指纹生成代码python

import cv2 import numpy as np import random FG_SHAPE_NONE = 0 FG_SHAPE_THUMB = 1 FG_SHAPE_INDEX = 2 FG_SHAPE_MIDDLE = 3 FG_SHAPE_RING = 4 FG_SHAPE_LITTLE = 5 FG_CLASS_NONE = 0 FG_CLASS_ARCH = 1 FG_CLASS_TENTED_ARCH = 2 FG_CLASS_LEFT_LOOP = 3 FG_CLASS_RIGHT_LOOP = 4 FG_CLASS_WHORL = 5 FG_CLASS_TWIN_LOOP = 6 FG_MODE_RUN = 0 FG_MODE_DEBUG = 1 FG_CACHE_DEG = 36 FG_CACHE_FREQ = 20 FG_GABOR_FILTER_SIZE = 10 class FinGe(): def __init__(self): self.minF = 1.0 / 15 self.maxF = 1.0 / 5 self.nCore = 0 self.nDelta = 0 self.mode = 0 def set_mode(self,mode): self.mode = mode def set_shape(self,left,right,top,bottom,middle): # 这一步产生了轮廓图和密度图 self.sL = left self.sR = right self.sT = top self.sB = bottom self.sM = middle self.size = (self.sT + self.sM + self.sB,self.sL + self.sR) self.size_cv = (self.sL + self.sR,self.sT + self.sM + self.sB) self.generate_shape() def generate_shape(self): self.shape = np.zeros(self.size, dtype=np.float64) # 轮廓图 cv2.ellipse(self.shape, (self.sL, self.sT), (self.sL, self.sT), 0, 180, 270, 1, -1) cv2.ellipse(self.shape, (self.sL - 1, self.sT), (self.sR, self.sT), 0, 270, 360, 1, -1) cv2.ellipse(self.shape, (self.sL, self.sT + self.sM - 1), (self.sL, self.sB), 0, 90, 180, 1, -1) cv2.ellipse(self.shape, (self.sL - 1, self.sT + self.sM - 1), (self.sR, self.sB), 0, 0, 90, 1, -1) cv2.rectangle(self.shape, (0, self.sT), (self.sL + self.sR, self.sT + self.sM), 1, -1) if self.mode == FG_MODE_DEBUG: # FG_MODE_DEBUG self.show_shape() print("Step 1: Generate shape") # input("Press any key to go to Step 2 ...") self.generate_density() def show_shape(self): cv2.imshow("Finger shape", self.shape) cv2.waitKey(0) def generate_density(self): resolution = [5, 6, 7] padding = 10 random.seed() self.density = np.zeros(self.size, dtype=np.float64) self.density_steps = np.zeros((self.size[0] + padding * 2,self.size[1] * 3 + padding * 4), dtype=np.float64) print("self.density_steps:",self.density_steps.shape) print("Step 2: Density map (Frequency map)") for i in range(3): # 用zoom产生噪声 zoom_img = np.random.uniform(self.minF, self.maxF, (resolution[i], resolution[i])).astype(np.float64) zoom_img = cv2.resize(zoom_img, self.size_cv, interpolation=cv2.INTER_CUBIC) self.density += zoom_img / 3.0 self.density_steps[ padding:padding + self.size[0], i * self.size[1] + (i + 1) * padding:(i+1) * self.size[1] + (i + 1) * padding ] = zoom_img.copy() if self.mode == FG_MODE_DEBUG: # FG_MODE_DEBUG cv2.imshow("Density", self.density_steps) print("Density layer", i + 1) cv2.waitKey(0) if self.mode == FG_MODE_DEBUG: # FG_MODE_DEBUG self.show_density() def show_density(self): cv2.imshow("Density", self.density) cv2.waitKey(0) def set_points(self, nCore, cores, nDelta, deltas): self.nCore = nCore self.nDelta = nDelta self.cores = cores.copy() self.deltas = deltas.copy() self.generate_orientation() # 生成方向图 def generate_orientation(self): self.orientation = np.zeros(self.size, dtype=np.float64) for i in range(self.size[1]): for j in range(self.size[0]): theta = 0 for k in range(self.nCore): z = (self.cores[k][0]-i, self.cores[k][1]-j) theta += np.arctan2(z[1], z[0]) for