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https://github.com/olafenwamoses/deepstack_exdark
A DeepStack custom model for detecting common objects in dark/night images and videos.
https://github.com/olafenwamoses/deepstack_exdark
ai cctv computer-vision deep-learning deepstack machine-learning night-vision night-vision-camera object-detection yolo yolov3 yolov5
Last synced: 18 days ago
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A DeepStack custom model for detecting common objects in dark/night images and videos.
- Host: GitHub
- URL: https://github.com/olafenwamoses/deepstack_exdark
- Owner: OlafenwaMoses
- License: mit
- Created: 2021-05-05T12:59:02.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-05-12T08:29:07.000Z (over 3 years ago)
- Last Synced: 2024-10-17T15:39:26.923Z (28 days ago)
- Topics: ai, cctv, computer-vision, deep-learning, deepstack, machine-learning, night-vision, night-vision-camera, object-detection, yolo, yolov3, yolov5
- Language: Python
- Homepage:
- Size: 1.36 MB
- Stars: 118
- Watchers: 4
- Forks: 24
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://github.com/cs-chan/Exclusively-Dark-Image-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](https://docs.deepstack.cc/index.html#installation)
- **Download Custom Model:** Download the trained custom model `dark.pt` for ExDark from [this GitHub release](https://github.com/OlafenwaMoses/DeepStack_ExDark/releases/tag/v1). 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](https://docs.deepstack.cc/custom-models/deployment/index.html#starting-deepstack).
E.g
For a Windows version, you run the command below
```bash
deepstack --MODELSTORE-DETECTION "C\Users\MyUser\Documents\DeepStack-Models" --PORT 80
```
For a Linux machine
```bash
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 `images/image.jpg` of this repository
- Install Python and install the **DeepStack Python SDK** via the command below
```bash
pip install deepstack_sdk
```
- Run the Python file `detect.py` in this repository.
```bash
python detect.py
```
- After the code runs, you will find a new image in `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/](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 ](https://docs.deepstack.cc/custom-models/datasetprep/index.html)
- **Train your Model:** Train the model as [detailed here](https://docs.deepstack.cc/custom-models/training/index.html)