13_Process_validation_tears_against_themselves_only.ipynb

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    "# RUN Main import block and TODO list\n",
    "\n",
    "# TODO: see how uri calculated the ridges\n",
    "\n",
    "# TODO: Perform Histogram equalization - start with it\n",
    "# TODO: \n",
    "# take integral from the Highest peak+-0.005 divide by integral of the entire graph \n",
    "# This will be the peakness measure for the PSD ==> The desired ridge index\n",
    "# TODO:\n",
    "# take integral from the Highest peak+-0.005 divide by integral of the entire graph - it's the peakness measure for the PSD\n",
    "# must select a peak above a min threshold in order to ignore noisy frequency\n",
    "# must ignore peaks above a certain threshold in order to detect meaningful frequency\n",
    "# run the PSD in moving windows every 200 px (deduced from the below PSD pointing to a freq of 1/0.02=50-> times 4= 200px)\n",
    "# and medianf the result of the windows\n",
    "# TODO:\n",
    "# Another alternative: (with Yariv)\n",
    "# Run PSD column by column - get the phase, freq, peakness and reconstruct an artificial ridge slice\n",
    "# from this - reconstruct a \"clean\" artificial ridge image\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.image as img\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import urllib.request\n",
    "import os\n",
    "import shutil\n",
    "import glob\n",
    "from scipy import ndimage\n",
    "from scipy import signal\n",
    "#import cv2\n",
    "\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "\n",
    "import mahotas as mh\n",
    "from mahotas import polygon\n",
    "# import pymorph as pm\n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "from scipy import ndimage as nd\n",
    "import skimage.transform as transform\n",
    "import skimage.morphology as mp\n",
    "import skimage.io as sio\n",
    "import scipy.misc as sm\n",
    "from skimage.filters import threshold_otsu, threshold_adaptive\n",
    "from skimage.feature import hessian_matrix, hessian_matrix_eigvals\n",
    "from skimage import exposure\n",
    "from skimage import data, img_as_float\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "from bisect import bisect_left\n",
    "import math\n",
    "import warnings\n",
    "import csv\n",
    "import tensorflow as tf\n"
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    "# RUN Utility functions\n",
    "\n",
    "# One time init\n",
    "# with open('results.csv', 'w') as csvfile:\n",
    "#     csvout = csv.writer(csvfile)\n",
    "#     csvout.writerow([\"File\", \"Model\", \"Gap\", \"Slice_size\", \"Count\", \"Precision\", \"Recall\", \"F-score\", \"True Count\", \"Error Rate\"])\n",
    "\n",
    "#BASIC CROP FRAME\n",
    "X_START = 1000\n",
    "X_END = 6000\n",
    "Y_START = 800\n",
    "Y_END = 4300\n",
    "BG_2_OBJ_RATIO = 0.91\n",
    "CUBE_SIZE = 250\n",
    "EDGE_GAP = 50\n",
    "ROOT_FOLDER = \"/Users/il239838/Downloads/private/Thesis/Papyrus/\"\n",
    "LEARNING_RATE = 0.001\n",
    "BATCHES = 400\n",
    "BATCH_SIZE = 50\n",
    "BREAK_VAL = 1000\n",
    "\n",
    "\n",
    "# Simple crop by x/y ranges\n",
    "def crop(image, ymin, ymax, xmin, xmax):\n",
    "    return image[ymin:ymax, xmin:xmax]\n",
    "\n",
    "# returns a logical matrix of values beyond a threshld\n",
    "def thresholded(image, val): \n",
    "    return np.logical_and(*[image[...] > val  for t in enumerate([0, 0])])\n",
    "\n",
    "def find_min_max_without_orphand_pixels(nonzero_dimension, crop_filter=20):\n",
    "    sorted = np.sort(nonzero_dimension)\n",
    "    prev=-1\n",
    "    min_val = sorted[0]\n",
    "    for i, x in enumerate(sorted[:100]):\n",
    "        if prev >= 0 and x - prev > crop_filter:\n",
    "            min_val = x\n",
    "        prev = x\n",
    "    prev=-1\n",
    "    max_val = sorted[-1]\n",
    "    for i, x in enumerate(sorted[-100:]):\n",
    "        if prev >= 0 and x - prev > crop_filter:\n",
    "            max_val = prev\n",
    "            break\n",
    "        prev = x\n",
    "    \n",
    "    return min_val, max_val\n",
    "\n",
    "def calc_min_max_coordinates(image, crop_val=50):\n",
    "    temp = thresholded(image, crop_val)\n",
    "    temp = temp * 1\n",
    "    temp = np.nonzero(temp)\n",
    "    ymin, ymax = find_min_max_without_orphand_pixels(temp[0])\n",
    "    xmin,xmax = find_min_max_without_orphand_pixels(temp[1])\n",
    "    return ymin, ymax, xmin, xmax\n",
    "\n",
    "def calc_min_max_coordinates_dynamic(image, cutoff=1):\n",
    "    temp = exposure.equalize_adapthist(image, clip_limit=0.03)\n",
    "    flat = np.sort(np.matrix.getA1(temp))\n",
    "    sum_all = np.sum(flat)\n",
    "    index = np.argmin(flat.cumsum() < (sum_all * cutoff))\n",
    "\n",
    "    temp = thresholded(temp, flat[index])\n",
    "    temp = temp * 1\n",
    "    temp = np.nonzero(temp)\n",
    "    ymin, ymax = find_min_max_without_orphand_pixels(temp[0])\n",
    "    xmin,xmax = find_min_max_without_orphand_pixels(temp[1])\n",
    "    return ymin, ymax, xmin, xmax\n",
    "\n",
    "# initial static crop and a seondary dynamic crop based on signal2noise ratio\n",
    "def crop_full_scan(image):\n",
    "    temp = crop(image, Y_START, Y_END, X_START, X_END)\n",
    "    ymin, ymax, xmin, xmax = calc_min_max_coordinates_dynamic(temp, cutoff=BG_2_OBJ_RATIO)\n",
    "    temp = crop(image, Y_START+ymin, Y_START+ymax, X_START+xmin, X_START+xmax)\n",
    "    return temp\n",
    "\n",
    "def crop_thresholded(image):\n",
    "    temp = crop(image, 0, image.shape[0]-1, 0, image.shape[1]-1)\n",
    "    ymin, ymax, xmin, xmax = calc_min_max_coordinates(temp)\n",
    "    temp = crop(image, ymin, ymax, xmin, xmax)\n",
    "    return temp\n",
    "\n",
    "def read_and_crop(image_name):\n",
    "    if \"il239838\" in os.getcwd():\n",
    "        image = img.imread(\"/Users/il239838/Downloads/private/Thesis/Papyrus/\" + image_name)\n",
    "    else:\n",
    "        f = urllib.request.urlopen(\"https://dl.dropboxusercontent.com/s/31b96942qdcn73k/\" + image_name)\n",
    "        image = img.imread(f, format='jpeg')\n",
    "\n",
    "    # Smart-crop the image to get rid of all the noise and redundant area\n",
    "    # return crop_full_scan(image)\n",
    "    cropped = crop_full_scan(image)\n",
    "    return exposure.equalize_adapthist(cropped, clip_limit=0.03)\n",
    "\n",
    "\n",
    "# TODO: fix performance!!! http://scikit-image.org/docs/dev/user_guide/tutorial_parallelization.html\n",
    "def combine_3_images_to_RGB(red, green, blue):\n",
    "    new_image = np.empty((blue.shape[0],blue.shape[1],3))\n",
    "    for x in range(0, blue.shape[0]):\n",
    "        for y in range(0, blue.shape[1]):\n",
    "            new_image[x,y,0] = red[x,y]\n",
    "            new_image[x,y,1] = green[x,y]\n",
    "            new_image[x,y,2] = blue[x,y]\n",
