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image-compression
Commit 903776f0 authored by Nathaniel Callens's avatar Nathaniel Callens
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entropy

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.ipynb_checkpoints/Encoding_decoding-checkpoint.ipynb 0 → 100644
View file @ 903776f0
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "14f74f21",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\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,linprog\n",
"import time\n",
"import seaborn as sns\n",
"from sklearn.neighbors import KernelDensity\n",
"import pandas as pd\n",
"from collections import Counter\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c16af61f",
"metadata": {},
"outputs": [],
"source": [
"def file_extractor(dirname=\"images\"):\n",
" files = os.listdir(dirname)\n",
" scenes = []\n",
" for file in files:\n",
" scenes.append(os.path.join(dirname, file))\n",
" return scenes\n",
"\n",
"def image_extractor(scenes):\n",
" image_folder = []\n",
" for scene in scenes:\n",
" files = os.listdir(scene)\n",
" for file in files:\n",
" image_folder.append(os.path.join(scene, file))\n",
" images = []\n",
" for folder in image_folder:\n",
" ims = os.listdir(folder)\n",
" for im in ims:\n",
" if im[-4:] == \".jp4\" or im[-7:] == \"_6.tiff\":\n",
" continue\n",
" else:\n",
" images.append(os.path.join(folder, im))\n",
" return images #returns a list of file paths to .tiff files in the specified directory given in file_extractor\n",
"\n",
"def im_distribution(images, num):\n",
" \"\"\"\n",
" Function that extracts tiff files from specific cameras and returns a list of all\n",
" the tiff files corresponding to that camera. i.e. all pictures labeled \"_7.tiff\" or otherwise\n",
" specified camera numbers.\n",
" \n",
" Parameters:\n",
" images (list): list of all tiff files, regardless of classification. This is NOT a list of directories but\n",
" of specific tiff files that can be opened right away. This is the list that we iterate through and \n",
" divide.\n",
" \n",
" num (str): a string designation for the camera number that we want to extract i.e. \"14\" for double digits\n",
" of \"_1\" for single digits.\n",
" \n",
" Returns:\n",
" tiff (list): A list of tiff files that have the specified designation from num. They are the files extracted\n",
" from the 'images' list that correspond to the given num.\n",
" \"\"\"\n",
" tiff = []\n",
" for im in images:\n",
" if im[-7:-5] == num:\n",
" tiff.append(im)\n",
" return tiff"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "aceba613",
"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",
" # 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": "6b965751",
"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": 5,
"id": "b7561883",
"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\n",
" \n",
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, boundary, bins = huffman(images[0])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2eb774d2",
"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": "8eeb40d0",
"metadata": {},
"outputs": [],
"source": [
"def decoder(A, encoded_matrix, encoding_dict):\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",
" the_keys = list(encode_dict.keys())\n",
" the_values = list(encode_dict.values())\n",
" error_matrix = encoded_matrix.copy()\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_keys[the_values.index(error_matrix[i,j])])\n",
" \n",
" elif i == 0 or i == error_matrix.shape[0]-1 or j == 0 or j == error_matrix.shape[1]-1:\n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])]) + error_matrix[0][0]\n",
" else:\n",
" \"\"\"z0, z1, z2, z3 = error_matrix[i-1][j-1], error_matrix[i-1][j], \\\n",
" error_matrix[i-1][j+1], error_matrix[i][j-1]\n",
" y = np.vstack((-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3))\"\"\"\n",
" \n",
" error_matrix[i][j] = int(the_keys[the_values.index(error_matrix[i,j])])\n",
" \n",
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3e0e9742",
"metadata": {},
"outputs": [],
"source": [
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, bound, bins = huffman(images[0])\n",
"encoded_matrix = encoder(np.reshape(new_error,(512,640)), [encode1, encode2, encode3, encode4, encode5], diff, bound, bins)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e6ea4f99",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['01100010001' '11000110' '100101010' ... '101110011' '00010100'\n",
