js",
   "gypfile": true,
   "dependencies": {
     "bindings": "~1.2.1",
     "nan": "^2.0.0"
   },
-  "devDependencies": {
-    "fluent-ffmpeg": "^2.1.0"
-  },
-  "license": "MIT"
+  "license": "MIT",
+  "engines": {
+    "node": ">=4"
+  }
 }

test/README.md

@@ -1,6 +1,7 @@
 You already have the config and data file for YOLO in the `test/cfg` and `test/data` subfolders. You will have to download the pre-trained weight file [here (258 MB)](http://pjreddie.com/media/files/yolo.weights). Or just run this:
 
 ```sh
+cd node-yolo/test
 wget http://pjreddie.com/media/files/yolo.weights
 ```