    "    return new_image\n",
    "\n",
    "def slice_image_left_edge(original, width=200, rotate=0):\n",
    "    rot = ndimage.rotate(original, rotate)\n",
    "    # Slice the left slice of the so-called \"blue\" image\n",
    "    left_edge_orig = crop(rot, 1, 1400, 1, width)\n",
    "    left_edge_orig = crop_thresholded(left_edge_orig)\n",
    "\n",
    "    # Copy to a new array so we don't thrash the origin\n",
    "    left_edge = np.empty_like (left_edge_orig)\n",
    "    np.copyto(left_edge, left_edge_orig)\n",
    "\n",
    "    # Zero down low level \"noise\" values\n",
    "    low_values_indices = left_edge < 30  # Where values are low\n",
    "    left_edge[low_values_indices] = 0  # All low values set to 0\n",
    "    return left_edge\n",
    "\n",
    "def get_best_angle_rotation(original, crop=True, width=200):\n",
    "    min_var = 99999999999\n",
    "    best_angle = -10\n",
    "    for x in range(-5,5):\n",
    "        if crop:            \n",
    "            rot_edge = slice_image_left_edge(original, width, x)\n",
    "        else:\n",
    "            rot_edge = ndimage.rotate(original, x)\n",
    "        left_var = np.var(rot_edge, axis=1)\n",
    "        # left_var = np.apply_along_axis(lambda v: np.var(v[np.nonzero(v)]), 1, rot_edge)\n",
    "        var_sum = np.sum(left_var)\n",
    "        if (var_sum < min_var):\n",
    "            min_var = var_sum\n",
    "            best_angle = x\n",
    "    print (\"best_angle=\"+str(best_angle))\n",
    "    return best_angle\n",
    "\n",
    "#     import pdb; pdb.set_trace()\n",
    "def calc_neighbors(slice_map, col, row):\n",
    "    # import pdb; pdb.set_trace()\n",
    "    if ((col-1, row) in slice_map and slice_map[(col-1, row)] != None):\n",
    "        slice_map[(col, row)][\"left\"] = slice_map[(col-1, row)]\n",
    "        slice_map[(col-1, row)][\"right\"] = slice_map[(col, row)]\n",
    "    if ((col+1, row) in slice_map and slice_map[(col+1, row)] != None):\n",
    "        slice_map[(col, row)][\"right\"] = slice_map[(col+1, row)]\n",
    "        slice_map[(col+1, row)][\"left\"] = slice_map[(col, row)]\n",
    "    if ((col, row-1) in slice_map and slice_map[(col, row-1)] != None):\n",
    "        slice_map[(col, row)][\"top\"] = slice_map[(col, row-1)]\n",
    "        slice_map[(col, row-1)][\"bottom\"] = slice_map[(col, row)]\n",
    "    if ((col, row+1) in slice_map and slice_map[(col, row+1)] != None):\n",
    "        slice_map[(col, row)][\"bottom\"] = slice_map[(col, row+1)]\n",
    "        slice_map[(col, row+1)][\"top\"] = slice_map[(col, row)]\n",
    "    \n",
    "\n",
    "\n",
    "def VAL_create_cube(name, raw, x, y):\n",
    "    cube = {}\n",
    "    cube[\"cube\"] = raw\n",
    "    cube[\"file\"] = name\n",
    "    if name.find('P') == 0:\n",
    "        cube[\"index\"] = int(name[name.find('P')+1:name.find('P')+4]) * 1000 + int(name[name.find('Fg')+2:name.find('Fg')+5])\n",
    "    else:\n",
    "#        print(\"Found a ZERO index cube with the name:\"+name)\n",
    "        cube[\"index\"] = 0\n",
    "    cube[\"top_row\"] = x\n",
    "    cube[\"left_col\"] = y\n",
    "    cube[\"right_col\"] = y + CUBE_SIZE\n",
    "    return cube\n",
    "    \n",
    "\n",
    "ZERO_CUBE = VAL_create_cube(\"ZERO\", np.zeros((CUBE_SIZE, CUBE_SIZE), dtype=np.int), -1, -2)\n",
    "\n",
    "# slice an image to cubes with 250X250 pixel size\n",
    "def VAL_slice_to_static_slices(name, cropped_original):\n",
    "    structure = []\n",
    "    # cropped_original = cropped_original / 256 # divide by 256 to \"normalize\" between 0 and 1\n",
    "\n",
    "    # import pdb; pdb.set_trace()\n",
    "    x, y = cropped_original.shape\n",
    "    # print (x,y)\n",
    "    n = 0\n",
    "    # every 250 pixels on the x axis == rows\n",
    "    while ((n + 1) * CUBE_SIZE < x):\n",
    "        # cut a cube of 250X250 at the first column\n",
    "        cube = (crop(cropped_original, n * CUBE_SIZE, (n + 1) * CUBE_SIZE, EDGE_GAP, CUBE_SIZE + EDGE_GAP))\n",
    "        # keep only cubes for which half of the pixels have some \"color\"\n",
    "        if np.median(cube) > 0.2: # aligned with the normalization 0.2 correlates to 50\n",
    "            # keep the cube\n",
    "            new_cube = VAL_create_cube(name, cube, n * CUBE_SIZE, EDGE_GAP)\n",
    "            new_cube[\"col\"] = 0\n",
    "            new_cube[\"row\"] = n\n",
    "            new_cube[\"last\"] = False\n",
    "            structure.append(new_cube)\n",
    "\n",
    "        # cut a cube of 250X250 at the last column\n",
    "        cube = crop(cropped_original, n * CUBE_SIZE, (n + 1) * CUBE_SIZE, y - CUBE_SIZE - EDGE_GAP, y - EDGE_GAP)\n",
    "        # keep only cubes for which half of the pixels have some \"color\"\n",
    "        if np.median(cube) > 0.2: # aligned with the normalization 0.2 correlates to 50\n",
    "            # keep the cube\n",
    "            new_cube = VAL_create_cube(name, cube, n * CUBE_SIZE, y - CUBE_SIZE - EDGE_GAP)\n",
    "            new_cube[\"col\"] = 1\n",
    "            new_cube[\"row\"] = n\n",
    "            new_cube[\"last\"] = True\n",
    "            structure.append(new_cube)\n",
    "            \n",
    "        n += 1\n",
    "            \n",
    "    # return the data structure with all the cubes and the counters of the rows and columns\n",
    "    return structure\n",
    "\n",
    "def pad_above(original, above, amount):\n",
    "    res = np.insert(original[\"cube\"], np.zeros(amount), above[\"cube\"][-amount:], axis=0)\n",
    "    res = np.delete(res, np.arange(CUBE_SIZE,CUBE_SIZE+amount), axis=0)\n",
    "    return create_cube(res, original[\"top_row\"] - amount, original[\"left_col\"])\n",
    "  \n",
    "\n",
    "def pad_below(original, below, amount):\n",
    "    res = np.insert(original[\"cube\"], np.full(amount, CUBE_SIZE), below[\"cube\"][:amount], axis=0)\n",
    "    res = np.delete(res, np.arange(0, amount), axis=0)\n",
    "    return create_cube(res, original[\"top_row\"] + amount, original[\"left_col\"])\n",
    "\n",
    "def pad_left(original, left, amount):\n",
    "    res = np.insert(original[\"cube\"], np.zeros(amount, dtype=int), left[\"cube\"][:,-amount:], axis=1)\n",
    "    res = np.delete(res, np.arange(CUBE_SIZE, CUBE_SIZE+amount), axis=1)\n",
    "    return create_cube(res, original[\"top_row\"], original[\"left_col\"] - amount)\n",
    "\n",
    "def pad_right(original, right, amount):\n",
    "    res = np.insert(original[\"cube\"], [CUBE_SIZE], right[\"cube\"][:,:amount], axis=1)\n",
    "    res = np.delete(res, np.arange(0, amount), axis=1)\n",
    "    return create_cube(res, original[\"top_row\"], original[\"left_col\"] + amount)\n",
    "    \n",
    "\n",
    "# \"Shave\" the right edge of the cube with <gap> pixels and pad with zeros on the left\n",
    "def shave_right(original, amount):\n",
