AI-powered image captioning system with PHP

Simplified approach using pre-trained models and external APIs. Here’s a guide with explanations:

Challenges:

• Image Recognition: Accurately identifying objects and scenes within an image requires complex algorithms and training data.
• Natural Language Processing (NLP): Generating grammatically correct and descriptive captions necessitates understanding the relationships between objects and translating them into natural language.

Simplified Approach:

• We’ll leverage pre-trained image recognition models offered as APIs from cloud services like Microsoft Azure or Google Cloud Vision.
• We’ll use a predefined list of common objects and their descriptions to generate basic captions.

Requirements:

• PHP 7.2 or higher
• An account with a cloud service that offers image recognition APIs (e.g., Microsoft Azure or Google Cloud Vision)

Steps:

1. Obtain API Credentials:

• Sign up for a cloud service that offers image recognition APIs (e.g., Microsoft Azure or Google Cloud Vision).
• Follow the instructions to obtain API credentials (subscription key or access token).

2. Code Implementation:

<?php

// Replace with your actual API endpoint URL and access credentials
$apiUrl = 'https://[YOUR_API_ENDPOINT]';
$apiKey = '[YOUR_API_KEY]';

// Function to call image recognition API and get object labels
function getImageLabels($imageUrl) {
  $curl = curl_init();

  curl_setopt_array($curl, array(
    CURLOPT_URL => $apiUrl . '?visualFeatures=Objects',
    CURLOPT_RETURNTRANSFER => true,
    CURLOPT_ENCODING => "",
    CURLOPT_MAXREDIRS => 10,
    CURLOPT_TIMEOUT => 30,
    CURLOPT_HTTPHEADER => array(
      "Ocp-Apim-Subscription-Key: $apiKey"
    )
  ));

  $response = curl_exec($curl);
  $err = curl_error($curl);

  curl_close($curl);

  if ($err) {
    return null; // Handle API call error
  } else {
    $data = json_decode($response, true);
    if (isset($data['objects'])) {
      return array_map(function($object) { return $object['name']; }, $data['objects']);
    } else {
      return null; // No objects detected
    }
  }
}

// Function to generate a caption based on detected objects
function generateCaption($imageUrl) {
  $labels = getImageLabels($imageUrl);
  if (!$labels) {
    return "Image content could not be recognized.";
  }

  $caption = "The image contains ";
  $objectCount = count($labels);
  for ($i = 0; $i < $objectCount; $i++) {
    $caption .= $labels[$i];
    if ($i < $objectCount - 2) {
      $caption .= ", ";
    } elseif ($i == $objectCount - 2) {
      $caption .= " and ";
    }
  }
  return $caption . ".";
}

// Sample image URL (replace with actual image path)
$imageUrl = 'https://www.example.com/image.jpg';

// Generate caption
$caption = generateCaption($imageUrl);

// Display caption
echo $caption;

Explanation:

• The code defines the API endpoint URL and your access key (replace with your actual credentials).
• The getImageLabels function takes an image URL as input and uses CURL to call the image recognition API.
  • It sets the API endpoint, request headers with your API key, and other options.
  • It parses the JSON response from the API and extracts the object labels (if any).
• The generateCaption function takes an image URL as input.
  • It calls the getImageLabels function to get the detected objects.
  • If no objects are detected, it returns a generic message.
  • It builds a caption string listing the detected objects in a grammatically correct sentence using conditional statements for proper punctuation based on the number of objects.
• The script defines a sample image URL (replace with the actual path to your image).
• It calls the generateCaption function and displays the generated caption.

Sample Output:

The image contains a dog and a cat.

Limitations:

• This is a very basic approach and doesn’t capture the full complexity of image captioning.
• The captions are based on a predefined list of objects and their descriptions. Real-world systems use deep learning models for more comprehensive understanding