Import Images

Updated: February 21, 2017

Loads images from Azure BLOB Storage into a dataset

Category: OpenCV Library Modules

You can use the Import Images module to get multiple images from Azure Blob storage and create an image dataset from them.

As you read each image from blob storage into your workspace using Import Images, the image is represented as a series of numeric values for the red, green, and blue channels, together with the image file name. A dataset of such images consist of multiple rows in a table, each with a different set of RGB values and corresponding image file names. You can then pass this dataset to the Score Model module, and connect a pre-trained image classification model to predict the image type.

You can import any kind of images used for machine learning. For instructions about how to prepare your images and connect to blob storage see How to Import Images. For limitations on images that cacn be processed, see the Technical Notes section.

Note
If you intend to use the Pretrained Cascade Image Classification module, be aware that it currently supports only recognition of faces in frontal view; other image classifiers are not yet available. You cannot use image datasets with these modules: Train and Cross-Validate Model.

This example assumes that you have uploaded multiple images to your account in Azure blob storage. The images are in a container designated for that purpose only. As a rule, each image must be fairly small and have the same dimensions and color channels.

Important
See the Technical Notes section for a detailed list of requirements that apply to images.

1. Add the Import Images module to your experiment.

2. Add the Pretrained Cascade Image Classification and the Score Model module.

3. Double-click the Import Images module and configure the location of the images, as well as the authentication method, private or public:

   • If the image set is in a blob that has been configured for public access through Shared Access Signatures(SAS), type the URL to the container that holds the images.

   • If the images are stored in a private account in Azure storage, select Account, and then type the account name as it appears in the management portal. Then, paste in the primary or secondary account key.

   • For Path to container, type just the container name, and no other path elements.

4. Connect the output of Import Images to the Score Model module.

5. Run the experiment.

Results

Each row of the output dataset contains data from one image. The rows are sorted alphabetically by image name, and the columns contain the following information, in this order:

• The first column contains image names.
• All other columns contain flattened data from the red, green, and blue color channels, in that order.
• The transparency channel is ignored.

Important
Depending on the color depth of the image and the image format, there could be many thousands of columns for a single image. Therefore, to view the results of the experiment, we recommend that you add the Select Columns in Dataset module, and select only these columns: Image Name Scored Labels Scored Probabilities

This section contains details about how images are processed, as well as requirements for images.

Supported Image Formats

Import Images determines the type of an image by reading the first few bytes of the content, not by the file extension. Based on that information, it determines whether the image is one of the supported image formats.

• Windows bitmap files: *.bmp, *.dib
• JPEG files: *.jpeg, *.jpg, *.jpe
• JPEG 2000 files: *.jp2
• Portable Network Graphics: *.png
• Portable image format: *.pbm, *.pgm, *.ppm
• Sun Raster: *.sr, *.ras
• TIFF files: *.tiff, *.tif

Image Requirements

There are strict requirements that apply to images processed by the Import Images module:

• All images must be the same shape.

• All images must have the same color channels. For example, you cannot mix grayscale images with RBG images.

• There is a limit of 65536 pixels per image. However, the number of images is not limited.

• If you specify a blob container as the source, the container must not contain other types of data. Ensure that the container contains only images before running the module.

For a list of all error codes, see Module Error Codes.

Pretrained Cascade Image Classification
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