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Getting started with Presidio

Simple flow

Using Presidio's modules as Python packages to get started:

  1. Install Presidio

    pip install presidio-analyzer
pip install presidio-anonymizer
python -m spacy download en_core_web_lg

  2. Analyze + Anonymize

    from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

text="My phone number is 212-555-5555"

# Set up the engine, loads the NLP module (spaCy model by default) 
# and other PII recognizers
analyzer = AnalyzerEngine()

# Call analyzer to get results
results = analyzer.analyze(text=text,
                           entities=["PHONE_NUMBER"],
                           language='en')
print(results)

# Analyzer results are passed to the AnonymizerEngine for anonymization

anonymizer = AnonymizerEngine()

anonymized_text = anonymizer.anonymize(text=text,analyzer_results=results)

print(anonymized_text)

  1. Install Presidio

    pip install "presidio-analyzer[transformers]"
pip install presidio-anonymizer
python -m spacy download en_core_web_sm

  2. Analyze + Anonymize

    from presidio_analyzer import AnalyzerEngine
from presidio_analyzer.nlp_engine import TransformersNlpEngine
from presidio_anonymizer import AnonymizerEngine

text = "My name is Don and my phone number is 212-555-5555"

# Define which transformers model to use
model_config = [{"lang_code": "en", "model_name": {
    "spacy": "en_core_web_sm",  # use a small spaCy model for lemmas, tokens etc.
    "transformers": "dslim/bert-base-NER"
    }
}]

nlp_engine = TransformersNlpEngine(models=model_config)

# Set up the engine, loads the NLP module (spaCy model by default) 
# and other PII recognizers
analyzer = AnalyzerEngine(nlp_engine=nlp_engine)

# Call analyzer to get results
results = analyzer.analyze(text=text, language='en')
print(results)

# Analyzer results are passed to the AnonymizerEngine for anonymization

anonymizer = AnonymizerEngine()

anonymized_text = anonymizer.anonymize(text=text, analyzer_results=results)

print(anonymized_text)

    Tip: Downloading models

    If not available, the transformers model and the spacy model would be downloaded on the first call to the AnalyzerEngine. To pre-download, see this doc.

Simple flow: Images

  1. Install presidio-image-redactor

    pip install presidio-image-redactor

  2. Redact PII from image

    from presidio_image_redactor import ImageRedactorEngine
from PIL import Image

image = Image.open(path_to_image_file)

redactor = ImageRedactorEngine()
redactor.redact(image=image)

  1. Install presidio-image-redactor

    pip install presidio-image-redactor

  2. Redact text PII from DICOM image

    import pydicom
from presidio_image_redactor import DicomImageRedactorEngine

# Set input and output paths
input_path = "path/to/your/dicom/file.dcm"
output_dir = "./output"

# Initialize the engine
engine = DicomImageRedactorEngine()

# Option 1: Redact from a loaded DICOM image
dicom_image = pydicom.dcmread(input_path)
redacted_dicom_image = engine.redact(dicom_image, fill="contrast")

# Option 2: Redact from DICOM file
engine.redact_from_file(input_path, output_dir, padding_width=25, fill="contrast")

# Option 3: Redact from directory
engine.redact_from_directory("path/to/your/dicom", output_dir, padding_width=25, fill="contrast")

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