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Machine Vision

Estimate Object Size From Image (Unknown Distance)

By Jan on 03/12/2025

Learn how to determine the actual size of objects in images even without knowing the camera-to-object distance using clever techniques and readily available tools.

Estimate Object Size From Image (Unknown Distance)

Table of Contents

Introduction

You can't tell how big something is in real life just by looking at a picture of it. You need more information, just like you can't tell how far away a car is just by its size in your rearview mirror. This is because of perspective – things look smaller the farther away they are. A small object up close can look the same size as a large object far away in a photo.

Step-by-Step Guide

You can't determine the real-life size of an object from a single image without additional information. It's like trying to tell how far away a car is just by looking at its size in your rearview mirror – you need more context.

Here's why:

  • Perspective: Objects appear smaller the farther they are from the camera. A small object close to the camera can look the same size as a large object far away.

To calculate real-life size, you need at least one of these:

  1. Known Distance: If you know the distance between the camera and the object, you can use geometry and the image's metadata (like focal length and sensor size) to calculate the object's size.

    object_height = (image_height * object_distance) / focal_length

  2. Reference Object: Include an object of known size in the image. By comparing the image sizes of the known object and the target object, you can estimate the target object's size.

  3. Depth Information: Use a depth camera (like the ZED 2i) or techniques like stereo vision to obtain depth information for each pixel. This allows you to calculate distances and then object sizes.

Without these, you're left with estimations and assumptions, which might not be accurate.

Code Example

This Python code demonstrates the difficulty of estimating an object's real-world size from a single image. It uses OpenCV to detect a ball in an image and calculate its pixel diameter. However, it highlights that without additional information like the distance to the ball or a reference object, determining the actual size is impossible. The code emphasizes that while image analysis can extract features, real-world measurements require more context.

This code example uses Python and OpenCV to illustrate the challenge of determining real-life object size from a single image.

Scenario: We have an image with a ball. We want to determine its real-life diameter.

import cv2

# Load the image
image = cv2.imread("ball_image.jpg")

# Assume we identified the ball in the image
# (x, y) are the top-left corner coordinates of the bounding box
# w, h are the width and height of the bounding box
x, y, w, h = 100, 50, 80, 80 

# Draw the bounding box around the ball
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

# Display the image with the bounding box
cv2.imshow("Ball Detection", image)
cv2.waitKey(0)

# We can get the pixel size of the ball
pixel_diameter = w 

# But without knowing the distance to the ball or having a reference object,
# we cannot determine its real-life size.

# Example: Assuming we know the distance
distance_to_ball_cm = 50  # Example: 50 cm

# We would need camera parameters like focal length to calculate real size
# This is a simplified example and actual calculation is more complex
# object_diameter_cm = (pixel_diameter * distance_to_ball_cm) / focal_length 

print("Pixel diameter of the ball:", pixel_diameter)
print("Without additional information, we cannot determine the real diameter.")

Explanation:

  1. Image Loading and Object Detection: We load the image and simulate detecting the ball by defining its bounding box coordinates.
  2. Pixel Size: We obtain the ball's diameter in pixels.
  3. Missing Information: We highlight that without knowing the distance to the ball or having a reference object, we cannot calculate the real-life diameter.
  4. Placeholder for Calculation: The commented-out code snippet demonstrates that even with the distance, we would need camera parameters like focal length for accurate size calculation.

This example emphasizes that while we can easily extract information from an image, determining real-world measurements requires additional information like distance, reference objects, or depth data.

Additional Notes

  • Human Vision vs. Single Images: Our brains use cues like stereo vision (two eyes), experience with object sizes, and environmental context to judge size and distance. A single image lacks this richness of information.
  • Importance of Camera Parameters: Focal length, sensor size, and lens distortion all influence how objects are projected onto the image, affecting size calculations.
  • Applications of Size Estimation: Despite the challenges, estimating size from images is crucial in fields like robotics (navigation, object manipulation), augmented reality (placing virtual objects realistically), and medical imaging (measuring tumor size).
  • Limitations of Reference Objects: The accuracy of using a reference object depends on knowing its exact size and ensuring it's at roughly the same distance from the camera as the target object.
  • Advancements in Depth Sensing: Technologies like structured light (used in some smartphone cameras) and LiDAR are making depth information more accessible, improving size estimation in real-time applications.
  • Ethical Considerations: Be mindful of the limitations of size estimation from images. Don't rely solely on images for critical measurements, especially in situations where accuracy is paramount.

Summary

Problem: You can't accurately determine an object's real-life size from a single image without additional information due to perspective distortion.

Why? Objects appear smaller as their distance from the camera increases. This makes it impossible to differentiate between a small, close object and a large, distant one based on image size alone.

Solutions: To calculate real-life size, you need at least one of the following:

  1. Known Distance: Knowing the distance between the camera and the object allows for size calculation using geometry, focal length, and sensor size information from the image metadata.
  2. Reference Object: Including an object of known size in the image provides a scale for comparison, enabling size estimation of the target object.
  3. Depth Information: Utilizing depth cameras or techniques like stereo vision provides depth data for each pixel, allowing for distance and subsequently object size calculation.

Conclusion: Without additional information like distance, a reference object, or depth data, determining real-life object size from a single image relies on estimations and assumptions, leading to potentially inaccurate results.

Conclusion

Just like you can't determine a car's distance solely by its size in your rearview mirror, you can't determine an object's real-life size from a single image without additional information. Perspective makes objects appear smaller the farther they are from the camera, making it impossible to discern the true size of an object from a single image. To accurately calculate real-life size, you need extra information such as known distance to the object, a reference object of known size in the image, or depth information provided by depth cameras or techniques like stereo vision. Without these, size estimations remain unreliable. While image analysis can extract features and pixel dimensions, accurate real-world measurements require additional context and information.

References

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