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image-compression
Commit c2c824c5 authored by Kelly Chang's avatar Kelly Chang
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Merge branch 'master' of https://git.elphel.com/Elphel/master

parents 22a69826 fe19ba5e
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.ipynb_checkpoints/Error_to_Image-checkpoint.ipynb
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......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"id": "dbef8759",
"metadata": {
"id": "dbef8759"
......@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "9ed20f84",
"metadata": {
"id": "9ed20f84"
......@@ -218,7 +218,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 8,
"id": "bb11dcd0",
"metadata": {},
"outputs": [],
......@@ -266,7 +266,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 9,
"id": "c01fda28",
"metadata": {},
"outputs": [],
......@@ -335,7 +335,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 10,
"id": "ffa858e8",
"metadata": {},
"outputs": [
......@@ -346,8 +346,8 @@
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/384786850.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mencode_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morig_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/3253315524.py\u001b[0m in \u001b[0;36mencoder\u001b[1;34m(images, i, plot)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;31m#update on throughout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m#new_error[1:-1,1:-1] = np.reshape(error[1:-1,1:-1],(510, 638))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#Set the inside of the updating matrix to be the same as the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;31m#error matrix retreived from predicting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0mkeep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#The top left entry stays the same\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/384786850.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mencode_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morig_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/3253315524.py\u001b[0m in \u001b[0;36mencoder\u001b[1;34m(images, i, plot)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;31m#update on throughout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m#new_error[1:-1,1:-1] = np.reshape(error[1:-1,1:-1],(510, 638))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#Set the inside of the updating matrix to be the same as the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;31m#error matrix retreived from predicting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0mkeep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#The top left entry stays the same\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (508,636) into shape (510,638)"
]
}
......@@ -456,12 +456,25 @@
},
{
"cell_type": "code",
"execution_count": 137,
"execution_count": 13,
"id": "30b1c87e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.7830123821108295\n"
]
}
],
"source": [
"def entropy_func(images):\n",
" \"\"\"\n",
" Computes the entropy for all pictures (tiff files) in the images list.\n",
" This gives an idea of how many bits it would take on average to encode the\n",
" given image. The output is a list of entropies, one per image.\n",
" \"\"\"\n",
" entr = []\n",
" for i in range(len(images)):\n",
" prediction, diff, im, err, A = predict(images, i)\n",
......@@ -470,30 +483,26 @@
" entr.append(sp.stats.entropy(counts))\n",
" return entr\n",
"\n",
"e = entropy_func(images)"
"e = entropy_func(images)\n",
"print(np.mean(e))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 15,
"id": "4c268907",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'read'",
"ename": "IndexError",
"evalue": "boolean index did not match indexed array along dimension 0; dimension is 510 but corresponding boolean dimension is 512",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m 2971\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2972\u001b[1;33m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2973\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUnsupportedOperation\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;31mAttributeError\u001b[0m: 'list' object has no attribute 'seek'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/4042219417.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mscenes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_extractor\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[0;32m 73\u001b[0m \u001b[0mimages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage_extractor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscenes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mencode1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhuffman\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/4042219417.py\u001b[0m in \u001b[0;36mhuffman\u001b[1;34m(image)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#Convert to an array, leaving out the first row because the first row is just housekeeping data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m 2972\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2973\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUnsupportedOperation\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-> 2974\u001b[1;33m \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\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[0m\u001b[0;32m 2975\u001b[0m \u001b[0mexclusive_fp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2976\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'read'"
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/1618652474.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mscenes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_extractor\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[0;32m 73\u001b[0m \u001b[0mimages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage_extractor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscenes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mencode1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhuffman\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/1618652474.py\u001b[0m in \u001b[0;36mhuffman\u001b[1;34m(image)\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m25\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mstring\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\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[0m\u001b[0;32m 21\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCounter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring\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[0;32m 22\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 510 but corresponding boolean dimension is 512"
]
}
],
......@@ -501,7 +510,7 @@
"def huffman(image):\n",
" origin, predicty, diff, error, A = predict(image,0)\n",
" \n",
" image = Image.open(image)\n",
" image = Image.open(image[0])\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",
" \n",
......
