5-simple-image-filtering.ipynb

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    "# 5. Simple image filtering"
   ]
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   "source": [
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.signal\n",
    "import IPython.display\n",
    "%matplotlib widget\n",
    "from cued_sf2_lab.familiarisation import load_mat_img, plot_image\n",
    "from cued_sf2_lab.simple_image_filtering import halfcos, convse"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [],
   "source": [
    "# load the image as we did before\n",
    "X, cmaps_dict = load_mat_img(img='lighthouse.mat', img_info='X', cmap_info={'map', 'map2'})"
   ]
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    "An effective image lowpass filter, of odd length $N$, may be obtained by defining the impulse\n",
    "response $h(n)$ to be a sampled half-cosine pulse:\n",
    "\n",
    "$$h(n) = G \\cos \\left(\\frac{n\\pi}{N + 1}\\right),\\qquad \\text{for} \\qquad \\frac{-(N - 1)}{2} \\le n \\le \\frac{N - 1}{2}$$\n",
    "\n",
    "where $G$ is a gain factor, which, in order to give unity gain at zero frequency, should be calculated such that\n",
    "\n",
    "$$\\sum_{n=-(N-1)/2}^{(N-1)/2} h(n) = 1$$\n",
    "\n",
    "(This may be done most easily by first calculating h(n) with G = 1, summing all terms,\n",
    "and then dividing them all by the result.)\n",
    "\n",
    "Take a look at the `halfcos` function below and check that it generates h for a given N:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">halfcos</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"p\">:</span> <span class=\"nb\">int</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Create a half-cosine function of length N samples.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters:</span>\n",
       "<span class=\"sd\">        N: Usually odd.</span>\n",
       "<span class=\"sd\">    Returns:</span>\n",
       "<span class=\"sd\">        h: discrete samples of the half-cosine</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"c1\"># The amplitude gives unit gain at zero frequency.</span>\n",
       "    <span class=\"n\">h</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">cos</span><span class=\"p\">((</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">N</span><span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">))</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">N</span><span class=\"o\">+</span><span class=\"mi\">1</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"mf\">0.5</span><span class=\"p\">)</span> <span class=\"o\">*</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">pi</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">h</span> <span class=\"o\">=</span> <span class=\"n\">h</span> <span class=\"o\">/</span> <span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">h</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">h</span>\n",
       "</pre></div>\n"
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       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{halfcos}\\PY{p}{(}\\PY{n}{N}\\PY{p}{:} \\PY{n+nb}{int}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Create a half\\PYZhy{}cosine function of length N samples.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters:}\n",
       "\\PY{l+s+sd}{        N: Usually odd.}\n",
       "\\PY{l+s+sd}{    Returns:}\n",
       "\\PY{l+s+sd}{        h: discrete samples of the half\\PYZhy{}cosine}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{c+c1}{\\PYZsh{} The amplitude gives unit gain at zero frequency.}\n",
       "    \\PY{n}{h} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{cos}\\PY{p}{(}\\PY{p}{(}\\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{N}\\PY{o}{+}\\PY{l+m+mi}{1}\\PY{p}{)}\\PY{p}{)}\\PY{o}{/}\\PY{p}{(}\\PY{n}{N}\\PY{o}{+}\\PY{l+m+mi}{1}\\PY{p}{)} \\PY{o}{\\PYZhy{}} \\PY{l+m+mf}{0.5}\\PY{p}{)} \\PY{o}{*} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pi}\\PY{p}{)}\n",
       "    \\PY{n}{h} \\PY{o}{=} \\PY{n}{h} \\PY{o}{/} \\PY{n+nb}{sum}\\PY{p}{(}\\PY{n}{h}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{h}\n",
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      "text/plain": [
       "def halfcos(N: int) -> np.ndarray:\n",
       "    \"\"\"\n",
       "    Create a half-cosine function of length N samples.\n",
       "\n",
       "    Parameters:\n",
       "        N: Usually odd.\n",
       "    Returns:\n",
       "        h: discrete samples of the half-cosine\n",
       "    \"\"\"\n",
       "    # The amplitude gives unit gain at zero frequency.\n",
       "    h = np.cos((np.array(range(1, N+1))/(N+1) - 0.5) * np.pi)\n",
       "    h = h / sum(h)\n",
       "    return h"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "# this is just to make it appear in a cell - you can use `halfcos??` to quickly read any function\n",
    "IPython.display.Code(inspect.getsource(halfcos), language=\"python\")"
   ]
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   "source": [
    "Use the [`np.convolve` function in a for loop](https://numpy.org/doc/stable/reference/generated/numpy.convolve.html) to convolve a 15-sample half-cosine with each row of the test image, Lighthouse. \n",
    "\n",
    "Observe the resulting image `Xf` and note the increased width and the gradual fade to black at the edges, caused by the `convolve` assuming the signal is zero outside the range of the input vectors (the behavior when `mode='full'`)."
