Update with YOLO model in OpenCV

Author: adipandas

README.md

@@ -1,12 +1,15 @@
+[output_video]: ./assets/sample-output.gif "Sample Output"
+
 # multi-object-tracker
-object detection using deep learning and multi-object tracking 
+object detection using deep learning and multi-object tracking
 
 The work here is based on the following literature available:
 1. http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf
-2. https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/
-3. https://github.com/Wenuka/correlationTracker
+2. Pyimagesearch [1](https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/), [2](https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/)
+3. [correlationTracker](https://github.com/Wenuka/correlationTracker)
 4. [Caffemodel zoo](http://caffe.berkeleyvision.org/model_zoo.html)
 5. [Caffemodel zoo GitHub](https://github.com/BVLC/caffe/tree/master/models)
+6. [YOLO v3](https://pjreddie.com/media/files/papers/YOLOv3.pdf)
 
 Use the caffemodel zoo from the reference [4,5] mentioned above to vary the CNN models and Play around with the codes.
 
@@ -15,9 +18,27 @@ Pip install for OpenCV (version 3.4.3 or later) is available [here](https://pypi
 
 `pip install opencv-contrib-python`
 
+### Run with YOLO
+
+1. Open the terminal
+2. Go to `yolo_dir` in this repository: `cd ./yolo_dir`
+3. Run: `sudo chmod +x ./get_yolo.sh`
+4. Run: `./get_yolo.sh`
+
+The model and the config files will be downloaded in `./yolo_dir`. These will be used `tracking-yolo-model.ipynb`.
+
+- The video input can be specified in the cell named `Initiate opencv video capture object` in the notebook.
+- To make the source as the webcam, use `video_src=0` else provide the path of the video file (example: `video_src="/path/of/videofile.mp4"`).
 
+Example video link: https://flic.kr/p/L6qyxj
 
+Output of the example video:
 
-NOTE: The algorithm shared in this repository was programmed and tested on Windows PC.
+![Output Sample][output_video]
 
 
+### Run with Caffemodel
+- You have to use `tracking-caffe-model.ipynb`.
+- The model for use is provided in the folder named `caffemodel_dir`.
+- The video input can be specified in the cell named `Initiate opencv video capture object` in the notebook.
+- To make the source as the webcam, use `video_src=0` else provide the path of the video file (example: `video_src="/path/of/videofile.mp4"`).

assets/sample-output.gif

@@ -110,12 +110,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Loading Object Detector Model\n",
+    "#### Loading Object Detector Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Face Detection and Tracking\n",
     "\n",
     "Here, the Face Detection Caffe Model is used.\n",
     "\n",
     "The files are taken from the following link:\n",
-    "https://github.com/opencv/opencv_3rdparty/tree/dnn_samples_face_detector_20170830\n"
+    "https://github.com/opencv/opencv_3rdparty/tree/dnn_samples_face_detector_20170830"
    ]
   },
   {
@@ -124,8 +131,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "caffemodel = {\"prototxt\":\"./deploy.prototxt\",\n",
-    "              \"model\":\"./res10_300x300_ssd_iter_140000.caffemodel\",\n",
+    "caffemodel = {\"prototxt\":\"./caffemodel_dir/deploy.prototxt\",\n",
+    "              \"model\":\"./caffemodel_dir/res10_300x300_ssd_iter_140000.caffemodel\",\n",
     "              \"acc_threshold\":0.50                  # neglected detections with probability less than acc_threshold value\n",
     "             }\n",
     "\n",
@@ -255,7 +262,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,