Process Images Datasets
WARNING auKsys/5 has been released as stable, but this documentation has not been fully updated with changes since auKsys/4. In case of questions or mistakes, you can use the support project.
In this tutorial we are going to learn how to use pralin/compose
to process images stored in a kDB dataset. We will see how to iterate over the images, reduce them in size, and insert them in a new dataset.
We will use a kDB store for the purpose of that tutorial, lets start it with:
kdb store --path tuto_kdb_store --extension kDBSensing
We will use the dataset of images acquired in the water near the Gränsö castle. It can be downloaded and uncompressed with the following command:
curl -o images_sea.kdb_dataset.xz "https://gitlab.liu.se/lks/tutorials_data/-/raw/main/images_sea.kdb_dataset.xz?ref_type=heads&inline=false"
xz -d images_sea.kdb_dataset.xz
The dataset can be imported into the database using the following command:
kdb datasets import --path tuto_kdb_store --filename images_sea.kdb_dataset
Show images from a dataset
The first example that we will look at is how to show the images from a dataset. The full composition is shown bellow
compose:
parameters:
kdb_host: !required null
kdb_port: 1242
source_dataset_uri: !required null
process:
# This create a connection to the kDB database, according to the parameter `kdb_host`
# and `kdb_port`
- kdb/create_connection_handle:
id: connection
parameters:
host: !param kdb_host
port: !param kdb_port
# This start a query for an image dataset, using the connection and the dataset
# specified in parameters
- kdb/sensing/query_image_dataset:
id: qid
inputs: ["connection[0]"]
parameters:
query: !param source_dataset_uri
# This loop over the images in the dataset
- for_each:
id: iterate_image
iterator: qid[0]
process:
# This shows the images using OpenCV
- opencv/highgui/imshow:
id: imwrite
inputs: [ "iterate_image[0]"]
parameters:
wait_delay: 1000
It requires the two parameters, the kdb_host
and the source_dataset_uri
. When using ROS, the kdb/create_connection_handle
can be replaced with ros_kdb/create_connection_handle
to automatically connect to the database (see ROS Computation Server tutorial).
When using the Gränsö dataset, and the tuto_kdb_store
database, the composition can be started with:
pralin compose --parameters "{ kdb_host: 'tuto_kdb_store', source_dataset_uri: 'http://askco.re/examples#images_granso' }" --
Export images from a dataset
The second example will use OpenCV to write the images to files.
compose:
variables:
# This variable will be used to increment the number used to index the file
counter: &counter [0, 'add[0]']
parameters:
kdb_host: !required null
kdb_port: 1242
source_dataset_uri: !required null
process:
# This create a connection to the kDB database, according to the parameter `kdb_host`
# and `kdb_port`
- kdb/create_connection_handle:
id: connection
parameters:
host: !param kdb_host
port: !param kdb_port
# This start a query for an image dataset, using the connection and the dataset
# specified in parameters
- kdb/sensing/query_image_dataset:
id: qid
inputs: ["connection[0]"]
parameters:
query: !param source_dataset_uri
# This loop over the images in the dataset
- for_each:
id: iterate_image
iterator: qid[0]
process:
# This create the filename
- pralin/values/string/format:
id: filename
inputs: [ [*counter] ]
parameters:
format: "{:04}.jpg"
- opencv/imgcodecs/imwrite:
id: imwrite
inputs: [ "filename[0]", "iterate_image[0]"]
- pralin/arithmetic/addition:
id: add
inputs: [ *counter, 1]
Show scale down images from a dataset
This example iterate through the images in the dataset, reduce their size and display the results.
compose:
parameters:
kdb_host: !required null
kdb_port: 1242
source_dataset_uri: !required null
destination_dataset_uri: !required null
process:
# This create a connection to the kDB database, according to the parameter `kdb_host`
# and `kdb_port`
- kdb/create_connection_handle:
id: connection
parameters:
host: !param kdb_host
port: !param kdb_port
# Query the source dataset
- kdb/datasets/get:
id: source_dataset
inputs: ["connection[0]"]
parameters:
query: !param source_dataset_uri
# Create the dataset, as a subdataset from the source dataset
- kdb/datasets/create:
inputs: ["connection[0]", "source_dataset[0]"]
parameters:
uri: !param destination_dataset_uri
# This start a query for an image dataset, using the connection and the dataset
# specified in parameters
- kdb/sensing/query_image_dataset:
id: qid
inputs: ["connection[0]"]
parameters:
query: !param source_dataset_uri
# This loop over the images in the dataset
- for_each:
id: iterate_image
iterator: qid[0]
process:
# This resize the image
- opencv/imgproc/resize:
id: resize
inputs: [ "iterate_image[0]"]
parameterkos:
scale: 0.25
# This insert the image in the dataset
- kdb/sensing/insert_image_in_dataset:
inputs: [ "connection[0]", "resize[0]"]
parameters:
query: !param destination_dataset_uri
Scale down images and save it into a dataset
This example iterate through the images in the dataset, reduce their size and store the results in a new dataset.
compose:
parameters:
kdb_host: !required null
kdb_port: 1242
source_dataset_uri: !required null
destination_dataset_uri: !required null
process:
# This create a connection to the kDB database, according to the parameter `kdb_host`
# and `kdb_port`
- kdb/create_connection_handle:
id: connection
parameters:
host: !param kdb_host
port: !param kdb_port
# Query the source dataset
- kdb/datasets/get:
id: source_dataset
inputs: ["connection[0]"]
parameters:
query: !param source_dataset_uri
# Create the dataset, as a subdataset from the source dataset, since we use a source
# dataset, we don't need to specify a type or a geometry as parameter, they will be
# copied from "source_dataset[0]", which is `source_dataset_uri`.
# We specify as parameter `destination_dataset_uri`.
- kdb/datasets/create:
inputs: ["connection[0]", "source_dataset[0]"]
parameters:
uri: !param destination_dataset_uri
# This start a query for an image dataset, using the connection and the dataset
# specified in parameters
- kdb/sensing/query_image_dataset:
id: qid
inputs: ["connection[0]"]
parameters:
query: !param source_dataset_uri
# This loop over the images in the dataset
- for_each:
id: iterate_image
iterator: qid[0]
process:
# This shows the images using OpenCV
- opencv/imgproc/resize:
id: resize
inputs: [ "iterate_image[0]"]
parameters:
scale: 0.25
# This save the scale down images in the database, with the
# `destination_dataset_uri` uri.
- kdb/sensing/insert_image_in_dataset:
inputs: [ "connection[0]", "resize[0]"]
parameters:
query: !param destination_dataset_uri
When running this composition, you need to specify a value for destination_dataset_uri
as parameter. You can check that everything went fine by using the visualisation composition from the beginning of this tutorial.