phi-anonymize-face 0.2.1

HIPAA-compliant face de-identification for medical images

phi_anonymize_face

HIPAA-compliant face de-identification for medical images.

Automatically detect and anonymize faces in clinical photographs, DICOM images, and standard image formats. Built for healthcare, research, and any workflow that requires Protected Health Information (PHI) removal from facial imagery.

Use it three ways: Python API, CLI, or GUI desktop app.

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Features

• Multi-detector cascade — MediaPipe (fast) → OpenCV DNN/Haar (robust) → RetinaFace (high-recall). Falls back automatically if a detector finds no faces.
• 3 anonymization methods — Gaussian blur, pixelation, or black-box redaction.
• Medical-grade padding — Configurable bounding-box expansion (default 1.3x) ensures ears, hairline, and chin are fully covered.
• Desktop GUI — PyQt6 app with side-by-side preview, tunable parameters, drag-and-drop, and batch processing.
• CLI tool — phi-anonymize command for scripting and pipelines.
• DICOM support — Read/write DICOM pixel data (optional pydicom extra).
• EXIF stripping — Removes metadata by default (EXIF can contain PHI).
• Batch processing — Parallel folder processing with progress bar (CLI, GUI, and API).
• Audit logging — CSV trail of every processed image for compliance.
• Verification mode — Re-run detection on output to confirm no residual faces.
• Accepts images or numpy arrays — Integrate into any Python pipeline.

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Installation

Core library (API + CLI):

pip install phi-anonymize-face

With GUI:

pip install phi-anonymize-face[gui]

With DICOM support:

pip install phi-anonymize-face[dicom]

With RetinaFace (highest recall):

pip install phi-anonymize-face[retinaface]

Everything:

pip install phi-anonymize-face[all]

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GUI Application

Launch the desktop app:

phi-anonymize-gui

Or from Python:

from phi_anonymize_face.gui.app import main
main()

GUI Features

Feature	Description
Side-by-side preview	Original and anonymized images shown together
Drag-and-drop	Drop an image directly onto the preview area
Tunable controls	Method, blur strength slider, detector, confidence, padding
Single image mode	Load Image → adjust settings → Anonymize → Save
Batch folder mode	Load Folder → select output folder → processes all with progress bar
Stats cards	Live display of faces detected, processing status, image dimensions
EXIF toggle	Strip or preserve EXIF metadata
Fallback toggle	Enable/disable detector cascade
Dark theme	Modern dark UI (Catppuccin Mocha)

GUI Workflow

Single image:

1. Click Load Image (or drag-and-drop onto the preview area)
2. Adjust settings in the sidebar (method, blur strength, detector, etc.)
3. Click Anonymize
4. Review the side-by-side before/after preview
5. Click Save Result to export

Batch folder:

1. Click Load Folder — all supported images are discovered (including subdirectories)
2. Adjust settings in the sidebar
3. Click Anonymize → select an output folder
4. Watch progress bar and live preview as each image is processed
5. Summary dialog shows total faces anonymized

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CLI Tool

The phi-anonymize command is available after installation.

Single Image

# Default blur
phi-anonymize -i patient.jpg -o anonymized.jpg

# Choose method
phi-anonymize -i photo.jpg -o out.jpg --method pixelate
phi-anonymize -i photo.jpg -o out.jpg --method blackbox

# Adjust blur strength and padding
phi-anonymize -i photo.jpg -o out.jpg --blur-strength 151 --padding 1.5

Folder Batch

# Process all images in a folder
phi-anonymize -i ./patient_photos/ -o ./anonymized/

# Recursive (includes subdirectories)
phi-anonymize -i ./patient_photos/ -o ./anonymized/ --recursive

Detection Options

# Use a specific detector
phi-anonymize -i photo.jpg -o out.jpg --detector opencv_dnn

# Auto cascade (try all detectors)
phi-anonymize -i photo.jpg -o out.jpg --detector auto

# Lower confidence to catch more faces
phi-anonymize -i photo.jpg -o out.jpg --confidence 0.3

# Disable fallback (use only selected detector)
phi-anonymize -i photo.jpg -o out.jpg --no-fallback

Compliance Options

# Enable audit trail (CSV log of every processed image)
phi-anonymize -i ./photos/ -o ./safe/ --audit-log audit.csv

# Verify output (re-run detection on anonymized images — should find 0 faces)
phi-anonymize -i photo.jpg -o safe.jpg --verify

