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Image Compression
Commit 332395dc authored by Kelly Chang's avatar Kelly Chang
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kelly

parents ffaf2ec6 e119fdf4
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.ipynb_checkpoints/Error_to_Image-checkpoint.ipynb
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......@@ -40,7 +40,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": null,
=======
"execution_count": 96,
>>>>>>> e119fdf48d4db1da99e5c8467b5c3ab9ac070ef5
"id": "9ed20f84",
"metadata": {
"id": "9ed20f84"
......@@ -113,7 +117,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": null,
=======
"execution_count": 202,
>>>>>>> e119fdf48d4db1da99e5c8467b5c3ab9ac070ef5
"id": "ba2881d9",
"metadata": {},
"outputs": [],
......@@ -125,7 +133,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 2,
=======
"execution_count": 203,
>>>>>>> e119fdf48d4db1da99e5c8467b5c3ab9ac070ef5
"id": "11e95c34",
"metadata": {},
"outputs": [
......@@ -147,7 +159,7 @@
},
{
"cell_type": "code",
"execution_count": 139,
"execution_count": 228,
"id": "434e4d2f",
"metadata": {},
"outputs": [],
......@@ -162,7 +174,7 @@
" 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: cd cdcd\n",
" Returns:\n",
" image (array): The reconstructed image\n",
" \"\"\"\n",
" new_e = error.copy()\n",
......@@ -173,12 +185,6 @@
" 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 == 1 and c == 1:\n",
" print(np.linalg.solve(A,y)[-1])\n",
" \n",
" #Real solution that works, DO NOT DELETE\n",
" #new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))\n",
" \n",
" new_e[r][c] = np.round(new_e[r][c] + np.linalg.solve(A,y)[-1], 1)\n",
" \n",
" return new_e.astype(int)\n",
......@@ -187,7 +193,7 @@
},
{
"cell_type": "code",
"execution_count": 140,
"execution_count": 210,
"id": "3cc609dc",
"metadata": {},
"outputs": [
......@@ -195,7 +201,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[22471.5]\n"
"22483 22521 22503 22481\n",
"[22491.]\n",
"-9\n",
"[22482.]\n"
]
}
],
......@@ -205,34 +214,34 @@
},
{
"cell_type": "code",
"execution_count": 136,
"execution_count": 211,
"id": "5d290a0c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[22546, 22514, 22513, ..., 22581, 22576, 22587],\n",
" [22488, 67, -21, ..., -1, -1, 22576],\n",
" [22514, -15, -3, ..., 10, -45, 22575],\n",
"array([[ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" ...,\n",
" [22317, 82, -2, ..., -64, 5, 22937],\n",
" [22335, -33, 18, ..., 47, -16, 22932],\n",
" [22333, 22339, 22362, ..., 22952, 22947, 22961]])"
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True]])"
]
},
"execution_count": 136,
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"err"
"im == new_error"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"id": "706f2816",
"metadata": {},
"outputs": [],
......@@ -256,13 +265,13 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"id": "530d2cab",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -276,12 +285,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"124\n"
"142\n"
]
},
{
"data": {
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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -295,12 +304,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"181\n"
"154\n"
]
},
{
"data": {
"image/png": 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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -314,12 +323,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"216\n"
