Shapes Detection OpenCV Python With Source Code

Shapes Detection OpenCV Python With Source Code

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Shapes Detection OpenCV Python With Source Code

The Shapes Detection OpenCV Python was developed using Python OpenCV , In this Shape Detection OpenCV Python project I am taking an image that contains shapes like triangle, square, rectangle, and circle. The image is then converted to grayscale using the cvtColor() function.

What is OpenCV?

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.

The Grayscaled image is then Thresholded using the THRESH_BINARY Method. The Thresholded image is then taken and contours are found on that image. The Contours obtained are then looped and the edges are counted using the approxPolyDP() function, which takes each contour. The edges of the Contour is then drawn on the image using drawContours() function.

In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

To start executing Shapes Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.

Shapes Detection OpenCV Python With Source Code : Steps on how to run the project

Time needed: 5 minutes

These are the steps on how to run Shapes Detection OpenCV Python With Source Code

  • Step 1: Download the given source code below.

    First, download the given source code below and unzip the source code.
    Shapes detection OpenCV download source code

  • Step 2: Import the project to your PyCharm IDE.

    Next, import the source code you’ve download to your PyCharm IDE.
    Shapes detection OpenCV open project

  • Step 3: Run the project.

    last, run the project with the command “py main.py”
    Shapes detection OpenCV run project

Installed Libraries

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

Complete Source Code

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt


def shapes():
    img = cv.imread('./img/shapess.jpg')
    img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

    _, thresh = cv.threshold(img_gray, 240, 255, cv.THRESH_BINARY)
    contours, _ = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)

    white = np.ones((img.shape[0], img.shape[1], 3))

    for c in contours:
        approx = cv.approxPolyDP(c, 0.01*cv.arcLength(c, True), True)
        cv.drawContours(img, [approx], 0, (0, 255, 0), 5)
        x = approx.ravel()[0]
        y = approx.ravel()[1] - 5
        if len(approx) == 3:
            cv.putText(img, "Triangle", (x, y),
                       cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)
        elif len(approx) == 4:
            x1, y1, w, h = cv.boundingRect(approx)
            aspect_ratio = float(w) / float(h)
            print(aspect_ratio)
            if aspect_ratio >= 0.95 and aspect_ratio <= 1.05:
                cv.putText(img, "Square", (x, y),
                           cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)
            else:
                cv.putText(img, "Rectangle", (x, y),
                           cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)
        elif len(approx) == 5:
            cv.putText(img, "Pentagon", (x, y),
                       cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)
        elif len(approx) == 10:
            cv.putText(img, "Star", (x, y),
                       cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)
        else:
            cv.putText(img, "Circle", (x, y),
                       cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 1)

    cv.imshow("Shapes", img)
    cv.waitKey()
    cv.destroyAllWindows()


if __name__ == "__main__":
    shapes()

Output

Download Source Code below

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Summary

In today’s article, we learned how to perform shape detection with OpenCV and Python.

To accomplish this, we leveraged contour approximation, the process of reducing the number of points on a curve to a more simple approximated version.

Then, based on this contour approximation, we examined the number of vertices each shape has. Given the vertex count, we were able to accurately label each of the shapes.

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Inquiries

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