" '1111000100']\n",
" ['10011100' '100001' '111000' ... '10111011' '00111' '1111001101']\n",
" ['10101111' '100100' '100000' ... '111100' '111000' '00010100']\n",
" ...\n",
" ['110001000' '100001' '111011' ... '1010010' '100000' '10011000']\n",
" ['0100011101' '111010' '00110' ... '1000101' '1100100' '10011010']\n",
" ['00100010' '110111101' '110110100' ... '00010010' '10100000'\n",
" '110110101']]\n"
]
}
],
"source": [
"print(encoded_matrix)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c07a23e",
"metadata": {},
"outputs": [],
"source": []
}
],
"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
}
.ipynb_checkpoints/Error_to_Image-checkpoint.ipynb
View file @ 903776f0
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"id": "dbef8759", "id": "dbef8759",
"metadata": { "metadata": {
"id": "dbef8759" "id": "dbef8759"
...@@ -28,7 +28,7 @@ ...@@ -28,7 +28,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 2,
"id": "9ed20f84", "id": "9ed20f84",
"metadata": { "metadata": {
"id": "9ed20f84" "id": "9ed20f84"
...@@ -193,16 +193,18 @@ ...@@ -193,16 +193,18 @@
"id": "5d290a0c", "id": "5d290a0c",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\calle\\AppData\\Local\\Temp/ipykernel_23384/389333.py:1: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" im == new_error\n"
]
},
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[ True, True, True, ..., True, True, True],\n", "False"
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" ...,\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True]])"
] ]
}, },
"execution_count": 8, "execution_count": 8,
...@@ -333,17 +335,30 @@ ...@@ -333,17 +335,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 62, "execution_count": 11,
"id": "ffa858e8", "id": "ffa858e8",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "ValueError",
"evalue": "could not broadcast input array from shape (508,636) into shape (510,638)",
"output_type": "error",
"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;31mValueError\u001b[0m: could not broadcast input array from shape (508,636) into shape (510,638)"
]
}
],
"source": [ "source": [
"encode_dict, encoding, error, orig_image = encoder(images, 2, plot=False)" "encode_dict, encoding, error, orig_image = encoder(images, 2, plot=False)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 121, "execution_count": 12,
"id": "825cc48c", "id": "825cc48c",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
...@@ -376,20 +391,44 @@ ...@@ -376,20 +391,44 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 122, "execution_count": 13,
"id": "ba1d2c2c", "id": "ba1d2c2c",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'encoding' 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_23384/3979147550.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'encoding' is not defined"
]
}
],
"source": [ "source": [
"em = decoder(A, encoding, encode_dict)" "em = decoder(A, encoding, encode_dict)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 123, "execution_count": 14,
"id": "b2cdce6d", "id": "b2cdce6d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'em' 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_23384/2268978435.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhopefully\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreconstruct\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mA\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 2\u001b[0m \u001b[1;31m#22487 22483 22521 22464\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 'em' is not defined"
]
}
],
"source": [ "source": [
"hopefully = reconstruct(em, A)\n", "hopefully = reconstruct(em, A)\n",
"#22487 22483 22521 22464" "#22487 22483 22521 22464"
...@@ -436,19 +475,39 @@ ...@@ -436,19 +475,39 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 125, "execution_count": 18,
"id": "4c268907", "id": "4c268907",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'read'",
"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'"
]
}
],
"source": [ "source": [
"def huffman(image, i):\n", "def huffman(image):\n",
" pred, diff, origin, error, A = predict(image, i)\n", " origin, predicty, diff, error, A = predict(image,0)\n",
" pred = np.ravel(pred[1:-1, 1:-1])\n", " \n",
" error = np.ravel(error)\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", " \n",