"217\n"
]
},
{
"data": {
"image/png": 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\n",
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -333,7 +342,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"251\n"
"176\n"
]
}
],
......@@ -355,7 +364,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"id": "bb11dcd0",
"metadata": {},
"outputs": [],
......@@ -403,14 +412,14 @@
},
{
"cell_type": "code",
"execution_count": 108,
"execution_count": 102,
"id": "c01fda28",
"metadata": {},
"outputs": [],
"source": [
"def enc_experiment(images, plot=True):\n",
" origin, predict, diff, error, A = plot_hist(images, 2)\n",
" image = Image.open(images[0]) #Open the image and read it as an Image object\n",
" image = Image.open(images[2]) #Open the image and read it as an Image object\n",
" image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
" image = image.astype(int)\n",
" new_error = np.copy(image)\n",
......@@ -455,7 +464,7 @@
},
{
"cell_type": "code",
"execution_count": 109,
"execution_count": 103,
"id": "ffa858e8",
"metadata": {},
"outputs": [],
......@@ -465,31 +474,28 @@
},
{
"cell_type": "code",
"execution_count": 110,
"execution_count": 104,
"id": "8dfdedc6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[22541 22531 22555 ... 22573 22589 22574]\n",
" [22548 22544 22530 ... 22607 22612 22618]\n",
" [22548 22544 22560 ... 22603 22605 22599]\n",
" ...\n",
" [22590 22593 22596 ... 22586 22627 22692]\n",
" [22568 22575 22555 ... 22625 22702 22749]\n",
" [22558 22541 22536 ... 22679 22748 22767]]\n"
]
"data": {
"text/plain": [
"0"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(orig_image)"
"error[1,6]"
]
},
{
"cell_type": "code",
"execution_count": 148,
"execution_count": 218,
"id": "825cc48c",
"metadata": {},
"outputs": [],
......@@ -507,53 +513,77 @@
" for j in range(error_matrix.shape[1]):\n",
" if i == 0 and j == 0:\n",
" error_matrix[i][j] = int(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",
" else:\n",
" if j == 1 and i == 1:\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",
"\n",
" y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))\n",
" #Real solution that works, DO NOT DELETE\n",
" #new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))\n",
"\n",
" print(int(the_keys[the_values.index(error_matrix[i,j])]))\n",
" print(np.linalg.solve(A,y)[-1])\n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])]) + \\\n",
" np.linalg.solve(A,y)[-1][0]\n",
" #error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])])\n",
" break\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",
" \n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])])\n",
" \n",
" return error_matrix"
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 149,
"execution_count": 219,
"id": "ba1d2c2c",
"metadata": {},
"outputs": [],
"source": [
"em = decoder(A, encoding, encode_dict)"
]
},
{
"cell_type": "code",
"execution_count": 220,
"id": "b2cdce6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"67\n",
"[22555.]\n"
"22483 22521 22503 22481\n",
"[22491.]\n",
"-9\n",
"[22482.]\n"
]
}
],
"source": [
"em = decoder(A, encoding, encode_dict)"
"hopefully = reconstruct(em, A)\n",
"#22487 22483 22521 22464"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2cdce6d",
"execution_count": 227,
"id": "2d2506c9",
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" ...,\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True]])"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hopefully == im"