    "    return pad_left(original, ZERO_CUBE, amount)\n",
    "    \n",
    "\n",
    "# \"Shave\" the left edge of the cube with <gap> pixels and pad with zeros on the right    \n",
    "def shave_left(original, amount):\n",
    "    return pad_right(original, ZERO_CUBE, amount)\n",
    "    \n",
    "\n",
    "# concatenate cubes \n",
    "def VAL_concatenate_cubes(left, right, slice_size):\n",
    "    right_img = right[\"cube\"]\n",
    "    # if the left cube is matched to another left cube (or right cube to another right cube) then rotate the right\n",
    "    # cube by 180 so we try to match it upside down, covering the option that the cube was pictured rotated\n",
    "    if ((left[\"col\"] == 0 and right[\"col\"] == 0) or (left[\"last\"] and right[\"last\"])):\n",
    "        right_img = np.rot90(right[\"cube\"], 2);\n",
    "    con = np.concatenate((left[\"cube\"][:,-slice_size:], right_img[:,:slice_size]), axis=1)\n",
    "    x_delta = right[\"top_row\"] - left[\"top_row\"]\n",
    "    y_delta = right[\"left_col\"] - left[\"right_col\"] \n",
    "    return con, x_delta, y_delta, left[\"file\"], right[\"file\"]\n",
    "    \n",
    "\n",
    "# concatenate cubes while artificially creating a gap between them. Pad the other end of the cube with zeros\n",
    "def concatenate_cubes_zero_pad_gaps(left_orig, right_orig, gap):\n",
    "    left = left_orig if gap == 0 else shave_right(left_orig, gap)\n",
    "    right = right_orig if gap == 0 else shave_left(right_orig, gap)\n",
    "    return concatenate_cubes(left, right)    \n",
    "\n",
    "# concatenate cubes while artificially creating a gap between them. Pad the other end of the cobe with the nearby\n",
    "# continuation of the cubes\n",
    "def concatenate_cubes_with_gap(left_orig, right_orig, gap, left_pad, right_pad, slice_size):\n",
    "    # import pdb; pdb.set_trace()\n",
    "    left = left_orig if gap == 0 else pad_left(left_orig, left_pad, gap)\n",
    "    right = right_orig if gap == 0 else pad_right(right_orig, right_pad, gap)\n",
    "    return concatenate_cubes(left, right, slice_size)        \n",
    "\n",
    "# convert the data structure of cubes into a train set of 2 arrays of images and labels\n",
    "# each image is a concatanation of 2 images from the original cubes set, covering all combinations of images\n",
    "# effectively creating Nx(N-1) images\n",
    "def VAL_build_train_set(cubes, slice_size, folder, curr):\n",
    "    for root, dirs, files in os.walk(folder):\n",
    "        for f in files:\n",
    "            os.unlink(os.path.join(root, f))\n",
    "    # import pdb; pdb.set_trace()\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "    train_imgs = []\n",
    "    train_x_delta = []\n",
    "    train_y_delta = []\n",
    "    train_x_file = []\n",
    "    train_y_file = []\n",
    "    # iterate over the others (effectively n^2)\n",
    "    for adj in cubes:\n",
    "        if (adj[\"file\"] != curr[\"file\"]) and curr[\"index\"] < adj[\"index\"]: # no need to test against self and avoid checking from both directions\n",
    "            # import pdb; pdb.set_trace()\n",
    "            # append the adjacent image to the current image\n",
    "            conc, x_delta, y_delta, x_file, y_file = VAL_concatenate_cubes(curr, adj, slice_size)\n",
    "            output = folder+x_file[:x_file.rfind(' ')]+\"_\"+str(curr[\"top_row\"])+\"_\"+str(curr[\"left_col\"])+\"---\"+y_file[:y_file.rfind(' ')]+\"_\"+str(adj[\"top_row\"])+\"_\"+str(adj[\"left_col\"])\n",
    "            np.save(output, conc)\n",
    "            train_imgs.append(output)\n",
    "            train_x_delta.append(x_delta)\n",
    "            train_y_delta.append(y_delta)\n",
    "            train_x_file.append(x_file)\n",
    "            train_y_file.append(y_file)\n",
    "\n",
    "    warnings.filterwarnings(\"default\")\n",
    "    return train_imgs,train_x_delta, train_y_delta, train_x_file, train_y_file"
   ]
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    "# RUN Define model util functions\n",
    "\n",
    "# initialize a shaped matrix of weights with random values\n",
    "def weight_variable(shape):\n",
    "  initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "  return tf.Variable(initial)\n",
    "\n",
    "# initialize a shaped matrix of bias with random values\n",
    "def bias_variable(shape):\n",
    "  initial = tf.constant(0.1, shape=shape)\n",
    "  return tf.Variable(initial)\n",
    "\n",
    "def conv2d(x, W):\n",
    "  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "def max_pool_2x2(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n",
    "                        strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "def max_pool_1x2(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 1, 2, 1],\n",
    "                        strides=[1, 1, 2, 1], padding='SAME')\n",
    "\n",
    "def max_pool_2x1(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 2, 1, 1],\n",
    "                        strides=[1, 2, 1, 1], padding='SAME')\n",
    "\n",
    "def max_pool_1x1(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 1, 1, 1],\n",
    "                        strides=[1, 1, 1, 1], padding='SAME')\n",
    "\n",
    "def max_pool_5x5(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 5, 5, 1],\n",
    "                        strides=[1, 5, 5, 1], padding='SAME')\n",
    "\n",
    "def max_pool_5x2(x):\n",
    "  return tf.nn.max_pool(x, ksize=[1, 5, 2, 1],\n",
    "                        strides=[1, 5, 2, 1], padding='SAME')\n"
   ]
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   "source": [
    "# RUN Image utility functions (external source)\n",
    "def branchedPoints(skel):\n",
    "    branch1=np.array([[2, 1, 2], [1, 1, 1], [2, 2, 2]])\n",
    "    branch2=np.array([[1, 2, 1], [2, 1, 2], [1, 2, 1]])\n",
    "    branch3=np.array([[1, 2, 1], [2, 1, 2], [1, 2, 2]])\n",
    "    branch4=np.array([[2, 1, 2], [1, 1, 2], [2, 1, 2]])\n",
    "    branch5=np.array([[1, 2, 2], [2, 1, 2], [1, 2, 1]])\n",
    "    branch6=np.array([[2, 2, 2], [1, 1, 1], [2, 1, 2]])\n",
    "    branch7=np.array([[2, 2, 1], [2, 1, 2], [1, 2, 1]])\n",
    "    branch8=np.array([[2, 1, 2], [2, 1, 1], [2, 1, 2]])\n",
    "    branch9=np.array([[1, 2, 1], [2, 1, 2], [2, 2, 1]])\n",
    "    br1=mh.morph.hitmiss(skel,branch1)\n",
    "    br2=mh.morph.hitmiss(skel,branch2)\n",
    "    br3=mh.morph.hitmiss(skel,branch3)\n",
    "    br4=mh.morph.hitmiss(skel,branch4)\n",
    "    br5=mh.morph.hitmiss(skel,branch5)\n",
    "    br6=mh.morph.hitmiss(skel,branch6)\n",
    "    br7=mh.morph.hitmiss(skel,branch7)\n",
    "    br8=mh.morph.hitmiss(skel,branch8)\n",
    "    br9=mh.morph.hitmiss(skel,branch9)\n",
    "    return br1+br2+br3+br4+br5+br6+br7+br8+br9\n",
    "\n",
    "def endPoints(skel):\n",
    "    endpoint1=np.array([[0, 0, 0],\n",
    "                        [0, 1, 0],\n",
    "                        [2, 1, 2]])\n",
