Commited minutiae
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Testing CoarseNet\n",
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"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\n",
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"\n",
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"If you use whole or partial function in this code, please cite paper:\n",
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"\n",
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" @inproceedings{Nguyen_MinutiaeNet,\n",
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"\tauthor = {Dinh-Luan Nguyen and Kai Cao and Anil K. Jain},\n",
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"\ttitle = {Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge},\n",
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"\tbooktitle = {The 11th International Conference on Biometrics, 2018},\n",
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"\tyear = {2018},\n",
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"\t}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To run this script, you need to prepare dataset as follows:\n",
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"`path/to/dataset/`:\n",
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" - img_files/*.bmp\n",
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"\n",
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"If using groundtruth mask instead of mask generated by CoarseNet:\n",
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" - seg_files/*.bmp\n",
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" \n",
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"## CoarseNet can run with any image size\n",
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"See [CoarseNet_run.py](https://github.com/luannd/MinutiaeNet/blob/master/CoarseNet/CoarseNet_run.py) if running from command line.\n",
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"\n",
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"CoarseNet can be improved by:\n",
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"- Train on new dataset instead of FVC\n",
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"- Correct the orientation\n",
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"- Tune threshold for different dataset\n",
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"\n",
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"## CoarseNet can provides:\n",
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"- Orientation field estimation\n",
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"- Mask for fingerprint area\n",
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"- Minutiae location and orientation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"from __future__ import absolute_import\n",
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"from __future__ import division\n",
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"\n",
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"import sys, os\n",
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"sys.path.append(os.path.realpath('../CoarseNet'))\n",
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"\n",
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"os.environ[\"CUDA_VISIBLE_DEVICES\"] = '1'\n",
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"os.environ['KERAS_BACKEND'] = 'tensorflow'\n",
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"\n",
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"\n",
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"from keras import backend as K\n",
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"\n",
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"from MinutiaeNet_utils import *\n",
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"from CoarseNet_utils import *\n",
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"from CoarseNet_model import *\n",
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"import argparse\n",
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"\n",
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"\n",
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"config = K.tf.ConfigProto(gpu_options=K.tf.GPUOptions(allow_growth=True))\n",
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"sess = K.tf.Session(config=config)\n",
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"K.set_session(sess)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# Prepare dataset for testing. \n",
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"inference_set = ['../Dataset/CoarseNet_test/',]\n",
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"\n",
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"CoarseNet_path = '../Models/CoarseNet.h5'\n",
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"\n",
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"output_dir = '../output_CoarseNet/'+datetime.now().strftime('%Y%m%d-%H%M%S')\n",
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"\n",
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"FineNet_path = '../Models/FineNet.h5'\n",
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"\n",
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"logging = init_log(output_dir)\n",
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"\n",
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"# If use FineNet to refine, set into True\n",
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"isHavingFineNet = False"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This can test with different folders.\n",
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"\n",
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"Threshold for each image is automatically chosen"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"for i, deploy_set in enumerate(inference_set):\n",
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" set_name = deploy_set.split('/')[-2]\n",
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"\n",
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" # Read image and GT\n",
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" img_name, folder_name, img_size = get_maximum_img_size_and_names(deploy_set)\n",
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"\n",
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" mkdir(output_dir + '/'+ set_name + '/')\n",
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" mkdir(output_dir + '/' + set_name + '/mnt_results/')\n",
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" mkdir(output_dir + '/'+ set_name + '/seg_results/')\n",
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" mkdir(output_dir + '/' + set_name + '/OF_results/')\n",
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"\n",
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" logging.info(\"Predicting \\\"%s\\\":\" % (set_name))\n",
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"\n",
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"\n",
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" main_net_model = CoarseNetmodel((None, None, 1), CoarseNet_path, mode='deploy')\n",
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"\n",
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" # ====== Load FineNet to verify\n",
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" if isHavingFineNet == True:\n",
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" model_FineNet = FineNetmodel(num_classes=2,\n",
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" pretrained_path=FineNet_path,\n",
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" input_shape=(224,224,3))\n",
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"\n",
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" model_FineNet.compile(loss='categorical_crossentropy',\n",
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" optimizer=Adam(lr=0),\n",
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" metrics=['accuracy'])\n",
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"\n",
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" for i in xrange(0, len(img_name)):\n",
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" \n",
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" logging.info(\"\\\"%s\\\" %d / %d: %s\" % (set_name, i + 1, len(img_name), img_name[i]))\n",
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"\n",
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" image = misc.imread(deploy_set + 'img_files/' + img_name[i] + '.bmp', mode='L')# / 255.0\n",
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"\n",
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" img_size = image.shape\n",
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" img_size = np.array(img_size, dtype=np.int32) // 8 * 8\n",
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" image = image[:img_size[0], :img_size[1]]\n",
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"\n",
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" original_image = image.copy()\n",
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"\n",
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" # Generate OF\n",
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" texture_img = FastEnhanceTexture(image, sigma=2.5, show=False)\n",
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" dir_map, fre_map = get_maps_STFT(texture_img, patch_size=64, block_size=16, preprocess=True)\n",
