Unveiling the Hidden World: Real-World Camera Calibration and Uncertainty Analysis

This repository contains the Python notebook and associated files for a computer vision project focused on calibrating real-world cameras. The core of this project is to determine a camera's precise 3D geographic location (latitude, longitude, altitude) and intrinsic properties (calibration matrix K) from 2D image data.

A key differentiator of this project is its two-part structure: first, a rigorous uncertainty analysis using synthetic data, and second, a practical application to find the location of live webcams.

Companion Blog Post

For a detailed narrative, insights into the methodology, and a full discussion of the project's challenges and findings, please read the companion article on Medium:

Unveiling the Hidden World: Calibrating Real-World Cameras and Understanding Their...

---

Table of Contents

• Core Objectives
• Tools & Technologies
• Project Methodology
  • Part 1: Uncertainty Analysis with Synthetic Data
  • Part 2: Real-World Camera Calibration
• How to Use This Repository
• Expected Outputs

---

Core Objectives

• Estimate Camera Pose: Determine the camera's 3D position (latitude, longitude, altitude), orientation, and its intrinsic calibration matrix (K).
• Uncertainty Analysis: Quantify the reliability and robustness of the camera parameter estimations using statistical methods.
• Real-World Application: Apply these calibration techniques to determine the 3D locations of live webcams.

Tools & Technologies

• Python Libraries: pandas, matplotlib, pyproj, pymap3d, numpy, cv2 (OpenCV).
• Geospatial Software: Google Earth Pro (for manual 3D coordinate extraction from real-world scenes).

Project Methodology

This project is divided into two main parts:

Part 1: Uncertainty Analysis with Synthetic Data

This section uses a pre-provided dataset of 3D-to-2D point correspondences to perform an in-depth uncertainty analysis and validate the model's stability.

• 1a) Leave-One-Out (LOO) Analysis:

  • Objective: To assess the sensitivity of the estimated camera location to individual data points.
  • Method: The calibration is run 'N' times, each time excluding one of the 'N' data points.
  • Output: A 3D scatter plot of the 'N' recovered camera locations, visually illustrating the estimation's stability.

• 1b) Noise Analysis:

  • Objective: To evaluate the robustness of the camera parameters (location and focal length) under simulated real-world noise.
  • Method: The calibration is run 100 times. In each iteration, Gaussian zero-mean noise is added to both the 3D coordinates (std dev 1m) and 2D pixel locations (std dev 1px).
  • Output: A 3D scatter plot of the 100 recovered camera locations and a histogram of the estimated focal lengths.

Part 2: Real-World Camera Calibration

This section applies the validated methodology to determine the 3D geographic locations of live webcams.

• Methodology:
  1. Select Webcams: Identify live streams (e.g., YouTube, NPS webcams).
  2. Landmark Identification: Find distinct, static features visible in the webcam and on satellite imagery.
  3. 3D Coordinate Extraction: Use Google Earth Pro to find the precise latitude, longitude, and absolute altitude of these landmarks.
  4. 2D Coordinate Extraction: Record the corresponding (x, y) pixel coordinates for each landmark from the webcam image.
  5. Data Compilation: Aggregate the 3D and 2D points into a structured CSV file (e.g., bostonharbor.csv).
  6. Calibration & Validation: Run the Python calibration script on the real-world data and generate a Reprojection Plot to visually validate the accuracy by projecting the 3D points back onto the image.
  7. Refinement: Iteratively refine landmark selection based on the reprojection error.

How to Use This Repository

1. Clone the repository:

   git clone [https://github.com/Anaswara23/OpenCV-Camera-Calibration]

2. Install dependencies:

   pip install pandas matplotlib pyproj pymap3d numpy opencv-python

3. Run the notebook:
   • Open calibrate.ipynb (or your notebook's name) in a Jupyter environment or Google Colab.
4. Part 1 (Uncertainty Analysis):
   • Ensure the synthetic data CSV is present.
   • Run the cells in the "Part 1" section to generate the LOO and noise analysis plots.
5. Part 2 (Real-World Calibration):
   • Follow the methodology in "Part 2" to create your own CSV file.
   • Update the notebook to load your CSV file.
   • Run the calibration and validation cells to find the camera's location and see the reprojection plot.

Expected Outputs

• A 3D point cloud visualizing the Leave-One-Out (LOO) analysis.
• A 3D point cloud and histogram for the Noise Analysis.
• For Part 2, a table of collected 3D/2D coordinates, the final recovered 3D GPS location (lat, long, alt) for the webcam, and a reprojection plot showing the calibration accuracy.