notebook and data for iris expts using CASIA1 data

notebooks/.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+ "metadata": {},
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+ "nbformat_minor": 2
+}

notebooks/.ipynb_checkpoints/iris-mmu-expt-checkpoint.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MMU2 Dataset -- Multimedia University\n",
+    "\n",
+    "* 100 subjects\n",
+    "* 995 iris images (5 per eye) \n",
+    "    * 5 images excluded due to cataract\n",
+    "* 199 unique iris-identities\n",
+    "* Capture device: Panasonic BM-ET100US camera\n",
+    "    * Near-Infrared (NIR) wavelength band\n",
+    "    * Resolution: 320 $\\times$ 240\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib.patches import Circle\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import Circle\n",
+    "import cv2\n",
+    "from lib.segment import segment\n",
+    "from lib.normalize import normalize\n",
+    "from lib.encode import encode\n",
+    "\n",
+    "import os\n",
+    "from scipy import misc\n",
+    "\n",
+    "\n",
+    "\n",
+    "#######\n",
+    "# Segmentation parameters\n",
+    "eyelashes_thres = 80\n",
+    "\n",
+    "# Normalisation parameters\n",
+    "radial_res = 20\n",
+    "angular_res = 240\n",
+    "\n",
+    "# Feature encoding parameters\n",
+    "minWaveLength = 18\n",
+    "mult = 1\n",
+    "sigmaOnf = 0.5\n",
+    "\n",
+    "#######\n",
+    "\n",
+    "#probe_dir = '../MMU1/1/left'\n",
+    "probe_dir = \"data/MMU1/1/left\"\n",
+    "probe_file = 'aeval1.bmp'\n",
+    "#probe_dir = 'data/MMU2'\n",
+    "#probe_file = '990201.bmp'\n",
+    "\n",
+    "probe_img_file = os.path.join(probe_dir, probe_file)\n",
+    "#probe_img_file = 'data/MMU1/3/right/chingycr1.bmp'\n",
+    "\n",
+    "print('Loading probe image: %s' % probe_img_file)\n",
+    "eye_im = plt.imread(probe_img_file, format='bmp')\n",
+    "probe_im = eye_im[:,:,0]\n",
+    "cv2.imwrite(\"images/probe_image.png\", probe_im)\n",
+    "print('probe image dims: ', probe_im.shape)\n",
+    "#plt.imshow(eye_im[:,:,0])\n",
+    "#assert 0>1, 'stop'\n",
+    "\n",
+    "ciriris, cirpupil, imwithnoise = segment(probe_im, eyelashes_thres, False)\n",
+    "print(ciriris)\n",
+    "print(cirpupil)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Probe image\n",
+    "![](images/probe_image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "ref_im = plt.imread('images/probe_image.png', format='png')\n",
+    "print('ref_im shape:', ref_im.shape)\n",
+    "#assert 0>1, 'stop'\n",
+    "line_width = 2\n",
+    "\n",
+    "cv2.circle(eye_im,(cirpupil[1], cirpupil[0]), cirpupil[2], (255,255,255), line_width)\n",
+    "cv2.circle(eye_im,(ciriris[1], ciriris[0]), ciriris[2], (255,255,255), line_width)\n",
+    "\n",
+    "cv2.imwrite(\"images/iris_det.png\", eye_im)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Iris Detection\n",
+    "![](images/iris_det.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys\n",
+    "import numpy as np\n",
+    "import scipy.io as sio\n",
+    "\n",
+    "# Perform normalization\n",
+    "polar_array, noise_array = normalize(imwithnoise, ciriris[1], ciriris[0], ciriris[2],\n",
+    "                                    cirpupil[1], cirpupil[0], cirpupil[2],\n",
+    "                                    radial_res, angular_res)\n",
+    "\n",
+    "print(imwithnoise.shape)\n",
+    "cv2.imwrite('images/iris_noisy.png', imwithnoise)\n",
+    "\n",
+    "print(type(polar_array[0,0]))\n",
+    "print(np.amax(polar_array))\n",
+    "#equ = cv2.equalizeHist(polar_array)\n",
+    "#cv2.imwrite('images/iris_polar.png', equ) #polar_array)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](images/iris_noisy.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Perform feature encoding\n",
+    "probe_iris_code, probe_mask = encode(polar_array, noise_array, minWaveLength, mult, sigmaOnf)\n",
+    "cv2.imwrite('images/iris_code.png', probe_iris_code)\n",
+    "cv2.imwrite('images/iris_mask.png', probe_mask)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "'Probe IrisCode'              | 'Probe Mask'\n",
+    "------------------------------|-------------------------\n",
+    "![](images/iris_code.png) | ![](images/iris_mask.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_dir = 'templates/MMU2'\n",
+    "enrolled_template_file = '010101.bmp.mat'\n",
+    "enrol_template = sio.loadmat(os.path.join(temp_dir, enrolled_template_file))\n",
+    "#enrol_template = sio.loadmat('%s%s'% (temp_dir, enrolled_template_file))\n",
+    "enrol_iris_code = enrol_template['template']\n",
+    "enrol_mask = enrol_template['mask']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fnc.matching_singlethread import calHammingDist\n",
+    "# Calculate the Hamming distance\n",
+    "hm_dist = calHammingDist(probe_iris_code, probe_mask, enrol_iris_code, enrol_mask)\n",
+    "#return (file_temp_name, hm_dist)\n",
+    "print(hm_dist)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.14"
+  }
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