YOLOv11 Mono-Camera Distance Estimation

This a Python-based machine learning project that combines depth estimation and object detection using pre-trained models. Building on Apple's DepthPro, it offers tools to test depth and object detection on custom inputs efficiently.

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Features

• Depth Estimation: Predict depth maps from input images using pre-trained models.
• Object Detection: Perform object detection on test images or datasets.
• Custom Input Support: Easily integrate and test custom images or datasets.
• Pre-trained Model Support: Simple script provided to download pre-trained models.
• Example Scripts: Provided for testing and evaluating models.

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Installation

Prerequisites

• Python: 3.8+
• PyTorch: Installation instructions can be found on the PyTorch website
• Other dependencies listed in requirements.txt

Steps

1. Clone the Repository

   Clone this project to your local machine:

   git clone https://github.com/CagriCatik/YOLOv11-Mono-Camera-Distance-Estimation
   cd YOLOv11-Mono-Camera-Distance-Estimation

2. Install Dependencies

   Install all required dependencies using pip:

   pip install -r requirements.txt

3. Download Pre-trained Models

   Run the provided script to download the necessary pre-trained models:

   chmod +x get_pretrained_models.sh
   ./get_pretrained_models.sh

   The models will be saved in the checkpoints/ directory.

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Usage

1. Depth Estimation

To run depth estimation on your input images, execute the following command:

python test_depth.py

2. Object Detection

To perform object detection using the pre-trained model, use the following command:

python test_detection.py

3. Depth Estimation and Object Detection Combined

Run both depth estimation and object detection in one go:

python test_depth_detection.py

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Input and Output Structure

• Input: Place images in the data_input/ directory for processing.
• Output: Results such as depth maps and detection outputs will be saved in the data_output/ directory