kelly changes

Encoding_Kelly.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "8868bc30",
    "metadata": {},
    "outputs": [],
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "id": "76317b02",
    "metadata": {},
    "outputs": [],
@@ -33,10 +33,13 @@
     "    files = os.listdir(dirname)\n",
     "    scenes = []\n",
     "    for file in files:\n",
-    "        scenes.append(os.path.join(dirname, file))\n",
+    "        if file == '.DS_Store':\n",
+    "            continue\n",
+    "        else:\n",
+    "            scenes.append(os.path.join(dirname, file))\n",
     "    return scenes\n",
     "\n",
-    "def image_extractor(scenes):\n",
+    "'''def image_extractor(scenes):\n",
     "    image_folder = []\n",
     "    for scene in scenes:\n",
     "        files = os.listdir(scene)\n",
@@ -51,6 +54,20 @@
     "            else:\n",
     "                images.append(os.path.join(folder, im))\n",
     "    return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor\n",
+    "'''\n",
+    "\n",
+    "def image_extractor(scenes):\n",
+    "    image_folder = []\n",
+    "    for scene in scenes:\n",
+    "        files = os.listdir(scene)\n",
+    "        for file in files:\n",
+    "            #if file[-4:] == \".jp4\" or file[-7:] == \"_6.tiff\":\n",
+    "            if file[-5:] != \".tiff\" or file[-7:] == \"_6.tiff\":\n",
+    "                print(file)\n",
+    "                continue\n",
+    "            else:\n",
+    "                image_folder.append(os.path.join(scene, file))\n",
+    "    return image_folder\n",
     "\n",
     "def im_distribution(images, num):\n",
     "    \"\"\"\n",
@@ -79,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "be1ff8a1",
    "metadata": {},
    "outputs": [],
@@ -125,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "8483903e",
    "metadata": {},
    "outputs": [],
@@ -171,62 +188,6 @@
     "    return nodes[0][0]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "64a3a193",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def reconstruct(error, A):\n",
-    "    \"\"\"\n",
-    "    Function that reconstructs the original image\n",
-    "    from the error matrix and using the predictive\n",
-    "    algorithm developed in the encoding.\n",
-    "    \n",
-    "    Parameters:\n",
-    "        error (array): matrix of errors computed in encoding. Same \n",
-    "                       shape as the original image (512, 640) in this case\n",
-    "        A (array): Matrix used for the system of equations to create predictions\n",
-    "    Returns: \n",
-    "        image (array): The reconstructed image\n",
-    "    \"\"\"\n",
-    "    new_e = error.copy()\n",
-    "    rows, columns = new_e.shape\n",
-    "\n",
-    "    for r in range(1, rows-1):\n",
-    "        for c in range(1, columns-1):\n",
-    "            z0, z1, z2, z3 = new_e[r-1][c-1], new_e[r-1][c], new_e[r-1][c+1], new_e[r][c-1]\n",
-    "            y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))\n",
-    "\n",
-    "            '''if r == 345 and c == 421:\n",
-    "                print(new_e[r][c])\n",
-    "                print(np.linalg.solve(A,y)[-1])\n",
-    "                print(new_e[r][c] + np.linalg.solve(A,y)[-1])\n",
-    "                print(np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1]))\n",
-    "                \n",
-    "                y0 = np.ravel(-z0+z2-z3)\n",
-    "    y1 = np.ravel(z0+z1+z2)\n",
-    "    y2 = np.ravel(-z0-z1-z2-z3)\n",
-    "    y = np.vstack((y0,y1,y2))\n",
-    "    # use numpy solver to solve the system of equations all at once\n",
-    "    predict = np.floor(np.linalg.solve(A,y)[-1])\n",
-    "    # flatten the neighbor pixlels and stack them together\n",
-    "    z0 = np.ravel(z0)\n",
-    "    z1 = np.ravel(z1)\n",
-    "    z2 = np.ravel(z2)\n",
-    "    z3 = np.ravel(z3)\n",
-    "    neighbor = np.vstack((z0,z1,z2,z3)).T'''\n",
-    "        \n",
-    "            #Real solution that works, DO NOT DELETE\n",
-    "            print(new_e[r][c]+ np.floor(np.linalg.solve(A,y)[-1]))\n",
-    "            new_e[r][c] = new_e[r][c] + np.floor(np.linalg.solve(A,y)[-1])\n",
-    "            print(new_e[r][c])\n",
-    "            #new_e[r][c] = np.ceil(new_e[r][c]) + np.floor(np.linalg.solve(A,y)[-1])\n",
-    "            \n",
-    "    return new_e"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "c7104fbf",
@@ -237,21 +198,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 401,
+   "execution_count": 11,
    "id": "a43f3f1c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.444949904726781\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "scenes = file_extractor()\n",
-    "images = image_extractor(scenes)\n",
+    "\n",
     "def huffman_nb(image):\n",
     "    origin, predict, diff, error, A = plot_hist(image)\n",
     "    image = Image.open(image)\n",
@@ -295,10 +247,7 @@
     "\n",
     "        \n",
     "    return c_len/o_len\n",
-    "\n",
-    "\n",
-    "encoding, error, image = huffman_nb(images[0])\n",
-    "print(compress_rate_nb(image, error, encoding))"
+    "\n"
    ]
   },
   {
@@ -931,7 +880,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.11"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,