# Keep EXIF metadata (not recommended for PHI)
phi-anonymize -i photo.jpg -o out.jpg --keep-exif

Full CLI Reference

Usage: phi-anonymize [OPTIONS]

Options:
  -i, --input TEXT        Image or folder path (required)
  -o, --output TEXT       Output path (required)
  -m, --method TEXT       blur | pixelate | blackbox (default: blur)
  --blur-strength INT     Blur kernel size (default: 99)
  --padding FLOAT         Bounding-box padding factor (default: 1.3)
  --detector TEXT         mediapipe | opencv_dnn | retinaface | auto (default: mediapipe)
  --confidence FLOAT      Min detection confidence (default: 0.5)
  --no-fallback           Disable detector cascade fallback
  --recursive             Recurse into subdirectories (folder mode)
  --keep-exif             Preserve EXIF metadata
  --audit-log TEXT        Path for CSV audit log
  --verify                Verify output images have no residual faces
  --version               Show version
  -h, --help              Show help

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Python API

Quick Start

from phi_anonymize_face import anonymize_image, anonymize_folder, FaceAnonymizer

# 1. Quick single-image anonymization
result = anonymize_image("patient_photo.jpg", method="blur", output_path="anon.jpg")
print(f"Faces found: {result.faces_detected}")

# 2. Quick folder batch
results = anonymize_folder("input_dir/", output_dir="output_dir/", method="pixelate")

# 3. Configured anonymizer
anon = FaceAnonymizer(
    method="blur",
    blur_strength=99,
    padding=1.3,
    detector="mediapipe",      # or "opencv_dnn", "retinaface", "auto"
    confidence_threshold=0.5,
    fallback=True,             # cascade to next detector if 0 faces found
    audit_log="audit.csv",
)
result = anon.process("patient.jpg", output_path="anonymized.jpg")
results = anon.process_folder("photos/", output_dir="safe_photos/")

Working with Results

result = anonymize_image("photo.jpg", output_path="anon.jpg")

if result.success:
    print(f"Faces: {result.faces_detected}")
    for box in result.bounding_boxes:
        print(f"  x={box.x}, y={box.y}, w={box.w}, h={box.h}, conf={box.confidence:.2f}")
    # result.image is the anonymized numpy array (BGR)
else:
    print(f"Error: {result.error}")

Using with Numpy Arrays

import cv2
from phi_anonymize_face import anonymize_image

img = cv2.imread("photo.jpg")
result = anonymize_image(img, method="pixelate")
cv2.imwrite("anon.jpg", result.image)

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How Detection Cascade Works

Input Image
    │
    ▼
┌─────────────┐   faces found?   ┌──────────┐
│  MediaPipe   │ ──── YES ──────► │  Apply    │
│  (primary)   │                  │  method   │
└─────┬───────┘                  └──────────┘
      │ NO
      ▼
┌─────────────┐   faces found?   ┌──────────┐
│  OpenCV DNN  │ ──── YES ──────► │  Apply    │
│  (fallback)  │                  │  method   │
└─────┬───────┘                  └──────────┘
      │ NO
      ▼
┌─────────────┐   faces found?   ┌──────────┐
│  RetinaFace  │ ──── YES ──────► │  Apply    │
│  (optional)  │                  │  method   │
└─────┬───────┘                  └──────────┘
      │ NO
      ▼
  Return image unchanged (log warning)

Set fallback=False to use only the selected detector.

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Anonymization Methods

Method	Description	Use Case
blur	Gaussian blur (configurable kernel)	Default — preserves image context
pixelate	Block-based downscale/upscale	Visible anonymization indicator
blackbox	Solid black fill	Maximum privacy, no facial info retained

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DICOM Support

from phi_anonymize_face import anonymize_image

# Works with .dcm files when pydicom is installed
result = anonymize_image("scan.dcm", output_path="anon.dcm")

Install with pip install phi-anonymize-face[dicom].

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API Reference

FaceAnonymizer

The main class for configuring and running face anonymization.

