Measure Size of Object in Images ACCURATELY using OpenCV Python

Summary

In this tutorial, the creator demonstrates how to measure the size of objects and images using OpenCV and Python. Starting with object detection, the video covers using a YOLO detector to recognize objects like phones. It delves into rotating objects for accurate dimension measurement, utilizing Principal Component Analysis (PCA) to find the width and height. The tutorial also shows how to convert pixel measurements into real-world units using a known reference object like a caliper. The process is well-suited for generalizing object detection across varying backgrounds and angles, yielding accurate results.

Takeaways

• 😀 OpenCV and Python can be used to measure the size of objects in images by using object detection and image processing techniques.
• 📱 The Coco dataset provides pre-trained object recognition models for common objects, such as phones, with indexed classes for easier identification.
• 🔧 If the object isn't in the Coco dataset, custom training is necessary to recognize it with YOLO detectors.
• 📸 It's crucial to ensure the camera is mounted perpendicular to the surface to reduce measurement inaccuracies when capturing images.
• 🔍 YOLO object detection is used to identify and localize objects in an image, including calculating bounding boxes for dimension extraction.
• 🌀 Principal Component Analysis (PCA) is applied to the object to find the two primary axes, which helps determine the width and height of rotated objects.
• 🎯 The PCA method allows measuring the dimensions of an object at any rotation by calculating its principal components and rotating the object back to its aligned axes.
• 📏 The width and height of the object can be measured in pixels after applying PCA to the rotated object, with pixel dimensions being extracted from the bounding box.
• 🖼️ A reference object like a caliper or ruler is used to establish a conversion factor between pixels and real-world measurements, ensuring accuracy.
• 📐 The script calculates the size of the object in millimeters based on the pixel measurements and conversion factors, providing accurate dimension estimates.
• 🔬 The final measurements, though accurate, may have minor errors (like tilt during image capture), but overall the results can be very close to true physical dimensions (e.g., 0.24% error in width).

Q & A

What is the first step in the process of measuring an object's size using OpenCV and Python?

The first step is object detection, where you identify the object in the image using a pre-trained model like YOLO. In this case, the script uses a phone from the COCO dataset, which contains common objects.

What is the COCO dataset, and why is it used in this process?

The COCO dataset (Common Objects in Context) is a large collection of images with labeled objects. It contains 80 object classes commonly found in everyday scenarios. It's used here because the YOLO model can recognize objects from this dataset, like phones.

What should you do if your object is not in the COCO dataset?

If the object is not in the COCO dataset, you need to perform custom training for the YOLO model so it can recognize the object you want to measure.

Why is it important to ensure that the camera is perpendicular to the surface during object detection?

The camera should be perpendicular to the surface to minimize distortion and inaccuracies in measurements. If the camera is tilted, it can lead to errors in the detection and dimension calculation, as seen in the script's example.

How does rotating the object help in the measurement process?

Rotating the object helps because it allows you to find the dimensions of the object at any angle, not just in its default orientation. This is useful for generalizing the measurement process, especially when the object is not aligned properly.

What is the role of PCA (Principal Component Analysis) in measuring the object?

PCA helps by identifying the two main axes of the object. It allows you to find the width and height of the object even when it's rotated, by aligning the object's axis with the image axes, making it easier to measure accurately.

What happens after PCA is applied to the object?

After PCA is applied, the object is rotated based on the calculated angle of the principal components. This transformation aligns the object’s axes with the image, making it easier to measure the width and height in pixels.

How do you convert the pixel dimensions of the object into real-world measurements?

To convert pixels into real-world measurements, you use a reference object with a known size in the image, like a ruler or caliper. For example, the script uses a caliper, where 100 millimeters corresponds to 629 pixels.

How accurate are the measurements compared to the real-world size?

The measurements are generally accurate, with the width being off by only 0.24%, but there is some error in the length due to the camera's angle. This error is minimized by ensuring the object is positioned correctly and the camera is perpendicular.

What is the significance of using a YOLO model for object detection in this process?

YOLO (You Only Look Once) is an efficient real-time object detection model. It is used in this process to quickly and accurately detect objects in images, allowing the subsequent measurement process to focus on known or trained objects.