Episode 04: Exploring OpenCV and Machine Learning with Scikit Learn in Python

Welcome to Advanced Python Episode 04! In this tutorial, we will explore the powerful combination of OpenCV and Machine Learning with Scikit Learn.

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It offers a wide range of functionalities for image and video processing, including object detection, face recognition, and image segmentation. On the other hand, Scikit Learn is a popular machine learning library in Python that provides tools for data processing, classification, regression, clustering, and more.

In this tutorial, we will combine the strengths of both libraries to build a simple machine learning model that can recognize objects in images.

To get started, make sure you have both OpenCV and Scikit Learn installed on your system. You can install them using the following commands:

pip install opencv-python
pip install scikit-learn

Once you have installed the libraries, create a new Python script and import the necessary modules:

import cv2
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

Next, we will load a dataset of images using the scikit-learn library. For this tutorial, we will use the Iris dataset, which is a popular dataset for classification tasks. You can load the dataset using the following code:

# Load the Iris dataset
iris = datasets.load_iris()

# Get the input data (images) and labels (targets)
X = iris.data
y = iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Now, we can train a machine learning model on the dataset using the Support Vector Machine (SVM) algorithm from Scikit Learn. SVM is a powerful classification algorithm that works well for image recognition tasks. You can train the model using the following code:

# Create an SVM classifier
clf = SVC(kernel='linear', C=1.0, random_state=42)

# Train the classifier on the training data
clf.fit(X_train, y_train)

Now that we have trained the model, we can test it on new images. For this tutorial, we will use images from the OpenCV library. You can load an image using the following code:

# Load an image using OpenCV
img = cv2.imread('image.jpg')

# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Resize the image to match the input data size
resized = cv2.resize(gray, (150, 150))

Next, we need to preprocess the image before feeding it into the model. You can preprocess the image using the following code:

# Flatten the image
flattened = resized.flatten()

# Make a prediction using the trained model
prediction = clf.predict([flattened])

# Print the predicted label
print('Predicted label:', prediction)

That’s it! You have successfully built a simple machine learning model for image recognition using OpenCV and Scikit Learn. You can now experiment with different algorithms, datasets, and image processing techniques to improve the model performance. Have fun exploring the possibilities of computer vision and machine learning with Python!