fingerprint_pipline.py

import cv2 as cv
from glob import glob
import os
import numpy as np
from utils.poincare import calculate_singularities
from utils.segmentation import create_segmented_and_variance_images
from utils.normalization import normalize
from utils.gabor_filter import gabor_filter
from utils.frequency import ridge_freq
from utils import orientation
from utils.crossing_number import calculate_minutiaes
from tqdm import tqdm
from utils.skeletonize import skeletonize


def fingerprint_pipline(input_img):
    block_size = 16

    # pipe line picture re https://www.cse.iitk.ac.in/users/biometrics/pages/111.JPG
    # normalization -> orientation -> frequency -> mask -> filtering

    # normalization - removes the effects of sensor noise and finger pressure differences.
    normalized_img = normalize(input_img.copy(), float(100), float(100))

    # color threshold
    # threshold_img = normalized_img
    # _, threshold_im = cv.threshold(normalized_img,127,255,cv.THRESH_OTSU)
    # cv.imshow('color_threshold', normalized_img); cv.waitKeyEx()

    # ROI and normalisation
    (segmented_img, normim, mask) = create_segmented_and_variance_images(normalized_img, block_size, 0.2)

    # orientations
    angles = orientation.calculate_angles(normalized_img, W=block_size, smoth=False)
    orientation_img = orientation.visualize_angles(segmented_img, mask, angles, W=block_size)
    
    # find the overall frequency of ridges in Wavelet Domain
    freq = ridge_freq(normim, mask, angles, block_size, kernel_size=5, minWaveLength=5, maxWaveLength=15)

    # create gabor filter and do the actual filtering
    gabor_img = gabor_filter(normim, angles, freq)

    # thinning oor skeletonize
    thin_image = skeletonize(gabor_img)

    # minutias
    minutias, end, bif = calculate_minutiaes(thin_image)

    # singularities
    singularities_img, FingerType = calculate_singularities(thin_image, angles, 1, block_size, mask)

    # visualize pipeline stage by stage
#     output_imgs = [input_img, normalized_img, segmented_img, orientation_img, gabor_img, thin_image, minutias, singularities_img]
    output_imgs = [input_img, minutias, singularities_img]
    
#     for i in range(len(output_imgs)): # Lặp qua từng ảnh output
#         if len(output_imgs[i].shape) == 2: # Nếu ảnh thứ i trong output là ảnh Gray(có 2 chiều)
#             output_imgs[i] = cv.cvtColor(output_imgs[i], cv.COLOR_GRAY2RGB) # Chuyển thành ảnh RGB để concatenate với 2 ảnh cuối
    
    # Chuyển list các ảnh thành ảnh 4x4
#     results = np.concatenate([np.concatenate(output_imgs[:4], 1), np.concatenate(output_imgs[4:], 1)]).astype(np.uint8)

#     return results
    return output_imgs, np.array(end)[:,1:].astype(int), np.array(bif)[:,1:].astype(int), np.array(FingerType)[:,1:].astype(int)


if __name__ == '__main__':
    # open images
    img_dir = './sample_inputs/*'
    output_dir = './output/'
    def open_images(directory):
        images_paths = glob(directory)
        return np.array([cv.imread(img_path,0) for img_path in images_paths])

    images = open_images(img_dir)

    # image pipeline
    os.makedirs(output_dir, exist_ok=True)
    for i, img in enumerate(tqdm(images)):
        results = fingerprint_pipline(img)
        cv.imwrite(output_dir+str(i)+'.png', results)
        # cv.imshow('image pipeline', results); cv.waitKeyEx()