k in range(self.nDelta): z = (self.deltas[k][0] - i, self.deltas[k][1] - j) theta -= np.arctan2(z[1], z[0]) theta *= 0.5 self.orientation[j, i] = theta self.orientation = cv2.blur(self.orientation, (5, 5)) self.orientation_display = np.zeros(self.size, dtype=np.float64) for i in range(5, self.size[1], 10): for j in range(5, self.size[0], 10): center = (i, j) theta = self.orientation[j, i] p1 = (int(i - 4.5 * np.cos(theta)), int(j - 4.5 * np.sin(theta))) p2 = (int(i + 4.5 * np.cos(theta)), int(j + 4.5 * np.sin(theta))) cv2.line(self.orientation_display, p1, p2, 1) self.orientation_display = self.orientation_display * self.shape self.orientation_display = cv2.merge([self.orientation_display, self.orientation_display, self.orientation_display]) for k in range(self.nCore): cv2.circle(self.orientation_display, (int(self.cores[k][0]),int(self.cores[k][1])), 5, (0, 0, 255), 1) for k in range(self.nDelta): cv2.circle(self.orientation_display, (int(self.deltas[k][0]),int(self.deltas[k][1])), 5, (0, 255, 0), 1) if self.mode == FG_MODE_DEBUG: # FG_MODE_DEBUG self.show_orientation() # input("Press any key to go to step 4") self.generate_ridge() def show_orientation(self): cv2.imshow("Orientation", self.orientation_display) cv2.waitKey(0) def generate_ridge(self): gb_cache = np.empty((FG_CACHE_DEG * FG_CACHE_FREQ,), dtype=object) for i in range(FG_CACHE_DEG): for j in range(FG_CACHE_FREQ): theta = self.index_to_val(i, 0, 2 * np.pi, FG_CACHE_DEG) + np.pi / 2.0 F = self.index_to_val(j, self.minF, self.maxF, FG_CACHE_FREQ) sigma = np.sqrt(-9.0 / (8.0 * F * F * np.log(0.001))) gb_cache[self.FG_CACHE_INDEX(i, j)] = cv2.getGaborKernel( (2 * FG_GABOR_FILTER_SIZE, 2 * FG_GABOR_FILTER_SIZE), sigma, theta, 1.0 / F, 1, 0) finger = np.zeros(self.size, dtype=np.float64) cv2.randu(finger,0,1) finger = np.where(finger < 0.001, 1, 0).astype(np.float64) if self.mode == FG_MODE_DEBUG: # FG_MODE_DEBUG cv2.imshow("Master Fingerprint", finger) cv2.waitKey(0) b = FG_GABOR_FILTER_SIZE while True: layer = np.zeros(finger.shape, dtype=np.float64) for i in range(self.size[0]): for j in range(self.size[1]): theta = self.orientation[i, j] theta = theta + 2 * np.pi if theta < 0 else theta F = self.density[i, j] index = self.FG_CACHE_INDEX(self.val_to_index(theta, 0, 2 * np.pi, FG_CACHE_DEG), self.val_to_index(F, self.minF, self.maxF, FG_CACHE_FREQ)) fil_rec = (max(b - i, 0), max(b - j, 0), min(min(2 * b, finger.shape[0] + b - i), b + i), min(min(2 * b, finger.shape[1] + b - j), b + j)) # print("fil_rec",fil_rec) fng_rec = (max(i - b, 0), max(j - b, 0), fil_rec[2], fil_rec[3]) # print("fng_rec",fng_rec) t = np.sum(gb_cache[index][fil_rec[0]:fil_rec[0]+fil_rec[2],fil_rec[1]:fil_rec[1]+fil_rec[3]]* finger[fng_rec[0]:fng_rec[0]+fng_rec[2],fng_rec[1]:fng_rec[1]+fng_rec[3]]) if t < 0: t = 0 if t > 0: t = 1 layer[i,j] = t if np.abs(np.count_nonzero(finger) - np.count_nonzero(layer)) / (self.size[0] * self.size[1]) > 0.001: finger = layer if self.mode == FG_MODE_DEBUG: cv2.imshow("Master Fingerprint", finger) cv2.waitKey(0) else: break self.master_fng = finger * self.shape def sh