]
}
],
"metadata": {
......
Error_to_Image.ipynb
View file @ 332395dc
......@@ -39,7 +39,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": null,
=======
"execution_count": 96,
>>>>>>> e119fdf48d4db1da99e5c8467b5c3ab9ac070ef5
"id": "9ed20f84",
"metadata": {
"id": "9ed20f84"
......@@ -112,7 +116,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": null,
=======
"execution_count": 202,
>>>>>>> e119fdf48d4db1da99e5c8467b5c3ab9ac070ef5
"id": "ba2881d9",
"metadata": {},
"outputs": [],
......@@ -124,7 +132,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 2,
=======
"execution_count": 203,
>>>>>>> e119fdf48d4db1da99e5c8467b5c3ab9ac070ef5
"id": "11e95c34",
"metadata": {},
"outputs": [
......@@ -146,7 +158,7 @@
},
{
"cell_type": "code",
"execution_count": 139,
"execution_count": 228,
"id": "434e4d2f",
"metadata": {},
"outputs": [],
......@@ -161,7 +173,7 @@
" 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: cd cdcd\n",
" Returns:\n",
" image (array): The reconstructed image\n",
" \"\"\"\n",
" new_e = error.copy()\n",
......@@ -172,12 +184,6 @@
" 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 == 1 and c == 1:\n",
" print(np.linalg.solve(A,y)[-1])\n",
" \n",
" #Real solution that works, DO NOT DELETE\n",
" #new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))\n",
" \n",
" new_e[r][c] = np.round(new_e[r][c] + np.linalg.solve(A,y)[-1], 1)\n",
" \n",
" return new_e.astype(int)\n",
......@@ -186,7 +192,7 @@
},
{
"cell_type": "code",
"execution_count": 140,
"execution_count": 210,
"id": "3cc609dc",
"metadata": {},
"outputs": [
......@@ -194,7 +200,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[22471.5]\n"
"22483 22521 22503 22481\n",
"[22491.]\n",
"-9\n",
"[22482.]\n"
]
}
],
......@@ -204,34 +213,34 @@
},
{
"cell_type": "code",
"execution_count": 136,
"execution_count": 211,
"id": "5d290a0c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[22546, 22514, 22513, ..., 22581, 22576, 22587],\n",
" [22488, 67, -21, ..., -1, -1, 22576],\n",
" [22514, -15, -3, ..., 10, -45, 22575],\n",
"array([[ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" ...,\n",
" [22317, 82, -2, ..., -64, 5, 22937],\n",
" [22335, -33, 18, ..., 47, -16, 22932],\n",
" [22333, 22339, 22362, ..., 22952, 22947, 22961]])"
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True]])"
]
},
"execution_count": 136,
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"err"
"im == new_error"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 9,
"id": "706f2816",
"metadata": {},
"outputs": [],
......@@ -255,13 +264,13 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 10,
"id": "530d2cab",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -275,12 +284,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"124\n"
"142\n"
]
},
{
"data": {
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\n",
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m6O1JbFqA7ZIkLZAZg6KqPjKgfFPH+tuB7QPq48B5A+ovA5fN1IckaTj8ZrYkqZNBIUnqZFBIkjoZFJKkTgaFJKmTQSFJ6mRQSJI6GRSSpE4GhSSpk0EhSepkUEiSOhkUkqROBoUkqZNBIUnqZFBIkjoZFJKkTgaFJKnTjEGR5OYkB5I82lc7Nck9SZ5oj6f0LbsqyUSSfUku7KtfkOSRtuy6du9s2v2172j13UlWLvA2SpLmYTZ7FLcAG6bVtgH3VtUq4N72nCTn0Lvn9bltzPVJFrUxNwBbgVVtmnrNLcALVXU2cC1wzVw3RpK08GYMiqr6HvD8tPJGYEeb3wFc0le/vaoOVdWTwASwNsky4KSqur+qCrh12pip17oTWD+1tyFJGr65nqM4s6r2A7THM1p9OfBM33qTrba8zU+vHzGmqg4DLwKnDXrTJFuTjCcZP3jw4BxblyQdi4U+mT1oT6A66l1jXl2surGq1lTVmqVLl86xRUnSsZhrUDzXDifRHg+0+iSwom+9MeDZVh8bUD9