SketchBasedShapeRetrieval/IPython/07 Compute Matches from Input Sketch.ipynb

{
 "metadata": {
  "name": "07 Compute Matches from Input Sketch"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy\n",
      "import math\n",
      "import scipy\n",
      "import scipy.io\n",
      "import scipy.misc\n",
      "import scipy.cluster\n",
      "import scipy.cluster.vq\n",
      "import matplotlib\n",
      "import matplotlib.pyplot\n",
      "import os\n",
      "import IPython\n",
      "import IPython.parallel\n",
      "import itertools\n",
      "import random\n",
      "import sklearn\n",
      "import sklearn.decomposition\n",
      "import sklearn.manifold\n",
      "import sklearn.cluster\n",
      "import sklearn.feature_extraction\n",
      "import sklearn.feature_extraction.text\n",
      "\n",
      "base_path = \"/u/mlrobert/code/local/2013_drawing_assistant/data/\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_path    = base_path+\"renderings/bunny2/apparent_ridges.num_latitude_lines=20.num_longitude_lines=20/\"\n",
      "filter_path = base_path+\"filters/gabor/num_thetas=08/\"\n",
      "\n",
      "local_feature_cluster_centroids_path = \\\n",
      "    base_path+\"local_feature_cluster_centroids/bunny2/\"+ \\\n",
      "    \"apparent_ridges.num_latitude_lines=20.num_longitude_lines=20.gabor.num_thetas=08.galif.patch_width=15.num_samples=32.num_tiles=04.num_samples=1000000.k=5000/\"\n",
      "\n",
      "global_features_path = \\\n",
      "    base_path+\"global_features/bunny2/\"+ \\\n",
      "    \"apparent_ridges.num_latitude_lines=20.num_longitude_lines=20.gabor.num_thetas=08.galif.patch_width=15.num_samples=32.num_tiles=04.num_samples=1000000.k=5000/\"\n",
      "\n",
      "img_names_exts                                = sorted(os.listdir(img_path))\n",
      "local_feature_cluster_centroids_path_name_ext = local_feature_cluster_centroids_path+\"local_feature_cluster_centroids.mat\"\n",
      "global_features_names_exts                    = sorted(os.listdir(global_features_path))\n",
      "num_thetas                                    = 8\n",
      "num_images                                    = len(img_names_exts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num_thetas  = 8\n",
      "filter_path = base_path+\"filters/gabor/num_thetas=08/\"\n",
      "\n",
      "tmp = []\n",
      "\n",
      "for theta_index in range(num_thetas):\n",
      "    \n",
      "    current_g_mat = scipy.io.loadmat(filter_path+\"theta=%02d.mat\" % theta_index)\n",
      "    current_g     = current_g_mat[\"g\"]\n",
      "    \n",
      "    tmp.append(current_g)\n",
      "\n",
      "g = numpy.array(tmp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "local_feature_cluster_centroids_mat = scipy.io.loadmat(local_feature_cluster_centroids_path_name_ext)\n",
      "local_feature_cluster_centroids     = local_feature_cluster_centroids_mat[\"local_feature_cluster_centroids\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_global_features = []\n",
      "\n",
      "for global_features_name_ext in global_features_names_exts:\n",
      "    global_features_mat = scipy.io.loadmat(global_features_path+global_features_name_ext)\n",
      "    global_features     = global_features_mat[\"global_features\"].ravel()\n",
      "    all_global_features.append(global_features)\n",
      "    \n",
      "all_global_features_2d        = numpy.array(all_global_features)\n",
      "tf_idf_transformer            = sklearn.feature_extraction.text.TfidfTransformer()\n",
      "tf_idf_all_global_features_2d = numpy.array(tf_idf_transformer.fit_transform(all_global_features_2d).todense()).squeeze()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "img_index = 124\n",
      "img       = scipy.misc.imread(img_path+img_names_exts[img_index]).astype(numpy.float32)[:,:,0]\n",
      "img       = (255.0 - img) / 255.0\n",
      "\n",
      "imshow(img, cmap=\"gray\");\n",
      "colorbar();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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58mWJtsHhcBAQEIArV65IVDBv3boFAI3cNurr6/HixQu8ePECf/31FzQ0NPDb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\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "# compute filter responses\n",
      "#\n",
      "filter_responses = []\n",
      "\n",
      "for theta_index in range(num_thetas):\n",
      "    \n",
      "    F_img = numpy.fft.fft2(img)\n",
      "    F_img_centered = numpy.fft.fftshift(F_img)\n",
      "    F_img_centered_filtered = F_img_centered * g[theta_index]\n",
      "    F_img_centered_filtered_uncentered = numpy.fft.ifftshift(F_img_centered_filtered)\n",
      "    filter_response = numpy.fft.ifft2(F_img_centered_filtered_uncentered)\n",
      "    \n",
      "    filter_responses.append(filter_response)\n",
      "\n",
      "filter_responses = numpy.array(filter_responses)\n",
      "\n",
      "\n",
      "\n",
      "#\n",
      "# compute local features\n",
      "#\n",
      "patch_width_as_fraction_of_width    = 0.15\n",
      "num_samples_width                   = 32\n",
      "num_tiles_per_dimension_per_patch   = 4\n",
