AzureVision (version 1.0.2)

add_images: Add, list and remove images for a project

Description

Add, list and remove images for a project

Usage

add_images(project, ...)
# S3 method for classification_project
add_images(project, images, tags = NULL, ...)
# S3 method for object_detection_project
add_images(project, images, regions = NULL, ...)
list_images(project, include = c("all", "tagged", "untagged"),
  as = c("ids", "dataframe", "list"), iteration = NULL)
remove_images(project, image_ids = list_images(project, "untagged", as =
  "ids"), confirm = TRUE)

Arguments

project

A Custom Vision project.

...

Arguments passed to lower-level functions.

images

For add_images, the images to add (upload) to the project.

tags

Optional tags to add to the images. Only for classification projects.

regions

Optional list of regions in the images that contain objects. Only for object detection projects.

include

For list_images, which images to include in the list: untagged, tagged, or both (the default).

as

For list_images, the return value: a vector of image IDs, a data frame of image metadata, or a list of metadata.

iteration

For list_images, the iteration ID (roughly, which model generation to use). Defaults to the latest iteration.

image_ids

For remove_images, the IDs of the images to remove from the project.

confirm

For remove_images, whether to ask for confirmation first.

Value

For add_images, the vector of IDs of the uploaded images.

For list_images, based on the value of the as argument. The default is a vector of image IDs; as="list" returns a (nested) list of image metadata with one component per image; and as="dataframe" returns the same metadata but reshaped into a data frame.

Details

The images to be uploaded can be specified as:

• A vector of local filenames. JPG, PNG and GIF file formats are supported.

• A vector of publicly accessible URLs.

• A raw vector, or a list of raw vectors, holding the binary contents of the image files.

Uploaded images can also have tags added (for a classification project) or regions (for an object detection project). Classification tags can be specified in the following ways:

• For a regular classification project (one tag per image), as a vector of strings. The tags will be applied to the images in order. If the length of the vector is 1, it will be recycled to the length of image_ids.

• For a multilabel classification project (multiple tags per image), as a list of vectors of strings. Each vector in the list contains the tags to be assigned to the corresponding image. If the length of the list is 1, it will be recycled to the length of image_ids.

If the length of the vector is 1, it will be recycled to the length of image_ids.

Object detection projects also have tags, but they are specified as part of the regions argument. The regions to add should be specified as a list of data frames, with one data frame per image. Each data frame should have one row per region, and the following columns:

• left, top, width, height: the location and dimensions of the region bounding box, normalised to be between 0 and 1.

• tag: the name of the tag to associate with the region.

Any other columns in the data frame will be ignored. If the length of the list is 1, it will be recycled to the length of image_ids.

Note that once uploaded, images are identified only by their ID; there is no general link back to the source filename or URL. If you don't include tags or regions in the add_images call, be sure to save the returned IDs and then call add_image_tags or add_image_regions as appropriate.

See Also

add_image_tags and add_image_regions to add tags and regions to images, if not done at upload time

add_tags, list_tags, remove_tags

customvision_project

Examples

# NOT RUN {
endp <- customvision_training_endpoint(url="endpoint_url", key="key")

# classification
proj1 <- create_classification_project(endp, "myproject")
list_images(proj1)
imgs <- dir("path/to/images", full.names=TRUE)

# recycling: apply one tag to all images
add_images(proj1, imgs, tags="mytag")
list_images(proj1, include="tagged", as="dataframe")

# different tags per image
add_images(proj1, c("cat.jpg", "dog.jpg", tags=c("cat", "dog"))

# adding online images
host <- "https://mysite.example.com/"
img_urls <- paste0(host, c("img1.jpg", "img2.jpg", "img3.jpg"))
add_images(proj1, img_urls, tags="mytag")

# multiple label classification
proj2 <- create_classification_project(endp, "mymultilabelproject", multiple_tags=TRUE)

add_images(proj2, imgs, tags=list(c("tag1", "tag2")))
add_images(proj2, c("catanddog.jpg", "cat.jpg", "dog.jpg"),
    tags=list(
        c("cat", "dog"),
        "cat",
        "dog"
    )
)

# object detection
proj3 <- create_object_detection_project(endp, "myobjdetproj")

regions <- list(
    data.frame(
        tag=c("cat", "dog"),
        left=c(0.1, 0.5),
        top=c(0.25, 0.28),
        width=c(0.24, 0.21),
        height=c(0.7, 0.6)
    ),
    data.frame(
        tag="cat", left=0.5, top=0.35, width=0.25, height=0.62
    ),
    data.frame(
        tag="dog", left=0.07, top=0.12, width=0.79, height=0.5
    )
)
add_images(proj3, c("catanddog.jpg", "cat.jpg", "dog.jpg"), regions=regions)

# }

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