.ipynb_checkpoints/Scout_Clean_Code_with_EncDec-checkpoint.ipynb 0 → 100644
View file @ c2c824c5
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "0d67d099",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from prediction_MSE_Scout import file_extractor, image_extractor, im_distribution\n",
"from matplotlib import pyplot as plt\n",
"from itertools import product\n",
"import os\n",
"import sys\n",
"from PIL import Image\n",
"from scipy.optimize import minimize\n",
"from time import time\n",
"from numpy import linalg as la\n",
"from scipy.stats import gaussian_kde\n",
"import seaborn as sns\n",
"from collections import Counter\n",
"import pandas as pd\n",
"import scipy as sp"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fc76b964",
"metadata": {},
"outputs": [],
"source": [
"def plot_hist(tiff_list):\n",
" \"\"\"\n",
" This function is the leftovers from the first attempt to plot histograms.\n",
" As it stands it needs some work in order to function again. We will\n",
" fix this later. 1/25/22\n",
" \"\"\"\n",
" \n",
" image = tiff_list\n",
" image = Image.open(image) #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",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
" z0 = image[0:-2,0:-2] # get all the first pixel for the entire image\n",
" z1 = image[0:-2,1:-1] # get all the second pixel for the entire image\n",
" z2 = image[0:-2,2::] # get all the third pixel for the entire image\n",
" z3 = image[1:-1,0:-2] # get all the forth pixel for the entire image\n",
" # calculate the out put of the system of equation\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",
" 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",
" z2 = np.ravel(z2)\n",
" z3 = np.ravel(z3)\n",
" neighbor = np.vstack((z0,z1,z2,z3)).T\n",
" # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
" \n",
" # flatten the image to a vector\n",
" image = np.ravel(image[1:-1,1:-1])\n",
" error = image-predict\n",
" \n",
" return image, predict, diff, error, A"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b781115b",
"metadata": {},
"outputs": [],
"source": [
"class NodeTree(object):\n",
" def __init__(self, left=None, right=None):\n",
" self.left = left\n",
" self.right = right\n",
"\n",
" def children(self):\n",
" return self.left, self.right\n",
"\n",
" def __str__(self):\n",
" return self.left, self.right\n",
"\n",
"\n",
"def huffman_code_tree(node, binString=''):\n",
" '''\n",
" Function to find Huffman Code\n",
" '''\n",
" if type(node) is str:\n",
" return {node: binString}\n",
" (l, r) = node.children()\n",
" d = dict()\n",
" d.update(huffman_code_tree(l, binString + '0'))\n",
" d.update(huffman_code_tree(r, binString + '1'))\n",
" return d\n",
"\n",
"\n",
"def make_tree(nodes):\n",
" '''\n",
" Function to make tree\n",
" :param nodes: Nodes\n",
" :return: Root of the tree\n",
" '''\n",
" while len(nodes) > 1:\n",
" (key1, c1) = nodes[-1]\n",
" (key2, c2) = nodes[-2]\n",
" nodes = nodes[:-2]\n",
" node = NodeTree(key1, key2)\n",
" nodes.append((node, c1 + c2))\n",
" nodes = sorted(nodes, key=lambda x: x[1], reverse=True)\n",
" return nodes[0][0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fe145ec0",
"metadata": {},
"outputs": [],
"source": [
"def huffman(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\n",
" \n",
" image = Image.open(image)\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",
" \n",
" boundary = np.hstack((image[0,:],image[-1,:],image[1:-1,0],image[1:-1,-1]))\n",
" boundary = boundary - image[0,0]\n",
" boundary[0] = image[0,0]\n",
"\n",
" string = [str(i) for i in boundary]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode1 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff <= 25\n",
" string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode2 = huffman_code_tree(node)\n",
"\n",
" \n",
" mask = diff > 25\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 40\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode3 = huffman_code_tree(node)\n",
" \n",
"\n",
" mask = diff > 40\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 70\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode4 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff > 70\n",
