"""Code for FineNet in paper "Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge" at ICB 2018 https://arxiv.org/pdf/1712.09401.pdf If you use whole or partial function in this code, please cite paper: @inproceedings{Nguyen_MinutiaeNet, author = {Dinh-Luan Nguyen and Kai Cao and Anil K. Jain}, title = {Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge}, booktitle = {The 11th International Conference on Biometrics, 2018}, year = {2018}, } """ from __future__ import absolute_import from __future__ import division from time import time from datetime import datetime from CoarseNet_utils import * from scipy import misc, ndimage, signal, sparse, io import scipy.ndimage import cv2 import sys,os sys.path.append(os.path.realpath('../FineNet')) from FineNet_model import FineNetmodel from keras.models import Model from keras.layers import Input from keras import layers from keras.layers.core import Flatten,Activation,Lambda, Dropout from keras.layers.convolutional import Conv2D,MaxPooling2D,UpSampling2D,AveragePooling2D from keras.layers.normalization import BatchNormalization from keras.layers.advanced_activations import PReLU from keras.regularizers import l2 from keras.optimizers import SGD, Adam from keras.utils import plot_model import tensorflow as tf from MinutiaeNet_utils import * from LossFunctions import * def conv_bn(bottom, w_size, name, strides=(1,1), dilation_rate=(1,1)): top = Conv2D(w_size[0], (w_size[1],w_size[2]), kernel_regularizer=l2(5e-5), padding='same', strides=strides, dilation_rate=dilation_rate, name='conv-'+name)(bottom) top = BatchNormalization(name='bn-'+name)(top) return top def conv_bn_prelu(bottom, w_size, name, strides=(1,1), dilation_rate=(1,1)): if dilation_rate == (1,1): conv_type = 'conv' else: conv_type = 'atrousconv' top = Conv2D(w_size[0], (w_size[1],w_size[2]), kernel_regularizer=l2(5e-5), padding='same', strides=strides, dilation_rate=dilation_rate, name=conv_type+name)(bottom) top = BatchNormalization(name='bn-'+name)(top) top = PReLU(alpha_initializer='zero', shared_axes=[1,2], name='prelu-'+name)(top) # top = Dropout(0.25)(top) return top def CoarseNetmodel(input_shape=(400,400,1), weights_path=None, mode='train'): # Change network architecture here!! img_input=Input(input_shape) bn_img=Lambda(img_normalization, name='img_normalized')(img_input) # Main part conv = conv_bn_prelu(bn_img, (64, 5, 5), '1_0') conv = conv_bn_prelu(conv, (64, 3, 3), '1_1') conv = conv_bn_prelu(conv, (64, 3, 3), '1_2') conv = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(conv) # =======Block 1 ======== conv1 = conv_bn_prelu(conv, (128, 3, 3), '2_1') conv = conv_bn_prelu(conv1, (128, 3, 3), '2_2') conv = conv_bn_prelu(conv, (128, 3, 3), '2_3') conv = layers.add([conv, conv1]) conv1 = conv_bn_prelu(conv, (128, 3, 3), '2_1b') conv = conv_bn_prelu(conv1, (128, 3, 3), '2_2b') conv = conv_bn_prelu(conv, (128, 3, 3), '2_3b') conv = layers.add([conv, conv1]) conv1 = conv_bn_prelu(conv, (128, 3, 3), '2_1c') conv = conv_bn_prelu(conv1, (128, 3, 3), '2_2c') conv = conv_bn_prelu(conv, (128, 3, 3), '2_3c') conv = layers.add([conv, conv1]) conv_block1 = MaxPooling2D(pool_size=(2,2),strides=(2,2))(conv) # ========================== # =======Block 2 ======== conv1 = conv_bn_prelu(conv_block1, (256,3,3), '3_1') conv = conv_bn_prelu(conv1, (256,3,3), '3_2') conv = conv_bn_prelu(conv, (256,3,3), '3_3') conv = layers.add([conv, conv1]) conv1 = conv_bn_prelu(conv, (256, 3, 3), '3_1b') conv = conv_bn_prelu(conv1, (256, 3, 3), '3_2b') conv = conv_bn_prelu(conv, (256, 3, 3), '3_3b') conv = layers.add([conv, conv1]) conv_block2 = MaxPooling2D(pool_size=(2,2),strides=(2,2))(conv) # ========================== # =======Block 3 ======== conv1 = conv_bn_prelu(conv_block2, (512, 3, 3), '3_1c') conv = conv_bn_prelu(conv1, (512, 3, 3), '3_2c') conv = conv_bn_prelu(conv, (512, 3, 3), '3_3c') conv = layers.add([conv, conv1]) conv_block3 = conv_bn_prelu(conv, (256, 3, 3), '3_4c') #conv_block3 = MaxPooling2D(pool_size=(2,2),strides=(2,2))(conv) # ========================== # multi-scale ASPP level_2=conv_bn_prelu(conv_block3, (256,3,3), '4_1', dilation_rate=(1,1)) ori_1=conv_bn_prelu(level_2, (128,1,1), 