Verifying Result from Computer Vision API: True or False?

Verifying Result from Computer Vision API: True or False?

Table of Contents

1. Introduction
2. Verifying the Result Retrieved from the Computer Vision API
3. Code Segment 1: Returning Brand Name with Confidence Level
4. Code Segment 2: Returning Coordinates of the Brand Logo
5. Code Segment 3: Returning Coordinates to the Bottom Right Corner
6. Developing an Application Using the Face API
7. Adding Multiple Images to a Person Group
8. Using the Computer Vision Client Library
9. Face Recognition in the Code
10. Listing Tags and Associated Confidence
11. Reading a File from the Local File System
12. Performing Optical Character Recovery (OCR)
13. Preventing the Get Read Result Async Method Occurring Before Completion
14. Developing a Method that Performs OCR
15. Using the Computer Vision Client Library for Optical Character Recovery
16. Utilizing Smart Cropping Feature in Computer Vision Resource

📷 Verifying the Result Retrieved from the Computer Vision API

In this section, we will discuss how to develop a test method to verify the result retrieved from a call to the computer vision API. The computer vision API is utilized to analyze the existence of company logos within images. The API returns a collection of brands and named brands. We will examine the given code segment to determine if the following statements are true:

1. The code will return the name of each detected brand with a confidence equal to or greater than 75 percent.
2. The code will return coordinates for the top left corner of the rectangle that contains the brand logo.
3. The code will return coordinates to the bottom right corner.

Let's analyze each statement and evaluate whether it is true or false.

📍 1. Code will return the name of each detected brand with confidence equal to or greater than 75 percent

The given code segment loops over the brands and checks if the confidence is greater than or equal to 75. If the condition is met, it returns the name of the brand. Therefore, the first statement is true.

📍 2. Code will return coordinates for the top left corner of the rectangle

The code segment includes code to calculate the coordinates of the top left corner of the rectangle that contains the brand logo. It also returns the brand name and coordinates. Therefore, the Second statement is true.

📍 3. Code will return coordinates to the bottom right corner

However, the code does not include any information or calculation related to the bottom right corner coordinates. Thus, the third statement is false.

In conclusion, the first two statements are correct, while the third statement is incorrect.

👤 Developing an Application Using the Face API

In this section, we will discuss the development of an application that utilizes the Face API. The objective is to add multiple images to a person group. The application should be able to complete the code to achieve this task.

📄 Using the Computer Vision Client Library

In this section, we will examine the usage of the Computer Vision Client Library for various tasks. The library provides functionalities for analyzing images and performing specific operations such as face recognition, reading text, and more.

💡Pros:

• Simplifies the integration of computer vision capabilities into applications
• Provides a wide range of features and functionalities
• Enables efficient image analysis and processing

⚠️ Cons:

• Requires understanding of library documentation and API usage
• May require additional setup and configuration

Now let's explore the capabilities and usage of the Computer Vision Client Library in different scenarios.

👀 Face Recognition in the Code

In this section, we will analyze a code segment that utilizes the Computer Vision Client Library for face recognition. The code analyzes an image and provides Captions and associated confidence levels. However, it does not perform face recognition. Thus, the code does not recognize faces, and the statement is false.

🔖 Listing Tags and Associated Confidence

The code segment listed in this section demonstrates the listing of tags and their associated confidence levels. The code reads a file from the local file system and retrieves the tags along with their confidence values. Therefore, the code does list tags and associated confidence, making the statement true.

💾 Reading a File from the Local File System

Within this section, we observe code that reads a file/image from the local file system. The code utilizes the "open-read" method to read the local image file. As a result, the code reads a file from the local file system, fulfilling the statement.

📃 Performing Optical Character Recovery (OCR)

In this section, we will discuss the development of a method that performs optical character recovery (OCR). The designated method uses the Computer Vision Client Library. However, there is an issue in the given code where the call to the "get read result async" method occurs before the read operation is complete. To address this, we need to modify the code to prevent the premature execution of the "get read result async" method. Let's explore the options:

• Remove "GRID.passOperationId" parameter: This change is unrelated to the issue at HAND, and it does not prevent the premature execution. Hence, it is incorrect.
• Add code to verify the "result.status.value": This addition would allow us to check the status of the result before proceeding. Thus, this option is correct.
• Add code to wrap the call to "getReadResultAsync" within a loop with a delay: By incorporating a loop and delay, we can ensure that the get operation starts after the read operation is complete. Hence, this option is also correct.

Therefore, to prevent the premature execution of the "get read result async" method, we should add code to verify the status of the result and wrap the call within a loop.

💡 Highlights:

• Properly verifying the result retrieved from the Computer Vision API is essential.
• The Computer Vision Client Library simplifies integrating computer vision capabilities into applications.
• Face recognition requires specific functionality and cannot be accomplished with the analyzed code.
• The library provides the ability to list tags with their associated confidence levels.
• Reading files from the local file system is achievable with the provided code.
• Attention must be paid to prevent premature execution in methods such as "get read result async".

📌 Utilizing Smart Cropping Feature in Computer Vision Resource

This section explores the utilization of the smart cropping feature in a computer vision resource named "Contoso One." The objective is to resize product photos by utilizing the smart cropping feature. Given that the computer vision resource is hosted on the West US Azure region, we need to construct the API URL appropriately. The correct option is to use the URL "westus.api.micro" since the resource is located in the West US region. Additionally, we should use the "generate thumbnail" function to achieve the desired smart cropping effect.

Frequently Asked Questions

Q: Can the code in the first section return the detected brand with a confidence level of 80 percent? A: No, the code will only return the detected brand if the confidence level is equal to or greater than 75 percent.

Q: How does the computer vision client library simplify the integration of computer vision capabilities into applications? A: The library provides a range of features and functionalities that can be easily integrated into applications, streamlining the development process.

Q: Can the code in the second section recognize faces? A: No, the code in the second section is focused on analyzing images and providing captions, but not face recognition.

Q: What is the correct URL to utilize the smart cropping feature in the Contoso One computer vision resource? A: The correct URL is "westus.api.micro," considering the resource is hosted in the West US Azure region.