ODaM - Object Detection and Monitoring

v0.9.0

ODaM is project aimed to do monitoring such as: pedestrian detection and counting, vehicle detection and counting, speed estimation of objects, sending detected objects to gRPC server for detailed analysis.

YOLOv4 + Kalman filter for tracking	YOLOv4 + simple centroid tracking

YOLOv4 Tiny + Kalman filter for tracking	YOLOv4 Tiny + simple centroid tracking

Work in progress

We are working on this.

Not too fast, but it is what it is.

Former version (until #21) of this repository were containing a lot of business logic via go-darknet. Current version depends on OpenCV's DNN module.

ODaM - Object Detection and Monitoring
v0.9.0

About

ODaM is tool for doing monitoring via Darknet's neural network called Yolo V4 (paper: https://arxiv.org/abs/2004.10934).

It's built on top of GoCV.

QA section

Who are you and what do you do?

There is info about me here: https://github.com/LdDl

You can have chat with me in Telegram/Gmail

Is this library / software or even framework?

I think about it as software with library capabilities.

What it capable of?

Not that much currently:

Object detection via darknet: OpenCV::ddn module is used
Object tracking via two possible techniques: Kalman tracking (filtering) or Centroid tracking;
Sending data to dedicated gRPC server;
MJPEG / imshow optional visual output;
Speed estimation based of GIS calculations (via matching pixels to WGS84).

Why Go?

Well, C++ is a killer in computer vision field and Python has a great battery included bindings for C++ code.

But I do no think that I'm ready to build gRPC/REST or any other web components of this software in C++ or Python (C++ is not that easy and Python...I just don't like Python syntax). That's why I prefer to stick with Go.

Why did you pick JSON for configuration purposes instead of TOML/YAML/INI or any other well-suited formats?

Compared to TOML, JSON is not that 'human friendly', but still readable.
It is in standart Go's library.
Well, it is in standart Go's library.
You got the idea.

Why bindings to Darknet instead of Opencv included stuff?

Sometimes you just do not need full OpenCV installation for object detection. I have such ANPR projet here: https://github.com/LdDl/license_plate_recognition I guess when I'm done with stable core I might switch from Go's Darknet bindings to OpenCV one (since ODaM-project requires OpenCV installation obviously)

What are your plans?

There is ROADMAP.md, but overall I am planning to extend capabilities of software:

Improve perfomance
Implement some cool tracking techniques (e.g. SORT)
Do gRPC accepting microservice for enabling software to catch information from external devices/systems/microservices and etc. E.g: you want to send message 'there is red light on traffic light" to instance of software, then it would look like grpcServer.Send('there is red light on traffic light'). After that any captured object will have state with message above in it. So you can catch traffic offenders.
Introduce convex polygon based calculations (same as virtual lines but for polygons)

How to help you?

If you are here, then you are already helped a lot, since you noticed my existence :harold_face:

If you want to make PR for some undone features (algorithms mainly) I'll glad to take a look.

Installation

notice: targeted for Linux users (no Windows/OSX instructions currenlty)

Need to enable CUDA (GPU) in every installation step where it's possible.

Install CUDA (we recommend version 10.2)

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda
echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:LD_LIBRARY_PATH'  >> ~/.bashrc
source ~/.bashrc

Install cuDNN (we recommend version v7.6.5 (November 18th, 2019), for CUDA 10.2) Go to NVIDIA's site and download .deb package. After downloading .deb package install it:

sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.2_amd64.deb
sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.2_amd64.deb
sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.2_amd64.deb

Do not forget to check if cuDNN installed properly:

cp -r /usr/src/cudnn_samples_v7/ $HOME
cd  $HOME/cudnn_samples_v7/mnistCUDNN
make clean && make
./mnistCUDNN
cd -

GoCV - instructions link.
Blob tracking library - instructions link
If you want to use gRPC client-server model: gRPC - instructions link

You need to implement your gRPC server as following proto-file: https://github.com/LdDl/odam/blob/master/yolo_grpc.proto.

If you need to rebuild *.pb.go file, call this is from project root folder:
```
protoc -I . yolo_grpc.proto --go_out=plugins=grpc:.
```
In case of my needs I need to detect license plates on vehicles and do OCR on server-side: you can take a look on https://github.com/LdDl/license_plate_recognition for gRPC server example

After steps above done:

go install github.com/LdDl/odam/cmd/odam

Check if executable available

odam -h

and you will see something like this:

Usage of ./odam:
-settings string
        Path to application's settings (default "conf.json")

Usage

notice: targeted for Linux users (no Windows/OSX instructions currenlty)

Prepare neural network stuff

Download YOLO's weights, configuration file and *.names file. Your way may warry, but here is our script: download_data.sh
```
./download_data_v4.sh
```

Make sure there is link to *.names file in YOLO's configuration file:

[yolo]
mask = 0,1,2
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
names = coco.names # <<========= here is the link to 'coco.names' file

Prepare configuration file for application. Example of file: conf.json. Description of fields:

{
"video_settings": { # Video input settings
    "source": "rtsp://127.0.0.1:554/h264", # Link to RTSP stream
    "width": 1920, # Width of image in video source
    "height": 1080, # Height of image in video source
    "reduced_width": 640, # Desired width of image (for imshow and MJPEG streaming, also reduces inference time (processing > accuracy) for neural network)
    "reduced_height": 360, # Desired height of image (for imshow and MJPEG streaming, also reduces inference time (processing > accuracy) for neural network)
    "camera_id": "f2abe45e-aad8-40a2-a3b7-0c610c0f3dda" # Unique ID for video source (useful for 'client-server' model)
},
"neural_network_settings": { # YOLO neural network settings
    "darknet_cfg": "yolov3.cfg", # Path to configuration.file
    "darknet_weights": "yolov3.weights", # Path to weights wile
    "darknet_classes": "coco.names", # Path to *.names file (labels of objects)
    "conf_threshold": 0.2, # Confidence threshold
    "nms_threshold": 0.4, # NMS threshold (postprocessing)
    "target_classes": ["car", "motorbike", "bus", "train", "truck"] # What classes you want to detect (if you want to use public dataset, but ignore some classes)
},
"cuda_settings":{ # CUDA settings, currently useless
    "enable": true # CUDA settings, currently useless
},
"mjpeg_settings":{ # MJPEG streaming settings
    "imshow_enable": false, # Do you want to enable imshow() feature (useful for testing purposes)
    "enable": true, # Do you want to enable this feature?
    "port": 35678 # Listening port fo connections
},
"grpc_settings": { # gRPC 'client-server' model settings
    "enable": true, # Do you want to enable this feature?
    "server_ip": "localhost", # gRPC server's IP
    "server_port": 50051 # gRPC server's listening port
},
"classes_settings": [ # classes settings (according to 'target_classes' in 'neural_network_settings')
    {
        "class_name": "car", # Corresponding class label
        "drawing_settings": {
            "bbox_settings": { # Setting for bounding boxes (detected objects)
                "rgba": [255, 255, 0, 0], # Color of bounding box border
                "thickness": 2 # Thickness as is
            },
            "centroid_settings": { # Setting for centroid of bounding boxes
                "rgba": [255, 0, 0, 0], # Color of circle
                "radius": 4, # Radius of circle
                "thickness": 2 # Thickness as is
            },
            "text_settings": { # Setting for text above bounding boxes
                "rgba": [0, 255, 0, 0], # Text color
                "scale": 0.5, # Size of text
                "thickness": 1, # Thickness as is
                "font": "hershey_simplex" # Text font
            },
            "display_object_id": true # If you want to display object identifier
        }
    },
    {
        "class_name": "motorbike", # see "car" ref.
        "drawing_settings": {} # if propetry is empty, then default values are used
    },
    {
        "class_name": "bus", # see "car" ref.
        "drawing_settings": {} # if propetry is empty, then default values are used
    },
    {
        "class_name": "train", # see "car" ref.
        "drawing_settings": {} # if propetry is empty, then default values are used
    },
    {
        "class_name": "truck", # see "car" ref.
        "drawing_settings": {} # if propetry is empty, then default values are used
    }
],
"tracker_settings": { # Tracked settings
    "tracker_type": "simple/kalman" # Use one of supported trackers. Simple tracker should fit realy simple scenes, while Kalman should be used with complicated scenes.
    "max_points_in_track": 150, # Restriction for maximum points in single track (>=1). Default value 10 (in case of value less than 1)
    "lines_settings":[
        {
            "line_id": 1, # Unique ID for line id (useful for 'client-server' model)
            "begin": [150, 800], # [X1,Y1], start point of line (usually, left side)
            "end": [1600, 800], # [X2,Y2], end point of line (usually, right side)
            "direction": "to_detector", # Direction of line (possible values: 'to_detector' and 'from_detector')
            "detect_classes": ["car", "motorbike", "bus", "train", "truck"], # What classes must be cropped (as detected objects) that were captured by detection line.
            "rgba": [255, 0, 0, 0], # Color of detection line
            "crop_mode": "crop" # When 'grpc_settings' field 'enable' is set to TRUE this option will be used for sending either cropped detected object (bbox==crop) or full image with bbox info to gRPC server-side application. Default is 'crop'
        }
    ],
    "speed_estimation_settings": { # Setting for speed estimation bas on GIS convertion between different spatial systems
        "enabled": false, # Enable this feature or not
        "mapper": [ # Map pixel coordinate to EPSG4326 coordinates
            # You should provide coordinates in correct order.
            # E.g. right bottom -> left bottom -> left top -> right top
            # Coordinates should match reduced_width and reduces_height attributes.
            {"image_coordinates": [640, 360], "epsg4326": [37.61891380882616, 54.20564268115055]},
            {"image_coordinates": [640, 0], "epsg4326": [37.61875545294513, 54.20546281228973]},
            {"image_coordinates": [0, 0], "epsg4326": [37.61903085447736, 54.20543126804313]},
            {"image_coordinates": [0, 360], "epsg4326": [37.61906183714973, 54.20562590237201]}
        ]
    }
},
"matpprof_settings": { # pprof for GoCV. Useful for debugging
    "enable": true # Do you want to enable this feature?
}
}

Run
```
odam --settings=conf.json
```

Screenshots

gocv.Imshow() output:
MJPEG streaming output:

Support

If you have troubles or questions please open an issue. Feel free to make PR's (we do not have contributing guidelines currently, but we will someday)

Roadmap

Please see ROADMAP.md

Dependencies

Bindings to OpenCV - GoCV. License is Apache-2.0
MJPEG streaming via GoCV - mjpeg. No license currently
Darknet (AlexeyAB's fork) - darknet. License is YOLO LICENSE
Tracking objects - gocv-blob. No license currently
gRPC for doing "'client-server'" application - grpc. License is Apache-2.0

License

You can check it here

Developers

LdDl https://github.com/LdDl

Pavel7824 https://github.com/Pavel7824

Former one: cpllbstr https://github.com/cpllbstr

LdDl / odam

readme

ODaM - Object Detection and Monitoring

v0.9.0

Work in progress

Table of Contents

About

QA section

Installation

notice: targeted for Linux users (no Windows/OSX instructions currenlty)

Usage

notice: targeted for Linux users (no Windows/OSX instructions currenlty)

Screenshots

Support

Roadmap

Dependencies

License

Developers