'ori_1_1') ori_1=Conv2D(90, (1,1), padding='same', name='ori_1_2')(ori_1) seg_1=conv_bn_prelu(level_2, (128,1,1), 'seg_1_1') seg_1=Conv2D(1, (1,1), padding='same', name='seg_1_2')(seg_1) level_3=conv_bn_prelu(conv_block2, (256,3,3), '4_2', dilation_rate=(4,4)) ori_2=conv_bn_prelu(level_3, (128,1,1), 'ori_2_1') ori_2=Conv2D(90, (1,1), padding='same', name='ori_2_2')(ori_2) seg_2=conv_bn_prelu(level_3, (128,1,1), 'seg_2_1') seg_2=Conv2D(1, (1,1), padding='same', name='seg_2_2')(seg_2) level_4=conv_bn_prelu(conv_block2, (256,3,3), '4_3', dilation_rate=(8,8)) ori_3=conv_bn_prelu(level_4, (128,1,1), 'ori_3_1') ori_3=Conv2D(90, (1,1), padding='same', name='ori_3_2')(ori_3) seg_3=conv_bn_prelu(level_4, (128,1,1), 'seg_3_1') seg_3=Conv2D(1, (1,1), padding='same', name='seg_3_2')(seg_3) # sum fusion for ori ori_out=Lambda(merge_sum)([ori_1, ori_2, ori_3]) ori_out_1=Activation('sigmoid', name='ori_out_1')(ori_out) ori_out_2=Activation('sigmoid', name='ori_out_2')(ori_out) # sum fusion for segmentation seg_out=Lambda(merge_sum)([seg_1, seg_2, seg_3]) seg_out=Activation('sigmoid', name='seg_out')(seg_out) # ---------------------------------------------------------------------------- # enhance part filters_cos, filters_sin = gabor_bank(stride=2, Lambda=8) filter_img_real = Conv2D(filters_cos.shape[3],(filters_cos.shape[0],filters_cos.shape[1]), weights=[filters_cos, np.zeros([filters_cos.shape[3]])], padding='same', name='enh_img_real_1')(img_input) filter_img_imag = Conv2D(filters_sin.shape[3],(filters_sin.shape[0],filters_sin.shape[1]), weights=[filters_sin, np.zeros([filters_sin.shape[3]])], padding='same', name='enh_img_imag_1')(img_input) ori_peak = Lambda(ori_highest_peak)(ori_out_1) ori_peak = Lambda(select_max)(ori_peak) # select max ori and set it to 1 # Use this function to upsample image upsample_ori = UpSampling2D(size=(8,8))(ori_peak) seg_round = Activation('softsign')(seg_out) upsample_seg = UpSampling2D(size=(8,8))(seg_round) mul_mask_real = Lambda(merge_mul)([filter_img_real, upsample_ori]) enh_img_real = Lambda(reduce_sum, name='enh_img_real_2')(mul_mask_real) mul_mask_imag = Lambda(merge_mul)([filter_img_imag, upsample_ori]) enh_img_imag = Lambda(reduce_sum, name='enh_img_imag_2')(mul_mask_imag) enh_img = Lambda(atan2, name='phase_img')([enh_img_imag, enh_img_real]) enh_seg_img = Lambda(merge_concat, name='phase_seg_img')([enh_img, upsample_seg]) # ---------------------------------------------------------------------------- # mnt part # =======Block 1 ======== mnt_conv1 = conv_bn_prelu(enh_seg_img, (64, 9, 9), 'mnt_1_1') mnt_conv = conv_bn_prelu(mnt_conv1, (64, 9, 9), 'mnt_1_2') mnt_conv = conv_bn_prelu(mnt_conv, (64, 9, 9), 'mnt_1_3') mnt_conv = layers.add([mnt_conv, mnt_conv1]) mnt_conv1 = conv_bn_prelu(mnt_conv, (64, 9, 9), 'mnt_1_1b') mnt_conv = conv_bn_prelu(mnt_conv1, (64, 9, 9), 'mnt_1_2b') mnt_conv = conv_bn_prelu(mnt_conv, (64, 9, 9), 'mnt_1_3b') mnt_conv = layers.add([mnt_conv, mnt_conv1]) mnt_conv = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(mnt_conv) # ========================== # =======Block 2 ======== mnt_conv1 = conv_bn_prelu(mnt_conv, (128, 5, 5), 'mnt_2_1') mnt_conv = conv_bn_prelu(mnt_conv1, (128, 5, 5), 'mnt_2_2') mnt_conv = conv_bn_prelu(mnt_conv, (128, 5, 5), 'mnt_2_3') mnt_conv = layers.add([mnt_conv, mnt_conv1]) mnt_conv1 = conv_bn_prelu(mnt_conv, (128, 5, 5), 'mnt_2_1b') mnt_conv = conv_bn_prelu(mnt_conv1, (128, 5, 5), 'mnt_2_2b') mnt_conv = conv_bn_prelu(mnt_conv, (128, 5, 5), 'mnt_2_3b') mnt_conv = layers.add([mnt_conv, mnt_conv1]) mnt_conv = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(mnt_conv) # ========================== # =======Block 3 ======== mnt_conv1 = conv_bn_prelu(mnt_conv, (256, 3, 3), 'mnt_3_1') mnt_conv2 = conv_bn_prelu(mnt_conv1, (256, 3, 3), 'mnt_3_2') mnt_conv3 = conv_bn_prelu(mnt_conv2, (256, 3, 3), 'mnt_3_3') mnt_conv3 = layers.add([mnt_conv3, mnt_conv1]) mnt_conv4 = conv_bn_prelu(mnt_conv3, (256, 3, 3), 'mnt_3_4') mnt_conv4 = layers.add([mnt_conv4, mnt_conv2]) mnt_conv = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(mnt_conv4) # ========================== mnt_o_1=Lambda(merge_concat)([mnt_conv, ori_out_1]) mnt_o_2=conv_bn_prelu(mnt_o_1, (256,1,1), 