Improve image loading and some small enhancements

images_ft.ipynb

@@ -19,8 +19,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 1440x360 with 5 Axes>",
-      "image/png": "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 truncated
+      "text/plain": "<Figure size 1440x360 with 4 Axes>",
+      "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "dark"
@@ -29,13 +29,14 @@
     }
    ],
    "source": [
-    "from PIL import Image\n",
-    "from scipy.misc import ascent\n",
-    "from scipy.signal import medfilt2d\n",
+    "import os\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
+    "from PIL import Image\n",
+    "from scipy.misc import ascent\n",
+    "\n",
     "dark_bg = True # Used to adjust matplotlib colors for dark backgrounds\n",
-    "# <editor-fold desc=\"Matplotlib config\">\n",
+    "# <editor-fold desc=\"Matplotlib config\" defaultstate=\"collapsed\">\n",
     "plt.rcParams[\"image.cmap\"] = \"gray\"\n",
     "if dark_bg:\n",
     "    plt.rcParams[\"text.color\"] = \"white\"\n",
@@ -43,22 +44,21 @@
     "plt.rcParams[\"ytick.color\"] = \"gray\"\n",
     "# </editor-fold>\n",
     "\n",
+    "# <editor-fold desc=\"Load images\" defaultstate=\"collapsed\">\n",
     "def gray_scale(im):\n",
     "    return np.dot(im, [0.07, 0.72, 0.21]) # grayscale weighted by human color perception\n",
     "\n",
-    "zebra = gray_scale(Image.open(\"zebra.jpg\", \"r\"))\n",
-    "crossing = gray_scale(Image.open(\"crossing.jpg\", \"r\"))\n",
-    "checkerboard_tunnel = gray_scale(Image.open(\"checkerboard-tunnel.jpg\", \"r\"))\n",
-    "task = medfilt2d(Image.open(\"task.tif\", \"r\"))\n",
-    "task_ft = Image.open(\"task_ft.png\", \"r\")\n",
+    "images = [ { \"image\": ascent(), \"name\": \"ascent\" } ]\n",
+    "\n",
+    "for image_path in os.listdir(\"img\"):\n",
+    "    image = Image.open(os.path.join(\"img\", image_path), \"r\")\n",
+    "    if len(np.shape(image)) == 3:\n",
+    "        image = gray_scale(image)\n",
+    "\n",
+    "    images.append({ \"image\": image, \"name\": os.path.splitext(os.path.basename(image_path))[0] })\n",
     "\n",
-    "images = [\n",
-    "    { \"image\": ascent(), \"name\": \"ascent\" },\n",