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here\n",
    "\n",
    "# fig, ax = plt.subplots()\n",
    "Xf = ...\n",
    "# plot_image(Xf, ax=ax)"
   ]
  },
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   "attachments": {},
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   "source": [
    "Trim the filtered image `Xf` to its correct size using `Xf[:, 7:256+7]` and display it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
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   "source": [
    "Note that darkening of the sides is still visible, since the lowpass filter\n",
    "assumes that the intensity is zero outside the image."
   ]
  },
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   "source": [
    "Image trimming and convolution of all the image rows can also be achieved using the [`scipy.signal.convolve`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.convolve.html) function with the `mode='same'` argument. Note we have to turn `h` into a 2d filter by wrapping it in `[]`:\n",
    "```python\n",
    "Xf = scipy.signal.convolve(X, [h])\n",
    "```"
   ]
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   "cell_type": "code",
   "execution_count": 8,
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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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   ],
   "source": [
    "# your code here - use `scipy.signal.convolve` instead of the for loop containing `np.convolve`:\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# define a 15-sample halfcosine\n",
    "h = halfcos(15)\n",
    "Xf = scipy.signal.convolve(X, [h], mode='same')\n",
    "\n",
    "plot_image(Xf, ax=ax);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symmetric extension is a technique to minimise edge effects when images of\n",
    "finite size are filtered.  It assumes that the image is surrounded by a\n",
    "flat mirror along each edge so it extends into mirror-images (symmetric\n",
    "extensions) of itself in all directions over an infinite plane.  If the filter\n",
    "impulse response is symmetrical about its mid point, then the filtered image\n",
    "will also be symmetrically extended in all directions with the same period as\n",
    "the original images.  Hence it is only necessary to define the filtered image\n",
    "over the same area as the original image, for it to be defined over the whole\n",
    "infinite plane.\n",
    "\n",
    "Let us consider a one-dimensional example for a 4-point input signal\n",
    "$a,b,c,d$.  This may be symmetrically extended in one of two ways:\n",
    "\n",
    "$$\n",
    " \\ldots d,c,b,\\underbrace{a,b,c,d,}_{\\text{original}}c,b,a \\ldots\n",
    "\\quad \\text{or} \\quad\n",
    " \\ldots d,c,b,a,\\underbrace{a,b,c,d,}_{\\text{original}}d,c,b,a \\ldots\n",
    "$$\n",
    "\n",
    "The left-hand method, where the end points are not repeated at each boundary, is most\n",
    "suitable when the signal is to be filtered by a filter of odd length. The other method is most suited to filters of even length.\n",
    "\n",
    "In Python, a matrix with symmetrically extended rows can be obtained with [`np.pad`](https://numpy.org/doc/stable/reference/generated/numpy.pad.html) using the `reflect` and `symmetric` modes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['c' 'b' 'a' 'b' 'c' 'd' 'c' 'b']\n",
      " ['C' 'B' 'A' 'B' 'C' 'D' 'C' 'B']]\n",
      "\n",
      "[['b' 'a' 'a' 'b' 'c' 'd' 'd' 'c']\n",
      " ['B' 'A' 'A' 'B' 'C' 'D' 'D' 'C']]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([\n",
    "    [\"a\", \"b\", \"c\", \"d\"],\n",
    "    [\"A\", \"B\", \"C\", \"D\"]])\n",
    "print(np.pad(x, [(0, 0), (2, 2)], mode='reflect'))    # for filters of odd length\n",
    "print()\n",
    "print(np.pad(x, [(0, 0), (2, 2)], mode='symmetric'))  # for filters of even length"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, `[(0, 0), (2, 2)]` reads as _\"pad with 0 entries above and below, and 2 entries to the left and right\"_."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `convse` make use of this to filter\n",
    "the rows of matrix `X` using the appropriate form of\n",
    "symmetric extension. The filtering is performed by accumulating shifted\n",
    "versions of `X` in `Xe`, each weighted by the appropriate element of\n",
    "`h`. Check that you understand how this function works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       ".output_html .vg { color: #19177C } /* Name.Variable.Global */\n",
       ".output_html .vi { color: #19177C } /* Name.Variable.Instance */\n",
