Update devel branch

.gitignore

@@ -1,10 +1,10 @@
 .ipynb_checkpoints/
-video_data/
+examples/video_data/
 .idea/
 examples/output.avi
-pretrained_models/caffemodel_weights/
-pretrained_models/tensorflow_weights/
-pretrained_models/yolo_weights/
+examples/pretrained_models/caffemodel_weights/
+examples/pretrained_models/tensorflow_weights/
+examples/pretrained_models/yolo_weights/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -1,27 +1,32 @@
-[cars-yolo-output]: ./assets/cars.gif "Sample Output with YOLO"
-[cows-tf-ssd-output]: ./assets/cows.gif "Sample Output with SSD"
+[cars-yolo-output]: examples/assets/cars.gif "Sample Output with YOLO"
+[cows-tf-ssd-output]: examples/assets/cows.gif "Sample Output with SSD"
 
 # Multi-object trackers in Python
 Object detection using deep learning and multi-object tracking
 
 [![DOI](https://zenodo.org/badge/148338463.svg)](https://zenodo.org/badge/latestdoi/148338463)
 
-### Available Trackers
-```
-SimpleTracker
+### Available Multi Object Trackers
 
-SimpleTracker2
+```
+CentroidTracker
+CentroidKF_Tracker
+IOUTracker
+SORT
 ```
 
-#### YOLO
-Video Source: [link](https://flic.kr/p/89KYXt)
-
-![Cars with YOLO][cars-yolo-output]
+### Available OpenCV-based object detectors:
 
-#### Tensorflow-SSD-MobileNet
-Video Source: [link](https://flic.kr/p/26WeEWy)
+```
+detector.TF_SSDMobileNetV2
+detector.Caffe_SSDMobileNet
+detector.YOLOv3
+```
 
-![Cows with tf-SSD][cows-tf-ssd-output]
+`YOLOv3 + CentroidTracker` |  `TF-MobileNetSSD + CentroidTracker`
+:-------------------------:|:-------------------------:
+![Cars with YOLO][cars-yolo-output]  |  ![Cows with tf-SSD][cows-tf-ssd-output]
+Video source: [link](https://flic.kr/p/89KYXt) | Video source: [link](https://flic.kr/p/26WeEWy)
 
 
 ### Installation
@@ -43,54 +48,43 @@ cd multi-object-tracker
 pip install -e .
 ```
 
-### YOLO
+For using the opencv `dnn`-based object detection modules provided in this repository with GPU, you may have to compile a CUDA enabled version of OpenCV from source.
+
+For building opencv from source, you can refer the following: [[link-1](https://docs.opencv.org/master/df/d65/tutorial_table_of_content_introduction.html)], [[link-2](https://www.pyimagesearch.com/2020/02/03/how-to-use-opencvs-dnn-module-with-nvidia-gpus-cuda-and-cudnn/)]
+
+### How to use?: Examples
+
+Examples for how to use `motrackers` are provided [examples](./examples/) folder of this repository.
+You can clone and run the examples as shown in the [readme](examples/readme.md) inside the [examples](./examples/) folder.
+
+### Pretrained Object Detection Models
 
-Do the following in the terminal to download a pretrained weights of YOLO:
+You will have to download the pretrained weights for the model. The shell scripts for downloading are provided in [examples](examples/) folder.
+
+##### YOLOv3
 ```
-cd ./pretrained_models/yolo_weights
+cd ./examples/pretrained_models/yolo_weights
 sudo chmod +x ./get_yolo.sh
 ./get_yolo.sh
 ```
 
-### TensorFlow model
-
-Do the following in the terminal to download a pretrained model:
+##### TensorFlow MobileNetSSDv2
 ```
 cd ./pretrained_models/tensorflow_weights
 sudo chmod +x ./get_ssd_model.sh
 ./get_ssd_model.sh
 ```
 
-**SSD-Mobilenet_v2_coco_2018_03_29** was used for this example.
-Other networks can be downloaded and ran: Go through `tracking-tensorflow-ssd_mobilenet_v2_coco_2018_03_29.ipynb` for more details.
-
-### Caffemodel
-
-Do the following in the terminal to download a pretrained model:
+##### Caffemodel
 ```
 cd ./pretrained_models/caffemodel_weights
 sudo chmod +x ./get_caffemodel.sh
 ./get_caffemodel.sh
 ```
 
-This is a MobileNet-SSD caffemodel.
-
-### Examples and How to use:
-
-For examples and how to use this repository, please refer [examples/](examples/) folder.
-
 ### References and Credits
-This work is based on the following literature:
-1. Bochinski, E., Eiselein, V., & Sikora, T. (2017, August). High-speed tracking-by-detection without using image information. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE. [[paper-pdf](http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf)]
-2. Pyimagesearch [link-1](https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/), [link-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.
 
