How to do image recognition using PHP?

With the development of artificial intelligence technology, image recognition is increasingly used in various fields. As a popular Web programming language, PHP can also be used for image recognition applications. This article will introduce how to use PHP for image recognition.

1. What is image recognition?

Image recognition, also known as image classification or image recognition, is the process of analyzing and understanding digital images through computer programs. To humans, we can easily distinguish the objects we see, but to computers, an image is just a collection of numbers and pixels. Therefore, the purpose of image recognition is to train computer programs to determine features in digital images.

2. How to implement image recognition?

Currently, the most popular image recognition technology is deep learning. Deep learning is a branch of artificial intelligence that simulates the human brain through neural network training. Deep learning can process massive amounts of data, automatically extract features from the data, and then learn how to correctly classify specific tasks.

One of the most popular frameworks for implementing deep learning is TensorFlow. TensorFlow is an open source machine learning library developed by Google that uses graphical representations to calculate mathematical operations and can be calculated in parallel across multiple CPUs and GPUs. TensorFlow can be written in a variety of programming languages ​​such as Python.

3. Use PHP to implement image recognition

Since image recognition requires processing a large amount of data and calculations, using PHP for image recognition is not the best choice. However, in some cases, using PHP for image recognition may be necessary. In this case, we can use PHP's extension library to manipulate images and use deep learning models written in Python or other languages ​​for image recognition.

1. Install the PHP extension library

First, you need to install the PHP extension library so that you can use PHP to process images. The most popular PHP image processing extensions are GD and ImageMagick. These extensions can be installed through Composer, PHP's package manager.

The following is a sample code to install the GD extension:

$ sudo apt-get install php-gd
$ sudo service apache2 restart

2. Write PHP code

Images can be easily read and processed using PHP code. Here is a simple example that uses the PHP GD library to load an image into memory and resize it to a specified dimension:

<?php
header('Content-type: image/jpeg');

$filename = 'example.jpg';

list($width, $height) = getimagesize($filename);
$ration = $width / $height;

$newWidth = 200;
$newHeight = (int) ($newWidth / $ration);

$thumb = imagecreatetruecolor($newWidth, $newHeight);
$source = imagecreatefromjpeg($filename);

imagecopyresampled($thumb, $source, 0, 0, 0, 0, $newWidth, $newHeight, $width, $height);

imagejpeg($thumb);
imagedestroy($thumb);
imagedestroy($source);
?>

3. Using a Python deep learning model

In order For image recognition, we need to use Python to write code to train the deep learning model. The image can then be loaded into memory using the PHP GD library and passed to the model for classification.

The following is a simple Python code example that uses TensorFlow and Keras framework to train a model and perform image classification:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import numpy as np

(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()

train_images = train_images.reshape((60000, 28, 28, 1))
test_images = test_images.reshape((10000, 28, 28, 1))

train_images, test_images = train_images / 255.0, test_images / 255.0

model = keras.Sequential([
    keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Flatten(),
    keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('
Test accuracy:', test_acc)

image_path = 'test_image.png'
img = keras.preprocessing.image.load_img(image_path, target_size=(28, 28), color_mode="grayscale")
img_array = keras.preprocessing.image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)

predictions = model.predict(img_array)
print(predictions)

In PHP code, we can use the following code to call the above Python model:

<?php
$command = escapeshellcmd('python3 path/to/python_script.py');
$output = shell_exec($command);
echo $output;
?>

4. Summary

Although using PHP for image recognition may not be the best choice, by using PHP’s extension library and deep learning models written in other languages, we Certain specific image recognition tasks can still be achieved. Deep learning is currently the most popular image recognition technology, and TensorFlow is one of the most popular frameworks for implementing deep learning.

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