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https://github.com/lbaiyat/computer-vision-distance-detector

This is a computer vision project using python3 and conventional computer vision techniques to determine a relative distance between two points in a given scene.
https://github.com/lbaiyat/computer-vision-distance-detector

canny-edge-detection color-histogram computer-vision computervision cv2 hough-transform k-means opencv opencv-python python3 sklearn

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This is a computer vision project using python3 and conventional computer vision techniques to determine a relative distance between two points in a given scene.

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README 

        
Computer Vision Distance Detector







Table of Contents









Overview:




This is a computer vision project created in Python with computer vision libraries to

determine the distance between two target objects in a given scene. The scenes and training images

are depicted from a popular sandbox game.








Instructions:





Follow the steps below to run the application.






Setup virtual environment





python3 -m venv venv;

pip install -r requirements.txt;






Run the application





python3 modules/main.py;









Steps:





Histogram Training






This step will process training images to get an understanding of

how the target block should look based on color values. The training images have (gold) target blocks shown

from different angles and under different lighting situations. Once the histogram training is complete,

the histogram model can be used to filter for target blocks by its most frequent colors..




For more info about Color Histograms:

    Color Histograms






Below are some sample images used to train the histogram model.

There are roughly 50 training images used for the histogram training.






  

 

 

 

 

 








Scene





For the purpose of this walkthrough, the scene image shown below will be used.









Canny Edge Detection



The Canny Edge Detection algorithm is used to detect the edges in a given image.


For more info about the Canny Edge Detection Algorithm:

    Canny Edge

        Detector



Applying the Canny Edge detector to the scene image gives us this result:







Projective Transformation



                        

The Projective Transformation technique is used to transform an image from a perspective

into a 2D overhead view.




For more info about the Projective Transformations:

    Projective Transformations




                        

The bounds for the Projective Transformation are determined and then highlighted in red for a visual representation.






                        

Once the Projective Transformation is completed, the result looks like the image shown below.








K-Means Clustering





K-Means Clustering will be used to find 2 points in the transformed image that

correspond to the center points of each of the target blocks.




For more info about K-Means Clustering:

    K-Means Clustering



Setting K=2 and using the pixels in the target blocks as data points, we get two points corresponding to the centers 

of the target blocks, which are marked by the red dots.









Hough Transformation






The Hough Transformation is used for the last result image to count the number of horizontal and vertical lines between the two highlight

points. These two values are used with the Euclidean Distance Formula to determine the distance between the two target blocks.




For more info about the Hough Transformation:

    Hough Transform