      "use_squared_filter_response         = True\n",
      "local_feature_norm_accept_threshold = 0.01\n",
      "codebook_size                       = 5000\n",
      "\n",
      "\n",
      "\n",
      "filter_response    = filter_responses[0,:,:]\n",
      "num_samples_height = (float(filter_response.shape[0])*float(num_samples_width))/float(filter_response.shape[1])\n",
      "samples_y          = numpy.linspace(0, filter_response.shape[0]-1, num_samples_height+2).astype(numpy.int32)\n",
      "samples_x          = numpy.linspace(0, filter_response.shape[1]-1, num_samples_width+2).astype(numpy.int32)\n",
      "patch_size         = int(filter_response.shape[1] * patch_width_as_fraction_of_width)\n",
      "\n",
      "if patch_size % 2 == 0:\n",
      "    patch_size = patch_size + 1\n",
      "\n",
      "patch_half_size = (patch_size - 1) / 2\n",
      "\n",
      "all_local_features = []\n",
      "\n",
      "for sample in list(itertools.product(samples_y, samples_x)):\n",
      "    \n",
      "    local_features = []\n",
      "    \n",
      "    for theta_index in range(num_thetas):\n",
      "    \n",
      "        filter_response = filter_responses[theta_index,:,:]\n",
      "        \n",
      "        if use_squared_filter_response:\n",
      "            filter_response_to_sample = abs(filter_response)*abs(filter_response)\n",
      "        else:\n",
      "            filter_response_to_sample = abs(filter_response)\n",
      "        \n",
      "        filter_response_to_sample_padded = numpy.pad(filter_response_to_sample, (patch_half_size,patch_half_size), \"constant\")\n",
      "\n",
      "        patch       = filter_response_to_sample_padded[sample[0]:sample[0]+patch_size, sample[1]:sample[1]+patch_size]\n",
      "        tile_bounds = numpy.linspace(0, patch_size, num_tiles_per_dimension_per_patch+1).astype(numpy.int32)\n",
      "        tile        = numpy.zeros((num_tiles_per_dimension_per_patch, num_tiles_per_dimension_per_patch), dtype=numpy.float32)\n",
      "    \n",
      "        for tile_index in list(itertools.product(range(num_tiles_per_dimension_per_patch), range(num_tiles_per_dimension_per_patch))):\n",
      "            tile[tile_index] = numpy.mean(patch[tile_bounds[tile_index[0]]:tile_bounds[tile_index[0]+1], \\\n",
      "                                                tile_bounds[tile_index[1]]:tile_bounds[tile_index[1]+1]])\n",
      "\n",
      "        tile_1d = tile.ravel()\n",
      "        local_features.append(tile_1d)\n",
      "        \n",
      "    local_features_1d = numpy.array(local_features).ravel()\n",
      "    \n",
      "    if numpy.linalg.norm(local_features_1d) > local_feature_norm_accept_threshold:\n",
      "        all_local_features.append(local_features_1d)\n",
      "\n",
      "\n",
      "\n",
      "#\n",
      "# compute global features\n",
      "#\n",
      "all_local_features_2d                             = numpy.array(all_local_features)\n",
      "all_local_features_2d_codebook_indices, distances = scipy.cluster.vq.vq(all_local_features_2d, local_feature_cluster_centroids)\n",
      "hist, bin_edges                                   = numpy.histogram(all_local_features_2d_codebook_indices, bins=codebook_size)\n",
      "tf_idf_hist                                       = numpy.array(tf_idf_transformer.transform(hist).todense()).squeeze()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print all_local_features_2d.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(601, 128)\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(tf_idf_hist);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cos_distances  = dot(tf_idf_all_global_features_2d, tf_idf_hist)\n",
      "distances      = arccos(cos_distances)\n",
      "sorted_indices = argsort(distances)\n",
      "\n",
      "print \"%s (distance = %f) \" % (img_names_exts[sorted_indices[0]], distances[sorted_indices[0]])\n",
      "print \"%s (distance = %f) \" % (img_names_exts[sorted_indices[1]], distances[sorted_indices[1]])\n",
      "print \"%s (distance = %f) \" % (img_names_exts[sorted_indices[2]], distances[sorted_indices[2]])\n",
      "print \"%s (distance = %f) \" % (img_names_exts[sorted_indices[3]], distances[sorted_indices[3]])\n",
      "print \"%s (distance = %f) \" % (img_names_exts[sorted_indices[4]], distances[sorted_indices[4]])\n",
      "\n",
      "plot(distances);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "latitude=07.longitude=04.png (distance = 0.000000) \n",
        "latitude=08.longitude=04.png (distance = 0.405799) \n",
        "latitude=09.longitude=04.png (distance = 0.451262) \n",
        "latitude=07.longitude=05.png (distance = 0.583955) \n",
        "latitude=09.longitude=05.png (distance = 0.627951) \n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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P4FJuSch/97u7YdYKqc3zJCwtzlNcXIw2bdpUfU5JSUFxcTFSUlKqpdW7Fqh0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\nVa0F2r17dwwZMgSTJ0/GuHH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      }
     ],
     "prompt_number": 32
    }
   ],
   "metadata": {}
  }
 ]
}