Building a Python Image Recognition System

Image recognition is a subset of computer vision and artificial intelligence that involves the ability of computer software or systems to identify and classify objects, people, text, and actions within digital images and videos. Put simply, image recognition allows computers to interpret visual input, like how people see and identify objects.

Image recognition has many prospects, including applications in healthcare, security, autonomous vehicles, banking, manufacturing, military surveillance, and so on. The most popular method for implementing image recognition is with the Python programming language, as it offers an easy-to-read programming language that’s incredibly flexible and widely supported by most image recognition tools.

In this article, we’ll explore implementing image recognition in Python, including some of the most popular libraries available, how image recognition works, and how to set up your own Python Image recognition tool.

In this article:

• How Does Image Recognition Work?
• Popular Python Image Recognition Libraries
• Creating a Python Image Recognition Tool with TensorFlow and Keras

How Does Image Recognition Work?

Image recognition refers to technologies or systems that identify animate subjects (e.g., humans and animals) and inanimate objects in digital images. It involves algorithms that leverage various methods such as machine learning, Convolutional Neural Networks (CNNs), and deep learning to recognize patterns and detect features within images, enabling the identification of objects. These algorithms analyze visual data to recognize shapes, colors, textures, and other characteristics, allowing machines to understand and interpret the content of images with increasing accuracy and efficiency.

At the very core, a typical image recognition algorithm involves a series of steps for correctly identifying the subjects and objects in an image. For example, using a deep learning algorithm, building an image recognition system will require the following workflow:

• Data collection and pre-processing – This involves gathering a large number of images representing the objects or categories you want to recognize and preparing them for training by resizing, converting formats, normalizing, and augmenting the data.
• Feature Extraction – Relevant features or patterns are extracted from the pre-processed image data. These features could include shapes, textures, colors, or other peculiar properties that help differentiate objects in the image.
• Model training – Next, the pre-processed images and their associated labels are fed into a machine learning model, such as a CNN, and its parameters are iteratively adjusted to learn the patterns and relationships between the images and labels.
• Classification – Once the model is trained, it can be used to classify or recognize objects in new and unseen images by extracting features and applying the learned patterns and relationships
• Evaluation – Next, we will assess the performance of the trained model on a new set of images that were not used during the model training, using metrics like accuracy and precision to identify areas for improvement.

However, image recognition should not be confused with image detection. Image detection deals with analyzing an image to find different subjects and objects in the image, while image recognition deals with recognizing images and classifying them into distinct categories.

Popular Python Image Recognition Libraries

Due to its versatility and ease of use, several Python libraries have made image recognition more accessible and efficient. Here are some of the popular libraries used for building image recognition systems in Python:

• OpenCV – An open-source computer vision and machine learning software library with extensive image processing and feature detection capabilities.
• TensorFlow – A popular machine learning library with a particular focus on the training and inference of deep neural networks. It’s also widely used for various image recognition and object detection tasks.
• Keras – Keras provides a high-level neural network interface for the TensorFlow library, which is used to build and train image recognition models.
• PyTorch – A dynamic computation graph library based on the Torch library. It’s commonly used for applications such as computer vision, natural language processing, building and training image recognition models, etc.
• Pillow (PIL Fork) – An accessible, straightforward library for opening, manipulating, and saving many different image file formats, making it ideal for tasks that require basic image editing or processing.

Each of these libraries offers something unique, and the best choice for your project will depend on your specific requirements, such as the complexity of the task, the level of control and flexibility you need, and the performance characteristics critical to your application.

Image Recognition in Python Using Tensorflow and Keras

In this section, we’ll guide you through the steps of creating your first image recognition application. As we mentioned earlier, the first step in any image recognition project is usually to gather the dataset on which the models will be trained. However, in this tutorial, we won’t have to start from scratch. Instead, we’ll use ResNet50, an open-source image classification/recognition model pre-trained with ImageNet.

ImageNet is an image database with over 14 million images that are annotated using WordNet synonym sets. It’s widely used in research and development for various computer vision tasks, such as training deep learning models for image recognition.

Some other popular examples of open-source pre-trained models include:

• ResNet
• VGG-19

Step 1 – Install and Import Necessary Packages

Before running the code, we recommend creating a virtual environment for your project. Run the code below to install the required packages for the application:

pip install numpy keras tensorflow matplotlib opencv-python

Next, create a file named main.py and add the following code to it:

import numpy as np 
import matplotlib.pyplot as plt 
import os 
import cv2 from keras.applications.resnet50 
import ResNet50 from keras.preprocessing 
import image from keras.applications.resnet50 
import preprocess_input, decode_predictions

Then, we’ll load the pre-trained ResNet50 model:

model = ResNet50(weights='imagenet')

Step 2 – Load and Pre-process the Image Using OpenCV

img_path = 'football.jpg' # The image to classify
img = cv2.imread(img_path)

pythonCopy codeimg = cv2.resize(img, (224, 224))  # Resize the image to match the model's input size
x = image.img_to_array(img)  # Convert the image to a numpy array
x = np.expand_dims(x, axis=0)  # Add a batch dimension
x = preprocess_input(x)

In this example, we uploaded an image of a football to the root directory, football.jpg. This is the image we want to recognize in our application. Feel free to replace football.jpg with the image file name you wish to recognize.

Step 3 – Make Predictions Using ResNet50

Next, we’ll use the pre-trained ResNet50 model to make predictions on the input image:

# Make predictions
preds = model.predict(x)

# Decode and display predictions
print('Predicted:', decode_predictions(preds, top=3)[0])

Step 4 – Run the Code

Predicted: [('n04254680', 'soccer_ball', 0.99946004), ('n03793489', 'mouse', 0.00015638306), ('n04540053', 'volleyball', 8.9596644e-05)]

Finally, run python main.py to see the result of the prediction. Here’s the output we got for this example:

Predicted: [('n04254680', 'soccer_ball', 0.99946004), ('n03793489', 'mouse', 0.00015638306), ('n04540053', 'volleyball', 8.9596644e-05)]

Here, the model has predicted with a very high probability (0.99946004 or approximately 99.95%) that the input image contains a soccer ball. However, the other tuples in the list represent the second and third most probable predictions made by the model, which have much lower probabilities than the first prediction.

Here’s the complete code for your reference:

import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions

# Load the pre-trained ResNet50 model
model = ResNet50(weights='imagenet')

# Path to the input image
img_path = 'football.jpg' # The image to classify

# Load the image
img = cv2.imread(img_path)

# Preprocess the image
img = cv2.resize(img, (224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# Make predictions
preds = model.predict(x)

# Decode and display predictions
print('Predicted:', decode_predictions(preds, top=3)[0])

Summing It Up

At its core, Python image recognition technology allows computers and software to interpret and understand images in a way that mimics human vision, albeit with a capacity for speed and accuracy that significantly surpasses our own. Python, with its rich ecosystem of libraries and frameworks, provides a powerful and robust environment for developing image recognition applications.

Given the wide array of Python libraries available, developers have the tools at their fingertips to start experimenting and innovating. And if you want to get a head start on storing, optimizing, and taking advantage of AI tools, check out Cloudinary.