       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">convse</span><span class=\"p\">(</span><span class=\"n\">X</span><span class=\"p\">:</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">,</span> <span class=\"n\">h</span><span class=\"p\">:</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Filter rows of image X using filter h with symmetric extension of X.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters:</span>\n",
       "<span class=\"sd\">        X: Image matrix (Usually 256x256)</span>\n",
       "<span class=\"sd\">        h: Filter coefficients</span>\n",
       "<span class=\"sd\">    Returns:</span>\n",
       "<span class=\"sd\">        Y: Row - Filtered image</span>\n",
       "\n",
       "<span class=\"sd\">    PS: Need to apply `convse` twice to filter rows and columns.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"n\">X</span><span class=\"o\">.</span><span class=\"n\">shape</span>\n",
       "    <span class=\"n\">m</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">h</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">m2</span> <span class=\"o\">=</span> <span class=\"n\">m</span> <span class=\"o\">//</span> <span class=\"mi\">2</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">m</span> <span class=\"o\">%</span> <span class=\"mi\">2</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">X</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">pad</span><span class=\"p\">(</span><span class=\"n\">X</span><span class=\"p\">,</span> <span class=\"p\">[(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"n\">m2</span><span class=\"p\">,</span> <span class=\"n\">m2</span><span class=\"p\">)],</span> <span class=\"n\">mode</span><span class=\"o\">=</span><span class=\"s1\">&#39;reflect&#39;</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">else</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">X</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">pad</span><span class=\"p\">(</span><span class=\"n\">X</span><span class=\"p\">,</span> <span class=\"p\">[(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"n\">m2</span><span class=\"p\">,</span> <span class=\"n\">m2</span><span class=\"p\">)],</span> <span class=\"n\">mode</span><span class=\"o\">=</span><span class=\"s1\">&#39;symmetric&#39;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">Y</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">((</span><span class=\"n\">r</span><span class=\"p\">,</span> <span class=\"n\">c</span><span class=\"p\">))</span>\n",
       "    <span class=\"c1\"># Loop for each term in h. This loop is the convolution.</span>\n",
       "    <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">m</span><span class=\"p\">):</span>\n",
       "        <span class=\"n\">Y</span> <span class=\"o\">=</span> <span class=\"n\">Y</span> <span class=\"o\">+</span> <span class=\"n\">h</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">X</span><span class=\"p\">[:,</span> <span class=\"n\">i</span><span class=\"p\">:</span><span class=\"n\">i</span><span class=\"o\">+</span><span class=\"n\">c</span><span class=\"p\">]</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">Y</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{convse}\\PY{p}{(}\\PY{n}{X}\\PY{p}{:} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n}{h}\\PY{p}{:} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Filter rows of image X using filter h with symmetric extension of X.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters:}\n",
       "\\PY{l+s+sd}{        X: Image matrix (Usually 256x256)}\n",
       "\\PY{l+s+sd}{        h: Filter coefficients}\n",
       "\\PY{l+s+sd}{    Returns:}\n",
       "\\PY{l+s+sd}{        Y: Row \\PYZhy{} Filtered image}\n",
       "\n",
       "\\PY{l+s+sd}{    PS: Need to apply `convse` twice to filter rows and columns.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{r}\\PY{p}{,} \\PY{n}{c} \\PY{o}{=} \\PY{n}{X}\\PY{o}{.}\\PY{n}{shape}\n",
       "    \\PY{n}{m} \\PY{o}{=} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{h}\\PY{p}{)}\n",
       "    \\PY{n}{m2} \\PY{o}{=} \\PY{n}{m} \\PY{o}{/}\\PY{o}{/} \\PY{l+m+mi}{2}\n",
       "    \\PY{k}{if} \\PY{n}{m} \\PY{o}{\\PYZpc{}} \\PY{l+m+mi}{2}\\PY{p}{:}\n",
       "        \\PY{n}{X} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pad}\\PY{p}{(}\\PY{n}{X}\\PY{p}{,} \\PY{p}{[}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{)}\\PY{p}{,} \\PY{p}{(}\\PY{n}{m2}\\PY{p}{,} \\PY{n}{m2}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mode}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{reflect}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "    \\PY{k}{else}\\PY{p}{:}\n",