'mnt_o_1_1') mnt_o_3=Conv2D(180, (1,1), padding='same', name='mnt_o_1_2')(mnt_o_2) mnt_o_out=Activation('sigmoid', name='mnt_o_out')(mnt_o_3) mnt_w_1=conv_bn_prelu(mnt_conv, (256,1,1), 'mnt_w_1_1') mnt_w_2=Conv2D(8, (1,1), padding='same', name='mnt_w_1_2')(mnt_w_1) mnt_w_out=Activation('sigmoid', name='mnt_w_out')(mnt_w_2) mnt_h_1=conv_bn_prelu(mnt_conv, (256,1,1), 'mnt_h_1_1') mnt_h_2=Conv2D(8, (1,1), padding='same', name='mnt_h_1_2')(mnt_h_1) mnt_h_out=Activation('sigmoid', name='mnt_h_out')(mnt_h_2) mnt_s_1=conv_bn_prelu(mnt_conv, (256,1,1), 'mnt_s_1_1') mnt_s_2=Conv2D(1, (1,1), padding='same', name='mnt_s_1_2')(mnt_s_1) mnt_s_out=Activation('sigmoid', name='mnt_s_out')(mnt_s_2) if mode == 'deploy': model = Model(inputs=[img_input,], outputs=[enh_img, enh_img_imag, enh_img_real, ori_out_1, ori_out_2, seg_out, mnt_o_out, mnt_w_out, mnt_h_out, mnt_s_out]) else: model = Model(inputs=[img_input,], outputs=[ori_out_1, ori_out_2, seg_out, mnt_o_out, mnt_w_out, mnt_h_out, mnt_s_out]) if weights_path != None: model.load_weights(weights_path, by_name=True) return model def train(input_shape=(400,400), train_set = None,output_dir='../output_CoarseNet/'+datetime.now().strftime('%Y%m%d-%H%M%S'), pretrain_dir=None,batch_size=1,test_set=None, learning_config=None, logging=None): img_name, folder_name, img_size = get_maximum_img_size_and_names(train_set, None, max_size=input_shape) main_net_model = CoarseNetmodel((img_size[0], img_size[1], 1), pretrain_dir, 'train') # Save model architecture plot_model(main_net_model, to_file=output_dir+'/model.png',show_shapes=True) main_net_model.compile(optimizer=learning_config, loss={'seg_out':segmentation_loss, 'mnt_o_out': orientation_output_loss, 'mnt_w_out': orientation_output_loss, 'mnt_h_out': orientation_output_loss, 'mnt_s_out': minutiae_score_loss }, loss_weights={'seg_out': .5, 'mnt_w_out': .5, 'mnt_h_out': .5, 'mnt_o_out': 100., 'mnt_s_out': 50.}, metrics={'seg_out':[seg_acc_pos, seg_acc_neg, seg_acc_all], 'mnt_o_out': [mnt_acc_delta_10, ], 'mnt_w_out': [mnt_mean_delta, ], 'mnt_h_out': [mnt_mean_delta, ], 'mnt_s_out': [seg_acc_pos, seg_acc_neg, seg_acc_all]}) writer = tf.summary.FileWriter(output_dir) Best_F1_result = 0 Best_loss = 10000000 for epoch in range(1000): outdir = "%s/saved_best_loss/" % (output_dir) mkdir(outdir) for i, train in enumerate(load_data((img_name, folder_name, img_size), tra_ori_model, rand=True, aug=0.7, batch_size=batch_size)): loss = main_net_model.train_on_batch(train[0], {'seg_out': train[3], 'mnt_w_out': train[4], 'mnt_h_out': train[5], 'mnt_o_out': train[6], 'mnt_s_out': train[7] }) # Save the lowest loss for easy converge if Best_loss > loss[0]: savedir = "%s%s_%d_%s" % (outdir, str(epoch),i,str(loss[0])) main_net_model.save_weights(savedir, True) Best_loss = loss[0] # Write log on screen at every 20 epochs if i%(2/batch_size) == 0: logging.info("epoch=%d, step=%d", epoch, i) # Write details loss logging.info("%s", " ".join(["%s:%.4f\t"%(x) for x in zip(main_net_model.metrics_names, loss)])) # logging.info("Loss = %f Best loss = %f",loss[0],Best_loss) # Show in tensorboard for name, value in zip(main_net_model.metrics_names, loss): summary = tf.Summary(value=[tf.Summary.Value(tag=name,simple_value=value), ]) writer.add_summary(summary, i) # Evaluate every 5 epoch: for faster training if epoch%10 == 0: outdir = "%s/saved_models/" % (output_dir) mkdir(outdir) savedir = "%s%s" % (outdir, str(epoch)) main_net_model.save_weights(savedir, True) for folder in test_set: precision_test, recall_test, F1_test, precision_test_location, recall_test_location, F1_test_location = evaluate_training(savedir, [folder, ], logging=logging) summary = tf.Summary(value=[tf.Summary.Value(tag="Precision", simple_value=precision_test), tf.Summary.Value(tag="Recall", simple_value=recall_test), tf.Summary.Value(tag="F1", simple_value=F1_test), tf.Summary.Value(tag="Location Precision", simple_value=precision_test_location), tf.Summary.Value(tag="Location Recall", simple_value=recall_test_location), tf.Summary.Value(tag="Location F1", simple_value=F1_test_location), ]) writer.add_summary(summary, epoch) # Only save the best result if F1_test > Best_F1_result: Best_F1_result = F1_test # else: # os.remove(savedir) writer.close() return def evaluate_training(model_dir, test_set, logging=None, FineNet_path=None): logging.info("Evaluating %s:" % (test_set)) # Prepare input info img_name, folder_name, img_size = get_maximum_img_size_and_names(test_set) main_net_model = CoarseNetmodel((None, None, 1), model_dir, 'test') ave_prf_nms,ave_prf_nms_location = [],[] for j, test in enumerate( load_data((img_name, folder_name, img_size), tra_ori_model, rand=False, aug=0.0, batch_size=1)): # logging.info("%d / %d: %s"%(j+1, len(img_name), img_name[j])) ori_out_1, ori_out_2, seg_out, mnt_o_out, mnt_w_out, mnt_h_out, mnt_s_out = main_net_model.predict(test[0]) mnt_gt = label2mnt(test[7], test[4], test[5], test[6]) original_image = test[0].copy() mnt_s_out = mnt_s_out * seg_out # Does not useful to use this while training final_minutiae_score_threashold = 0.45 early_minutiae_thres = final_minutiae_score_threashold + 0.05 isHavingFineNet = False # In cases of small amount of minutiae given, try adaptive threshold while final_minutiae_score_threashold >= 0: mnt = label2mnt(mnt_s_out, mnt_w_out, mnt_h_out, mnt_o_out, thresh=early_minutiae_thres) # Previous exp: 0.2 mnt_nms_1 = py_cpu_nms(mnt, 0.5) mnt_nms_2 = nms(mnt) # Make sure good result is given if mnt_nms_1.shape[0] > 4 and mnt_nms_2.shape[0] > 4: break else: final_minutiae_score_threashold = final_minutiae_score_threashold - 0.05 early_minutiae_thres = early_minutiae_thres - 0.05 mnt_nms = fuse_nms(mnt_nms_1, mnt_nms_2) mnt_nms = mnt_nms[mnt_nms[:, 3] > early_minutiae_thres, :] mnt_refined = [] if isHavingFineNet == True: # ======= Verify using FineNet ============ patch_minu_radio = 22 if FineNet_path != None: for idx_minu in range(mnt_nms.shape[0]): try: # Extract patch from image x_begin = int(mnt_nms[idx_minu, 1]) - patch_minu_radio y_begin = int(mnt_nms[idx_minu, 0]) - patch_minu_radio patch_minu = original_image[x_begin:x_begin + 2 * patch_minu_radio, y_begin:y_begin + 2 * patch_minu_radio] patch_minu = cv2.resize(patch_minu, dsize=(224, 224), interpolation=cv2.INTER_NEAREST) ret = np.empty((patch_minu.shape[0], patch_minu.shape[1], 3), dtype=np.uint8) ret[:, :, 0] = patch_minu ret[:, :, 1] = patch_minu ret[:, :, 2] = patch_minu patch_minu = ret patch_minu = np.expand_dims(patch_minu, axis=0) # # Can use class as hard decision # # 0: minu 1: non-minu # [class_Minutiae] = np.argmax(model_FineNet.predict(patch_minu), axis=1) # # if class_Minutiae == 0: # mnt_refined.append(mnt_nms[idx_minu,:]) # Use soft decision: merge FineNet score with CoarseNet score [isMinutiaeProb] = model_FineNet.predict(patch_minu) isMinutiaeProb = isMinutiaeProb[0] # print isMinutiaeProb tmp_mnt = mnt_nms[idx_minu, :].copy() tmp_mnt[3] = (4 * tmp_mnt[3] + isMinutiaeProb) / 5 mnt_refined.append(tmp_mnt) except: mnt_refined.append(mnt_nms[idx_minu, :]) else: mnt_refined = mnt_nms mnt_nms = np.array(mnt_refined) if mnt_nms.shape[0] > 0: mnt_nms = mnt_nms[mnt_nms[:, 3] > final_minutiae_score_threashold, :] p, r, f, l, o = metric_P_R_F(mnt_gt, mnt_nms, 16, np.pi/6) ave_prf_nms.append([p, r, f, l, o]) p, r, f, l, o = metric_P_R_F(mnt_gt, mnt_nms, 16, np.pi) ave_prf_nms_location.append([p, r, f, l, o]) logging.info("Average testing results:") ave_prf_nms = np.mean(np.array(ave_prf_nms), 0) ave_prf_nms_location = np.mean(np.array(ave_prf_nms_location), 0) logging.info( "Precision: %f\tRecall: %f\tF1-measure: %f\tLocation_dis: %f\tOrientation_delta:%f\n----------------\n" % ( ave_prf_nms[0], ave_prf_nms[1], ave_prf_nms[2], ave_prf_nms[3], ave_prf_nms[4])) return ave_prf_nms[0], ave_prf_nms[1], ave_prf_nms[2], ave_prf_nms_location[0], ave_prf_nms_location[1], ave_prf_nms_location[2] def fuse_minu_orientation(dir_map, mnt, mode=1,block_size=16): # mode is the way to fuse output minutiae with orientation # 1: use orientation; 2: use minutiae; 3: fuse average blkH, blkW = dir_map.shape dir_map = dir_map%(2*np.pi) if mode == 1: for k in range(mnt.shape[0]): # Choose nearest orientation ori_value = dir_map[int(mnt[k, 1]//block_size),int(mnt[k, 0]//block_size)] if 0 < mnt[k, 2] and mnt[k, 2] <= np.pi/2: if 0 < ori_value and ori_value <= np.pi / 2: mnt[k, 2] = ori_value if