palm-roi 0.1.2

A lightweight palm region-of-interest (ROI) extraction tool using hand landmarks and OpenCV.

palm_roi - A region-of-interest extraction tool 🖐️

A lightweight palm region-of-interest (ROI) extraction Python library, originally built for use in a palm-vein biometric imaging device. It makes use of OpenCV and hand landmarks (using tools like MediaPipe) to compute an ROI of a palm in an image, calculate a rotation matrix to straighten it out, and process coordinates.

The original Raspberry Pi example with a live camera feed has been moved to the examples/ directory. Check out the example's README.md for details on how to set it up!

Installation

You can install palm_roi easily via pip:

pip install palm_roi

Note: Depending on your setup (e.g., using MediaPipe to get the hand landmarks), your Python version compatibility will be constrained. The palm_roi library currently works with Python 3.8+, but MediaPipe currently requires Python <3.13.

Usage

Here's an example using Mediapipe hands with palm_roi to extract hand landmarks and perform ROI computation:

import palm_roi
import cv2
import mediapipe as mp

# 1. Provide an image and get hand landmarks from MediaPipe (or another tool)
image = # - your image data - #
hands = mp.solutions.hands.Hands(static_image_mode=True, max_num_hands=1)
results = hands.process(cv2.cvtColor(image, cv2.COLOR_GRAY2RGB))

if results.multi_hand_landmarks:
    INDEX_FINGER_MCP = results.multi_hand_landmarks[0].landmark[5]
    PINKY_MCP = results.multi_hand_landmarks[0].landmark[17]
    WRIST = results.multi_hand_landmarks[0].landmark[0]

    # 2. Apply padding, rotation, and cropping to your image automatically.
    output, error = palm_roi.extract(image, INDEX_FINGER_MCP, PINKY_MCP, WRIST)

    # Note: If you do not have or prefer not to provide a wrist landmark, pass `upside_down=True` or `False` directly:
    # output, error = palm_roi.extract(image, INDEX_FINGER_MCP, PINKY_MCP, upside_down=False)

    if not error:
        cv2.imwrite(f"output.png", output) # Save your processed ROI!

    # Alternatively, get the ROI boundaries, rotation matrix, and orientation for manual application.
    R, roi_height, l1, l2, upside_down = palm_roi.get_coords(image, INDEX_FINGER_MCP, PINKY_MCP, WRIST)
    # R, roi_height, l1, l2, upside_down = palm_roi.get_coords(image, INDEX_FINGER_MCP, PINKY_MCP, upside_down=False)

The landmark parameters (INDEX_FINGER_MCP, PINKY_MCP, and WRIST) accept standard (x, y) coordinate tuples, lists, as well as direct landmark objects from tools like MediaPipe.

See examples/main.py for a full implementation demonstrating real-time camera processing and Mediapipe.