" string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode5 = huffman_code_tree(node)\n",
"\n",
"\n",
" new_error = np.copy(image)\n",
" new_error[1:-1,1:-1] = np.reshape(error,(510, 638))\n",
" keep = new_error[0,0]\n",
" new_error[0,:] = new_error[0,:] - keep\n",
" new_error[-1,:] = new_error[-1,:] - keep\n",
" new_error[1:-1,0] = new_error[1:-1,0] - keep\n",
" new_error[1:-1,-1] = new_error[1:-1,-1] - keep\n",
" new_error[0,0] = keep\n",
" \n",
" \n",
" #new_error = np.ravel(new_error)\n",
" \n",
" 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, predict, A\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4fb8f5d0",
"metadata": {},
"outputs": [],
"source": [
"def encoder(error, list_dic, diff, bound, bins):\n",
" encoded = np.copy(error).astype(int).astype(str).astype(object)\n",
" \n",
" diff = np.reshape(diff,(510,638))\n",
" \n",
" for i in range(encoded.shape[0]):\n",
" for j in range(encoded.shape[1]):\n",
" if i == 0 or i == encoded.shape[0]-1 or j == 0 or j == encoded.shape[1]-1:\n",
" encoded[i][j] = list_dic[0][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[0]:\n",
" encoded[i][j] = list_dic[1][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[1] and diff[i-1][j-1] > bins[0]:\n",
" encoded[i][j] = list_dic[2][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[2] and diff[i-1][j-1] > bins[1]:\n",
" encoded[i][j] = list_dic[3][encoded[i][j]]\n",
" else: \n",
" encoded[i][j] = list_dic[4][encoded[i][j]]\n",
"\n",
" \n",
" return encoded"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9a39a65b",
"metadata": {},
"outputs": [],
"source": [
"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",
"\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_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_keys0[the_values0.index(encoded_matrix[i,j])]) + error_matrix[0][0]\n",
" else:\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",
" \n",
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fc4d80bd",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, bound, bins, predict, A = huffman(images[0])\n",
"encoded_matrix = encoder(np.reshape(new_error,(512,640)), [encode1, encode2, encode3, encode4, encode5], diff, bound, bins)\n",
"list_dic = [encode1, encode2, encode3, encode4, encode5]\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2d82e61a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reconstruct_image = decoder(A, encoded_matrix, list_dic, bins)\n",
"np.allclose(image.reshape(512,640), reconstruct_image)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e35be607",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.720647237500972\n"
]
}
],
"source": [
"\n",
"\"\"\"plt.hexbin(x,y,cmap=\"rocket\")\n",
"plt.colorbar()\n",
"plt.xlim(0,50)\n",
"plt.ylim(0,100)\"\"\"\n",
"\n",
"def rel_freq(x):\n",
" freqs = [x.count(value) / len(x) for value in set(x)] \n",
" return freqs\n",
"print(sp.stats.entropy(rel_freq(list(diff))))\n",
"\n",
"def entropy_check(x, y):\n",
" #freq = rel_freq(list(np.ravel(o)))\n",
" means = []\n",
" for i in range(len(images)):\n",
" p, d, o, e, A = predict(images,0)\n",
" d = d.reshape((510,638))\n",
" x = np.abs(np.ravel(e))\n",
" y = np.ravel(d)\n",
" \n",
" mask1 = y <= 25\n",
" x_masked1 = x[mask1]\n",
"\n",
" mask2 = y > 25\n",
" x_masked2 = x[mask2]\n",
" mask2 = y[mask2] <= 40\n",
" x_masked2 = x_masked2[mask2]\n",
"\n",
" mask3 = y > 40\n",
" x_masked3 = x[mask3]\n",
" mask3 = y[mask3] <= 75\n",
" x_masked3 = x_masked3[mask3]\n",
" \n",
" mask4 = y > 75\n",
" x_masked4 = x[mask4]\n",
"\n",
"\n",
" e_m1 = sp.stats.entropy(rel_freq(list(x_masked1)))\n",
" e_m2 = sp.stats.entropy(rel_freq(list(x_masked2)))\n",
" e_m3 = sp.stats.entropy(rel_freq(list(x_masked3)))\n",
" e_m4 = sp.stats.entropy(rel_freq(list(x_masked4)))\n",
" means.append([e_m1, e_m2, e_m3, e_m4])\n",
" return np.mean(np.array(means).reshape(len(images),4), axis=0)\n",
" \n",
"#print(entropy_check(x, y))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Encoding_decoding.ipynb
View file @ c2c824c5
......@@ -573,7 +573,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.8.11"
}
},
"nbformat": 4,
......