Parameter	Type	Default	Description
method	str	"blur"	Anonymization method: "blur", "pixelate", "blackbox"
blur_strength	int	99	Kernel size for blur / block size for pixelate
padding	float	1.3	Bounding-box expansion factor (1.3 = 30% larger)
detector	str	"mediapipe"	Backend: "mediapipe", "opencv_dnn", "retinaface", "auto"
confidence_threshold	float	0.5	Minimum detection confidence to keep a face
fallback	bool	True	Cascade to next detector if current finds 0 faces
strip_exif	bool	True	Remove EXIF metadata from output (recommended for PHI)
audit_log	str|None	None	Path for CSV audit trail file

Methods:

Method	Signature	Description
process()	process(source, output_path=None) → AnonymizationResult	Anonymize a single image (path, Path, or numpy array)
process_folder()	process_folder(folder, output_dir, recursive=False) → list[AnonymizationResult]	Batch-process all images in a folder
verify()	verify(source) → bool	Returns True if no faces are detected (post-anonymization check)

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anonymize_image

anonymize_image(source, method="blur", output_path=None, **kwargs) → AnonymizationResult

Quick function for single images. All **kwargs are forwarded to FaceAnonymizer.

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anonymize_folder

anonymize_folder(folder, output_dir, method="blur", recursive=False, **kwargs) → list[AnonymizationResult]

Quick function for batch processing. All **kwargs are forwarded to FaceAnonymizer.

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AnonymizationResult

Dataclass returned by all processing methods.

Field	Type	Description
image	np.ndarray|None	The anonymized image (BGR numpy array)
faces_detected	int	Number of faces found
bounding_boxes	list[BoundingBox]	Padded bounding boxes of detected faces
output_path	str|None	Path where the image was saved (if requested)
success	bool	Whether processing completed without error
error	str|None	Error message if success is False

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Step-by-Step Usage Guide

Option 1: GUI (Desktop App) — No coding required

1. Install: pip install phi-anonymize-face[gui]
2. Launch: Run phi-anonymize-gui in your terminal
3. Load an image: Click Load Image in the sidebar, or drag-and-drop onto the preview area
4. Adjust settings: Use the sidebar controls to pick a method (blur/pixelate/blackbox), set blur strength, choose a detector, and adjust confidence and padding
5. Anonymize: Click the blue Anonymize button
6. Review: Compare the original and anonymized images side-by-side
7. Save: Click Save Result and choose where to export

For a folder of images:

1. Click Load Folder instead of Load Image
2. Adjust settings as needed
3. Click Anonymize → select an output folder when prompted
4. Watch the progress bar — each image previews as it completes
5. A summary dialog appears when done

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Option 2: CLI (Command Line) — For scripting and automation

1. Install: pip install phi-anonymize-face
2. Single image: phi-anonymize -i photo.jpg -o anonymized.jpg
3. Choose a method: Add --method pixelate or --method blackbox
4. Adjust blur: Add --blur-strength 151 (higher = more blur)
5. Batch a folder: phi-anonymize -i ./photos/ -o ./safe/ --recursive
6. Add audit log: Append --audit-log audit.csv
7. Verify output: Append --verify to confirm no residual faces

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Option 3: Python API — For integration into code/pipelines

1. Install: pip install phi-anonymize-face
2. Quick anonymize:

   from phi_anonymize_face import anonymize_image
   result = anonymize_image("photo.jpg", output_path="anon.jpg")
   print(f"Faces blurred: {result.faces_detected}")
   

3. Choose method:

   anonymize_image("photo.jpg", method="pixelate", output_path="pix.jpg")
   

4. Batch a folder:

   from phi_anonymize_face import anonymize_folder
   results = anonymize_folder("./photos/", output_dir="./safe/")
   

5. Fine-tune settings:

   from phi_anonymize_face import FaceAnonymizer
   anon = FaceAnonymizer(
       method="blur", blur_strength=99, padding=1.3,
       detector="mediapipe", confidence_threshold=0.5,
       fallback=True, audit_log="audit.csv",
   )
   result = anon.process("photo.jpg", output_path="safe.jpg")
   

6. Use with numpy arrays:

   import cv2
   from phi_anonymize_face import anonymize_image
   img = cv2.imread("photo.jpg")
   result = anonymize_image(img, method="blur")
   cv2.imwrite("anon.jpg", result.image)
   

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Development

git clone https://github.com/ismailukman/phi_anonymize_face.git
cd phi_anonymize_face
pip install -e ".[dev,gui]"
pytest
ruff check src/ tests/

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License

MIT — see LICENSE.