pytdml 1.1.2

Parsing and encoding training datasets based on OGC Training Data Markup Language for AI (TrainingDML-AI) standard

pytdml

pytdml is a pure python parser and encoder for training datasets based on OGC Training Data Markup Language for AI standard.

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Installation

The package can be installed via pip.

Requirements

• Python 3 and above

Dependencies

Dependencies are listed in requirements.txt. Dependencies are automatically installed during pytdml's installation.

Installing the Package

pip install pytdml

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Usage

Encoding

From the command line

The training dataset can be encoded to TrainingDML-AI JSON format by YAML configuration file with command line.

pytdml/yaml_to_tdml.py --config=<YAML configuration file path> --output=<Output TrainingDML-AI JSON file path>

YAML configuration file schema is described in encoding YAML configuration file schema.

Using the API from python

The training dataset can also be encoded to TrainingDML-AI JSON format with Python API.

from pytdml.io import write_to_json
from pytdml.type import EOTrainingDataset, EOTrainingData, EOTask, EODataSource, SceneLabel

# generate EO training dataset
dataset = EOTrainingDataset(
    id='...',
    name='...',
    description='...',
    data=[
        EOTrainingData(
            id='...',
            labels=[
                SceneLabel(
                    label_class='...',
                    data_url='...',
                    date_time='...'),
                ...
            ]),
        ...
    ],
    version="...",
    amount_of_training_data=...,
    created_time="...",
    updated_time="...",
    providers=["..."],
    keywords=["...", "..."],
    tasks=[EOTask(task_type="...",
                  description="...")],
    data_sources=[EODataSource(
        id="...",
        data_type="...",
        resolution="..."
    )],
    classes=["...", "...", "..."],
    number_of_classes=...,
    bands=["...", "...", "..."],
    image_size="..."
)
# write to json
write_to_json(dataset, "dataset.json")

encoding training data from S3

# get training data from s3
s3_client = pytdml.io.S3Client('s3', "your_server", "your_ak", "your_sk")
td_list = []
bucket_name = "my-bucket"
obj_list = s3_client.list_objects(Bucket=bucket_name, Prefix="whu_rs19/")
for obj in obj_list:
    td = EOTrainingData(
        id=obj.split(".")[0],
        labels=[SceneLabel(label_class=obj.split("/")[1])],
        data_url=f"s3://{bucket_name}/{quote(obj)}",
        date_time="2010"
    )
    td_list.append(td)

Parsing

The training dataset described with TrainingDML-AI JSON file can be parsed with python API and transformed to PyTorch/TensorFlow dataset.

Read TrainingDataset object from JSON file

import pytdml

training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file
print("Load training dataset: " + training_dataset.name)
print("Number of training samples: " + str(training_dataset.amount_of_training_data))
print("Number of classes: " + str(training_dataset.number_of_classes))

read training data from s3

import pytdml

# Initialize S3client 
s3_client = pytdml.io.S3_reader.S3Client("s3", "your_server", "your_akey", "your_skey")
# Load the training dataset
training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file
for item in training_dataset.data:
    path = item.data_url
    if pytdml.io.S3_reader.pasrse_s3_path(path):
        bucket_name, key_name = pytdml.io.S3_reader.parse_s3_path(path)
        object_data = s3_client.get_object(bucket_name, key_name)
        # Process the S3 object data (read as PIL Image)
        with PIL.Image.open(BytesIO(object_data)) as img:
            # processing....
    else:
        print("Invalid S3 path:", path)

Transform to PyTorch dataset

• Scene classification dataset

import pytdml
from torchvision import transforms

# Load the training dataset
training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file

# Transform the training dataset
class_map = pytdml.ml.creat_class_map(training_dataset)  # create class map
train_dataset = pytdml.ml.TorchEOImageSceneTD(  # create Torch train dataset
    training_dataset.data,
    class_map,
    transform=transforms.Compose(  # transform for the training set
        [transforms.RandomResizedCrop(size=156, scale=(0.8, 1.0)),  # random resize
         transforms.RandomRotation(degrees=15),  # random rotate
         transforms.RandomHorizontalFlip(),  # random flip
         transforms.CenterCrop(size=124),  # center crop
         transforms.ToTensor(),  # transform to tensor
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # normalize
         ]
    ))

• Object detection dataset

import pytdml

# Load the training dataset
training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file

# Transform the training dataset
class_map = pytdml.ml.creat_class_map(training_dataset)  # create class map
train_dataset = pytdml.ml.TorchEOImageObjectTD(  # create Torch train dataset
    training_dataset.data,
    class_map,
    transform=pytdml.ml.BaseTransform([128, 128])
)

• Semantic segmentation dataset

import pytdml
from torchvision import transforms

# Load the training dataset
training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file

# Transform the training dataset
class_map = pytdml.ml.creat_class_map(training_dataset)  # create class map
train_dataset = pytdml.ml.TorchEOImageSegmentationTD(  # create Torch train dataset
    training_dataset.data,
    class_map,
    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])
)

Transform to TensorFlow dataset

• Scene classification dataset

import pytdml

# Load the training dataset
training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file

# Transform the training dataset
class_map = pytdml.ml.creat_class_map(training_dataset)  # create class map
train_dataset = pytdml.ml.TensorflowEOImageSceneTD(  # create TensorFlow train dataset
    training_dataset.data,
    class_map
)
tf_train_dataset = train_dataset.create_dataset()

• Object detection dataset

import pytdml

# Load the training dataset
training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file

# Transform the training dataset
class_map = pytdml.ml.creat_class_map(training_dataset)  # create class map
train_dataset = pytdml.ml.TensorflowEOImageObjectTD(  # create TensorFlow train dataset
    training_dataset.data,
    class_map
)
tf_train_dataset = train_dataset.create_dataset()

• Semantic segmentation dataset

import pytdml

# Load the training dataset
training_dataset = pytdml.io.read_from_json("dataset.json")  # read from TDML json file

# Transform the training dataset
class_map = pytdml.ml.creat_class_map(training_dataset)  # create class map
train_dataset = pytdml.ml.TensorflowEOImageSegmentationTD(  # create TensorFlow train dataset
    training_dataset.data,
    class_map
)
tf_train_dataset = train_dataset.create_dataset()

Image Cropping

The images of training dataset in TrainingDML-AI JSON format can be cropped with command line for preprocessing.

pytdml/tdml_image_crop.py  --input=<Input original TrainingDML-AU file path> --output_json=<Output result TrainingDML-AI JSON file path>
                          --output_images=<Output dir of result cropped images> --size=<Crop size of images>