Error_to_Image.ipynb
View file @ c2c824c5
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"id": "dbef8759",
"metadata": {
"id": "dbef8759"
......@@ -28,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "9ed20f84",
"metadata": {
"id": "9ed20f84"
......@@ -218,7 +218,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 8,
"id": "bb11dcd0",
"metadata": {},
"outputs": [],
......@@ -266,7 +266,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 9,
"id": "c01fda28",
"metadata": {},
"outputs": [],
......@@ -335,7 +335,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 10,
"id": "ffa858e8",
"metadata": {},
"outputs": [
......@@ -346,8 +346,8 @@
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/384786850.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mencode_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morig_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/3253315524.py\u001b[0m in \u001b[0;36mencoder\u001b[1;34m(images, i, plot)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;31m#update on throughout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m#new_error[1:-1,1:-1] = np.reshape(error[1:-1,1:-1],(510, 638))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#Set the inside of the updating matrix to be the same as the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;31m#error matrix retreived from predicting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0mkeep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#The top left entry stays the same\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/384786850.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mencode_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morig_image\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/3253315524.py\u001b[0m in \u001b[0;36mencoder\u001b[1;34m(images, i, plot)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[1;31m#update on throughout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m#new_error[1:-1,1:-1] = np.reshape(error[1:-1,1:-1],(510, 638))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#Set the inside of the updating matrix to be the same as the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 24\u001b[0m \u001b[1;31m#error matrix retreived from predicting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[0mkeep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#The top left entry stays the same\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: could not broadcast input array from shape (508,636) into shape (510,638)"
]
}
......@@ -456,12 +456,25 @@
},
{
"cell_type": "code",
"execution_count": 137,
"execution_count": 13,
"id": "30b1c87e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.7830123821108295\n"
]
}
],
"source": [
"def entropy_func(images):\n",
" \"\"\"\n",
" Computes the entropy for all pictures (tiff files) in the images list.\n",
" This gives an idea of how many bits it would take on average to encode the\n",
" given image. The output is a list of entropies, one per image.\n",
" \"\"\"\n",
" entr = []\n",
" for i in range(len(images)):\n",
" prediction, diff, im, err, A = predict(images, i)\n",
......@@ -470,30 +483,26 @@
" entr.append(sp.stats.entropy(counts))\n",
" return entr\n",
"\n",
"e = entropy_func(images)"
"e = entropy_func(images)\n",
"print(np.mean(e))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 15,
"id": "4c268907",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'read'",
"ename": "IndexError",
"evalue": "boolean index did not match indexed array along dimension 0; dimension is 510 but corresponding boolean dimension is 512",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m 2971\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2972\u001b[1;33m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2973\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUnsupportedOperation\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;31mAttributeError\u001b[0m: 'list' object has no attribute 'seek'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/4042219417.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mscenes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_extractor\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[0;32m 73\u001b[0m \u001b[0mimages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage_extractor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscenes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mencode1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhuffman\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_23384/4042219417.py\u001b[0m in \u001b[0;36mhuffman\u001b[1;34m(image)\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredicty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mA\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m#Convert to an array, leaving out the first row because the first row is just housekeeping data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(fp, mode, formats)\u001b[0m\n\u001b[0;32m 2972\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2973\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUnsupportedOperation\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-> 2974\u001b[1;33m \u001b[0mfp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\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[0m\u001b[0;32m 2975\u001b[0m \u001b[0mexclusive_fp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2976\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'read'"
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/1618652474.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[0mscenes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfile_extractor\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[0;32m 73\u001b[0m \u001b[0mimages\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimage_extractor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscenes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mencode1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mboundary\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhuffman\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimages\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/1618652474.py\u001b[0m in \u001b[0;36mhuffman\u001b[1;34m(image)\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdiff\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m25\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0mstring\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\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[0m\u001b[0;32m 21\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCounter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring\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[0;32m 22\u001b[0m \u001b[0mfreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: boolean index did not match indexed array along dimension 0; dimension is 510 but corresponding boolean dimension is 512"
]
}
],
......@@ -501,7 +510,7 @@
"def huffman(image):\n",
" origin, predicty, diff, error, A = predict(image,0)\n",
" \n",
" image = Image.open(image)\n",
" image = Image.open(image[0])\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",
" \n",
......@@ -767,7 +776,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 16,
"id": "64832ca7",
"metadata": {},
"outputs": [
......@@ -775,7 +784,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
"6.736892561802416\n"
"[58 38 13 ... 65 97 32]\n"
]
},
{
"ename": "NameError",
"evalue": "name 'o' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_2620/2795330121.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mfreqs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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[0;32m 8\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfreqs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mentropy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrel_freq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\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[0m\u001b[0;32m 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mentropy_check\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\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;31mNameError\u001b[0m: name 'o' is not defined"
]
}
],
......@@ -785,7 +805,7 @@
"plt.colorbar()\n",
"plt.xlim(0,50)\n",
"plt.ylim(0,100)\"\"\"\n",
"\n",
"print(diff)\n",
"def rel_freq(x):\n",
" freqs = [x.count(value) / len(x) for value in set(x)] \n",
" return freqs\n",
......
