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Kernel applied on Matrix #17

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142 changes: 142 additions & 0 deletions Class15/.ipynb_checkpoints/Apply_Kernel-checkpoint.ipynb
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Original file line number Diff line number Diff line change
@@ -0,0 +1,142 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def applyKernel(mat, kernel):\n",
" rows = mat.shape[0] - kernel.shape[0] + 1\n",
" cols = mat.shape[1] - kernel.shape[0] + 1\n",
" ker_rows = kernel.shape[0]\n",
" ker_cols = kernel.shape[1]\n",
" res = np.zeros((rows, cols))\n",
" \n",
" for ix in range(rows):\n",
" for iy in range(cols):\n",
" neighborhood = mat[ix : ix + ker_rows, iy : iy + ker_cols]\n",
" product = neighborhood * kernel\n",
" res[ix, iy] = product.sum()\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix : \n",
"[[ 56 29 144 227 32 1 59]\n",
" [ 72 22 134 11 35 18 98]\n",
" [ 63 161 177 108 0 11 45]\n",
" [232 95 37 126 79 119 3]\n",
" [102 132 86 39 142 8 51]\n",
" [ 15 152 188 154 163 241 179]\n",
" [170 74 65 160 132 216 107]]\n",
"Kernel : \n",
"[[-1 -4 0]\n",
" [ 4 -4 4]\n",
" [-3 4 0]]\n"
]
}
],
"source": [
"matrix = np.random.randint(0, 255, size=(7,7))\n",
"print(\"Matrix : \")\n",
"print(matrix)\n",
"\n",
"kernel = np.random.randint(-5, 5, size=(3,3))\n",
"print(\"Kernel : \")\n",
"print(kernel)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result : \n",
"[[1019. -784. -519. -703. 468.]\n",
" [-160. -327. 491. 263. 268.]\n",
" [ 211. -185. -751. 1007. -586.]\n",
" [ 175. 393. 267. -632. 660.]\n",
" [-640. 34. 991. 369. 698.]]\n"
]
}
],
"source": [
"result = applyKernel(matrix, kernel)\n",
"print(\"Result : \")\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nAuthor : Prashant Singh\\n'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Author : Prashant Singh\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
142 changes: 142 additions & 0 deletions Class15/Apply_Kernel.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,142 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def applyKernel(mat, kernel):\n",
" rows = mat.shape[0] - kernel.shape[0] + 1\n",
" cols = mat.shape[1] - kernel.shape[0] + 1\n",
" ker_rows = kernel.shape[0]\n",
" ker_cols = kernel.shape[1]\n",
" res = np.zeros((rows, cols))\n",
" \n",
" for ix in range(rows):\n",
" for iy in range(cols):\n",
" neighborhood = mat[ix : ix + ker_rows, iy : iy + ker_cols]\n",
" product = neighborhood * kernel\n",
" res[ix, iy] = product.sum()\n",
" return res"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix : \n",
"[[ 56 29 144 227 32 1 59]\n",
" [ 72 22 134 11 35 18 98]\n",
" [ 63 161 177 108 0 11 45]\n",
" [232 95 37 126 79 119 3]\n",
" [102 132 86 39 142 8 51]\n",
" [ 15 152 188 154 163 241 179]\n",
" [170 74 65 160 132 216 107]]\n",
"Kernel : \n",
"[[-1 -4 0]\n",
" [ 4 -4 4]\n",
" [-3 4 0]]\n"
]
}
],
"source": [
"matrix = np.random.randint(0, 255, size=(7,7))\n",
"print(\"Matrix : \")\n",
"print(matrix)\n",
"\n",
"kernel = np.random.randint(-5, 5, size=(3,3))\n",
"print(\"Kernel : \")\n",
"print(kernel)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Result : \n",
"[[1019. -784. -519. -703. 468.]\n",
" [-160. -327. 491. 263. 268.]\n",
" [ 211. -185. -751. 1007. -586.]\n",
" [ 175. 393. 267. -632. 660.]\n",
" [-640. 34. 991. 369. 698.]]\n"
]
}
],
"source": [
"result = applyKernel(matrix, kernel)\n",
"print(\"Result : \")\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nAuthor : Prashant Singh\\n'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Author : Prashant Singh\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}