Detecting shapes from an image is one of the coolest things you can perform with Python and OpenCV. There are many methods and techniques available in the OpenCV library to detect shapes in an image, and for most of them, we first find out the edges in the image and then detect its shape.

This code segment is responsible for exploring and analyzing a dataset, specifically designed for shape recognition tasks. The dataset is structured into training, testing, and validation sets, with each set residing in its respective directory.

There is another popular shape detection method called the Hough Transform technique present in OpenCV, but it is limited to detecting a circle. The approxPolyDP() technique is emerging as a better alternative when we want to detect different shapes in an image.

Open up the shapedetector.py file and insert the following code: def __init__(self): pass def detect(self, c): # initialize the shape name and approximate the contour shape = "unidentified" peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.04 * peri, True) Line 4 starts the definition of our ShapeDetector class.