Real-Time Object Distance Estimator Using OpenCV

This project is a real-time computer vision system built with OpenCV to: - Detect yellow-colored objects in a video stream - Label them with unique IDs - Estimate and display the distance between the objects in centimeters - Provide a visual overlay including bounding boxes, centers, and measured distances

It uses HSV color filtering, contour detection, and basic geometric calculations to determine spatial relationships between detected objects.

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🔍 Features

• Real-time video capture with OpenCV
• Detection of yellow objects using HSV masking
• Calculates center of each object and assigns labels (A, B, C...)
• Computes Euclidean distance between detected object centers
• Converts pixel distance to real-world centimeters using a fixed threshold

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Technologies Used

• Python 3.x
• OpenCV (cv2)
• NumPy (numpy)
• Basic Math operations (math)

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📦 Installation

1. Clone the Repository

git clone https://github.com/your-username/real-time-object-distance-estimator.git
cd real-time-object-distance-estimator

2. Install Dependencies

Ensure you have Python 3 installed. Then install the required libraries:

pip install opencv-python numpy

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🎥 Camera Setup

This project uses camera index 10 in cv2.VideoCapture(10).
⚠️ Important: Make sure your desired camera is mapped to index 10, or change this value accordingly.

To find your working camera index:

cv2.VideoCapture(0)  # Try different indexes if 0 doesn't work

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🚀 Usage

Simply run the script:

python object_distance_estimator.py

Controls

• Press q to quit the program safely.

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📌 How It Works

1. Color Detection

The system uses the HSV color space to isolate yellow objects:

lower_yellow = np.array([20, 100, 100])
upper_yellow = np.array([30, 255, 255])

2. Object Detection

Contours are detected from the mask and filtered based on area (> 500px) to avoid noise.

3. Labeling

Each detected object is labeled using alphabet characters (A, B, C...).

4. Distance Measurement

• Centers of detected bounding boxes are calculated.
• Distance between consecutive objects is calculated using the Euclidean formula: \( d = \sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2} \
• This pixel distance is then multiplied by a distance calibration factor (distance_threshold = 0.06912) to convert to centimeters.

5. Display

• Bounding boxes (green)
• Center points (red circles)
• Blue lines = measured distance between objects
• Red text = real-world distance in centimeters

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📷 Output Sample

Here's what you see in the final output window:

• Green rectangles = detected objects
• Red dot = center of object
• Blue lines = measured distance between objects
• Red text = real-world distance in centimeters

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📐 Calibration Note

distance_threshold = 0.06912 is a hardcoded value that maps pixel distance to centimeters.
To improve accuracy: - Use a known reference object size in frame - Calibrate using real-world measurements

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🧼 Cleanup

On pressing q, the script: - Breaks out of the loop - Releases the camera feed - Destroys all OpenCV windows

cv2.destroyAllWindows()

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🤝 Contributing

Contributions are welcome!
To contribute:

1. Fork the repository
2. Create a new branch (git checkout -b feature-name)
3. Commit your changes
4. Push and create a PR

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📄 License

This project is licensed under the MIT License.
Feel free to use, modify, and distribute.

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✉️ Contact

Have suggestions, feedback, or collaboration ideas?

Abhijeet Singh
📧 abhijeet8800434205@gmail.com