When you send an image annotation request to the Cloud Vision service in the Google Cloud Platform, the JSON response you receive contains valuable information about the image and its annotations. This response provides a comprehensive analysis of the image, including various features such as labels, landmarks, logos, text, and facial expressions.
The JSON response is structured in a hierarchical format, allowing you to easily access different components of the annotation. The top-level structure contains general information about the image, such as its width, height, and format. It also includes a list of annotations, each representing a specific feature detected in the image.
Each annotation in the list consists of several fields. The most common field is "description," which provides a textual representation of the detected feature. For example, if the image contains a cat, the description field might contain the word "cat." This field can be particularly useful when you want to extract labels from the image.
In addition to the description, annotations may also include other fields depending on the detected feature. For instance, if the image contains landmarks, the annotation may include a "locations" field that provides the latitude and longitude coordinates of the landmark. Similarly, if the image contains text, the annotation may include a "locale" field indicating the language of the text.
Furthermore, annotations related to facial expressions can provide information about emotions detected in the faces present in the image. These annotations include fields such as "joyLikelihood," "sorrowLikelihood," "angerLikelihood," and "surpriseLikelihood," which represent the likelihood of each emotion being present in the face.
Apart from the annotations themselves, the JSON response also includes a field called "error" in case any issues occurred during the annotation process. This field can help you identify and handle potential errors in your application.
The JSON response you receive after sending an image annotation request to the Cloud Vision service provides a detailed analysis of the image, including labels, landmarks, logos, text, and facial expressions. By parsing this response, you can extract valuable information about the content of the image and use it for various purposes, such as image classification, content moderation, or sentiment analysis.
Other recent questions and answers regarding EITC/CL/GCP Google Cloud Platform:
- What is the difference between Cloud AutoML and Cloud AI Platform?
- What is the difference between Big Table and BigQuery?
- How to configure the load balancing in GCP for a use case of multiple backend web servers with WordPress, assuring that the database is consistent accross the many back-ends (web servwers) WordPress instances?
- Does it make sense to implement load balancing when using only a single backend web server?
- If Cloud Shell provides a pre-configured shell with the Cloud SDK and it does not need local resources, what is the advantage of using a local installation of Cloud SDK instead of using Cloud Shell by means of Cloud Console?
- Is there an Android mobile application that can be used for management of Google Cloud Platform?
- What are the ways to manage the Google Cloud Platform ?
- What is cloud computing?
- What is the difference between Bigquery and Cloud SQL
- What is the difference between cloud SQL and cloud spanner
View more questions and answers in EITC/CL/GCP Google Cloud Platform