Scout_Clean_Code_with_EncDec.ipynb 0 → 100644
View file @ c2c824c5
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "0d67d099",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from prediction_MSE_Scout import file_extractor, image_extractor, im_distribution\n",
"from matplotlib import pyplot as plt\n",
"from itertools import product\n",
"import os\n",
"import sys\n",
"from PIL import Image\n",
"from scipy.optimize import minimize\n",
"from time import time\n",
"from numpy import linalg as la\n",
"from scipy.stats import gaussian_kde\n",
"import seaborn as sns\n",
"from collections import Counter\n",
"import pandas as pd\n",
"import scipy as sp"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "fc76b964",
"metadata": {},
"outputs": [],
"source": [
"def plot_hist(tiff_list):\n",
" \"\"\"\n",
" This function is the leftovers from the first attempt to plot histograms.\n",
" As it stands it needs some work in order to function again. We will\n",
" fix this later. 1/25/22\n",
" \"\"\"\n",
" \n",
" image = tiff_list\n",
" image = Image.open(image) #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",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
" z0 = image[0:-2,0:-2] # get all the first pixel for the entire image\n",
" z1 = image[0:-2,1:-1] # get all the second pixel for the entire image\n",
" z2 = image[0:-2,2::] # get all the third pixel for the entire image\n",
" z3 = image[1:-1,0:-2] # get all the forth pixel for the entire image\n",
" # calculate the out put of the system of equation\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",
" 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",
" z2 = np.ravel(z2)\n",
" z3 = np.ravel(z3)\n",
" neighbor = np.vstack((z0,z1,z2,z3)).T\n",
" # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
" \n",
" # flatten the image to a vector\n",
" image = np.ravel(image[1:-1,1:-1])\n",
" error = image-predict\n",
" \n",
" return image, predict, diff, error, A"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b781115b",
"metadata": {},
"outputs": [],
"source": [
"class NodeTree(object):\n",
" def __init__(self, left=None, right=None):\n",
" self.left = left\n",
" self.right = right\n",
"\n",
" def children(self):\n",
" return self.left, self.right\n",
"\n",
" def __str__(self):\n",
" return self.left, self.right\n",
"\n",
"\n",
"def huffman_code_tree(node, binString=''):\n",
" '''\n",
" Function to find Huffman Code\n",
" '''\n",
" if type(node) is str:\n",
" return {node: binString}\n",
" (l, r) = node.children()\n",
" d = dict()\n",
" d.update(huffman_code_tree(l, binString + '0'))\n",
" d.update(huffman_code_tree(r, binString + '1'))\n",
" return d\n",
"\n",
"\n",
"def make_tree(nodes):\n",
" '''\n",
" Function to make tree\n",
" :param nodes: Nodes\n",
" :return: Root of the tree\n",
" '''\n",
" while len(nodes) > 1:\n",
" (key1, c1) = nodes[-1]\n",
" (key2, c2) = nodes[-2]\n",
" nodes = nodes[:-2]\n",
" node = NodeTree(key1, key2)\n",
" nodes.append((node, c1 + c2))\n",
" nodes = sorted(nodes, key=lambda x: x[1], reverse=True)\n",
" return nodes[0][0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "fe145ec0",
"metadata": {},
"outputs": [],
"source": [
"def huffman(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\n",
" \n",
" image = Image.open(image)\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",
" \n",
" boundary = np.hstack((image[0,:],image[-1,:],image[1:-1,0],image[1:-1,-1]))\n",
" boundary = boundary - image[0,0]\n",
" boundary[0] = image[0,0]\n",
"\n",
" string = [str(i) for i in boundary]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode1 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff <= 25\n",
" string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode2 = huffman_code_tree(node)\n",
"\n",
" \n",
" mask = diff > 25\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 40\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode3 = huffman_code_tree(node)\n",
" \n",
"\n",
" mask = diff > 40\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 70\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode4 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff > 70\n",
" string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" encode5 = huffman_code_tree(node)\n",
"\n",
"\n",
" new_error = np.copy(image)\n",
" new_error[1:-1,1:-1] = np.reshape(error,(510, 638))\n",
" keep = new_error[0,0]\n",
" new_error[0,:] = new_error[0,:] - keep\n",
" new_error[-1,:] = new_error[-1,:] - keep\n",
" new_error[1:-1,0] = new_error[1:-1,0] - keep\n",
" new_error[1:-1,-1] = new_error[1:-1,-1] - keep\n",
" new_error[0,0] = keep\n",
" \n",
" \n",
" #new_error = np.ravel(new_error)\n",