np.pi / 2 < ori_value and ori_value <= np.pi: if (ori_value - mnt[k, 2]) < (np.pi - ori_value + mnt[k, 2]): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value + np.pi if np.pi < ori_value and ori_value <= 3*np.pi/2: mnt[k, 2] = ori_value - np.pi if 3*np.pi/2 < ori_value and ori_value <= 2 * np.pi: if (np.pi*2 - ori_value + mnt[k, 2]) < (ori_value - np.pi - mnt[k, 2]): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value - np.pi if np.pi/2 < mnt[k, 2] and mnt[k, 2] <= np.pi: if 0 < ori_value and ori_value <= np.pi / 2: if (mnt[k, 2] - ori_value) < (np.pi - ori_value + mnt[k, 2]): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value + np.pi if np.pi / 2 < ori_value and ori_value <= np.pi: mnt[k, 2] = ori_value if np.pi < ori_value and ori_value <= 3*np.pi/2: if (ori_value - mnt[k, 2]) < (mnt[k, 2] - ori_value + np.pi): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value - np.pi if 3*np.pi/2 < ori_value and ori_value <= 2 * np.pi: mnt[k, 2] = ori_value - np.pi if np.pi < mnt[k, 2] and mnt[k, 2] <= 3*np.pi/2: if 0 < ori_value and ori_value <= np.pi / 2: mnt[k, 2] = ori_value + np.pi if np.pi / 2 < ori_value and ori_value <= np.pi: if (mnt[k, 2] - ori_value) < (ori_value + np.pi - mnt[k, 2]): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value + np.pi if np.pi < ori_value and ori_value <= 3*np.pi/2: mnt[k, 2] = ori_value if 3*np.pi/2 < ori_value and ori_value <= 2 * np.pi: if (ori_value - mnt[k, 2]) < (mnt[k, 2] - ori_value + np.pi): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value - np.pi if 3*np.pi/2 < mnt[k, 2] and mnt[k, 2] <= 2*np.pi: if 0 < ori_value and ori_value <= np.pi / 2: if (np.pi - mnt[k, 2] + ori_value) < (mnt[k, 2] - np.pi - ori_value): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value + np.pi if np.pi / 2 < ori_value and ori_value <= np.pi: mnt[k, 2] = ori_value + np.pi if np.pi < ori_value and ori_value <= 3*np.pi/2: if (mnt[k, 2] - ori_value) < (np.pi*2 - mnt[k, 2] + ori_value - np.pi): mnt[k, 2] = ori_value else: mnt[k, 2] = ori_value - np.pi if 3*np.pi/2 < ori_value and ori_value <= 2 * np.pi: mnt[k, 2] = ori_value elif mode == 2: return elif mode ==3: for k in range(mnt.shape[0]): # Choose nearest orientation ori_value = dir_map[int(mnt[k, 1] // block_size), int(mnt[k, 0] // block_size)] if 0 < mnt[k, 2] and mnt[k, 2] <= np.pi / 2: if 0 < ori_value and ori_value <= np.pi / 2: fixed_ori = ori_value if np.pi / 2 < ori_value and ori_value <= np.pi: if (ori_value - mnt[k, 2]) < (np.pi - ori_value + mnt[k, 2]): fixed_ori = ori_value else: fixed_ori = ori_value + np.pi if np.pi < ori_value and ori_value <= 3 * np.pi / 2: fixed_ori = ori_value - np.pi if 3 * np.pi / 2 < ori_value and ori_value <= 2 * np.pi: if (np.pi * 2 - ori_value + mnt[k, 2]) < (ori_value - np.pi - mnt[k, 2]): fixed_ori = ori_value else: fixed_ori = ori_value - np.pi if np.pi / 2 < mnt[k, 2] and mnt[k, 2] <= np.pi: if 0 < ori_value and ori_value <= np.pi / 2: if (mnt[k, 2] - ori_value) < (np.pi - ori_value + mnt[k, 2]): fixed_ori = ori_value else: fixed_ori = ori_value + np.pi if np.pi / 2 < ori_value and ori_value <= np.pi: fixed_ori = ori_value if np.pi < ori_value and ori_value <= 3 * np.pi / 2: if (ori_value - mnt[k, 2]) < (mnt[k, 2] - ori_value + np.pi): fixed_ori = ori_value else: fixed_ori = ori_value - np.pi if 3 * np.pi / 2 < ori_value and ori_value <= 2 * np.pi: fixed_ori = ori_value - np.pi if np.pi < mnt[k, 2] and mnt[k, 2] <= 3 * np.pi / 2: if 0 < ori_value and ori_value <= np.pi / 2: fixed_ori = ori_value + np.pi if np.pi / 2 < ori_value and ori_value <= np.pi: if (mnt[k, 2] - ori_value) < (ori_value + np.pi - mnt[k, 2]): fixed_ori = ori_value else: fixed_ori = ori_value + np.pi if np.pi < ori_value and ori_value <= 3 * np.pi / 2: fixed_ori = ori_value if 3 * np.pi / 2 < ori_value and ori_value <= 2 * np.pi: if (ori_value - mnt[k, 2]) < (mnt[k, 2] - ori_value + np.pi): fixed_ori = ori_value else: fixed_ori = ori_value - np.pi if 3 * np.pi / 2 < mnt[k, 2] and mnt[k, 2] <= 2 * np.pi: if 0 < ori_value and ori_value <= np.pi / 2: if (np.pi - mnt[k, 2] + ori_value) < (mnt[k, 2] - np.pi - ori_value): fixed_ori = ori_value else: fixed_ori = ori_value + np.pi if np.pi / 2 < ori_value