Table of Contents
- Introduction
- Prerequisites
- Setup and Installation
- Loading and Preprocessing Images
- Identifying Shapes
- Conclusion
Introduction
In this tutorial, we will explore how to use Python for image recognition to identify different shapes. We will start by discussing the prerequisites and setup needed to follow along with the tutorial. Then, we will cover loading and preprocessing images, followed by the main topic of identifying shapes within the images. By the end of this tutorial, you will be able to write Python code to recognize and classify various shapes in images.
Prerequisites
To fully understand and follow this tutorial, you should have a basic understanding of Python programming language syntax and concepts. Familiarity with the following Python libraries will also be beneficial:
- NumPy
- OpenCV
Setup and Installation
Before we begin, let’s make sure we have all the necessary libraries installed. Open a terminal and run the following command to install NumPy:
python
pip install numpy
Next, we need to install OpenCV. Run the following command in the terminal to install it:
python
pip install opencv-python
With the required packages installed, we are now ready to proceed with the rest of the tutorial.
Loading and Preprocessing Images
Before we can start recognizing shapes, we need to load and preprocess the images. For this tutorial, let’s say we have a directory called “images” that contains several image files. We will focus on a single image for simplicity.
First, let’s import the necessary libraries:
python
import cv2
import numpy as np
Next, we load the image using the cv2.imread() function:
python
image = cv2.imread("images/shapes.jpg")
The cv2.imread() function reads the image file from disk and returns a NumPy array representing the image. We can then perform various operations on this array to preprocess the image. Preprocessing steps may include resizing, grayscale conversion, and noise removal, among others. However, for the purpose of shape identification, we will keep it simple and won’t apply any additional preprocessing.
Identifying Shapes
Now that we have our image loaded and preprocessed (if necessary), it’s time to identify the shapes present in the image. We will use OpenCV’s contour detection functionality for this task.
Contours are simply the boundaries of shapes present in an image. To find contours, we need to convert the image to grayscale. We can use the cv2.cvtColor() function for this:
python
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
Next, we can apply a threshold to the grayscale image to convert it into a binary image. This process helps in isolating the shapes from the rest of the image. We will use the cv2.threshold() function to perform this operation:
python
_, threshold_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)
The cv2.threshold() function takes the grayscale image, a threshold value (127 in this case), a maximum value (255), and a thresholding method (cv2.THRESH_BINARY). The function returns two outputs; the first one is the threshold value used, and the second one is the thresholded image.
Now that we have the thresholded image, we can find the contours using the cv2.findContours() function:
python
contours, _ = cv2.findContours(threshold_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
The cv2.findContours() function takes the thresholded image, a contour retrieval mode (cv2.RETR_EXTERNAL), and a contour approximation method (cv2.CHAIN_APPROX_SIMPLE). It returns a list of contours and a hierarchy.
To visualize the contours on the original image, we can use the cv2.drawContours() function:
python
cv2.drawContours(image, contours, -1, (0, 255, 0), 3)
The cv2.drawContours() function takes the original image, the list of contours, a contour index (-1 to draw all contours), a color (in BGR format), and a thickness value for the contour lines.
Finally, we can display the original image with the identified shapes using the cv2.imshow() and cv2.waitKey() functions:
python
cv2.imshow("Identified Shapes", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
The cv2.imshow() function displays an image window with the given title, and the cv2.waitKey() function waits for a key event (0 here) before closing the window. The cv2.destroyAllWindows() function closes all open windows.
Conclusion
In this tutorial, we learned how to use Python for image recognition to identify shapes. We started by loading and preprocessing the image, then used contour detection to find the shapes present in the image. Finally, we visualized the identified shapes on the original image.
By understanding the concepts and code provided in this tutorial, you can build upon them to create more complex image recognition systems or explore other applications of Python in computer vision.
Remember, practice is key to improving your skills. Experiment with different images and explore additional functionalities provided by the OpenCV library to further enhance your knowledge in image recognition and computer vision.