https://github.com/gkamboj/image-features-extractor
Detect object in any image along with its features like color, etc.
https://github.com/gkamboj/image-features-extractor
color-classification connexion feature-extraction flask image-classification image-recognition machine-learning numpy opencv python resnet
Last synced: 2 months ago
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Detect object in any image along with its features like color, etc.
- Host: GitHub
- URL: https://github.com/gkamboj/image-features-extractor
- Owner: gkamboj
- License: mit
- Created: 2024-12-28T19:57:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-29T08:23:04.000Z (over 1 year ago)
- Last Synced: 2025-05-30T02:45:06.801Z (about 1 year ago)
- Topics: color-classification, connexion, feature-extraction, flask, image-classification, image-recognition, machine-learning, numpy, opencv, python, resnet
- Language: Python
- Homepage:
- Size: 6.58 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# image-features-extractor
This is my first attempt on creating a basic Flask application. This application allows to detect object in any image along with its features like color, etc. Inspirarion behind it was a POC to enable the image based search in the e-commerce website project I worked earlier.
## Table of Contents
- [Setup Instructions](#setup-instructions)
- [Usage](#usage)
- [Demo and Screenshots](#demo-and-screenshots)
- [Future Improvements](#future-improvements)
- [License](#license)
## Setup Instructions
After cloning the repository, follow these steps to set up the project:
1. **Set Up your development environment**:
- Download and install JetBrains PyCharm IDE or your preferred IDE.
- The following instructions will focus on PyCharm, but most IDEs provide similar features.
2. **Open the project**:
- In PyCharm, navigate to `File -> Open` and select the cloned repository folder.
3. **Set Up a local virtual environment**:
- Go to `Settings` > `Project: finance-gpt` > `Python Interpreter` > `Add Interpreter`.
- Choose `Add Local Interpreter` > `Virtualenv Environment`.
1. Select `Environment` -> `New`.
2. Set `Base Interpreter` to your installed Python version (e.g., Python 3.x).
3. Click `OK`.
4. **Install dependencies**:
Install required dependencies by running the following command in terminal through IDE:
```bash
pip install -r requirements.txt
```
5. **Run the application**:
Application can be started by running the following command from the root folder:
```bash
python -m cx_img.app
```
## Usage
The application does not include a user interface (UI) but provides two APIs that can be accessed using Postman or any other API client. These APIs are:
1. **Training API**
- This API generates a training data file based on [data source color images](https://github.com/gkamboj/image-features-extractor/tree/main/cx_img/resources/color_detection/training_dataset/training_dataset).
- It uses color classification to extract and store RGB values for each image in the data source.
- Special thanks to [color_recognition by ahmetozlu](https://github.com/ahmetozlu/color_recognition) for inspiring this approach.
2. **Image Classification API**
- This API processes input images and returns data about the detected objects, including their confidence scores and features such as color.
- The object detection model is based on ResNet40.
- For color detection, area of main object in the image is identified first through **GrabCut algorithm**. [KNN algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) is then applied on this object by referencing the training data to determine the color corresponding to the object's RGB values.
### Notes:
- The **Training API** must be executed once during setup to generate the necessary training data for accurate image classification results.
- To access the Swagger UI for API documentation, navigate to [http://localhost:5001/ui](http://localhost:5001/ui) (or the appropriate port if different).
## Demo and Screenshots
Here are some screenshots showcasing working deployments of the application.
- _Image Classification_ API and sample response:
- Postman:
- Sample input image:
- Response corresponding to the sample input image:
```
{
"result": {
"attributes": {
"color": {
"name": [
"black"
],
"rgb": "46,46,46"
}
},
"detection": [
{
"confidence": "0.96429",
"model": "resnet50",
"name": "running_shoe"
}
]
},
"success": true
}
```
- Swagger UI:
## Future Improvements
Potential enhancements for future development include:
- Improving color detection accuracy by expanding the dataset with more diverse training images.
- Adding additional features such as:
- Object type detection.
- Features specific to object types (e.g., collar type, pattern, and color for shirts; brand and color for mobile phones).
- The current use of **ResNet40**, a pretrained model, may limit accuracy for object detection. To achieve higher accuracy, a custom model can be created and trained with a dataset relevant to your usecase.
- The current approach of using **KNN for color detection** does not perform well for images with multiple colors. A more advanced method is required to handle such scenarios effectively.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.