and ori_value <= np.pi: fixed_ori = ori_value + np.pi if np.pi < ori_value and ori_value <= 3 * np.pi / 2: if (mnt[k, 2] - ori_value) < (np.pi * 2 - mnt[k, 2] + ori_value - np.pi): fixed_ori = ori_value else: fixed_ori = ori_value - np.pi if 3 * np.pi / 2 < ori_value and ori_value <= 2 * np.pi: fixed_ori = ori_value mnt[k, 2] = (mnt[k, 2] + fixed_ori)/2.0 else: return def deploy_with_GT(deploy_set, output_dir, model_path, FineNet_path=None, set_name=None): if set_name is None: set_name = deploy_set.split('/')[-2] # Read image and GT img_name, folder_name, img_size = get_maximum_img_size_and_names(deploy_set) mkdir(output_dir + '/'+ set_name + '/') mkdir(output_dir + '/' + set_name + '/mnt_results/') mkdir(output_dir + '/'+ set_name + '/seg_results/') mkdir(output_dir + '/' + set_name + '/OF_results/') logging.info("Predicting %s:" % (set_name)) isHavingFineNet = False main_net_model = CoarseNetmodel((None, None, 1), model_path, mode='deploy') if isHavingFineNet == True: # ====== Load FineNet to verify model_FineNet = FineNetmodel(num_classes=2, pretrained_path=FineNet_path, input_shape=(224,224,3)) model_FineNet.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0), metrics=['accuracy']) time_c = [] ave_prf_nms=[] for i, test in enumerate( load_data((img_name, folder_name, img_size), tra_ori_model, rand=False, aug=0.0, batch_size=1)): print i, img_name[i] logging.info("%s %d / %d: %s" % (set_name, i + 1, len(img_name), img_name[i])) time_start = time() image = misc.imread(deploy_set + 'img_files/' + img_name[i] + '.bmp', mode='L')# / 255.0 mask = misc.imread(deploy_set + 'seg_files/' + img_name[i] + '.bmp', mode='L') / 255.0 img_size = image.shape img_size = np.array(img_size, dtype=np.int32) // 8 * 8 image = image[:img_size[0], :img_size[1]] mask = mask[:img_size[0], :img_size[1]] original_image = image.copy() # Generate OF texture_img = FastEnhanceTexture(image, sigma=2.5, show=False) dir_map, fre_map = get_maps_STFT(texture_img, patch_size=64, block_size=16, preprocess=True) image = np.reshape(image, [1, image.shape[0], image.shape[1], 1]) enh_img, enh_img_imag, enhance_img, ori_out_1, ori_out_2, seg_out, mnt_o_out, mnt_w_out, mnt_h_out, mnt_s_out \ = main_net_model.predict(image) time_afterconv = time() # Use post processing to smooth image round_seg = np.round(np.squeeze(seg_out)) seg_out = 1 - round_seg kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10)) seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_CLOSE, kernel) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7)) seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_OPEN, kernel) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) seg_out = cv2.dilate(seg_out, kernel) # If use mask from outside # seg_out = cv2.resize(mask, dsize=(seg_out.shape[1], seg_out.shape[0])) mnt_gt = label2mnt(test[7], test[4], test[5], test[6]) final_minutiae_score_threashold = 0.45 early_minutiae_thres = final_minutiae_score_threashold + 0.05 # In cases of small amount of minutiae given, try adaptive threshold while final_minutiae_score_threashold >= 0: mnt = label2mnt(np.squeeze(mnt_s_out) * np.round(np.squeeze(seg_out)), mnt_w_out, mnt_h_out, mnt_o_out, thresh=early_minutiae_thres) # Previous exp: 0.2 mnt_nms_1 = py_cpu_nms(mnt, 0.5) mnt_nms_2 = nms(mnt) # Make sure good result is given if mnt_nms_1.shape[0] > 4 and mnt_nms_2.shape[0] > 4: break else: final_minutiae_score_threashold = final_minutiae_score_threashold - 0.05 early_minutiae_thres = early_minutiae_thres - 0.05 mnt_nms = fuse_nms(mnt_nms_1, mnt_nms_2) mnt_nms = mnt_nms[mnt_nms[:, 3] > early_minutiae_thres, :] mnt_refined = [] if isHavingFineNet == True: # ======= Verify using FineNet ============ patch_minu_radio = 22 if FineNet_path != None: for idx_minu in range(mnt_nms.shape[0]): try: # Extract patch from image x_begin = int(mnt_nms[idx_minu, 1]) - patch_minu_radio y_begin = int(mnt_nms[idx_minu, 0]) - patch_minu_radio patch_minu = original_image[x_begin:x_begin + 2 * patch_minu_radio, y_begin:y_begin + 2 * patch_minu_radio] patch_minu = cv2.resize(patch_minu, dsize=(224, 224), interpolation=cv2.INTER_NEAREST) ret = np.empty((patch_minu.shape[0], patch_minu.shape[1], 