Add Darknet YOLO object detection example and references

Author: Johnson Su

examples/dnnDarknetYOLORealTimeObjectDetection.js

@@ -0,0 +1,120 @@
+/**
+ * Please refer to the python version of "YOLO object detection with OpenCV" by Adrian Rosebrock.
+ * For more detail: https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/
+ */
+const fs = require("fs");
+const path = require("path");
+const { cv, drawBlueRect, runVideoDetection } = require("./utils");
+
+if (!cv.xmodules.dnn) {
+  throw new Error("exiting: opencv4nodejs compiled without dnn module");
+}
+
+// replace with path where you unzipped darknet model
+const darknetPath = "../data/dnn/yolo-object-detection";
+
+const cfgFile = path.resolve(darknetPath, "yolov3-tiny.cfg");
+const weightsFile = path.resolve(darknetPath, "yolov3-tiny.weights");
+const labelsFile = path.resolve(darknetPath, "coco.names");
+
+if (
+  !fs.existsSync(weightsFile) ||
+  !fs.existsSync(cfgFile) ||
+  !fs.existsSync(labelsFile)
+) {
+  console.log("could not find darknet model");
+  console.log("download the model from: https://pjreddie.com/darknet/yolo/");
+  throw new Error("exiting");
+}
+
+// set webcam port
+const webcamPort = 0;
+
+const minConfidence = 0.5;
+const nmsThreshold = 0.3;
+
+// read classNames and store them in an array
+const labels = fs
+  .readFileSync(labelsFile)
+  .toString()
+  .split("\n");
+
+// initialize tensorflow darknet model from modelFile
+const net = cv.readNetFromDarknet(cfgFile, weightsFile);
+
+const classifyImg = img => {
+  // object detection model works with 416 x 416 images
+  const size = new cv.Size(416, 416);
+  const vec3 = new cv.Vec(0, 0, 0);
+  const [imgHeight, imgWidth] = img.sizes;
+
+  // network accepts blobs as input
+  const inputBlob = cv.blobFromImage(img, 1 / 255.0, size, vec3, true, true);
+  net.setInput(inputBlob);
+
+  // specify two layers "yolo_16" and "yolo_23"
+  const layerNames = ["yolo_16", "yolo_23"];
+
+  console.time("net.forward");
+  // forward pass input through entire network
+  const layerOutputs = net.forward(layerNames);
+  console.timeEnd("net.forward");
+
+  let boxes = [];
+  let confidences = [];
+  let classIDs = [];
+
+  layerOutputs.forEach(mat => {
+    const output = mat.getDataAsArray();
+    output.forEach(detection => {
+      const scores = detection.slice(5);
+      const classId = scores.indexOf(Math.max(...scores));
+      const confidence = scores[classId];
+
+      if (confidence > minConfidence) {
+        const box = detection.slice(0, 4);
+
+        const centerX = parseInt(box[0] * imgWidth);
+        const centerY = parseInt(box[1] * imgHeight);
+        const width = parseInt(box[2] * imgWidth);
+        const height = parseInt(box[3] * imgHeight);
+
+        const x = parseInt(centerX - width / 2);
+        const y = parseInt(centerY - height / 2);
+
+        boxes.push(new cv.Rect(x, y, width, height));
+        confidences.push(confidence);
+        classIDs.push(classId);
+
+        const indices = cv.NMSBoxes(
+          boxes,
+          confidences,
+          minConfidence,
+          nmsThreshold
+        );
+
+        indices.forEach(i => {
+          const rect = boxes[i];
+          const imgRect = new cv.Rect(rect.x, rect.y, rect.width, rect.height);
+          drawBlueRect(img, imgRect);
+          const text = labels[classIDs[i]];
+          img.putText(
+            text,
+            new cv.Point(rect.x, rect.y + 0.1 * imgHeight),
+            cv.FONT_ITALIC,
+            2,
+            {
+              color: new cv.Vec(255, 0, 0),
+              thickness: 2
+            }
+          );
+          drawBlueRect(img, imgRect);
+        });
+      }
+    });
+  });
+
+  cv.imshow("Darknet YOLO Object Detection", img);
+};
+
+runVideoDetection(webcamPort, classifyImg);

examples/dnnTensorflowObjectDetection.js

@@ -1,3 +1,7 @@
+/**
+ * Please refer to the python version of "ExploreOpencvDnn" by Saumya Shovan Roy.
+ * For more detail: https://github.com/rdeepc/ExploreOpencvDnn
+ */
 const fs = require("fs");
 const path = require("path");
 const classNames = require("./dnnTensorflowObjectDetectionClassNames");