DeepStack_ExDark

This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for detecting 12 common objects (including people) in the dark/night images and videos. The Model was trained on the ExDark dataset dataset.

Create API and Detect Objects
Discover more Custom Models
Train your own Model

Create API and Detect Objects

The Trained Model can detect the following objects in dark/night images and videos.

Bicycle
Boat
Bottle
Bus
Chair
Car
Cat
Cup
Dog
Motorbike
People
Table

To start detecting, follow the steps below

Install DeepStack: Install DeepStack AI Server with instructions on DeepStack's documentation via https://docs.deepstack.cc
Download Custom Model: Download the trained custom model dark.pt for ExDark from this GitHub release. Create a folder on your machine and move the downloaded model to this folder.

E.g A path on Windows Machine C\Users\MyUser\Documents\DeepStack-Models, which will make your model file path C\Users\MyUser\Documents\DeepStack-Models\dark.pt
Run DeepStack: To run DeepStack AI Server with the custom ExDark model, run the command that applies to your machine as detailed on DeepStack's documentation linked here.

E.g

For a Windows version, you run the command below
```
deepstack --MODELSTORE-DETECTION "C\Users\MyUser\Documents\DeepStack-Models" --PORT 80
```
For a Linux machine
```
sudo docker run -v /home/MyUser/Documents/DeepStack-Models:/modelstore/detection -p 80:5000 deepquestai/deepstack
```
Once DeepStack runs, you will see a log like the one below in your Terminal/Console

That means DeepStack is running your custom dark.pt model and now ready to start detecting objects in night/dark images via the API endpoint http://localhost:80/v1/vision/custom/dark or http://your_machine_ip:80/v1/vision/custom/dark
Detect Objects in night image: You can detect objects in an image by sending a POST request to the url mentioned above with the paramater image set to an image using any proggramming language or with a tool like POSTMAN. For the purpose of this repository, we have provided a sample Python code below.
- A sample image can be found in https://raw.githubusercontent.com/OlafenwaMoses/DeepStack_ExDark/main/images/image.jpg of this repository
- Install Python and install the DeepStack Python SDK via the command below
```
pip install deepstack_sdk
```
- Run the Python file detect.py in this repository.
```
python detect.py
```
- After the code runs, you will find a new image in https://raw.githubusercontent.com/OlafenwaMoses/DeepStack_ExDark/main/images/image_detected.jpg with the detection visualized, with the following results printed in the Terminal/Console.
```
Name: People
Confidence: 0.74210495
x_min: 616
x_max: 672
y_min: 224
y_max: 323
-----------------------
Name: Dog
Confidence: 0.82523036
x_min: 250
x_max: 327
y_min: 288
y_max: 349
-----------------------
Name: Dog
Confidence: 0.86660975
x_min: 403
x_max: 485
y_min: 283
y_max: 341
-----------------------
Name: Dog
Confidence: 0.87793124
x_min: 508
x_max: 609
y_min: 309
y_max: 370
-----------------------
Name: Dog
Confidence: 0.89132285
x_min: 286
x_max: 372
y_min: 316
y_max: 393
-----------------------
```
- You can try running detection for other night/dark images.

Discover more Custom Models

For more custom DeepStack models that has been trained and ready to use, visit the Custom Models sample page on DeepStack's documentation https://docs.deepstack.cc/custom-models-samples/ .

Train your own Model

If you will like to train a custom model yourself, follow the instructions below.

Prepare and Annotate: Collect images on and annotate object(s) you plan to detect as detailed here
Train your Model: Train the model as detailed here