" \n",
" 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, predict, A\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4fb8f5d0",
"metadata": {},
"outputs": [],
"source": [
"def encoder(error, list_dic, diff, bound, bins):\n",
" encoded = np.copy(error).astype(int).astype(str).astype(object)\n",
" \n",
" diff = np.reshape(diff,(510,638))\n",
" \n",
" for i in range(encoded.shape[0]):\n",
" for j in range(encoded.shape[1]):\n",
" if i == 0 or i == encoded.shape[0]-1 or j == 0 or j == encoded.shape[1]-1:\n",
" encoded[i][j] = list_dic[0][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[0]:\n",
" encoded[i][j] = list_dic[1][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[1] and diff[i-1][j-1] > bins[0]:\n",
" encoded[i][j] = list_dic[2][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[2] and diff[i-1][j-1] > bins[1]:\n",
" encoded[i][j] = list_dic[3][encoded[i][j]]\n",
" else: \n",
" encoded[i][j] = list_dic[4][encoded[i][j]]\n",
"\n",
" \n",
" return encoded"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9a39a65b",
"metadata": {},
"outputs": [],
"source": [
"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",
"\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_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_keys0[the_values0.index(encoded_matrix[i,j])]) + error_matrix[0][0]\n",
" else:\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",
" \n",
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fc4d80bd",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, bound, bins, predict, A = huffman(images[0])\n",
"encoded_matrix = encoder(np.reshape(new_error,(512,640)), [encode1, encode2, encode3, encode4, encode5], diff, bound, bins)\n",
"list_dic = [encode1, encode2, encode3, encode4, encode5]\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "2d82e61a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reconstruct_image = decoder(A, encoded_matrix, list_dic, bins)\n",
"np.allclose(image.reshape(512,640), reconstruct_image)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e35be607",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.720647237500972\n"
]
}
],
"source": [
"\n",
"\"\"\"plt.hexbin(x,y,cmap=\"rocket\")\n",
"plt.colorbar()\n",
"plt.xlim(0,50)\n",
"plt.ylim(0,100)\"\"\"\n",
"\n",
"def rel_freq(x):\n",
" freqs = [x.count(value) / len(x) for value in set(x)] \n",
" return freqs\n",
"print(sp.stats.entropy(rel_freq(list(diff))))\n",
"\n",
"def entropy_check(x, y):\n",
" #freq = rel_freq(list(np.ravel(o)))\n",
" means = []\n",
" for i in range(len(images)):\n",
" p, d, o, e, A = predict(images,0)\n",
" d = d.reshape((510,638))\n",
" x = np.abs(np.ravel(e))\n",
" y = np.ravel(d)\n",
" \n",
" mask1 = y <= 25\n",
" x_masked1 = x[mask1]\n",
"\n",
" mask2 = y > 25\n",
" x_masked2 = x[mask2]\n",
" mask2 = y[mask2] <= 40\n",
" x_masked2 = x_masked2[mask2]\n",
"\n",
" mask3 = y > 40\n",
" x_masked3 = x[mask3]\n",
" mask3 = y[mask3] <= 75\n",
" x_masked3 = x_masked3[mask3]\n",
" \n",
" mask4 = y > 75\n",
" x_masked4 = x[mask4]\n",
"\n",
"\n",
" e_m1 = sp.stats.entropy(rel_freq(list(x_masked1)))\n",
" e_m2 = sp.stats.entropy(rel_freq(list(x_masked2)))\n",
" e_m3 = sp.stats.entropy(rel_freq(list(x_masked3)))\n",
" e_m4 = sp.stats.entropy(rel_freq(list(x_masked4)))\n",
" means.append([e_m1, e_m2, e_m3, e_m4])\n",
" return np.mean(np.array(means).reshape(len(images),4), axis=0)\n",
" \n",
"#print(entropy_check(x, y))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Scout_Compression_Exp.ipynb deleted 100644 → 0
View file @ 22a69826
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"id": "9d3f0b36",
"metadata": {},
"outputs": [],
"source": [
"from prediction_MSE_Scout import file_extractor, image_extractor, im_distribution\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import os\n",
"import sys\n",
"from PIL import Image\n",
"import math"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e20525b8",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"num_images = im_distribution(images, \"_1\")"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "837df9c4",
"metadata": {},
"outputs": [],
"source": [
"def compress(inputFile):\n",
" twoBytes = 256*256\n",
" # Read the input file into a numpy array of 8-bit values\n",
" #\n",
" # The img.shape is a 3-type with rows,columns,channels, where\n",
" # channels is the number of components in each pixel. The img.dtype\n",
" # is 'uint8', meaning that each component is an 8-bit unsigned\n",
" # integer.\n",
"\n",
" #img = netpbm.imread(inputFile).astype('uint8')\n",
" img = Image.open(inputFile) #Open the image and read it as an Image object\n",