    "    \n",
    "    endpoint2=np.array([[0, 0, 0],\n",
    "                        [0, 1, 2],\n",
    "                        [0, 2, 1]])\n",
    "    \n",
    "    endpoint3=np.array([[0, 0, 2],\n",
    "                        [0, 1, 1],\n",
    "                        [0, 0, 2]])\n",
    "    \n",
    "    endpoint4=np.array([[0, 2, 1],\n",
    "                        [0, 1, 2],\n",
    "                        [0, 0, 0]])\n",
    "    \n",
    "    endpoint5=np.array([[2, 1, 2],\n",
    "                        [0, 1, 0],\n",
    "                        [0, 0, 0]])\n",
    "    \n",
    "    endpoint6=np.array([[1, 2, 0],\n",
    "                        [2, 1, 0],\n",
    "                        [0, 0, 0]])\n",
    "    \n",
    "    endpoint7=np.array([[2, 0, 0],\n",
    "                        [1, 1, 0],\n",
    "                        [2, 0, 0]])\n",
    "    \n",
    "    endpoint8=np.array([[0, 0, 0],\n",
    "                        [2, 1, 0],\n",
    "                        [1, 2, 0]])\n",
    "    \n",
    "    ep1=mh.morph.hitmiss(skel,endpoint1)\n",
    "    ep2=mh.morph.hitmiss(skel,endpoint2)\n",
    "    ep3=mh.morph.hitmiss(skel,endpoint3)\n",
    "    ep4=mh.morph.hitmiss(skel,endpoint4)\n",
    "    ep5=mh.morph.hitmiss(skel,endpoint5)\n",
    "    ep6=mh.morph.hitmiss(skel,endpoint6)\n",
    "    ep7=mh.morph.hitmiss(skel,endpoint7)\n",
    "    ep8=mh.morph.hitmiss(skel,endpoint8)\n",
    "    ep = ep1+ep2+ep3+ep4+ep5+ep6+ep7+ep8\n",
    "    return ep\n",
    "\n",
    "def pruning(skeleton, size):\n",
    "    '''remove iteratively end points \"size\" \n",
    "       times from the skeleton\n",
    "    '''\n",
    "    for i in range(0, size):\n",
    "        endpoints = endPoints(skeleton)\n",
    "        endpoints = np.logical_not(endpoints)\n",
    "        skeleton = np.logical_and(skeleton,endpoints)\n",
    "    return skeleton\n",
    "\n",
    "def plot_comparison(original, filtered, filter_name):\n",
    "\n",
    "    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True)\n",
    "    ax1.imshow(original, cmap=plt.cm.gray)\n",
    "    ax1.set_title('original')\n",
    "    ax1.axis('off')\n",
    "    ax1.set_adjustable('box-forced')\n",
    "    ax2.imshow(filtered, cmap=plt.cm.gray)\n",
    "    ax2.set_title(filter_name)\n",
    "    ax2.axis('off')\n",
    "    ax2.set_adjustable('box-forced')\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# RUN model_tf - Define the model - 250, 125, 62, 25\n",
    "def model_tf(input_width): \n",
    "    global accuracy, correct_prediction, train_step, x, y_, y_conv, keep_prob #, W_fc, b_fc, cost, y_conv_temp\n",
    "    \n",
    "    # foundation of the model - the input layer of the image 250 x input_width*2\n",
    "    x = tf.placeholder(tf.float32, [None, 250, input_width*2], \"001\")\n",
    "    x_image = tf.reshape(x, [-1,250,input_width*2,1], \"0011\") # 1 is the number of color channels\n",
    "\n",
    "    # the target digits of the model\n",
    "    y_ = tf.placeholder(tf.float32, [None, 2], \"002\") # 1\n",
    "\n",
    "    # first convolutional layer: one input image and 32 output filters of 5x5\n",
    "    W_conv1 = weight_variable([5, 5, 1, 32])\n",
    "    b_conv1 = bias_variable([32])\n",
    "    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1, \"0021\")\n",
    "    if (input_width == 250):\n",
    "        h_pool1 = max_pool_2x2(h_conv1) # size is reduced to 125x250\n",
    "    elif (input_width == 125):\n",
    "        h_pool1 = max_pool_2x1(h_conv1) # size is reduced to 125x250\n",
    "    elif (input_width == 62):\n",
    "        h_pool1 = max_pool_2x1(h_conv1) # size is reduced to 125x125\n",
    "    elif (input_width == 25):\n",
    "        h_pool1 = max_pool_2x1(h_conv1) # size is reduced to 125x50\n",
    "    else:\n",
    "        print(\"ERROR - unsupported slice width\")\n",
    "        return\n",
    "\n",
    "    # second convolutional layer: 32 input (filtered) images and 32 output filters of 5x5\n",
    "    W_conv2 = weight_variable([5, 5, 32, 32])\n",
    "    b_conv2 = bias_variable([32])\n",
    "    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2, \"0022\")\n",
    "    if (input_width == 62):\n",
    "        h_pool2 = max_pool_1x1(h_conv2) # size is reduced to 125x125\n",
    "    elif (input_width == 25):\n",
    "        h_pool2 = max_pool_1x1(h_conv2) # size is reduced to 125x50\n",
    "    else:\n",
    "        h_pool2 = max_pool_1x2(h_conv2) # size is reduced to 125x125\n",
    "\n",
    "\n",
    "    # third convolutional layer: 32 input (filtered) images and 32 output filters of 5x5\n",
    "    W_conv3 = weight_variable([5, 5, 32, 32])\n",
    "    b_conv3 = bias_variable([32])\n",
    "    h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3, \"0023\")\n",
    "    if (input_width == 25):\n",
    "        h_pool3 = max_pool_5x2(h_conv3) # size is reduced to 25x25\n",
    "    else:\n",
    "        h_pool3 = max_pool_5x5(h_conv3) # size is reduced to 25x25\n",
    "\n",
    "\n",
    "    h_pool3_flat = tf.reshape(h_pool3, [-1, 25*25*32]) # shape as an array \n",
    "\n",
    "    # fourth layer - fully connected with input 25*25*128 and output 1024\n",
    "    W_fc1 = weight_variable([25*25*32, 1024])\n",
    "    b_fc1 = bias_variable([1024])\n",
    "    h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1, \"0024\")\n",
    "\n",
    "    # a drop layer with probability \n",
    "    keep_prob = tf.placeholder(tf.float32, name=\"003\")\n",
    "    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob, name=\"0031\")\n",
    "\n",
    "    #     # final layer - reduce to one \"class\" for the linear regression\n",
    "    #     W_fc = weight_variable([1024, 1]) \n",
    "    #     b_fc = bias_variable([1])         \n",
    "    #     y_conv_temp = tf.matmul(h_fc1_drop, W_fc, name=\"0032\") + b_fc\n",
    "    #     y_conv = tf.minimum(y_conv_temp, tf.constant(BREAK_VAL, tf.float32))\n",
    "\n",
    "    #     #     # minimize loss function\n",
    "    #     #     cross_entropy = tf.reduce_mean(\n",
    "    #     #       tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))\n",
    "    #     # cost = tf.reduce_sum(tf.pow(y_conv - y_, 2))/(2*BATCHES*BATCH_SIZE) # Mean squared error\n",
    "    #     cost = tf.reduce_mean(tf.square(y_conv_temp - y_), name=\"0033\") # Mean squared error\n",
    "\n",
    "    #     #     # define train step and rate\n",
    "    #     #     train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "    #     train_step = tf.train.AdamOptimizer(LEARNING_RATE).minimize(cost) # Gradient descent\n",
    "\n",
    "    #     # evaluate the prediction and the accuracy on the train test - needed only for printing during the training\n",
    "    #     correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n",
    "    #     accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    \n",