3), dtype=np.uint8) ret[:, :, 0] = patch_minu ret[:, :, 1] = patch_minu ret[:, :, 2] = patch_minu patch_minu = ret patch_minu = np.expand_dims(patch_minu, axis=0) # # Can use class as hard decision # # 0: minu 1: non-minu # [class_Minutiae] = np.argmax(model_FineNet.predict(patch_minu), axis=1) # # if class_Minutiae == 0: # mnt_refined.append(mnt_nms[idx_minu,:]) # Use soft decision: merge FineNet score with CoarseNet score [isMinutiaeProb] = model_FineNet.predict(patch_minu) isMinutiaeProb = isMinutiaeProb[0] # print isMinutiaeProb tmp_mnt = mnt_nms[idx_minu, :].copy() tmp_mnt[3] = (4*tmp_mnt[3] + isMinutiaeProb) / 5 mnt_refined.append(tmp_mnt) except: mnt_refined.append(mnt_nms[idx_minu, :]) else: mnt_refined = mnt_nms mnt_nms = np.array(mnt_refined) if mnt_nms.shape[0] > 0: mnt_nms = mnt_nms[mnt_nms[:, 3] > final_minutiae_score_threashold, :] final_mask = ndimage.zoom(np.round(np.squeeze(seg_out)), [8, 8], order=0) # Show the orientation show_orientation_field(original_image, dir_map + np.pi, mask=final_mask, fname="%s/%s/OF_results/%s_OF.jpg" % (output_dir, set_name, img_name[i])) fuse_minu_orientation(dir_map, mnt_nms, mode=3) time_afterpost = time() mnt_writer(mnt_nms, img_name[i], img_size, "%s/%s/mnt_results/%s.mnt" % (output_dir, set_name, img_name[i])) draw_minutiae_overlay_with_score(image, mnt_nms, mnt_gt[:, :3], "%s/%s/%s_minu.jpg"%(output_dir, set_name, img_name[i]),saveimage=True) # misc.imsave("%s/%s/%s_score.jpg"%(output_dir, set_name, img_name[i]), np.squeeze(mnt_s_out_upscale)) misc.imsave("%s/%s/seg_results/%s_seg.jpg" % (output_dir, set_name, img_name[i]), final_mask) time_afterdraw = time() time_c.append([time_afterconv - time_start, time_afterpost - time_afterconv, time_afterdraw - time_afterpost]) logging.info( "load+conv: %.3fs, seg-postpro+nms: %.3f, draw: %.3f" % (time_c[-1][0], time_c[-1][1], time_c[-1][2])) # Metrics calculating p, r, f, l, o = metric_P_R_F(mnt_gt, mnt_nms) ave_prf_nms.append([p, r, f, l, o]) print p,r,f time_c = np.mean(np.array(time_c), axis=0) ave_prf_nms = np.mean(np.array(ave_prf_nms), 0) print "Precision: %f\tRecall: %f\tF1-measure: %f" % (ave_prf_nms[0], ave_prf_nms[1], ave_prf_nms[2]) logging.info( "Average: load+conv: %.3fs, oir-select+seg-post+nms: %.3f, draw: %.3f" % (time_c[0], time_c[1], time_c[2])) return def inference(deploy_set, output_dir, model_path, FineNet_path=None, set_name=None, file_ext='.bmp', isHavingFineNet = False): if set_name is None: set_name = deploy_set.split('/')[-2] mkdir(output_dir + '/'+ set_name + '/') mkdir(output_dir + '/' + set_name + '/mnt_results/') mkdir(output_dir + '/'+ set_name + '/seg_results/') mkdir(output_dir + '/' + set_name + '/OF_results/') logging.info("Predicting %s:" % (set_name)) _, img_name = get_files_in_folder(deploy_set+ 'img_files/', file_ext) print deploy_set # ====== Load FineNet to verify if isHavingFineNet == True: model_FineNet = FineNetmodel(num_classes=2, pretrained_path=FineNet_path, input_shape=(224,224,3)) model_FineNet.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0), metrics=['accuracy']) time_c = [] main_net_model = CoarseNetmodel((None, None, 1), model_path, mode='deploy') for i in xrange(0, len(img_name)): print i image = misc.imread(deploy_set + 'img_files/'+ img_name[i] + file_ext, mode='L') # / 255.0 img_size = image.shape img_size = np.array(img_size, dtype=np.int32) // 8 * 8 # read the mask from files try: mask = misc.imread(deploy_set + 'seg_files/' + img_name[i] + '.jpg', mode='L') / 255.0 except: mask = np.ones((img_size[0],img_size[1])) image = image[:img_size[0], :img_size[1]] mask = mask[:img_size[0], :img_size[1]] original_image = image.copy() texture_img = FastEnhanceTexture(image, sigma=2.5, show=False) dir_map, fre_map = get_maps_STFT(texture_img, patch_size=64, block_size=16, preprocess=True) image = image*mask logging.info("%s %d / %d: %s" % (set_name, i + 1, len(img_name), img_name[i])) time_start = time() image = np.reshape(image, [1, image.shape[0], image.shape[1], 1]) enh_img, enh_img_imag, enhance_img, ori_out_1, ori_out_2, seg_out, mnt_o_out, mnt_w_out, mnt_h_out, mnt_s_out \ = main_net_model.predict(image) time_afterconv = time() # If use mask from model round_seg = np.round(np.squeeze(seg_out)) seg_out = 1 - round_seg kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10)) seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_CLOSE, kernel) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7)) seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_OPEN, kernel) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) seg_out = cv2.dilate(seg_out, kernel) # If use mask from outside # seg_out = cv2.resize(mask, dsize=(seg_out.shape[1], seg_out.shape[0])) max_num_minu = 20 min_num_minu = 6 early_minutiae_thres = 0.5 # New adaptive threshold mnt = label2mnt(np.squeeze(mnt_s_out) * np.round(np.squeeze(seg_out)), mnt_w_out, mnt_h_out, mnt_o_out, thresh=0) # Previous exp: 0.2 mnt_nms_1 = py_cpu_nms(mnt, 0.5) mnt_nms_2 = nms(mnt) mnt_nms_1.view('f8,f8,f8,f8').sort(order=['f3'], axis=0) mnt_nms_1 = mnt_nms_1[::-1] mnt_nms_1_copy = mnt_nms_1.copy() mnt_nms_2_copy = mnt_nms_2.copy() # Adaptive threshold goes here # Make sure the maximum number of minutiae is max_num_minu # Sort minutiae by score while early_minutiae_thres > 0: mnt_nms_1 = mnt_nms_1_copy[mnt_nms_1_copy[:, 3] > early_minutiae_thres, :] mnt_nms_2 = mnt_nms_2_copy[mnt_nms_2_copy[:, 3] > early_minutiae_thres, :] if mnt_nms_1.shape[0]>max_num_minu or mnt_nms_2.shape[0]>max_num_minu: mnt_nms_1 = mnt_nms_1[:max_num_minu,:] mnt_nms_2 = mnt_nms_2[:max_num_minu, :] if mnt_nms_1.shape[0] > min_num_minu and mnt_nms_2.shape[0] > min_num_minu: break early_minutiae_thres = early_minutiae_thres - 0.05 mnt_nms = fuse_nms(mnt_nms_1, mnt_nms_2) final_minutiae_score_threashold = early_minutiae_thres - 0.05 print early_minutiae_thres, final_minutiae_score_threashold mnt_refined = [] if isHavingFineNet == True: # ======= Verify using FineNet ============ patch_minu_radio = 22 if FineNet_path != None: for idx_minu in range(mnt_nms.shape[0]): try: # Extract patch from image x_begin = int(mnt_nms[idx_minu, 1]) - patch_minu_radio y_begin = int(mnt_nms[idx_minu, 0]) - patch_minu_radio patch_minu = original_image[x_begin:x_begin + 2 * patch_minu_radio, y_begin:y_begin + 2 * patch_minu_radio] patch_minu = cv2.resize(patch_minu, dsize=(224, 224),interpolation=cv2.INTER_NEAREST) ret = np.empty((patch_minu.shape[0], patch_minu.shape[1], 3), dtype=np.uint8) ret[:, :, 0] = patch_minu ret[:, :, 1] = patch_minu ret[:, :, 2] = patch_minu patch_minu = ret patch_minu = np.expand_dims(patch_minu, axis=0) # # Can use class as hard decision # # 0: minu 1: non-minu # [class_Minutiae] = np.argmax(model_FineNet.predict(patch_minu), axis=1) # # if class_Minutiae == 0: # mnt_refined.append(mnt_nms[idx_minu,:]) # Use soft decision: merge FineNet score with CoarseNet score [isMinutiaeProb] = model_FineNet.predict(patch_minu) isMinutiaeProb = isMinutiaeProb[0] #print isMinutiaeProb tmp_mnt = mnt_nms[idx_minu, :].copy() tmp_mnt[3] = (4*tmp_mnt[3] + isMinutiaeProb)/5 mnt_refined.append(tmp_mnt) except: mnt_refined.append(mnt_nms[idx_minu, :]) else: mnt_refined = mnt_nms mnt_nms_backup = mnt_nms.copy() mnt_nms = np.array(mnt_refined) if mnt_nms.shape[0] > 0: mnt_nms = mnt_nms[mnt_nms[:,3]>final_minutiae_score_threashold,:] final_mask = ndimage.zoom(np.round(np.squeeze(seg_out)), [8, 8], order=0) # Show the orientation show_orientation_field(original_image, dir_map + np.pi, mask=final_mask, fname="%s/%s/OF_results/%s_OF.jpg" % (output_dir, set_name, img_name[i])) fuse_minu_orientation(dir_map, mnt_nms, mode=3) time_afterpost = time() mnt_writer(mnt_nms, img_name[i], img_size, "%s/%s/mnt_results/%s.mnt"%(output_dir, set_name, img_name[i])) draw_minutiae(original_image, mnt_nms, "%s/%s/%s_minu.jpg"%(output_dir, set_name, img_name[i]),saveimage=True) misc.imsave("%s/%s/seg_results/%s_seg.jpg" % (output_dir, set_name, img_name[i]), final_mask) time_afterdraw = time() time_c.append([time_afterconv - time_start, time_afterpost - time_afterconv, time_afterdraw - time_afterpost]) logging.info( "load+conv: %.3fs, seg-postpro+nms: %.3f, draw: %.3f" % (time_c[-1][0], time_c[-1][1], time_c[-1][2])) # time_c = np.mean(np.array(time_c), axis=0) # logging.info( # "Average: load+conv: %.3fs, oir-select+seg-post+nms: %.3f, draw: %.3f" % (time_c[0], time_c[1], time_c[2])) return