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.
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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.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
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, img.shape, 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() y = approx.ravel() - 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()
Download Source Code below
Anyway, if you want to level up your programming knowledge, especially Python OpenCV, try this new article I’ve made for you Best OpenCV Projects With Source Code For Beginners 2021.
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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