Shinobi CE officially lands on Gitlab

tools/coco/testCoco.js

@@ -0,0 +1,103 @@
+var cv = require('opencv4nodejs')
+const fs = require('fs');
+const path = require('path');
+const classNames = require('./dnnCocoClassNames');
+const { extractResults } = require('./dnn/ssdUtils');
+
+if (!cv.xmodules.dnn) {
+  throw new Error('exiting: opencv4nodejs compiled without dnn module');
+}
+
+// replace with path where you unzipped inception model
+const ssdcocoModelPath = '../data/dnn/coco-SSD_300x300';
+
+const prototxt = path.resolve(ssdcocoModelPath, 'deploy.prototxt');
+const modelFile = path.resolve(ssdcocoModelPath, 'VGG_coco_SSD_300x300_iter_400000.caffemodel');
+
+if (!fs.existsSync(prototxt) || !fs.existsSync(modelFile)) {
+  console.log('could not find ssdcoco model');
+  console.log('download the model from: https://drive.google.com/file/d/0BzKzrI_SkD1_dUY1Ml9GRTFpUWc/view');
+  throw new Error('exiting: could not find ssdcoco model');
+}
+
+// initialize ssdcoco model from prototxt and modelFile
+const net = cv.readNetFromCaffe(prototxt, modelFile);
+
+function classifyImg(img) {
+  // ssdcoco model works with 300 x 300 images
+  const imgResized = img.resize(300, 300);
+
+  // network accepts blobs as input
+  const inputBlob = cv.blobFromImage(imgResized);
+  net.setInput(inputBlob);
+
+  // forward pass input through entire network, will return
+  // classification result as 1x1xNxM Mat
+  let outputBlob = net.forward();
+  // extract NxM Mat
+  outputBlob = outputBlob.flattenFloat(outputBlob.sizes[2], outputBlob.sizes[3]);
+
+  return extractResults(outputBlob, img)
+    .map(r => Object.assign({}, r, { className: classNames[r.classLabel] }));
+}
+
+const makeDrawClassDetections = predictions => (drawImg, className, getColor, thickness = 2) => {
+  predictions
+    .filter(p => classNames[p.classLabel] === className)
+    .forEach(p => drawRect(drawImg, p.rect, getColor(), { thickness }));
+  return drawImg;
+};
+
+const runDetectDishesExample = () => {
+  const img = cv.imread('../data/dishes.jpg');
+  const minConfidence = 0.2;
+
+  const predictions = classifyImg(img).filter(res => res.confidence > minConfidence);
+
+  const drawClassDetections = makeDrawClassDetections(predictions);
+
+  const classColors = {
+    fork: new cv.Vec(0, 255, 0),
+    bowl: new cv.Vec(255, 0, 0),
+    'wine glass': new cv.Vec(0, 0, 255),
+    cup: new cv.Vec(0, 255, 255)
+  };
+
+  const legendLeftTop = new cv.Point(580, 20);
+  const alpha = 0.4;
+  cv.drawTextBox(
+    img,
+    legendLeftTop,
+    Object.keys(classColors).map(className => ({
+      text: className,
+      fontSize: 0.8,
+      color: classColors[className]
+    })),
+    alpha
+  );
+
+  Object.keys(classColors).forEach((className) => {
+    const color = classColors[className];
+    // draw detections
+    drawClassDetections(img, className, () => color);
+  });
+
+  cv.imshowWait('img', img);
+};
+
+const runDetectPeopleExample = () => {
+  const img = cv.imread('../data/cars.jpeg');
+  const minConfidence = 0.4;
+
+  const predictions = classifyImg(img).filter(res => res.confidence > minConfidence);
+
+  const drawClassDetections = makeDrawClassDetections(predictions);
+
+  const getRandomColor = () => new cv.Vec(Math.random() * 255, Math.random() * 255, 255);
+
+  drawClassDetections(img, 'car', getRandomColor);
+  cv.imshowWait('img', img);
+};
+
+runDetectDishesExample();
+runDetectPeopleExample();