" img = np.array(img)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
" img = img.astype('uint8')\n",
"\n",
" # Compress the image\n",
" #\n",
" #\n",
" # Note that single-channel images will have a 'shape' with only two\n",
" # components: the y dimensions and the x dimension. So you will\n",
" # have to detect this and set the number of channels accordingly.\n",
" # Furthermore, single-channel images must be indexed as img[y,x]\n",
" # instead of img[y,x,1]. You'll need two pieces of similar code:\n",
" # one piece for the single-channel case and one piece for the\n",
" # multi-channel case.\n",
"\n",
" #startTime = time.time()\n",
"\n",
" outputBytes = bytearray()\n",
"\n",
" # initialize dictionary\n",
" d = {}\n",
" counter = 256\n",
" for i in range(-counter, counter):\n",
" d[str(i)] = i\n",
" # Set Dictionary limit\n",
"\n",
" # Make a list to hold bytes\n",
" tempBytes = []\n",
" # A counter for the number of bytes\n",
" numBytes = 0\n",
" multichannel = False\n",
" \n",
" # for a single channel image\n",
" if (len(img.shape) == 2) :\n",
" multichannel = False\n",
" \n",
" # Go through whole image\n",
" for y in range(img.shape[0]):\n",
" for x in range(img.shape[1]):\n",
" # Initialize prediction to image value\n",
" prediction = img[y][x]\n",
" #\"\"\" \n",
" # Modify prediction to show the difference between prior pixels and current pixel\n",
" if(x != 0):\n",
" prediction = prediction - img[y][x-1]\n",
" elif(y != 0):\n",
" prediction = prediction - img[y-1][x]\n",
" else:\n",
" prediction = prediction - (img[y][x-1]/3 + img[y-1][x]/3 + img[y-1][x-1]/3)\n",
" #\"\"\"\n",
" # Add the predicted value to the bytestream\n",
" tempBytes.append(prediction)\n",
" numBytes += 1\n",
" # Using a string variable as it allows for concatenation\n",
" s = \"\"\n",
" # Set s to the first value of the bytestream \n",
" s = str(int(tempBytes[0]))\n",
" # Go through all bytes\n",
" for i in range(1, numBytes):\n",
" # Do LZW encoding\n",
" # If trying to add entry larger than max size of the dictionary reinitialize the dictionary\n",
" if(counter >= twoBytes):\n",
" counter = 256\n",
" d = {}\n",
" for i in range(-counter, counter):\n",
" d[str(i)] = i\n",
"\n",
" # Add the next byte to the current string. Uses a delimeter to distinguish numbers\n",
" w = s +\"|\"+str(tempBytes[i])\n",
" \n",
" # Checking if it has been seen before\n",
" if w in d:\n",
" s = w\n",
" \n",
" else:\n",
" # Output bytes by splitting integer into two bytes, this allows for a larger dictionary\n",
" outputBytes.append((int(d[s]) >> 8) & 0xFF)\n",
" outputBytes.append(int(d[s]) & 0xFF)\n",
" # Add to dictionarry\n",
" d[w] = counter\n",
" counter += 1\n",
" s = str(int(tempBytes[i]))\n",
" # Check if the last byte was added or not \n",
" if s in d: \n",
" outputBytes.append((int(d[s]) >> 8) & 0xFF)\n",
" outputBytes.append(int(d[s]) & 0xFF)\n",
" \n",
" \n",
" \n",
" return outputBytes, img.shape[0], img.shape[1]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "dec67245",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"103\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\calle\\AppData\\Local\\Temp/ipykernel_12604/4289951463.py:56: RuntimeWarning: overflow encountered in ubyte_scalars\n",
" prediction = prediction - img[y][x-1]\n",
"C:\\Users\\calle\\AppData\\Local\\Temp/ipykernel_12604/4289951463.py:58: RuntimeWarning: overflow encountered in ubyte_scalars\n",
" prediction = prediction - img[y-1][x]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"245\n"
]
}
],
"source": [
"test = images[0]\n",
"out, rows, cols = compress(test)\n",
"print(out[19])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "51938ebb",
"metadata": {},
"outputs": [],
"source": [
"# Uncompress an image\n",
"\n",
"def uncompress(byteArray, rows, columns):\n",
" twoBytes = 256*256\n",
" # Check that it's a known file\n",
"\n",
" \"\"\"if inputFile.readline() != headerText + '\\n':\n",
" sys.stderr.write( \"Input is not in the '%s' format.\\n\" % headerText )\n",
" sys.exit(1)\"\"\"\n",
" \n",
" # Read the rows, columns, and channels. counter\n",
"\n",
" #rows, columns, channels = [ int(x) for x in inputFile.readline().split() ]\n",
"\n",
" # Read the raw bytes.\n",
"\n",
" inputBytes = byteArray\n",
"\n",
" # Build the image\n",
" #\n",
" # REPLACE THIS WITH YOUR OWN CODE TO CONVERT THE 'inputBytes' ARRAY INTO AN IMAGE IN 'img'.\n",
" \n",
"\n",
" result = []\n",
"\n",
" # initialize the dictionary in the opposite was as compress and use an array as the value\n",
" d = {} # create a dictionary\n",
" counter = 256\n",
" \n",
" # Initialize dictionary with values equalling keys from [-256,256]\n",
" for i in range(-counter, counter):\n",
" d[i] = [i]\n",
