Skip to content

I
image-compression
Commit dc5e7ac2 authored by Nathaniel Callens's avatar Nathaniel Callens
Browse files
Options

deletions made

parent 20af6366
Hide whitespace changes
Inline Side-by-side
Showing with 57 additions and 427 deletions
.ipynb_checkpoints/Error_to_Image-checkpoint.ipynb
View file @ dc5e7ac2
......@@ -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",
......@@ -470,7 +483,8 @@
" entr.append(sp.stats.entropy(counts))\n",
" return entr\n",
"\n",
"e = entropy_func(images)"
"e = entropy_func(images)\n",
"print(np.mean(e))"
]
},
{
......
Encoding_decoding.ipynb
View file @ dc5e7ac2
......@@ -432,7 +432,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.8.11"
}
},
"nbformat": 4,
......
Error_to_Image.ipynb
View file @ dc5e7ac2
......@@ -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",
......
Scout_Compression_Exp.ipynb deleted 100644 → 0
View file @ 20af6366
{
"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
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment