​
Logging images in different format

Log images under the currentrun at the given step.

Use the function log_images for a run.
PIL package is needed to log images. To install the PIL package, run

pip install pillow

Here is the sample code to log images from different sources:

from truefoundry.ml import get_client, Image
import numpy as np
import PIL.Image

client = get_client()
run = client.create_run(
    ml_repo="my-classification-project",
)

imarray = np.random.randint(low=0, high=256, size=(100, 100, 3))
im = PIL.Image.fromarray(imarray.astype("uint8")).convert("RGB")
im.save("result_image.jpeg")

images_to_log = {
    "logged-image-array": Image(data_or_path=imarray),
    "logged-pil-image": Image(data_or_path=im),
    "logged-image-from-path": Image(data_or_path="result_image.jpeg"),
}

run.log_images(images_to_log, step=1)
run.end()

​
Visualizing the logged images

You can visualize the logged images in the TrueFoundry dashboard.

Logged Images

You can also view the images logged step-wise by clicking in an image.

Visualizing image at different steps

​
Class mlfoundry.Image

Images are represented and logged using this class in TrueFoundry.

You can initialize truefoundry.ml.Image by either by using a local path or you can use a numpy array / PIL.Image object.

You can also log caption and the actual and prodicted values for an image as shown in the examples below.

​
Logging images with caption and a class label

from keras.datasets import mnist
from truefoundry.ml import get_client, Image
import time
import numpy as np

data = mnist.load_data()
(X_train, y_train), (X_test, y_test) = data

client = get_client()
run = client.create_run("mnist-sample")

actuals = list(y_test)
predictions = list(np.random.randint(9, size=10))

img_dict = {}
for i in range(10):
    img_dict[str(i)] = Image(
        data_or_path=X_train[i],
        caption="mnist sample",
        class_groups={
            "actuals": str(actuals[i]),
            "predictions": str(predictions[i])
            },
    )

run.log_images(img_dict)

The logged images can be visualized in the TrueFoundry dashboard.

MNIST sample images

​
Logging image with multi-label classification problems

images_to_log = {
    "logged-image-array": truefoundry.ml.Image(
        data_or_path=imarray,
        caption="testing image logging",
        class_groups={"actuals": ["dog", "human"], "predictions": ["cat", "human"]},
    ),
}

run.log_images(images_to_log, step=1)

​
Logging images in different format

Log images under the currentrun at the given step.

Use the function log_images for a run.
PIL package is needed to log images. To install the PIL package, run

pip install pillow

Here is the sample code to log images from different sources:

from truefoundry.ml import get_client, Image
import numpy as np
import PIL.Image

client = get_client()
run = client.create_run(
    ml_repo="my-classification-project",
)

imarray = np.random.randint(low=0, high=256, size=(100, 100, 3))
im = PIL.Image.fromarray(imarray.astype("uint8")).convert("RGB")
im.save("result_image.jpeg")

images_to_log = {
    "logged-image-array": Image(data_or_path=imarray),
    "logged-pil-image": Image(data_or_path=im),
    "logged-image-from-path": Image(data_or_path="result_image.jpeg"),
}

run.log_images(images_to_log, step=1)
run.end()

​
Visualizing the logged images

You can visualize the logged images in the TrueFoundry dashboard.

Logged Images

You can also view the images logged step-wise by clicking in an image.

Visualizing image at different steps

​
Class mlfoundry.Image

Images are represented and logged using this class in TrueFoundry.

You can initialize truefoundry.ml.Image by either by using a local path or you can use a numpy array / PIL.Image object.

You can also log caption and the actual and prodicted values for an image as shown in the examples below.

​
Logging images with caption and a class label

from keras.datasets import mnist
from truefoundry.ml import get_client, Image
import time
import numpy as np

data = mnist.load_data()
(X_train, y_train), (X_test, y_test) = data

client = get_client()
run = client.create_run("mnist-sample")

actuals = list(y_test)
predictions = list(np.random.randint(9, size=10))

img_dict = {}
for i in range(10):
    img_dict[str(i)] = Image(
        data_or_path=X_train[i],
        caption="mnist sample",
        class_groups={
            "actuals": str(actuals[i]),
            "predictions": str(predictions[i])
            },
    )

run.log_images(img_dict)

The logged images can be visualized in the TrueFoundry dashboard.

MNIST sample images

​
Logging image with multi-label classification problems

images_to_log = {
    "logged-image-array": truefoundry.ml.Image(
        data_or_path=imarray,
        caption="testing image logging",
        class_groups={"actuals": ["dog", "human"], "predictions": ["cat", "human"]},
    ),
}

run.log_images(images_to_log, step=1)