"\n",
" img = np.empty([rows,columns], dtype=np.uint8 )\n",
"\n",
" byteIter = iter(inputBytes)\n",
"\n",
" # Get encoding in the form of next two bytes\n",
" new = (byteIter.__next__() >> 8) + byteIter.__next__()\n",
" s = d[new]\n",
" \n",
" result.append(s[0])\n",
"\n",
" for i in range(1, len(inputBytes)//2):\n",
" \n",
" # again reset the dictionary if it reaches the limit\n",
" # Initialize dictionary with values equalling keys from [-256,256]\n",
"\n",
" if counter >= twoBytes:\n",
" d = {} # initialize blank dictionary\n",
" counter = 256\n",
" for i in range(-counter, counter):\n",
" d[i] = [i]\n",
" \n",
" \n",
" new = (byteIter.__next__() >> 8) + byteIter.__next__()\n",
"\n",
" #retrieve value of dictionary entry from dictionary or create entry assuming it has not yet been entered into dictionary\n",
" \n",
" if new in d:\n",
" d_value = d[new]\n",
" else:\n",
" d_value = []\n",
" for j in s:\n",
" d_value.append(j)\n",
" d_value.append(s[0])\n",
" \n",
" #add dictionary entry value to the result\n",
" for k in range(len(d_value)):\n",
" result.append(d_value[k])\n",
"\n",
" #Create entry in dictionary\n",
" temp = []\n",
" for j in s:\n",
" temp.append(j)\n",
" temp.append(s[0])\n",
" d[counter] = temp\n",
" counter += 1\n",
" \n",
" \n",
" # reset decoded string to dictionary entry value\n",
" s = d_value\n",
" \n",
" print(result[:20])\n",
" channels = 1\n",
" \n",
" #implement predictive encoding\n",
" prediction = 0\n",
" counter = 0\n",
"\n",
" # for a single channel image\n",
" if (channels == 1):\n",
" # Go through whole image\n",
" for y in range(rows):\n",
" for x in range(columns):\n",
" #'''\n",
" if(x != 0):\n",
" prediction = img[y][x-1]\n",
" elif(y != 0):\n",
" prediction = img[y-1][x]\n",
" else:\n",
" prediction = (img[y][x-1]/3 + img[y-1][x]/3 + img[y-1][x-1]/3)\n",
" #'''\n",
" img[y,x] = result[counter] + prediction\n",
" counter += 1\n",
" \n",
" return img\n",
"\n",
" # Output the image\n",
"\n",
" #netpbm.imsave( outputFile, img )\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "74528264",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[164, 246, 24, 0, 4, 239, 251, 13, 12, 245, 246, 6, 6, 250, 0, 0, 3, 4, 242, 0]\n"
]
},
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12604/601870618.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mimgg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0muncompress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrows\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12604/2882586717.py\u001b[0m in \u001b[0;36muncompress\u001b[1;34m(byteArray, rows, columns)\u001b[0m\n\u001b[0;32m 102\u001b[0m \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m3\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m3\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[1;31m#'''\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 104\u001b[1;33m \u001b[0mimg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcounter\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mprediction\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 105\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 106\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"imgg = uncompress(out, rows, cols)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "ea5c3c61",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
" 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
" 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
" 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
" 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
" 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
" 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
" 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
" 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155,\n",
" 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,\n",
" 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
" 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194,\n",
" 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207,\n",
" 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220,\n",
" 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,\n",
" 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246,\n",
" 247, 248, 249, 250, 251, 252, 253, 254, 255], dtype=uint8)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img = Image.open(test) #Open the image and read it as an Image object\n",
"img = np.array(img)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
"img = img.astype('uint8')\n",
"np.unique(img.ravel())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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