-***Suggestion**: If you are looking for speed go for SSD-mobilenet. If you are looking for accurracy and speed go with YOLO. The best way is to train and fine tune your models on your dataset. Although, Faster-RCNN gives more accurate object detections, you will have to compromise on the detection speed as it is slower as compared to YOLO.*
+Please see [references.md](REFERENCES.md)
 
 ### Citation
 

REFERENCES.md

@@ -0,0 +1,13 @@
+# References and Credits
+
+This work is based on the following literature:
+
+1. Bochinski, E., Eiselein, V., & Sikora, T. (2017, August). High-speed tracking-by-detection without using image information. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE. [[pdf](http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf)]
+2. Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016, September). Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP) (pp. 3464-3468). IEEE. [[arxiv](https://arxiv.org/abs/1602.00763)]
+3. YOLOv3. [[pdf](https://pjreddie.com/media/files/papers/YOLOv3.pdf)][[website](https://pjreddie.com/darknet/yolo/)]
+4. Kalman Filter. [[wiki](https://en.wikipedia.org/wiki/Kalman_filter)]
+5. TensorFlow Object Detection API [[github](https://github.com/tensorflow/models/tree/master/research/object_detection)]
+6. Caffe [[website](https://caffe.berkeleyvision.org/)][[github](https://github.com/BVLC/caffe)]
+
+
+#### Link to `multi-object-tracker` [[webpage](https://adipandas.github.io/multi-object-tracker/)][[GitHub Repo](https://github.com/adipandas/multi-object-tracker)]

_config.yml

@@ -1,2 +1,3 @@
 title: Multi-object trackers in Python
+description:
 theme: jekyll-theme-cayman

examples/IOUTracker-caffe-model-mobilenetSSD.ipynb → examples/example_notebooks/IOUTracker-caffe-model-mobilenetSSD.ipynb

@@ -8,8 +8,9 @@
    "source": [
     "import numpy as np\n",
     "import cv2 as cv\n",
-    "from motrackers import IOUTracker\n",
-    "from motrackers.utils import select_caffemodel, select_videofile"
+    "from motrackers.utils import select_caffemodel, select_videofile\n",
+    "from motrackers.iou_tracker import IOUTracker\n",
+    "from motrackers.detectors.caffe import Caffe_MobileNetSSD"
    ]
   },
   {
@@ -23,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,7 +33,7 @@
     "# display(video_file, prototxt, weights)\n",
     "\n",
     "video_file = \"./../video_data/people.mp4\"\n",
-    "prototxt = \"./../pretrained_models/caffemodel_weights/MobileNetSSD_deploy.prototxt\" \n",
+    "prototxt = \"./../pretrained_models/caffemodel_weights/MobileNetSSD_deploy.prototxt\"\n",
     "weights = \"./../pretrained_models/caffemodel_weights/MobileNetSSD_deploy.caffemodel\""
    ]
   },
@@ -156,6 +157,7 @@
     "        image_resized, model[\"pixel_std\"], model[\"input_size\"], \n",
     "        (model[\"pixel_mean\"], model[\"pixel_mean\"], model[\"pixel_mean\"]), False\n",
     "    )\n",
+    "    \n",
     "    net.setInput(blob)\n",
     "    detections = net.forward()\n",
     "\n",
@@ -192,7 +194,7 @@
     "            boxes.append([left, top, width, height])\n",
     "            confidences.append(float(confidence))\n",
     "            classIDs.append(int(class_id))\n",
-    "        \n",
+    "\n",
     "    indices = cv.dnn.NMSBoxes(boxes, confidences, model[\"confidence_threshold\"], model[\"threshold\"])\n",
     "    \n",
     "    class_ids_to_track = []\n",
@@ -222,7 +224,7 @@
     "                         cv.FILLED)\n",
     "            \n",
     "            cv.putText(image, label, (x, y_label), cv.FONT_HERSHEY_SIMPLEX, 0.5, clr, 2)\n",
-    "        \n",
+    "    \n",
     "    tracks = tracker.update(detections_bbox, class_ids_to_track, confidences_track)\n",
     "    \n",
     "    if print_tracks:\n",

examples/SimpleTracker-caffe-model-mobilenetSSD.ipynb → examples/example_notebooks/SimpleTracker-caffe-model-mobilenetSSD.ipynb

@@ -2,25 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import cv2 as cv\n",
-    "from motrackers import SimpleTracker\n",
+    "# from motrackers import SimpleTracker\n",
     "from motrackers.utils import select_caffemodel, select_videofile"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1d3c3a3a86324074b3461a40bda983ea",
+       "model_id": "cb30715c81274880b0f1ff9b0bab206f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -34,7 +34,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "46b604d3ae4d4853aa0a644f9edfe463",
+       "model_id": "14f91c21118d4e2ba2135fcdb0bbcce7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -48,7 +48,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f31f72dfb7446b29e258b97fa8f1a2f",
+       "model_id": "e256f1517fd44cbd9b1b89e950550807",
        "version_major": 2,
        "version_minor": 0
       },