-    "    { \"image\": zebra, \"name\": \"zebra\" },\n",
-    "    { \"image\": crossing, \"name\": \"crossing\" },\n",
-    "    { \"image\": checkerboard_tunnel, \"name\": \"checkerboard_tunnel\" },\n",
-    "]\n",
     "_, ax = plt.subplots(ncols=len(images), figsize=(20, 5))\n",
+    "# </editor-fold>\n",
     "\n",
     "for i, im in enumerate(images):\n",
     "    ax[i].set_title(im[\"name\"])\n",
@@ -76,12 +76,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 1440x1800 with 20 Axes>",
-      "image/png": "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 truncated
+      "text/plain": "<Figure size 1440x1440 with 16 Axes>",
+      "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "dark"
@@ -114,7 +114,7 @@
     "    ax[i][2].set_title(\"Logarithmic spectrum of FT\")\n",
     "    ax[i][2].imshow(im_log_spec, vmin=0, vmax=20)\n",
     "    ax[i][3].set_title(\"FT reverted\")\n",
-    "    ax[i][3].imshow(abs(ifft2(ilog_spectrum(im_log_spec, im[\"fourier_angle\"]))))"
+    "    ax[i][3].imshow(abs(ifft2(ilog_spectrum(im_log_spec, im[\"fourier_angle\"]))), vmin=0, vmax=255)"
    ],
    "metadata": {
     "collapsed": false,
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "outputs": [],
    "source": [
     "export_scale = 20.0 # Should be higher than the spectrum max\n",
@@ -164,8 +164,8 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 1440x1800 with 15 Axes>",
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJMAAAV+CAYAAADC3n9sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOz9eZhd554Whr57167acw2quUqTLduSbXk6x7ZkyZJ9GjfdNH3o0A8cTgPdHMK9B8gDyb1JuBC6iY6SG2gIIacDNwwJcOgmNHQI9xLCSXPSbo5t+Ui2NdiyBkvWPEs1V+259nD/WPV++12/WrsGSWXJR+v3PPVU1d5rfeub1m94f8MXaTQaCCmkkEIKKaSQQgoppJBCCimkkEIKKaSVUPRBdyCkkEIKKaSQQgoppJBCCimkkEIKKaQvD4VgUkghhRRSSCGFFFJIIYUUUkghhRRSSCumEEwKKaSQQgoppJBCCimkkEIKKaSQQgppxRSCSSGFFFJIIYUUUkghhRRSSCGFFFJIIa2YQjAppJBCCimkkEIKKaSQQgoppJBCCimkFVMIJoUUUkghhRRSSCGFFFJIIYUUUkghhbRiCsGkkB52+g6Af/oA7g0ppJBCCun+0l8G8D+vwbXLUQPAEy2++z8B/In79JyQQgoppEedNsPjubGF/y2P/X8DGAdwC8BGADkAbffhOWtJ38H9syfuZcwPI/0QwP+txXf3U46H9JBSCCaFdC90CcBbD7oTd0lvAqjDY+j8+TcATsr/NQAl+f8vP4iOGvoePEEcUkghhfQg6VsAPgVQgGcU/F0A3cvc81fRWum8l2vvhX4fgH/yBTznXuhNeAo74BlPDyN9C8AB89n3Fj7/1sLfIYUU0sNFlwBUAPSZz4/B4zWb78MzlMduBPCfAXgGwBCAKwAy8PTtR4UepjFvxtoCcl+UHA/pAVIIJoX0KNMNeAydP18H8Kz8/x6APyf//9UH081V0RfhoQkppJAebfrPAPx1AH8BQBeAnQA2Afi/AHS0uCfkTSGFFFJIDx9dBPAL8v9zAFJr9KyNACYA3Fmj9h8G+rLIui9LP0N6yCkEk0JaC4oD+C48sObGwt/xhe96APwfAMYATC38vV7ufQzAOwDm4Bkm1luyE8CPAEwD+ASex3al964FvQrgMIBZALcB/K2FzzfDQ/u/DW8ObgL4z+W+KIC/BOA8PMH6WwDWyfevoznOq/A8u98G8McA/L/QjKQCPM/SXwRwHEAenoCwaR3fQzOi6U0A1xbaubPQt/8AwM8AOAtgEg9HFFZIIYX08FEngP0A/jyA3wYwD48HfQMe3/vjC9d9B8C/hJcaMAuPh30H/lSBXwJwGR4P/CvwR7vqtZvh8bQ/Ac+rOw7gl6WdVwEchMcvbwL4O2gNaln6IZqe028BeB/Af7/Q1gUAuxY+vwqPX2q6xu+H58GfXfj+O6btpca3nAxYCf1JAKfhybwLAP60fNcHT75Ow+Pp76Gp820A8K/gyeEJePNF+g8X2pwC8O/ggYSkBoA/A+DzhXb/PwAiAJ4G8PcAvAZPNk2vchwhhRTSg6PfgMerSH8CwK+ba7oWPhuDx9N+BU1+0gbgb8Ljyxfg8UWlH8LjsW/B081H4PGJ72FxZEwXgH8Ij49fh6e3Mh1suedYIn+dA3AKwB+U774FL5Lyb8LjdRfhRVCRVmNPvAlPp/6L8KJ0/zGW5u92zD9cGOeP0NTtewH8L/Bky0fwR4g1APzH8OZgHMB/i+ZaROGtzWV48urX4c2pPvdPwZOjvwvg3YXvphee/drC/0vJgZ8E8BmAGXiyI7LE3HwHi+X4n4QnL6fgyZNX4Nkv0/DLoi0LfZxYGOf/An/081fgyd85AP8rgH8Bf+bGzwL4eKHdHwF4fol+hnQPFIJJIa0F/TI80OdFAC/AU/R/ZeG7KDxGuwmeh6IIP/P4ZwCOwGPc/zX8ivsogH8Lj1msgwfO/G8A+ldw71rRry38dMJjfL9lvv8agCcB/F54goaGxJ+HB+C8AU+wTsFTzAFvbv5PAH8b3thehMcQ/wE8Zvo30IykIv0CPMHaDaC6gn4PAUjAm9P/EsD/BM8I/CqAPfAMn8dW0E5IIYX0aNEueLzjX5nPcwC+D0/RJP0cPECpGx7vUnoGwP8IDyAfhqfwji7z7NcBbAXwe+DxracXPq8B+H/C4/2vLXz/H61wPJZ2wFNse+HJlH8OT9l9Ah6P/Dvw+C/ggfe/BG98vx/An4XH11cyvqVkgKUfouk4UcX9DjyFuROegv7fw1OwAS967Bo8GTIIz0HQgGeQ/R/wjI3NC3365wv3/NzCdT+/cN97AH7T9OVnF+bjeXgA4k/BMzr+DDxAL4Omwv8teAbj9xb+DimkkB4+OgSPhzwNjz98E4vrA/1teDzscXg865fg8RwA+L/D4wsvAXgZwB9q8ZzfgQfYMCvgWwHXfA+eDvvEQnu/F02wf6XPIZ2Hp892wXOA/FN4vJi0A8AZeHLjb8ADschfV2tPDMGzSzbBc/yuhr8D3pz/Ijx+vAUeL/3HC22eBrDPXP8H4c3BV+Dx7f9w4fNvLfx8Dd5aZeC3sbDQp6fh8e69C591L1x7EEvLgT54sv9XFv4+D2D3EuMKoh3w7KI/Ai/Y4Jfh2UbPwpMpbyxcFwHw1+DN39PwnCDfWfiuA8D/F95+WbfQPwULXwLwj+A5WHoB/H0A/zuagQ0h3UcKwaSQ1oL+GID/Cp6iOwaPif/iwncT8ACgAjw0+b9Bk3FshKek/hUAZXiI+b9Bk/44PGPl+/DqHf1f8KKCfmYF9wbRCDzEmj/fWO1A4Xnln4DHVHPwhLLSfngGx6fwBANDif8MPAZ6baG/34EnGGMA/ig8ofubC+1PwAOTlqL/AR7SX1xFv/+bhd//fKH/vwZvTU7C8+K8sMK2QgoppEeH+uB5CYNA65vwe3APAvj/wePXljf9IXg8+gC8mh3/JZavB7R/oZ1PFn7Io47A471VeNE/fx9NubJaugiPV9fgeTo3wJNnZQA/WOgroz5/CI+31+EBUL8pz11ufEvJgJXSv4WnzDfgedF/AM94AjzePgzPuJmHZxA04Dl3RuClKObh1QVkraM/A095Pw1vLv8qPGeGeqV/FZ68vALg3y98H1JIIX25idFJPwnv/b8u3xFg+i/g6YiXAPx3aOr134AHClyFFwX51+6yD4Pw9Pn/BzzedAceQP7Nu3zO/woPuKrD4+Wfw+N/pMvwHKk1eDWdhhf6cDf2RB0e4FOGJ6NWy9//MTxePgPPmXwenh1QXRjHS+b6vw5vDq7AmxPaFn8MXobEBXg2yX8Bb/70ud+BN7+t7IWl5MDPwLMR/iU8ufJdeNFYq6H/Gp7c+cFCP34T3lpfhyenONZz8Oy8Mjxb8m+hKV93Lozpf1jox78C8KE849vw9IAP0Fzf8sJ9Id1nCsGkkNaCRuAxadLlhc8ALw/77y98NguPSXfDE1ZE7/PmXtImAH8YfgDodXgCYLl7g+jGwrP5Y6OKVkJ/CsBT8EI+P4LnNVG6avrDedgED1WfXvg5DY/hDcIzXs6vsh9Xl7/ERxNoFv+jQLkt3xfR9L6HFFJIIZHG4QFGQUrx8ML3pKX40oj5vgCPLy1FqrQW0ORRT8GLtrkFT678Vdx9mrPlg0Gf8bk74AEqY/CMgD8jz11ufEvJgJXS74MHok0utPEz8vz/Fp4y/gN4hsVfWvh8AzxZFAQGboLnVGCfJuF5hzWiqtUahBRSSF9e+g14jsxvYXGKWx+AdizW68kXLK9bTvduRZsWnnMTTR709wEM3OVzfgnNNKdpANvhlwuWlwEeP7sbe2IMHkBCWi1/tzJmOX28lW0RZH/FzHOXsxeWkgN2DRoraM/SSsc6CM/ZfR2eXP+n8MvX6/A7aLQfm+BF507LzwY05ymk+0ghmBTSWtAN+D2ZGxc+A7yXeys8JbwTzRDLCDwB0gMgbe4lXYUn8LrlJw3PU7rcvWtFn8PzCAzA8xT8S9OHDaY/nIer8AyBbvlJwGOOV+GFuQZRK8+9/bwAfwHFoZYjCCmkkEJaOR2E5+H7efN5Bh5Pe1s+WyrS6Cb89fKS8MLR74b+LjxA/0l4cuUvY+k6DveL/hm80PkN8FIp/p48d7nxLSUDVkJxeFG+fxOe0t0NL2qXz5+DJ28fB/AHAPyn8NL/rsKTRUFg4FV4aQHapyS8ehPL0cN6ylxIIYW0PF2GF5X5M1icwjwOL/rD6vXkVTexWNe9G7oKT7b0ocl/OuGlP632OZvgRR39OXh8txvACaxMLtyNPWH5373y9+WolW0RZH9V4QdsGi3+Ji0lB+waRMz/95P+6kL/noO3D/44/PJ1FP711H5chZd90S0/KSxO2w7pPlAIJoV0r9QOj0HyJwbvZf0VeLm2ffDC+5l/nYWHPE/Dy3PVPODL8NLW9sPLh30d/rpA/3Th/5+CF8mUgFdHYv0K7l0r+uPwxllHs+hoXb7/K/AY2LPw8sv/xcLnfw8eoyPT74eXpwx4tUXeghfSG4MnCF9c+O42PONgOfoYnpepDcBP4+5TPkIKKaSQlGbg8dm/DY+3tMOrvfNb8EL6f2OF7fxLeDx6Fzye/R3cPQCUhee5zAHYBq920RdBWXhe2xK89Ik/Kt8tN76lZMBKqAMeoDQGz1j4ffDqi5B+Fl46XgTemtXgyaYP4SnivwrPWEqgWfPi78FLi6Dx1gUvGngldBueLF5p4fOQQgrp4aI/BeAn4I/IATze8Vvw+FUWHs/6T9HU638LXkHo9fBAmL+Eu6Ob8CIp/zt44EEUnmOV+utqnpOGB0SMLfz/J+FFJq2E7oc9ca/8fTn6C/DmYAOA/wRN2+I34dUPfAzNU6j/BVrXUh2DJxfUrlhKDvzbhc9/Hp598h9j7ZzVWXgyfQYecPQX5LuD8Pbln1vox8/Bn8L4P8GLFN4BTwam4dU1zK5RXx9pCsGkkO6Line truncated
+      "text/plain": "<Figure size 1440x1440 with 12 Axes>",
+      "image/png": "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 truncated
      },
      "metadata": {
       "needs_background": "dark"