fix(vision): Improve logo detection

[amcolin]

Description

Refactors Vision samples to use batchAnnotateImages instead of the high-level logoDetection method.

Why this is important:
To align the Node.js samples with Google Cloud’s Java, Go, and Python libraries, where single-feature convenience methods are being deprecated or discouraged.

To demonstrates the recommended "batch-first" architecture, which is the standard for real-world applications requiring high throughput.

Internal: b/470281106

Checklist

• I have followed guidelines from CONTRIBUTING.MD and Samples Style Guide
• Tests pass: npm test (see Testing)
• Lint pass: npm run lint (see Style)
• Required CI tests pass (see CI testing)
• These samples need a new API enabled in testing projects to pass (let us know which ones)
• These samples need a new/updated env vars in testing projects set to pass (let us know which ones)
• This pull request is from a branch created directly off of GoogleCloudPlatform/nodejs-docs-samples. Not a fork.
• This sample adds a new sample directory, and I updated the CODEOWNERS file with the codeowners for this sample
• This sample adds a new sample directory, and I created GitHub Actions workflow for this sample
• This sample adds a new Product API, and I updated the Blunderbuss issue/PR auto-assigner with the codeowners for this sample
• Please merge this PR for me once it is approved

Note: Any check with (dev), (experimental), or (legacy) can be ignored and should not block your PR from merging (see CI testing).

[gemini-code-assist]

Summary of Changes

Hello @amcolin, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request modernizes the Node.js Google Cloud Vision API samples by transitioning the logo detection functionality from a single-feature convenience method to the more robust "batchAnnotateImages" API. This change ensures consistency with other Google Cloud client libraries, which are deprecating single-feature methods, and promotes a "batch-first" approach for improved performance and scalability in real-world applications.

Highlights

• API Modernization: Refactored Vision API samples to utilize the "batchAnnotateImages" method, moving away from the high-level "logoDetection" convenience method.
• Consistency Across Libraries: Ensured Node.js samples align with Google Cloud's Java, Go, and Python libraries, where single-feature convenience methods are being deprecated or discouraged.
• Batch-First Architecture: Demonstrated the recommended "batch-first" architecture, which is standard for real-world applications requiring high throughput and efficient processing.

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[gemini-code-assist]

Code Review

The pull request refactors the detectLogos and detectLogosGCS functions in vision/detect.js to use the client.batchAnnotateImages method instead of the deprecated client.logoDetection method. For local file detection, the fs module is now imported to read the image file and convert it to a base64 string for the API request. Both functions now construct a detailed request object for batch annotation, specifying the image source and the LOGO_DETECTION feature. The response handling has also been updated to iterate through the batch responses, logging logo annotations and explicitly handling potential errors for each response.