iTJLFwMm8+lCXJGlI5hoUu4DNbX4zcFdffVO7kukseiet97TDUy8lWdfOP1w+bczUa10K3NfOY0iSRsDimVZI8hXgfcDpSSaBPwSuBnYm2QI8DVwGUFV7k+wEHgMOA1dW1Svtpa6gdwXVEuDuNgHcBNyWZILensSmBdkySdKCmDEoquojR1m0/ijrbwe2D6iPA+cNqL9MCxpJ0ujxm9mSpE4GhSSpk0EhSepkUEiSOhkUkqROBoUkqZNBIUnqZFBIkjoZFJKkTgaFJKnTjD/hIc3Xym3fHMr7PnX1xUN5X+lXjXsUkqROBoUkqZNBIUnqZFBIkjoZFJKkTgaFJKnTvIIiyVNJHknyYJLxVjs1yT1JnmiPp/Stf1WSiST7klzYV7+gvc5EkuvafbUlSSNgIfYofruqVlfVmvZ8G3BvVa0C7m3PSXIOvfthnwtsAK5PsqiNuQHYCqxq04YF6EuStACOx6GnjcCONr8DuKSvfntVHaqqJ4EJYG2SZcBJVXV/VRVwa98YSdKQzTcoCvh2kgeSbG21M6tqP0B7PKPVlwPP9I2dbLXlbX56/VWSbE0ynmT84MGD82xdkjQb8/0Jj/dW1bNJzgDuSfKDjnUHnXeojvqri1U3AjcCrFmzZuA6kqSFNa89iqp6tj0eAL4OrAWea4eTaI8H2uqTwIq+4WPAs60+NqAuSRoBcw6KJG9L8o6peeB3gEeBXcDmttpm4K42vwvYlOTNSc6id9J6Tzs89VKSde1qp8v7xkiShmw+h57OBL7ermRdDPxJVf23JH8B7EyyBXgauAygqvYm2Qk8BhwGrqyqV9prXQHcAiwB7m6TJGkEzDkoqup/A+8ZUP8JsP4oY7YD2wfUx4Hz5tqLJOn48ZvZkqROBoUkqZNBIUnqZFBIkjoZFJKkTgaFJKmTQSFJ6mRQSJI6zfdHAaWRtXLbN4fyvk9dffFQ3lc6XtyjkCR1MigkSZ0MCklSJ4NCktTJoJAkdTIoJEmdDApJUie/RyEtsGF9fwP8DoeOj5HZo0iyIcm+JBNJtg27H0lSz0gERZJFwH8Gfhc4B/hIknOG25UkCUbn0NNaYKLdh5sktwMbgceG2pX0GuPPluh4GJWgWA480/d8EvgH01dKshXY2p7+PMm+Ob7f6cCP5zj2RBjl/ka5Nxjt/ka5N5hHf7lmgTt5tV/Zv90J0t/frx3r4FEJigyo1asKVTcCN877zZLxqloz39c5Xka5v1HuDUa7v1HuDUa7v1HuDX71+xuJcxT09iBW9D0fA54dUi+SpD6jEhR/AaxKclaSNwGbgF1D7kmSxIgceqqqw0k+Afx3YBFwc1XtPY5vOe/DV8fZKPc3yr3BaPc3yr3BaPc3yr3Br3h/qXrVqQBJkv7WqBx6kiSNKINCktTpdRcUo/RTIUlWJPlOkseT7E3yyVY/Nck9SZ5oj6cMscdFSf4yyTdGsLd3JrkzyQ/a3/Afjlh//7r9uz6a5CtJ3jKs/pLcnORAkkf7akftJclV7TOyL8mFQ+rvP7Z/24eTfD3JO0epv75l/zZJJTl9GP0drbck/6q9/94kfzSv3qrqdTPRO1H+Q+DXgTcBDwHnDLGfZcBvtfl3AP+L3k+Y/BGwrdW3AdcMscd/A/wJ8I32fJR62wH8yzb/JuCdo9IfvS+RPgksac93Av98WP0B/xT4LeDRvtrAXtr/Bh8C3gyc1T4zi4bQ3+8Ai9v8NaPWX6uvoHcRzl8Bpw+jv6P87X4b+B/Am9vzM+bT2+ttj+Jvfyqkqv4GmPqpkKGoqv1V9f02/xLwOL3/wGyk9x9B2uMlw+gvyRhwMfDFvvKo9HYSvQ/ITQBV9TdV9dNR6a9ZDCxJshh4K73vBg2lv6r6HvD8tPLRetkI3F5Vh6rqSWCC3mfnhPZXVd+uqsPt6Z/T+37VyPTXXAv8Pkd+QfiE9neU3q4Arq6qQ22dA/Pp7fUWFIN+KmT5kHo5QpKVwPnAbuDMqtoPvTABzhhSW39M70Pwy77aqPT268BB4L+2Q2NfTPK2Uemvqn4E/CfgaWA/8GJVfXtU+muO1ssofk7+BXB3mx+J/pJ8CPhRVT00bdEo9PcbwD9JsjvJ/0zy9+fT2+stKGb1UyEnWpK3A18FPlVVPxt2PwBJPggcqKoHht3LUSymt7t9Q1WdD/wfeodPRkI73r+R3u793wXeluSjw+1q1kbqc5Lk08Bh4MtTpQGrndD+krwV+DTwHwYtHlA70X+/xcApwDrg3wE7k4Q59vZ6C4qR+6mQJG+kFxJfrqqvtfJzSZa15cuAA0cbfxy9F/hQkqfoHaJ7f5IvjUhv0Pu3nKyq3e35nfSCY1T6+wDwZFUdrKpfAF8D/tEI9UdHLyPzOUmyGfgg8M+qHWRnNPp7N73/E/BQ+4yMAd9P8ndGpL9J4GvVs4feUYHT59rb6y0oRuqnQlrC3wQ8XlWf71u0C9jc5jcDd53o3qrqqqoaq6qV9P5O91XVR0eht9bfXwPPJPl7rbSe3s/Sj0R/9A45rUvy1vbvvJ7eOahR6Y+OXnYBm5K8OclZwCpgz4luLskG4N8DH6qq/9u3aOj9VdUjVXVGVa1sn5FJehem/PUo9Af8KfB+gCS/Qe9ijx/PubfjeaXAKE7ARfSuLvoh8Okh9/KP6e32PQw82KaLgNOAe4En2uOpQ+7zffz/q55GpjdgNTDe/n5/Sm9Xe5T6+yzwA+BR4DZ6V5oMpT/gK/TOlfyC3n/UtnT1Qu+wyg+BfcDvDqm/CXrH06c+G/9llPqbtvwp2lVPJ7q/o/zt3gR8qf1v7/vA++fTmz/hIUnq9Ho79CRJOkYGhSSpk0EhSepkUEiSOhkUkqROBoUkqZNBIUnq9P8Af5IATMUnx5sAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -294,12 +303,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"181\n"
"154\n"
]
},
{
"data": {
"image/png": 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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -313,12 +322,12 @@
"name": "stdout",
"output_type": "stream",
"text": [
"216\n"
"217\n"
]
},
{
"data": {
"image/png": 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\n",
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -332,7 +341,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"251\n"
"176\n"
