Object Finder is a Python project that uses the DeepLabV3+ model to identify people in an image and highlight them with a colored overlay. The project features a user interface (UI) created with PyQt5 for an interactive experience.
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Python 3.x
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Install the required libraries using the following command:
pip install -r requirements.txt
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Clone the repository:
git clone https://github.com/your-username/ObjectFinder.git cd ObjectFinder
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Run the project using the provided script:
python3 ./src/main.py
- The project uses the DeepLabV3+ model from torchvision to perform semantic segmentation on the input image.
- Identified people are highlighted with a specified color and transparency level.
- OpenCV is utilized for image processing.
- The UI is built with PyQt5, providing a user-friendly interface for image manipulation.
Caption: The main interface of the Object Finder application.
Caption: Selecting an image using the UI.
src/main.py
: The main script to run the object finder.data/sample_images/
: Contains sample images for testing.requirements.txt
: Lists the required Python libraries and their versions.
- DeepLabV3+ model: torchvision.models.segmentation.deeplabv3_resnet101
- OpenCV: opencv-python
Note: This project adheres to specific guidelines. Numpy is intentionally excluded, and only input/output functions from OpenCV are used.
Feel free to customize the project to fit your specific needs!