python指纹识别算法（亲测代码可用）_python获取小分子指纹

代码已测试过，可以直接使用。

from os import path
import math
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
from utils import *
from ipywidgets import interact
 
fingerprint = cv.imread('samples/sample_1_1.png', cv.IMREAD_GRAYSCALE)
show(fingerprint, f'Fingerprint with size (w,h): {fingerprint.shape[::-1]}')

# Calculate the local gradient (using Sobel filters)
gx, gy = cv.Sobel(fingerprint, cv.CV_32F, 1, 0), cv.Sobel(fingerprint, cv.CV_32F, 0, 1)
show((gx, 'Gx'), (gy, 'Gy'))

Step 1: Fingerprint segmentation(指纹分割)

fingerprint = cv.imread('samples/sample_1_1.png', cv.IMREAD_GRAYSCALE)
show(fingerprint, f'Fingerprint with size (w,h): {fingerprint.shape[::-1]}')

gx, gy = cv.Sobel(fingerprint, cv.CV_32F, 1, 0), cv.Sobel(fingerprint, cv.CV_32F, 0, 1)
show((gx, 'Gx'), (gy, 'Gy'))

sum_gm = cv.boxFilter(gm, -1, (25, 25), normalize = False)
show(sum_gm, 'Integral of the gradient magnitude')

sum_gm = cv.boxFilter(gm, -1, (25, 25), normalize = False)
show(sum_gm, 'Integral of the gradient magnitude')

thr = sum_gm.max() * 0.2
mask = cv.threshold(sum_gm, thr, 255, cv.THRESH_BINARY)[1].astype(np.uint8)
show(fingerprint, mask, cv.merge((mask, fingerprint, fingerprint)))