]
}
],
......@@ -354,7 +363,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"id": "bb11dcd0",
"metadata": {},
"outputs": [],
......@@ -402,14 +411,14 @@
},
{
"cell_type": "code",
"execution_count": 108,
"execution_count": 102,
"id": "c01fda28",
"metadata": {},
"outputs": [],
"source": [
"def enc_experiment(images, plot=True):\n",
" origin, predict, diff, error, A = plot_hist(images, 2)\n",
" image = Image.open(images[0]) #Open the image and read it as an Image object\n",
" image = Image.open(images[2]) #Open the image and read it as an Image object\n",
" image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
" image = image.astype(int)\n",
" new_error = np.copy(image)\n",
......@@ -454,7 +463,7 @@
},
{
"cell_type": "code",
"execution_count": 109,
"execution_count": 103,
"id": "ffa858e8",
"metadata": {},
"outputs": [],
......@@ -464,31 +473,28 @@
},
{
"cell_type": "code",
"execution_count": 110,
"execution_count": 104,
"id": "8dfdedc6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[22541 22531 22555 ... 22573 22589 22574]\n",
" [22548 22544 22530 ... 22607 22612 22618]\n",
" [22548 22544 22560 ... 22603 22605 22599]\n",
" ...\n",
" [22590 22593 22596 ... 22586 22627 22692]\n",
" [22568 22575 22555 ... 22625 22702 22749]\n",
" [22558 22541 22536 ... 22679 22748 22767]]\n"
]
"data": {
"text/plain": [
"0"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(orig_image)"
"error[1,6]"
]
},
{
"cell_type": "code",
"execution_count": 148,
"execution_count": 218,
"id": "825cc48c",
"metadata": {},
"outputs": [],
......@@ -506,53 +512,77 @@
" for j in range(error_matrix.shape[1]):\n",
" if i == 0 and j == 0:\n",
" error_matrix[i][j] = int(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",
" else:\n",
" if j == 1 and i == 1:\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",
"\n",
" y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))\n",
" #Real solution that works, DO NOT DELETE\n",
" #new_e[r][c] = int(np.ceil(new_e[r][c] + np.linalg.solve(A,y)[-1]))\n",
"\n",
" print(int(the_keys[the_values.index(error_matrix[i,j])]))\n",
" print(np.linalg.solve(A,y)[-1])\n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])]) + \\\n",
" np.linalg.solve(A,y)[-1][0]\n",
" #error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])])\n",
" break\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",
" \n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])])\n",
" \n",
" return error_matrix"
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 149,
"execution_count": 219,
"id": "ba1d2c2c",
"metadata": {},
"outputs": [],
"source": [
"em = decoder(A, encoding, encode_dict)"
]
},
{
"cell_type": "code",
"execution_count": 220,
"id": "b2cdce6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"67\n",
"[22555.]\n"
"22483 22521 22503 22481\n",
"[22491.]\n",
"-9\n",
"[22482.]\n"
]
}
],
"source": [
"em = decoder(A, encoding, encode_dict)"
"hopefully = reconstruct(em, A)\n",
"#22487 22483 22521 22464"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b2cdce6d",
"execution_count": 227,
"id": "2d2506c9",
"metadata": {},
"outputs": [],
"source": []
"outputs": [
{
"data": {
"text/plain": [
"array([[ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" ...,\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True]])"
]
},
"execution_count": 227,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hopefully == im"
]
}
],
"metadata": {
......
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