       "        \\PY{n}{X} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pad}\\PY{p}{(}\\PY{n}{X}\\PY{p}{,} \\PY{p}{[}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{)}\\PY{p}{,} \\PY{p}{(}\\PY{n}{m2}\\PY{p}{,} \\PY{n}{m2}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{n}{mode}\\PY{o}{=}\\PY{l+s+s1}{\\PYZsq{}}\\PY{l+s+s1}{symmetric}\\PY{l+s+s1}{\\PYZsq{}}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{Y} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{zeros}\\PY{p}{(}\\PY{p}{(}\\PY{n}{r}\\PY{p}{,} \\PY{n}{c}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{c+c1}{\\PYZsh{} Loop for each term in h. This loop is the convolution.}\n",
       "    \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{m}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{Y} \\PY{o}{=} \\PY{n}{Y} \\PY{o}{+} \\PY{n}{h}\\PY{p}{[}\\PY{n}{i}\\PY{p}{]} \\PY{o}{*} \\PY{n}{X}\\PY{p}{[}\\PY{p}{:}\\PY{p}{,} \\PY{n}{i}\\PY{p}{:}\\PY{n}{i}\\PY{o}{+}\\PY{n}{c}\\PY{p}{]}\n",
       "    \\PY{k}{return} \\PY{n}{Y}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "def convse(X: np.ndarray, h: np.ndarray) -> np.ndarray:\n",
       "    \"\"\"\n",
       "    Filter rows of image X using filter h with symmetric extension of X.\n",
       "\n",
       "    Parameters:\n",
       "        X: Image matrix (Usually 256x256)\n",
       "        h: Filter coefficients\n",
       "    Returns:\n",
       "        Y: Row - Filtered image\n",
       "\n",
       "    PS: Need to apply `convse` twice to filter rows and columns.\n",
       "    \"\"\"\n",
       "    r, c = X.shape\n",
       "    m = len(h)\n",
       "    m2 = m // 2\n",
       "    if m % 2:\n",
       "        X = np.pad(X, [(0, 0), (m2, m2)], mode='reflect')\n",
       "    else:\n",
       "        X = np.pad(X, [(0, 0), (m2, m2)], mode='symmetric')\n",
       "\n",
       "    Y = np.zeros((r, c))\n",
       "    # Loop for each term in h. This loop is the convolution.\n",
       "    for i in range(m):\n",
       "        Y = Y + h[i] * X[:, i:i+c]\n",
       "    return Y"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IPython.display.Code(inspect.getsource(convse), language=\"python\")"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this `convse` is actually provided as part of scipy, as [`scipy.ndimage.convolve1d`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.convolve1d.html), but `convse` is much easier to understand the implementation of.\n",
    "\n",
    "Use `convse` to filter the rows of your image with the\n",
    "15-tap half-cosine filter, noting the absence of edge effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now filter the columns of the _row-filtered_ image by use of the Python transpose operation `.T`.\n",
    "\n",
    "(Note that unlike the Matlab version of this lab, the function `conv2se` has not been provided)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "Does it make any difference whether the rows or columns are filtered first? (You should test this accurately by measuring the maximum absolute pixel difference between the row-column and column-row filtered images. Beware of scientific notation, used by Python for very small numbers!)\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This process of separate row and column filtering is known as\n",
    "*separable* 2-D filtering, and is much more efficient than\n",
    "the more general non-separable 2-D filtering."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to construct a 2-D _high-pass_ filter by subtracting the 2-D low-pass\n",
    "result from the original. Note that your 2-D lowpass filter `h` _must_ have a DC gain (sum of all filter coefficients) of unity for this to correctly produce a highpass filter. The\n",
    "highpass image `Y` now contains negative, as well as positive\n",
    "pixel values, so it is sensible to display the result using `imshow(Y)` which automatically compensates for this."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "Try generating both low-pass and high-pass versions of `X` using a range of different odd-length half-cosine filters. Comment on the relative effects of these filters\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One way to assess sets of filtered images like these is to contrast the *energy* content. In this context, the energy `E` of an image `X` is given by the sum of the squares of the individual pixel values:\n",
    "```python\n",
    "E = np.sum(X**2.0)\n",
    "```\n",
    "Remember that $a^b$ is spelt `a**b` in Python, not `a^b`. We make sure to use `2.0` and not `2`, as `np.array(16, dtype=np.uint8)**2` overflows the bounds of `uint8` and gives `0`, while `2.0` tells numpy to use at least `float64` instead."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "What do you observe about the energy of the highpass images, compared with that of the lowpass images?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# your code here"
   ]
  }
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