ai-601.ipynb

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   "source": [
    "from PIL import Image\n",
    "import io\n",
    "from urllib import request\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "url = 'https://raw.githubusercontent.com/MachineIntellect/DeepLearner/master/J.png'\n",
    "resp = request.urlopen(url)\n",
    "s = resp.read()\n",
    "imgG = Image.open(io.BytesIO(s)).convert('L')\n",
    "img_data = np.asarray(imgG)\n",
    "filter3 = np.array([[1,1,1],\n",
    "          [0,0,0],\n",
    "          [-1,-1,-1]])\n",
    "def conv2D(x,kernel):\n",
    "    (n_H_prev, n_W_prev) = x.shape\n",
    "    (f, f) = kernel.shape\n",
    "    n_H = n_H_prev - f + 1\n",
    "    n_W = n_W_prev - f + 1\n",
    "    Z = np.zeros((n_H, n_W))\n",
    "    for h in range(n_H):\n",
    "        for w in range(n_W):\n",
    "            vert_start = h\n",
    "            vert_end = vert_start + f\n",
    "            horiz_start = w\n",
    "            horiz_end = horiz_start + f\n",
    "            a_slice_prev = img_data[vert_start:vert_end, horiz_start:horiz_end]\n",
    "            mat = np.multiply(a_slice_prev, kernel)\n",
    "            Z[h, w] = np.sum(mat)\n",
    "    return Z\n",
    "Zh = conv2D(img_data, filter3)\n",
    "Zv = conv2D(img_data, filter3.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "fig, axs = plt.subplots(1, 3, sharex=True,figsize=(12,4))\n",
    "for a in axs:\n",
    "    a.grid(False)\n",
    "axs[0].imshow(img_data, cmap='gray')\n",
    "axs[1].imshow(Zh, cmap='gray')\n",
    "axs[2].imshow(Zv, cmap='gray')\n",
    "plt.show()"
   ]
  }
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