Using Google Search and Image Comparison in Node.js

In industrial, retail, and other sectors, it is critical to accurately match component numbers to their images to effectively manage inventory, ensure product quality, and satisfy customer demands. Thanks to the latest programming technologies, this process can now be automated, greatly simplifying the work.

This article presents a practical method for matching part numbers with their images. Using the Google search engine, we will collect images based on the brand and part number, and then apply image comparison techniques using Node.js to identify the most relevant images for each part.

Stage 1: Search and collect images via Google

At the initial stage of our task, we will focus on finding images that correspond to our part numbers. To do this, we will use Google’s image search function, specifying the brand and part number of each spare part as the main search criteria.

Although there is no official Google API for image search, there are alternative APIs and tools for scraping. However, it is important to consider Google’s terms and conditions regarding the use of such tools.

const googleImages = require('google-images-scraper');

async function fetchImagesForPart(partNumber) {
    const results = await googleImages.search({
        keyword: `${partBrand} ${partNumber}`,
        num: 10
    });

    return results.map(result => result.url);
}

Stage 2: Image Similarity Analysis Using Node.js

After generating a collection of images corresponding to each spare part number, the stage of analyzing them for similarity begins. For this task, we will use Node.js in combination with libraries such as jimp for image processing and mathjs for similarity calculation.

Setting up your work environment

To get started, create a project in Node.js and install all the required dependencies:

npm init -y
npm install jimp mathjs

Development of a script for image similarity analysis

To evaluate the similarity of images, we will use a simple method such as calculating the Euclidean distance. However, more sophisticated techniques, including image feature comparison algorithms, can be used to improve the accuracy of the analysis.

 // imageComparison.js

  const Jimp = require('jimp');
  const math = require('mathjs');
  
  async function imageToVector(imagePath) {
      const image = await Jimp.read(imagePath);
      await image.resize(256, 256).grayscale(); // Resize and convert to grayscale
      let vector = [];

      for (let y = 0; y < image.bitmap.height; y++) {
          for (let x = 0; x < image.bitmap.width; x++) {
              const pixel = Jimp.intToRGBA(image.getPixelColor(x, y));
              vector.push(pixel.r); // Use red channel as grayscale value
          }
      }

      return vector;
  }

    async function calculateSimilarity(imagePath1, imagePath2) {
        const vector1 = await imageToVector(imagePath1);
        const vector2 = await imageToVector(imagePath2);

        // Using Euclidean distance as similarity measure
        const distance = math.distance(vector1, vector2);
        return distance;
    }
  }

Stage 3: Analysis and synthesis of comparison results

Upon completion of the process of calculating the degree of similarity for all combinations of images corresponding to a specific part number, the resulting data should be summarized. This will reveal the most suitable image for a particular part.

async function findMostSuitableImage(imagePaths) {
    let maxSimilarity = 0;
    let mostSuitableImagePair = [];

    for (let i = 0; i < imagePaths.length; i++) {
        for (let j = i + 1; j < imagePaths.length; j++) {
            const similarity = await calculateSimilarity(imagePaths[i], imagePaths[j]);
            if (similarity > maxSimilarity) {
                maxSimilarity = similarity;
                mostSuitableImagePair = [imagePaths[i], imagePaths[j]];
            }
        }
    }

    return mostSuitableImagePair;
}

Conclusion

In conclusion, the integration of automated image acquisition and comparison techniques provides a powerful tool for matching part numbers to the correct images. Node.js, with its rich ecosystem, offers a flexible platform for implementing these solutions. While the basic approach described here is a good starting point, further refinement and the use of more advanced image comparison algorithms can improve accuracy and reliability.

Remember that when automating image searches and comparisons, it is very important to respect the copyright and terms of use of the platforms you use. Always use legal APIs and respect the restrictions and legal guidelines set by service providers.