    "    # final layer - softmax reduction 2 outputs\n",
    "    W_fc2 = weight_variable([1024, 2])\n",
    "    b_fc2 = bias_variable([2])\n",
    "    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2\n",
    "\n",
    "    # minimize loss function\n",
    "    cross_entropy = tf.reduce_mean(\n",
    "      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))\n",
    "\n",
    "    # define train step and rate\n",
    "    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "\n",
    "    # evaluate the prediction and the accuracy on the train test - needed only for printing during the training\n",
    "    correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "code_folding": [],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def pre_process(folder):\n",
    "    print(\"#####################################################################\")\n",
    "    print(\"PRE_PROCESS:\"+folder)\n",
    "    print(\"#####################################################################\")\n",
    "    result = []\n",
    "    count = 0\n",
    "    for root, dirs, files in os.walk(folder):\n",
    "        for file_ in files:\n",
    "            # Read the image\n",
    "            # image = img.imread(os.path.join(root, file_))\n",
    "            image = np.load(os.path.join(root, file_))\n",
    "            # import pdb; pdb.set_trace()\n",
    "            cubes = VAL_slice_to_static_slices(file_, image)\n",
    "            count += len(cubes)\n",
    "            # print(\"File: %s >>> cubes: %d\"%(file_, len(cubes)))\n",
    "            result.extend(cubes)\n",
    "    \n",
    "    print(count)\n",
    "    print(\"#####################################################################\")\n",
    "    print(\"PRE_PROCESS ENDED\")\n",
    "    print(\"#####################################################################\")\n",
    "    print(\" \")\n",
    "    print(\" \")\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "code_folding": [],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def validate1(cubes, model, slice_size, folder, curr_cube):    \n",
    "    # VALIDATE prepare the data sets\n",
    "    test_imgs, test_x_delta, test_y_delta, test_x_file, test_y_file = VAL_build_train_set(cubes, slice_size, folder, curr_cube)\n",
    "    print(\"loaded %d images\"%(len(test_imgs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def validate2(folder, model, slice_size):\n",
    "    test_imgs = []\n",
    "    test_x_file = []\n",
    "    test_y_file = []\n",
    "    the_root = \"\"\n",
    "    for root, dirs, files in os.walk(folder):\n",
    "        the_root = root\n",
    "        for file_ in files:\n",
    "            test_imgs.append( os.path.join(root, file_) )\n",
    "            test_x_file.append(file_[:file_.rfind('---P')])\n",
    "            test_y_file.append(file_[file_.rfind('---P')+3:])\n",
    "            \n",
    "    print(len(test_imgs))\n",
    "    \n",
    "    # VALIDATE Prepare a test session \n",
    "\n",
    "    # Add ops to save and restore all the variables.\n",
    "    saver = tf.train.Saver()\n",
    "\n",
    "    # start session and initialize variables\n",
    "    sess = tf.InteractiveSession()\n",
    "\n",
    "    # Restore variables from disk.\n",
    "    saver.restore(sess, model)\n",
    "    print(\"Model restored.\")\n",
    "\n",
    "    # VALIDATE Validate the model\n",
    "\n",
    "    # import pdb; pdb.set_trace()\n",
    "    v1t = []\n",
    "    count = 0\n",
    "    length = len(test_imgs)\n",
    "    batch = 100\n",
    "    x_batch = []\n",
    "    # change the ranges in the loop below - first number is the start point (multiplied by batch size)\n",
    "    # second number is the end point (multiplied by batch size)\n",
    "    # third number is the jump from batch to batch\n",
    "    # use the length about to set the batch length\n",
    "    for start in range(0, length, batch):\n",
    "        for i in range(start, start+batch):\n",
    "            if (i < length):\n",
    "                image = np.load(test_imgs[i])\n",
    "                x_batch.append(image)\n",
    "                count += 1\n",
    "\n",
    "        # print(\"Validating start at #%d end at %d\"%(start*batch,(start+length)*batch))\n",
    "        my_prediction=tf.argmax(y_conv,1)\n",
    "        v1 = my_prediction.eval(feed_dict={x:x_batch, keep_prob: 1.0})\n",
    "        v1t = np.concatenate((v1t, v1), axis=0)\n",
    "        x_batch = []\n",
    "        print(\">>> step %d\"%(start+batch))\n",
    "\n",
    "\n",
    "    match_indexes = np.nonzero(v1t)[0]\n",
    "    A = np.array(test_x_file)\n",
    "    B = np.array(test_y_file)\n",
    "    C = np.array(test_imgs)\n",
    "    match_x_files = A[match_indexes]\n",
    "    match_y_files = B[match_indexes]\n",
    "    match_images = C[match_indexes]\n",
    "    \n",
    "    for matched_img in match_images:\n",
    "        load_img = np.load(matched_img)\n",
    "        plt.imsave(os.path.join(\"/Volumes/250GB/matched/\",matched_img[matched_img.rfind('/')+1:]+\".png\"), load_img, cmap=plt.cm.gray)\n",
    "    \n",
    "    for root, dirs, files in os.walk(folder):\n",
    "        for file_ in files:\n",
    "            os.remove( os.path.join(root, file_) ) # delete it from the FS\n",
    "                \n",
    "    with open('matches.csv', 'a') as csvfile:\n",
    "        csvout = csv.writer(csvfile)\n",
    "        for match_index in match_indexes:\n",
    "            print(\"MATCH %s === %s\"%(test_x_file[match_index], test_y_file[match_index]))\n",
    "            # print(\"MATCH %s === %s\"%(A[match_index], B[match_index]))\n",
    "            # csvout.writerow([A[match_index], B[match_index]])\n",
    "            csvout.writerow([test_x_file[match_index], test_y_file[match_index]])\n",
    "            # plt.imsave(\"match_\"+match_index+\".jpg\", C[match_index])\n",
    "\n",
    "    # Close the Session when we're done.\n",
    "    sess.close()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "code_folding": [
     0
    ],
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def validate2_old(folder, model, slice_size):\n",
    "    from random import randrange\n",
    "\n",
    "    test_imgs = []\n",
    "    test_x_file = []\n",
    "    test_y_file = []\n",
    "    the_root = \"\"\n",
    "    for root, dirs, files in os.walk(folder):\n",
    "        the_root = root\n",
    "        for file_ in files:\n",
    "            test_imgs.append( os.path.join(root, file_) )\n",
    "            test_x_file.append(file_[:file_.rfind('---P')])\n",
    "            test_y_file.append(file_[file_.rfind('---P')+3:])\n",
    "            \n",
    "    print(len(test_imgs))\n",
    "    \n",
    "    # VALIDATE Prepare a test session \n",
    "\n",
    "    # Add ops to save and restore all the variables.\n",