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" \n",
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" image = np.reshape(image, [1, image.shape[0], image.shape[1], 1])\n",
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"\n",
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" 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 \\\n",
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" = main_net_model.predict(image)\n",
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"\n",
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" # Use for output mask\n",
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" round_seg = np.round(np.squeeze(seg_out))\n",
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" seg_out = 1 - round_seg\n",
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" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 10))\n",
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" seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_CLOSE, kernel)\n",
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" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))\n",
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" seg_out = cv2.morphologyEx(seg_out, cv2.MORPH_OPEN, kernel)\n",
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" kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n",
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" seg_out = cv2.dilate(seg_out, kernel)\n",
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"\n",
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" #========== Adaptive threshold ==================\n",
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" final_minutiae_score_threashold = 0.45\n",
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" early_minutiae_thres = final_minutiae_score_threashold + 0.05\n",
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"\n",
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"\n",
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"\n",
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" # In cases of small amount of minutiae given, try adaptive threshold\n",
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" while final_minutiae_score_threashold >= 0:\n",
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" mnt = label2mnt(np.squeeze(mnt_s_out) * np.round(np.squeeze(seg_out)), mnt_w_out, mnt_h_out, mnt_o_out,\n",
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" thresh=early_minutiae_thres)\n",
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"\n",
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" mnt_nms_1 = py_cpu_nms(mnt, 0.5)\n",
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" mnt_nms_2 = nms(mnt)\n",
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" # Make sure good result is given\n",
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" if mnt_nms_1.shape[0] > 4 and mnt_nms_2.shape[0] > 4:\n",
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" break\n",
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" else:\n",
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" final_minutiae_score_threashold = final_minutiae_score_threashold - 0.05\n",
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" early_minutiae_thres = early_minutiae_thres - 0.05\n",
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"\n",
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"\n",
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" mnt_nms = fuse_nms(mnt_nms_1, mnt_nms_2)\n",
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"\n",
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" mnt_nms = mnt_nms[mnt_nms[:, 3] > early_minutiae_thres, :]\n",
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" mnt_refined = []\n",
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"\n",
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" if isHavingFineNet == True:\n",
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" # ======= Verify using FineNet ============\n",
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" patch_minu_radio = 22\n",
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" if FineNet_path != None:\n",
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" for idx_minu in range(mnt_nms.shape[0]):\n",
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" try:\n",
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" # Extract patch from image\n",
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" x_begin = int(mnt_nms[idx_minu, 1]) - patch_minu_radio\n",
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" y_begin = int(mnt_nms[idx_minu, 0]) - patch_minu_radio\n",
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" patch_minu = original_image[x_begin:x_begin + 2 * patch_minu_radio,\n",
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" y_begin:y_begin + 2 * patch_minu_radio]\n",
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"\n",
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" patch_minu = cv2.resize(patch_minu, dsize=(224, 224), interpolation=cv2.INTER_NEAREST)\n",
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"\n",
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" ret = np.empty((patch_minu.shape[0], patch_minu.shape[1], 3), dtype=np.uint8)\n",
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" ret[:, :, 0] = patch_minu\n",
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" ret[:, :, 1] = patch_minu\n",
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" ret[:, :, 2] = patch_minu\n",
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" patch_minu = ret\n",
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" patch_minu = np.expand_dims(patch_minu, axis=0)\n",
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"\n",
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" # # Can use class as hard decision\n",
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" # # 0: minu 1: non-minu\n",
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" # [class_Minutiae] = np.argmax(model_FineNet.predict(patch_minu), axis=1)\n",
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" #\n",
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" # if class_Minutiae == 0:\n",
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" # mnt_refined.append(mnt_nms[idx_minu,:])\n",
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"\n",
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" # Use soft decision: merge FineNet score with CoarseNet score\n",
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" [isMinutiaeProb] = model_FineNet.predict(patch_minu)\n",
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" isMinutiaeProb = isMinutiaeProb[0]\n",
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" # print isMinutiaeProb\n",
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" tmp_mnt = mnt_nms[idx_minu, :].copy()\n",
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" tmp_mnt[3] = (4*tmp_mnt[3] + isMinutiaeProb) / 5\n",
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" mnt_refined.append(tmp_mnt)\n",
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"\n",
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" except:\n",
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" mnt_refined.append(mnt_nms[idx_minu, :])\n",
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" else:\n",
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" mnt_refined = mnt_nms\n",
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"\n",
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" mnt_nms_backup = mnt_nms.copy()\n",
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" mnt_nms = np.array(mnt_refined)\n",
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"\n",
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" if mnt_nms.shape[0] > 0:\n",
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" mnt_nms = mnt_nms[mnt_nms[:, 3] > final_minutiae_score_threashold, :]\n",
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" \n",
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" final_mask = ndimage.zoom(np.round(np.squeeze(seg_out)), [8, 8], order=0)\n",
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" # Show the orientation\n",
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" 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]))\n",
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"\n",
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" fuse_minu_orientation(dir_map, mnt_nms, mode=3)\n",
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"\n",
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" time_afterpost = time()\n",
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" mnt_writer(mnt_nms, img_name[i], img_size, \"%s/%s/mnt_results/%s.mnt\"%(output_dir, set_name, img_name[i]))\n",
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" draw_minutiae(original_image, mnt_nms, \"%s/%s/%s_minu.jpg\"%(output_dir, set_name, img_name[i]),saveimage=True)\n",
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"\n",
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" misc.imsave(\"%s/%s/seg_results/%s_seg.jpg\" % (output_dir, set_name, img_name[i]), final_mask)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.15"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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