Python for Machine Learning: Image Recognition Exercise

Introduction
Prerequisites
Setup
Image Recognition Exercise
Conclusion

Introduction

In this tutorial, we will learn how to perform image recognition using Python. Image recognition has become an essential part of many applications, such as self-driving cars, facial recognition systems, and object detection in images. By the end of this tutorial, you will be able to write Python code to identify objects or patterns in images.

Prerequisites

To follow along with this tutorial, you should have a basic understanding of Python programming and familiarity with the concepts of machine learning. It would also be beneficial to have some knowledge of the NumPy and OpenCV libraries, as they are commonly used in image recognition tasks.

Setup

Before we begin, ensure that you have the following libraries installed:

NumPy: pip install numpy
OpenCV: pip install opencv-python

These libraries provide the necessary functionality for handling arrays and image processing in Python.

Image Recognition Exercise

For this exercise, we will use an image containing various fruits and build a model to recognize and classify these fruits. Follow the steps below to complete the exercise:

Import the required libraries:
```
import cv2
import numpy as np
```
Load the image:
```
image = cv2.imread("fruits_image.jpg")
```
Make sure to replace "fruits_image.jpg" with the path to your own image file.
Preprocess the image:

Before performing image recognition, we need to prepare the image by resizing it and converting it to grayscale. This step helps reduce the computational complexity and enhances the model’s accuracy.
```
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
resized_image = cv2.resize(gray_image, (100, 100))
```
Adjust the dimensions of resized_image as needed based on your requirements.
Load the pre-trained model:

For this exercise, we will use a pre-trained model called “fruit_classifier_model.h5” that has been trained to recognize various fruits. Load the model using the following code:
```
model = keras.models.load_model("fruit_classifier_model.h5")
```
Replace "fruit_classifier_model.h5" with the path to your own pre-trained model file.
Make predictions:

Now that we have the pre-trained model and the preprocessed image, we can make predictions on the image to identify the fruits present.
```
predictions = model.predict(np.expand_dims(resized_image, axis=0))
```
The predictions variable will contain the predicted probabilities for each fruit class.
Post-process the predictions:

To obtain the final predicted fruit, we need to post-process the predictions by identifying the fruit class with the highest probability.
```
fruit_classes = ["apple", "banana", "orange"]
predicted_class = fruit_classes[np.argmax(predictions)]
```
In this example, we assume that our model can recognize three types of fruits: apple, banana, and orange. Adjust fruit_classes to match your own model’s output.

Display the results:

Finally, let’s display the original image along with the predicted fruit class.

cv2.putText(
    image,
    f"Predicted Fruit: {predicted_class}",
    (10, 30),
    cv2.FONT_HERSHEY_SIMPLEX,
    1,
    (0, 0, 255),
    2
)

cv2.imshow("Image Recognition", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code will overlay the predicted fruit class on the image and display it in a window.

Congratulations! You have just built a simple image recognition model using Python. You can further enhance and expand this exercise by training your own model on a larger dataset or adding more classes to the existing model.

Conclusion

In this tutorial, we explored how to perform image recognition using Python. We learned how to load and preprocess an image, make predictions using a pre-trained model, and display the results. Image recognition is an exciting field with numerous applications, and Python provides powerful libraries like OpenCV and NumPy to make it accessible. With the knowledge gained from this tutorial, you can now start building your own image recognition models and explore the possibilities of this technology.

Remember to experiment with different models, datasets, and parameters to improve the accuracy and performance of your image recognition systems. Keep learning and exploring the world of machine learning and computer vision!

Published: 7 December 2019