    "    saver = tf.train.Saver()\n",
    "\n",
    "    # start session and initialize variables\n",
    "    sess = tf.InteractiveSession()\n",
    "    \n",
    "    # Restore variables from disk.\n",
    "    saver.restore(sess, model)\n",
    "    print(\"Model restored.\")\n",
    "\n",
    "    # VALIDATE Validate the model\n",
    "\n",
    "    # import pdb; pdb.set_trace()\n",
    "    v1t = []\n",
    "    pred = []\n",
    "    count = 0\n",
    "    length = len(test_imgs)\n",
    "    batch = 100\n",
    "    x_batch = []\n",
    "    # change the ranges in the loop below - first number is the start point (multiplied by batch size)\n",
    "    # second number is the end point (multiplied by batch size)\n",
    "    # third number is the jump from batch to batch\n",
    "    # use the length about to set the batch length\n",
    "\n",
    "    #     for i in range(0, batch):\n",
    "    #         random_index = randrange(0, length)\n",
    "    #         image = np.load(test_imgs[random_index])\n",
    "    for i in range(0, length):\n",
    "        image = np.load(test_imgs[i])\n",
    "        x_batch.append(image)\n",
    "        count += 1\n",
    "\n",
    "        my_prediction = y_conv.eval(feed_dict={x:x_batch, keep_prob: 1.0})\n",
    "        my_prediction = int(np.abs(my_prediction[0][0]))\n",
    "        ####print(\"%s --- %s === %s\"%(my_prediction, test_x_file[i], test_y_file[i]))\n",
    "        \n",
    "        \n",
    "        #         print(\"Epoch:\", '%04d' % (i+1), \"W=\", sess.run(W_fc), \n",
    "        #             \"b=\", sess.run(b_fc), \"y_conv=\", my_prediction)\n",
    "        v1t.append(1 if (my_prediction <= 100) else 0)\n",
    "        pred.append(my_prediction)\n",
    "        x_batch = []\n",
    "        #         # print(\"Validating start at #%d end at %d\"%(start*batch,(start+length)*batch))\n",
    "        #         my_prediction=tf.argmax(y_conv,1)\n",
    "        #         v1 = my_prediction.eval(feed_dict={x:x_batch, keep_prob: 1.0})\n",
    "        #         v1t = np.concatenate((v1t, v1), axis=0)\n",
    "        #         x_batch = []\n",
    "        #         print(\">>> step %d\"%(start+batch))\n",
    "\n",
    "\n",
    "    match_indexes = np.nonzero(v1t)[0]\n",
    "    #     A = np.array(test_x_file)\n",
    "    #     B = np.array(test_y_file)\n",
    "    #     C = np.array(test_imgs)\n",
    "    #     D = np.array(pred)\n",
    "    #     match_x_files = A[match_indexes]\n",
    "    #     match_y_files = B[match_indexes]\n",
    "    #     match_images = C[match_indexes]\n",
    "    #     match_pred = D[match_indexes]\n",
    "\n",
    "    #     for matched_img in match_images:\n",
    "    #         load_img = np.load(matched_img)\n",
    "    #         plt.imsave(os.path.join(\"/Volumes/250GB/matched/\",matched_img[matched_img.rfind('/')+1:]+\".png\"), load_img, cmap=plt.cm.gray)\n",
    "\n",
    "    with open('matches.csv', 'a') as csvfile:\n",
    "        csvout = csv.writer(csvfile)\n",
    "        for match_index in match_indexes:\n",
    "            matched_img = test_imgs[match_index]\n",
    "            load_img = np.load(matched_img)\n",
    "            plt.imsave(os.path.join(\"/Volumes/250GB/matched/\", str(pred[match_index]).zfill(3) + \" --- \" + matched_img[matched_img.rfind('/')+1:]+\".png\"), \n",
    "                       load_img, cmap=plt.cm.gray)\n",
    "\n",
    "            print(\"MATCH %s === %s\"%(test_x_file[match_index], test_y_file[match_index]))\n",
    "            # print(\"MATCH %s === %s\"%(A[match_index], B[match_index]))\n",
    "            # csvout.writerow([A[match_index], B[match_index]])\n",
    "            csvout.writerow([test_x_file[match_index], test_y_file[match_index], str(pred[match_index]).zfill(3)])\n",
    "            # plt.imsave(\"match_\"+match_index+\".jpg\", C[match_index])\n",
    "\n",
    "        \n",
    "    for root, dirs, files in os.walk(folder):\n",
    "        for file_ in files:\n",
    "            os.remove( os.path.join(root, file_) ) # delete it from the FS\n",
    "                \n",
    "\n",
    "    # Close the Session when we're done.\n",
    "    sess.close()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def iter_validate(cubes, model, slice_size, folder):\n",
    "    print(\"#####################################################################\")\n",
    "    print(\"VALIDATING\")\n",
    "    print(\"#####################################################################\")\n",
    "    cubes_len = len(cubes)\n",
    "    batch_size = 10\n",
    "    count = 0\n",
    "    # iterate over the cubes\n",
    "    for curr in cubes:\n",
    "        # import pdb; pdb.set_trace()\n",
    "        if count <= 100: # and count < 460: ### TEMP LIMITATION\n",
    "        #         if \"P589\" in curr[\"file\"] or \"P596\" in curr[\"file\"]:\n",
    "        #             continue\n",
    "        #         elif \"P597\" in curr[\"file\"] and \"Fg00\" in curr[\"file\"]:\n",
    "        #             continue\n",
    "        #         else:\n",
    "            print(\"COUNT=\"+str(count)+\":: CUBE:%s\"%(curr[\"file\"]+\"_\"+str(curr[\"top_row\"])+\"_\"+str(curr[\"left_col\"])))\n",
    "            validate1(cubes, model, slice_size, folder, curr)\n",
    "            validate2(folder, model, slice_size)\n",
    "        count += 1\n",
    "\n",
    "        \n",
    "    print(\"#####################################################################\")\n",
    "    print(\"VALIDATION ENDED\")\n",
    "    print(\"#####################################################################\")\n",
    "    print(\" \")\n",
    "    print(\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "code_folding": [],
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def run_all(folder, model, slice_size):\n",
    "    model_tf(slice_size)\n",
    "    cubes_set = pre_process(folder)\n",
    "    validate(cubes_set, model, slice_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run_all(\"/Volumes/250GB/cropped/\", \"/Users/il239838/Development/models/ML924__012_250px_model400x50.ckpt.meta\", 250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-6dfcee0a98bf>:88: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model_tf(250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################################\n",
      "PRE_PROCESS:/Volumes/250GB/cropped/\n",
      "#####################################################################\n",
      "770\n",
      "#####################################################################\n",
      "PRE_PROCESS ENDED\n",
      "#####################################################################\n",
      " \n",
      " \n"
     ]
    }
   ],
   "source": [
    "cubes_set = pre_process(\"/Volumes/250GB/cropped/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#####################################################################\n",
