Скачать или смотреть Detect Vehicle Color with OpenCV and Python

Learn how to detect the color of vehicles using `OpenCV` and `Python`, and easily save it for further analysis.
---
This video is based on the question https://stackoverflow.com/q/76973865/ asked by the user 'youwish' ( https://stackoverflow.com/u/22438267/ ) and on the answer https://stackoverflow.com/a/76994873/ provided by the user 'Natália' ( https://stackoverflow.com/u/5153043/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Detect vehicle color with image OpenCV Python

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Detect Vehicle Color with OpenCV and Python

When it comes to computer vision applications, detecting the color of vehicles can be quite useful. Whether you're working on a traffic analysis project or developing a feature-rich vehicle recognition system, understanding how to extract and identify vehicle colors from images is a significant task. In this post, we'll explore how to achieve vehicle color detection using the OpenCV library in Python, in combination with the YOLO object detection model.

Problem Statement

You are able to identify vehicles, specifically cars, in images using the YOLO object detection model. However, you want to go a step further: you want to determine the dominant color of the detected cars and save that information for further usage.

Solution Overview

To detect vehicle color after identifying cars in images using YOLO, we will follow these steps:

Import the Necessary Libraries: We need OpenCV, NumPy, and a couple of other libraries for color detection.

Load YOLO: Load the YOLO model to perform object detection.

Process Images: Read image files and apply YOLO to detect cars.

Extract the Dominant Color: Use KMeans clustering to find the dominant color in a detected car's image.

Map the Color to Its Name: Convert the RGB values of the detected color to a recognizable color name.

Save the Processed Image: Save the image with the detected color name in the filename.

Step-by-Step Implementation

1. Import the Necessary Libraries

First, we need to import all the relevant libraries:

[[See Video to Reveal this Text or Code Snippet]]

2. Load YOLO

You will also need to load the YOLO files (yolov3.weights, yolov3.cfg, and coco.names) which contain the model structure and trained weights.

[[See Video to Reveal this Text or Code Snippet]]

3. Process Images

Loop through your image directory and apply YOLO to each image:

[[See Video to Reveal this Text or Code Snippet]]

4. Extract the Dominant Color

After detecting the car, we will focus on extracting the dominant color using KMeans clustering:

[[See Video to Reveal this Text or Code Snippet]]

5. Map the Color to Its Name

We will convert the RGB values to a color name such as "Red", "Blue", etc., using the webcolors library:

[[See Video to Reveal this Text or Code Snippet]]

6. Save the Processed Image

Finally, we will save the image with the color name included in the filename:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In this guide, we have walked through the process of detecting vehicle colors using OpenCV and Python. By leveraging KMeans clustering to identify the dominant color and integrating this feature into your car detection workflow, you can enrich your dataset with valuable information. Enjoy experimenting with your vehicle color detection project!