The ML.CONVERT_IMAGE_TYPE function

This document describes the ML.CONVERT_IMAGE_TYPE scalar function, which lets you convert the data type of pixel values in an image to INT64 with a range of [0, 255). You can use ML.CONVERT_IMAGE_TYPE with the ML.PREDICT function or chain it with other functions or subqueries.

Syntax

ML.CONVERT_IMAGE_TYPE(image)

Arguments

ML.CONVERT_IMAGE_TYPE takes the following argument:

• image: a STRUCT<ARRAY<INT64>, ARRAY<FLOAT64>> value that represents an image.

  The first array in the struct must contain the dimensions of the image. It must contain three INT64 values, which represent the image height (H), width (W), and number of channels (C).

  The second array in the struct must contain the image data. The length of the array must be equivalent to H x W x C from the preceding array. Each value in the array must be between [0, 1).

  The struct value must be <= 60 MB.

Output

ML.CONVERT_IMAGE_TYPE returns a STRUCT<ARRAY<INT64>, ARRAY<INT64>> value that represents the image.

The first array in the struct represents the dimensions of the image, and the second array in the struct contains the image data, similar to the image input argument. Each value in the second array is between [0, 255).

Example

The SSD Mobilenet V2 Object detection model model requires input to be in tf.uint8. The following example changes the pixel values for the input images from floating point numbers to integers so that they work with this model:

CREATE OR REPLACE TABLE mydataset.detections
AS (
  SELECT uri, detection_scores
  FROM
    ML.PREDICT(
      MODEL `mydataset.mobilenet`,
      SELECT
        ML.CONVERT_IMAGE_TYPE(ML.DECODE_IMAGE(data))
          AS image,
        uri
      FROM `mydataset.images`)
);

What's next

• For information about feature preprocessing, see Feature preprocessing overview.

• For more information about supported SQL statements and functions for each model type, see the following documents:

  • End-to-end user journeys for generative AI models
  • End-to-end user journeys for time series forecasting models
  • End-to-end user journeys for ML models
  • End-to-end user journeys for imported models
  • Contribution analysis user journey