      "VALIDATING\n",
      "#####################################################################\n",
      "COUNT=0:: CUBE:P589-Fg001-V-C01-R01-D12022013-T111114-ML638 _018.jpg.npy_0_0\n",
      "loaded 764 images\n",
      "764\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=1:: CUBE:P589-Fg001-V-C01-R01-D12022013-T111114-ML638 _018.jpg.npy_250_1000\n",
      "loaded 764 images\n",
      "764\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=2:: CUBE:P589-Fg001-V-C01-R01-D12022013-T111114-ML638 _018.jpg.npy_500_0\n",
      "loaded 764 images\n",
      "764\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=3:: CUBE:P589-Fg001-V-C01-R01-D12022013-T111114-ML638 _018.jpg.npy_500_1000\n",
      "loaded 764 images\n",
      "764\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=4:: CUBE:P589-Fg001-V-C01-R01-D12022013-T111114-ML638 _018.jpg.npy_750_0\n",
      "loaded 764 images\n",
      "764\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=5:: CUBE:P589-Fg001-V-C01-R01-D12022013-T111114-ML638 _018.jpg.npy_750_1000\n",
      "loaded 764 images\n",
      "764\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=6:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_0_0\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=7:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_0_2500\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=8:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_250_0\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=9:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_250_2500\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "MATCH P589-Fg002-V-C01-R01-D12022013-T112400-ML638_250_2500 === P589-Fg003-V-C01-R01-D12022013-T114238-ML638_1000_2750.npy\n",
      "MATCH P589-Fg002-V-C01-R01-D12022013-T112400-ML638_250_2500 === P597-Fg051-V-C01-R01-D01122014-T125556-ML638_250_0.npy\n",
      "MATCH P589-Fg002-V-C01-R01-D12022013-T112400-ML638_250_2500 === P598-Fg013-V-C01-R01-D07012014-T132450-ML638_250_0.npy\n",
      "MATCH P589-Fg002-V-C01-R01-D12022013-T112400-ML638_250_2500 === P590-Fg004-V-C01-R01-D13022013-T142314-ML638_0_0.npy\n",
      "MATCH P589-Fg002-V-C01-R01-D12022013-T112400-ML638_250_2500 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_500_0.npy\n",
      "COUNT=10:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_500_2500\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=11:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_750_2500\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=12:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_1000_0\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=13:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_1000_2500\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=14:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_1250_0\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=15:: CUBE:P589-Fg002-V-C01-R01-D12022013-T112400-ML638 _018.jpg.npy_1250_2500\n",
      "loaded 754 images\n",
      "754\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=16:: CUBE:P589-Fg003-V-C01-R01-D12022013-T114238-ML638 _018.jpg.npy_250_0\n",
      "loaded 748 images\n",
      "748\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=17:: CUBE:P589-Fg003-V-C01-R01-D12022013-T114238-ML638 _018.jpg.npy_250_2750\n",
      "loaded 748 images\n",
      "748\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "MATCH P589-Fg003-V-C01-R01-D12022013-T114238-ML638_250_2750 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_500_0.npy\n",
      "COUNT=18:: CUBE:P589-Fg003-V-C01-R01-D12022013-T114238-ML638 _018.jpg.npy_500_0\n",
      "loaded 748 images\n",
      "748\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=19:: CUBE:P589-Fg003-V-C01-R01-D12022013-T114238-ML638 _018.jpg.npy_500_2750\n",
      "loaded 748 images\n",
      "748\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=20:: CUBE:P589-Fg003-V-C01-R01-D12022013-T114238-ML638 _018.jpg.npy_750_2750\n",
      "loaded 748 images\n",
      "748\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=21:: CUBE:P589-Fg003-V-C01-R01-D12022013-T114238-ML638 _018.jpg.npy_1000_2750\n",
      "loaded 748 images\n",
      "748\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "MATCH P589-Fg003-V-C01-R01-D12022013-T114238-ML638_1000_2750 === P597-Fg051-V-C01-R01-D01122014-T125556-ML638_250_0.npy\n",
      "MATCH P589-Fg003-V-C01-R01-D12022013-T114238-ML638_1000_2750 === P598-Fg005-V-C01-R01-D07012014-T113520-ML638_750_0.npy\n",
      "MATCH P589-Fg003-V-C01-R01-D12022013-T114238-ML638_1000_2750 === P590-Fg004-V-C01-R01-D13022013-T142314-ML638_0_0.npy\n",
      "MATCH P589-Fg003-V-C01-R01-D12022013-T114238-ML638_1000_2750 === P590-Fg035-V-C01-R01-D14022013-T154354-ML638_1000_0.npy\n",
      "MATCH P589-Fg003-V-C01-R01-D12022013-T114238-ML638_1000_2750 === P593-Fg004-V-C01-R01-D30122013-T150045-ML638_500_0.npy\n",
      "COUNT=22:: CUBE:P589-Fg004-V-C01-R01-D12022013-T115122-ML638 _018.jpg.npy_0_0\n",
      "loaded 743 images\n",
      "743\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=23:: CUBE:P589-Fg004-V-C01-R01-D12022013-T115122-ML638 _018.jpg.npy_250_0\n",
      "loaded 743 images\n",
      "743\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=24:: CUBE:P589-Fg004-V-C01-R01-D12022013-T115122-ML638 _018.jpg.npy_500_0\n",
      "loaded 743 images\n",
      "743\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=25:: CUBE:P589-Fg004-V-C01-R01-D12022013-T115122-ML638 _018.jpg.npy_750_0\n",
      "loaded 743 images\n",
      "743\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=26:: CUBE:P589-Fg004-V-C01-R01-D12022013-T115122-ML638 _018.jpg.npy_750_2750\n",
      "loaded 743 images\n",
      "743\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=27:: CUBE:P589-Fg005-V-C01-R01-D12022013-T115957-ML638 _018.jpg.npy_0_0\n",
      "loaded 737 images\n",
      "737\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=28:: CUBE:P589-Fg005-V-C01-R01-D12022013-T115957-ML638 _018.jpg.npy_0_750\n",
      "loaded 737 images\n",
      "737\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P597-Fg051-V-C01-R01-D01122014-T125556-ML638_250_0.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P598-Fg001-V-C01-R01-D07012014-T105717-ML638_250_0.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P598-Fg005-V-C01-R01-D07012014-T113520-ML638_750_0.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P598-Fg020-V-C01-R01-D07012014-T142020-ML638_750_0.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P598-Fg035-V-C01-R01-D08012014-T114742-ML638_250_1250.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P590-Fg004-V-C01-R01-D13022013-T142314-ML638_0_0.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_500_0.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_1000_1750.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_1250_1750.npy\n",
      "MATCH P589-Fg005-V-C01-R01-D12022013-T115957-ML638_0_750 === P590-Fg035-V-C01-R01-D14022013-T154354-ML638_1000_0.npy\n",
