Skip to content

Commit

Permalink
Update rpf.ipynb
Browse files Browse the repository at this point in the history
  • Loading branch information
@0oTinaKimo0
0oTinaKimo0 committed Nov 15, 2022
1 parent 062a896 commit ff84c15
Showing 1 changed file with 45 additions and 9 deletions.
54 changes: 45 additions & 9 deletions tools/rpf.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -23,33 +23,69 @@
"name": "stdout",
"output_type": "stream",
"text": [
"100 100 8 28\n"
"100 100 8 28\n",
"[ 5.82861328e+00 1.05459538e+01 2.56491732e-02 2.56491732e-02\n",
" 4.10386808e-02 -4.00000000e+02 2.95105316e+02 -1.33042297e+01\n",
" 0.00000000e+00 0.00000000e+00 0.00000000e+00 5.00000000e-01\n",
" 5.00000000e-01 8.00000012e-01 0.00000000e+00 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.25136793e-01\n",
" 3.10504138e-01 5.03994346e-01 0.00000000e+00 0.00000000e+00]\n"
]
}
],
"source": [
"with open('../scenes/sample.dat', 'rb') as f:\n",
" fileContent = f.read()\n",
" width, height, spp, SL = struct.unpack(\"QQQQ\", fileContent[:32])\n",
" m = struct.unpack(\"f\" * ((len(fileContent) - 32) // 4), fileContent[32:])\n",
"print(width, height, spp, SL)"
" samples = struct.unpack(\"f\" * ((len(fileContent) - 32) // 4), fileContent[32:])\n",
" raw = np.array(samples)\n",
" samples = np.reshape(raw, (width, height, spp, SL))\n",
"print(width, height, spp, SL)\n",
"print(samples[10,5,4,:]) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"def preprocess(x, y, b, M):\n",
" N = np.random.normal(scale=b/4, size=(M - spp, 1))"
" # Sample neighbors based on gaussian distribution\n",
" indexN = np.random.multivariate_normal(mean=[x, y], cov=(b/4) * np.identity(2), size=M-spp)\n",
" indexN = np.clip(indexN, 0, 100)\n",
" indexN = np.around(indexN)\n",
" randSampleInPixel = np.random.randint(7, size=(M-spp, 1))\n",
" indexN = np.concatenate((indexN,randSampleInPixel), axis = 1)\n",
" sampleMapper = lambda v: samples[int(v[0]), int(v[1]), int(v[2]),:]\n",
" N = np.apply_along_axis(sampleMapper, 1, indexN)\n",
" # TODO: cluster samples\n",
" # Standardize neighbors\n",
" N = scale(N)\n",
" return N"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 31,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-31-06a1d910b3c0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocess\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxNumOfSamples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-30-77019b851b6e>\u001b[0m in \u001b[0;36mpreprocess\u001b[1;34m(x, y, b, M)\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;31m# TODO: cluster samples\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;31m# Standardize neighbors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 13\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\tinak\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 70\u001b[0m FutureWarning)\n\u001b[0;32m 71\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 73\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\users\\tinak\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\sklearn\\preprocessing\\_data.py\u001b[0m in \u001b[0;36mscale\u001b[1;34m(X, axis, with_mean, with_std, copy)\u001b[0m\n\u001b[0;32m 164\u001b[0m \u001b[0mXr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrollaxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 165\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mwith_mean\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m \u001b[0mXr\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mmean_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 167\u001b[0m \u001b[0mmean_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnanmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mXr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 168\u001b[0m \u001b[1;31m# Verify that mean_1 is 'close to zero'. If X contains very\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"boxSizes = [55, 35, 17, 7]\n",
"for t in range(4):\n",
Expand Down Expand Up @@ -84,7 +120,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
"version": "3.7.0"
}
},
"nbformat": 4,
Expand Down

0 comments on commit ff84c15

Please sign in to comment.