Holistically-Nested Edge Detection Paper Example

Holistically-Nested Edge Detection Paper Example.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "import numpy\n",
    "\n",
    "%matplotlib notebook\n",
    "from matplotlib import pyplot\n",
    "\n",
    "from skimage.io import imread\n",
    "import caffe\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = imread('paper-example.jpg')\n",
    "\n",
    "# pyplot.figure()\n",
    "# pyplot.imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_input(X):\n",
    "    X = numpy.array(X, dtype=numpy.float32)\n",
    "    X = X[...,::-1]\n",
    "    X -= numpy.array([[[[104.00698793,116.66876762,122.67891434]]]])\n",
    "    return X.transpose(0, 3, 1, 2) / 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "images = image[None]\n",
    "preprocessed = preprocess_input(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.95842904, 1.1796329)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed.min(), preprocessed.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = caffe.Net(\n",
    "    'deploy.prototxt',\n",
    "    'hed_pretrained_bsds.caffemodel',\n",
    "    caffe.TEST\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.blobs['data'].reshape(*preprocessed.shape)\n",
    "net.blobs['data'].data[...] = preprocessed\n",
    "\n",
    "# run net and take argmax for prediction\n",
    "net.forward()\n",
    "out1 = net.blobs['sigmoid-dsn1'].data[0][0]\n",
    "out2 = net.blobs['sigmoid-dsn2'].data[0][0]\n",
    "out3 = net.blobs['sigmoid-dsn3'].data[0][0]\n",
    "out4 = net.blobs['sigmoid-dsn4'].data[0][0]\n",
    "out5 = net.blobs['sigmoid-dsn5'].data[0][0]\n",
    "fuse = net.blobs['sigmoid-fuse'].data[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pyplot.rcParams['figure.figsize'] = (9, 5)\n",
    "# for axis, out, title in zip(\n",
    "#     pyplot.subplots(2,3)[1].ravel(),\n",
    "#     [out1, out2, out3, out4, out5, numpy.zeros(out5.shape)],\n",
    "#     ['out1', 'out2', 'out3', 'out4', 'fusion', ''],\n",
    "# ):\n",
    "#     if out is not None:\n",
    "#         axis.imshow(out)\n",
    "#     axis.set_xticks([])\n",
    "#     axis.set_yticks([])\n",
    "#     axis.set_xlabel(title)\n",
    "# pyplot.tight_layout()"
   ]
  }
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