      "COUNT=29:: CUBE:P589-Fg005-V-C01-R01-D12022013-T115957-ML638 _018.jpg.npy_250_0\n",
      "loaded 737 images\n",
      "737\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=30:: CUBE:P589-Fg005-V-C01-R01-D12022013-T115957-ML638 _018.jpg.npy_250_750\n",
      "loaded 737 images\n",
      "737\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=31:: CUBE:P589-Fg005-V-C01-R01-D12022013-T115957-ML638 _018.jpg.npy_500_0\n",
      "loaded 737 images\n",
      "737\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=32:: CUBE:P589-Fg005-V-C01-R01-D12022013-T115957-ML638 _018.jpg.npy_500_750\n",
      "loaded 737 images\n",
      "737\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=33:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_0_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=34:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_250_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "MATCH P589-Fg006-V-C01-R01-D12022013-T121224-ML638_250_3750 === P597-Fg003-V-C01-R01-D30112014-T103141-ML638_500_0.npy\n",
      "MATCH P589-Fg006-V-C01-R01-D12022013-T121224-ML638_250_3750 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_500_0.npy\n",
      "MATCH P589-Fg006-V-C01-R01-D12022013-T121224-ML638_250_3750 === P590-Fg035-V-C01-R01-D14022013-T154354-ML638_1000_0.npy\n",
      "COUNT=35:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_500_0\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=36:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_500_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=37:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_750_0\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=38:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_750_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=39:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_1000_0\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=40:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_1000_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "MATCH P589-Fg006-V-C01-R01-D12022013-T121224-ML638_1000_3750 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_500_0.npy\n",
      "COUNT=41:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_1250_0\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=42:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_1250_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "COUNT=43:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_1500_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> step 100\n",
      ">>> step 200\n",
      ">>> step 300\n",
      ">>> step 400\n",
      ">>> step 500\n",
      ">>> step 600\n",
      ">>> step 700\n",
      ">>> step 800\n",
      "MATCH P589-Fg006-V-C01-R01-D12022013-T121224-ML638_1500_3750 === P590-Fg032-V-C01-R01-D14022013-T151728-ML638_500_0.npy\n",
      "COUNT=44:: CUBE:P589-Fg006-V-C01-R01-D12022013-T121224-ML638 _018.jpg.npy_1750_3750\n",
      "loaded 725 images\n",
      "725\n",
      "INFO:tensorflow:Restoring parameters from /Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\n",
      "Model restored.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/il239838/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/util/tf_inspect.py:45: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  if d.decorator_argspec is not None), _inspect.getargspec(target))\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-75e1c7ec16d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0miter_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcubes_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"/Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m250\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"/Volumes/250gb/mixed/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-10-d1d432d29249>\u001b[0m in \u001b[0;36miter_validate\u001b[0;34m(cubes, model, slice_size, folder)\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"COUNT=\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\":: CUBE:%s\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"file\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"top_row\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"_\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcurr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"left_col\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0mvalidate1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcubes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m             \u001b[0mvalidate2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mcount\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-badc8ed1ef5a>\u001b[0m in \u001b[0;36mvalidate2\u001b[0;34m(folder, model, slice_size)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0;31m# print(\"Validating start at #%d end at %d\"%(start*batch,(start+length)*batch))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mmy_prediction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_conv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0mv1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmy_prediction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mx_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m         \u001b[0mv1t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv1t\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0mx_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36meval\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m     \"\"\"\n\u001b[0;32m--> 635\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_eval_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_eval_using_default_session\u001b[0;34m(tensors, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m   4728\u001b[0m                        \u001b[0;34m\"the tensor's graph is different from the session's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4729\u001b[0m                        \"graph.\")\n\u001b[0;32m-> 4730\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4731\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1318\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1322\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1324\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1325\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/tf_gpu/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1300\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1301\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "iter_validate(cubes_set, \"/Users/il239838/Downloads/private/Thesis/Papyrus/model_binary/tear_model3.ckpt\", 250, \"/Volumes/250gb/mixed/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# iter_validate(cubes_set, \"/Users/il239838/Downloads/private/Thesis/Papyrus/model_for_y/tear_model.ckpt\", 250, \"/Volumes/250gb/mixed/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
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