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Commit 20af6366 authored by Kelly Chang's avatar Kelly Chang
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kelly push

parent 903776f0
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Encoding_decoding.ipynb
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......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 288,
"id": "14f74f21",
"metadata": {},
"outputs": [],
......@@ -24,7 +24,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 289,
"id": "c16af61f",
"metadata": {},
"outputs": [],
......@@ -79,7 +79,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 290,
"id": "aceba613",
"metadata": {},
"outputs": [],
......@@ -106,7 +106,8 @@
" 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",
" #predict = np.floor(np.linalg.solve(A,y)[-1])\n",
" predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1))\n",
" # flatten the neighbor pixlels and stack them together\n",
" z0 = np.ravel(z0)\n",
" z1 = np.ravel(z1)\n",
......@@ -125,7 +126,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 291,
"id": "6b965751",
"metadata": {},
"outputs": [],
......@@ -173,7 +174,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 292,
"id": "b7561883",
"metadata": {},
"outputs": [],
......@@ -247,16 +248,13 @@
" bins = [25,40,70]\n",
" \n",
" # return the huffman dictionary\n",
" return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, new_error, diff, boundary, bins\n",
" \n",
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, boundary, bins = huffman(images[0])"
" return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, new_error, diff, boundary, bins, predict\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 293,
"id": "2eb774d2",
"metadata": {},
"outputs": [],
......@@ -285,79 +283,134 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 294,
"id": "8eeb40d0",
"metadata": {},
"outputs": [],
"source": [
"def decoder(A, encoded_matrix, encoding_dict):\n",
"def decoder(A, encoded_matrix, list_dic, bins):\n",
" \"\"\"\n",
" Function that accecpts the prediction matrix A for the linear system,\n",
" the encoded matrix of error values, and the encoding dicitonary.\n",
" \"\"\"\n",
" the_keys = list(encode_dict.keys())\n",
" the_values = list(encode_dict.values())\n",
" error_matrix = encoded_matrix.copy()\n",
"\n",
" the_keys0 = list(list_dic[0].keys())\n",
" the_values0 = list(list_dic[0].values())\n",
" \n",
" the_keys1 = list(list_dic[1].keys())\n",
" the_values1 = list(list_dic[1].values())\n",
" \n",
" the_keys2 = list(list_dic[2].keys())\n",
" the_values2 = list(list_dic[2].values())\n",
" \n",
" the_keys3 = list(list_dic[3].keys())\n",
" the_values3 = list(list_dic[3].values())\n",
" \n",
" the_keys4 = list(list_dic[4].keys())\n",
" the_values4 = list(list_dic[4].values())\n",
" \n",
" error_matrix = np.zeros((512,640))\n",
" \n",
" for i in range(error_matrix.shape[0]):\n",
" for j in range(error_matrix.shape[1]):\n",
" if i == 0 and j == 0:\n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])])\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])])\n",
" \n",
" elif i == 0 or i == error_matrix.shape[0]-1 or j == 0 or j == error_matrix.shape[1]-1:\n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])]) + error_matrix[0][0]\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])]) + error_matrix[0][0]\n",
" else:\n",
" \"\"\"z0, z1, z2, z3 = error_matrix[i-1][j-1], error_matrix[i-1][j], \\\n",
" error_matrix[i-1][j+1], error_matrix[i][j-1]\n",
" y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))\"\"\"\n",
" z0 = error_matrix[i-1][j-1]\n",
" z1 = error_matrix[i-1][j]\n",
" z2 = error_matrix[i-1][j+1]\n",
" z3 = error_matrix[i][j-1]\n",
" y0 = int(-z0+z2-z3)\n",
" y1 = int(z0+z1+z2)\n",
" y2 = int(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n",
" difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)\n",
" predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))\n",
"\n",
" if difference <= bins[0]:\n",
" error_matrix[i][j] = int(the_keys1[the_values1.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[1] and difference > bins[0]:\n",
" error_matrix[i][j] = int(the_keys2[the_values2.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[2] and difference > bins[1]:\n",
" error_matrix[i][j] = int(the_keys3[the_values3.index(encoded_matrix[i,j])]) + int(predict)\n",
" else:\n",
" error_matrix[i][j] = int(the_keys4[the_values4.index(encoded_matrix[i,j])]) + int(predict)\n",
" \n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])])\n",
" \n",
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 295,
"id": "3e0e9742",
"metadata": {},
"outputs": [],
"source": [
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, bound, bins = huffman(images[0])\n",
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, bound, bins, predict = huffman(images[0])\n",
"encoded_matrix = encoder(np.reshape(new_error,(512,640)), [encode1, encode2, encode3, encode4, encode5], diff, bound, bins)\n",
"\n"
"list_dic = [encode1, encode2, encode3, encode4, encode5]\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e6ea4f99",
"execution_count": 296,
"id": "ceb0b957",
"metadata": {},
"outputs": [],
"source": [
"reconstruct_image = decoder(A, encoded_matrix, list_dic, bins)"
]
},
{
"cell_type": "code",
"execution_count": 297,
"id": "60297ad0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['01100010001' '11000110' '100101010' ... '101110011' '00010100'\n",
" '1111000100']\n",
" ['10011100' '100001' '111000' ... '10111011' '00111' '1111001101']\n",
" ['10101111' '100100' '100000' ... '111100' '111000' '00010100']\n",
" ...\n",
" ['110001000' '100001' '111011' ... '1010010' '100000' '10011000']\n",
" ['0100011101' '111010' '00110' ... '1000101' '1100100' '10011010']\n",
" ['00100010' '110111101' '110110100' ... '00010010' '10100000'\n",
" '110110101']]\n"
]
"data": {
"text/plain": [
"True"
]
},
"execution_count": 297,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(encoded_matrix)"
"np.allclose(image.reshape(512,640), reconstruct_image)"
]
},
{
"cell_type": "code",
"execution_count": 277,
"id": "f0948ab2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 277,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c07a23e",
"id": "7bc6e808",
"metadata": {},
"outputs": [],
"source": []
......@@ -379,7 +432,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.11"
"version": "3.9.1"
}
},
"nbformat": 4,
......
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