SimpleFingerprintRecognitionExample.zip

import math import numpy as np import cv2 as cv import urllib.request import IPython import base64 import html # Utility function to show an image def show(*images, enlarge_small_images = True, max_per_row = -1, font_size = 0): if len(images) == 2 and type(images[1])==str: images = [(images[0], images[1])] def convert_for_display(img): if img.dtype!=np.uint8: a, b = img.min(), img.max() if a==b: offset, mult, d = 0, 0, 1 elif a<0: offset, mult, d = 128, 127, max(abs(a), abs(b)) else: offset, mult, d = 0, 255, b img = np.clip(offset + mult*(img.astype(float))/d, 0, 255).astype(np.uint8) return img def convert(imgOrTuple): try: img, title = imgOrTuple if type(title)!=str: img, title = imgOrTuple, '' except ValueError: img, title = imgOrTuple, '' if type(img)==str: data = img else: img = convert_for_display(img) if enlarge_small_images: REF_SCALE = 100 h, w = img.shape[:2] if h<REF_SCALE or w<REF_SCALE: scale = max(1, min(REF_SCALE//h, REF_SCALE//w)) img = cv.resize(img,(w*scale,h*scale), interpolation=cv.INTER_NEAREST) data = 'data:image/png;base64,' + base64.b64encode(cv.imencode('.png', img)[1]).decode('utf8') return data, title if max_per_row == -1: max_per_row = len(images) rows = [images[x:x+max_per_row] for x in range(0, len(images), max_per_row)] font = f"font-size: {font_size}px;" if font_size else "" html_content = "" for r in rows: l = [convert(t) for t in r] html_content += "".join(["<table><tr>"] + [f"<td style='text-align:center;{font}'>{html.escape(t)}</td>" for _,t in l] + ["</tr><tr>"] + [f"<td style='text-align:center;'><img src='{d}'></td>" for d,_ in l] + ["</tr></table>"]) IPython.display.display(IPython.display.HTML(html_content)) # Utility function to load an image from an URL def load_from_url(url): resp = urllib.request.urlopen(url) image = np.asarray(bytearray(resp.read()), dtype=np.uint8) return cv.imdecode(image, cv.IMREAD_GRAYSCALE) # Utility function to draw orientations over an image def draw_orientations(fingerprint, orientations, strengths, mask, scale = 3, step = 8, border = 0): if strengths is None: strengths = np.ones_like(orientations) h, w = fingerprint.shape sf = cv.resize(fingerprint, (w*scale, h*scale), interpolation = cv.INTER_NEAREST) res = cv.cvtColor(sf, cv.COLOR_GRAY2BGR) d = (scale // 2) + 1 sd = (step+1)//2 c = np.round(np.cos(orientations) * strengths * d * sd).astype(int) s = np.round(-np.sin(orientations) * strengths * d * sd).astype(int) # minus for the direction of the y axis thickness = 1 + scale // 5 for y in range(border, h-border, step): for x in range(border, w-border, step): if mask is None or mask[y, x] != 0: ox, oy = c[y, x], s[y, x] cv.line(res, (d+x*scale-ox,d+y*scale-oy), (d+x*scale+ox,d+y*scale+oy), (255,0,0), thickness, cv.LINE_AA) return res # Utility function to draw a set of minutiae over an image def draw_minutiae(fingerprint, minutiae, termination_color = (255,0,0), bifurcation_color = (0,0,255)): res = cv.cvtColor(fingerprint, cv.COLOR_GRAY2BGR) for x, y, t, *d in minutiae: color = termination_color if t else bifurcation_color if len(d)==0: cv.drawMarker(res, (x,y), color, cv.MARKER_CROSS, 8) else: d = d[0] ox = int(round(math.cos(d) * 7)) oy = int(round(math.sin(d) * 7)) cv.circle(res, (x,y), 3, color, 1, cv.LINE_AA) cv.line(res, (x,y), (x+ox,y-oy), color, 1, cv.LINE_AA) return res # Utility function to generate gabor filter kernels _sigma_conv = (3.0/2.0)/((6*math.log(10))**0.5) # sigma is adjusted according to the ridge period, so that the filter does not contain more than three effective peaks def _gabor_sigma(ridge_period): return _sigma_conv * ridge_period def _gabor_size(ridge_period): p = int(round(ridge_period * 2 + 1)) if p % 2 == 0: p += 1 return (p, p) def gabor_kernel(period, orientation): f = cv.getGaborKernel(_gabor_size(period), _gabor_sigma(period), np.pi/2 - orientation, period, gamma = 1, psi = 0) f /= f.sum() f -= f.mean() return f # Utility functions for minutiae def angle_abs_difference(a, b): return math.pi - abs(abs(a - b) - math.pi) def angle_mean(a, b): return math.atan2((math.sin(a)+math.sin(b))/2, ((math.cos(a)+math.cos(b))/2)) # Utility functions for MCC def draw_minutiae_and_cylinder(fingerprint, origin_cell_coords, minutiae, values, i, show_cylinder = True): def _compute_actual_cylinder_coordinates(x, y, t, d): c, s = math.cos(d), math.sin(d) rot = np.array([[c, s],[-s, c]]) return (rot@origin_cell_coords.T + np.array([x,y])[:,np.newaxis]).T res = draw_minutiae(fingerprint, minutiae) if show_cylinder: for v, (cx, cy) in zip(values[i], _compute_actual_cylinder_coordinates(*minutiae[i])): cv.circle(res, (int(round(cx)), int(round(cy))), 3, (0,int(round(v*255)),0), 1, cv.LINE_AA) return res def draw_match_pairs(f1, m1, v1, f2, m2, v2, cells_coords, pairs, i, show_cylinders = True): #nd = _current_parameters.ND h1, w1 = f1.shape h2, w2 = f2.shape p1, p2 = pairs res = np.full((max(h1,h2), w1+w2, 3), 255, np.uint8) res[:h1,:w1] = draw_minutiae_and_cylinder(f1, cells_coords, m1, v1, p1[i], show_cylinders) res[:h2,w1:w1+w2] = draw_minutiae_and_cylinder(f2, cells_coords, m2, v2, p2[i], show_cylinders) for k, (i1, i2) in enumerate(zip(p1, p2)): (x1, y1, *_), (x2, y2, *_) = m1[i1], m2[i2] cv.line(res, (int(x1), int(y1)), (w1+int(x2), int(y2)), (0,0,255) if k!=i else (0,255,255), 1, cv.LINE_AA) return res