" boundary = np.hstack((origin[0,:],origin[-1,:],origin[1:-1,0],origin[1:-1,-1]))\n", " boundary = np.hstack((image[0,:],image[-1,:],image[1:-1,0],image[1:-1,-1]))\n",
" boundary = boundary - origin[0,0]\n", " boundary = boundary - image[0,0]\n",
" boundary[0] = origin[0,0]\n", " boundary[0] = image[0,0]\n",
"\n", "\n",
" string = [str(i) for i in boundary]\n", " string = [str(i) for i in boundary]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
...@@ -457,7 +516,7 @@ ...@@ -457,7 +516,7 @@
" encode1 = huffman_code_tree(node)\n", " encode1 = huffman_code_tree(node)\n",
" \n", " \n",
" \n", " \n",
" mask = diff <= 10\n", " mask = diff <= 25\n",
" string = [str(i) for i in error[mask].astype(int)]\n", " string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
...@@ -465,9 +524,9 @@ ...@@ -465,9 +524,9 @@
" encode2 = huffman_code_tree(node)\n", " encode2 = huffman_code_tree(node)\n",
"\n", "\n",
" \n", " \n",
" mask = diff > 10\n", " mask = diff > 25\n",
" new_error = error[mask]\n", " new_error = error[mask]\n",
" mask2 = diff[mask] <= 25\n", " mask2 = diff[mask] <= 40\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n", " string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
...@@ -475,9 +534,9 @@ ...@@ -475,9 +534,9 @@
" encode3 = huffman_code_tree(node)\n", " encode3 = huffman_code_tree(node)\n",
" \n", " \n",
"\n", "\n",
" mask = diff > 25\n", " mask = diff > 40\n",
" new_error = error[mask]\n", " new_error = error[mask]\n",
" mask2 = diff[mask] <= 45\n", " mask2 = diff[mask] <= 70\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n", " string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
...@@ -485,17 +544,15 @@ ...@@ -485,17 +544,15 @@
" encode4 = huffman_code_tree(node)\n", " encode4 = huffman_code_tree(node)\n",
" \n", " \n",
" \n", " \n",
" mask = diff > 45\n", " mask = diff > 70\n",
" string = [str(i) for i in error[mask].astype(int)]\n", " string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n", " node = make_tree(freq)\n",
" encode5 = huffman_code_tree(node)\n", " encode5 = huffman_code_tree(node)\n",
"\n", "\n",
" \n",
" \n",
"\n", "\n",
" new_error = np.copy(origin)\n", " new_error = np.copy(image)\n",
" new_error[1:-1,1:-1] = np.reshape(error,(510, 638))\n", " new_error[1:-1,1:-1] = np.reshape(error,(510, 638))\n",
" keep = new_error[0,0]\n", " keep = new_error[0,0]\n",
" new_error[0,:] = new_error[0,:] - keep\n", " new_error[0,:] = new_error[0,:] - keep\n",
...@@ -503,13 +560,18 @@ ...@@ -503,13 +560,18 @@
" new_error[1:-1,0] = new_error[1:-1,0] - 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[1:-1,-1] = new_error[1:-1,-1] - keep\n",
" new_error[0,0] = keep\n", " new_error[0,0] = keep\n",
" \n",
" \n",
" #new_error = np.ravel(new_error)\n", " #new_error = np.ravel(new_error)\n",
" \n", " \n",
" bins = [25,40,70]\n",
" \n",
" # return the huffman dictionary\n", " # return the huffman dictionary\n",
" return encode1, encode2, encode3, encode4, encode5, np.ravel(origin), error, diff, boundary\n", " return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, new_error, diff, boundary, bins\n",
"\n", " \n",
"\n", "scenes = file_extractor()\n",
"encode1, encode2, encode3, encode4, encode5, origin, error, diff, boundary = huffman(images, 0)" "images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, boundary, bins = huffman(images)"
] ]
}, },
{ {
...@@ -705,7 +767,7 @@ ...@@ -705,7 +767,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 204, "execution_count": 72,
"id": "64832ca7", "id": "64832ca7",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
...@@ -713,13 +775,56 @@ ...@@ -713,13 +775,56 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"True\n" "6.736892561802416\n"
] ]
} }
], ],
"source": [ "source": [
"\n", "\n",
"print('101011' in enc_mat)" "\"\"\"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(np.ravel(o)))))\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))"
] ]
} }
], ],
......
Encoding_decoding.ipynb
View file @ 903776f0
...@@ -379,7 +379,7 @@ ...@@ -379,7 +379,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.1" "version": "3.8.11"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
Error_to_Image.ipynb
View file @ 903776f0
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"id": "dbef8759", "id": "dbef8759",
"metadata": { "metadata": {
"id": "dbef8759" "id": "dbef8759"
...@@ -28,7 +28,7 @@ ...@@ -28,7 +28,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 2,
"id": "9ed20f84", "id": "9ed20f84",
"metadata": { "metadata": {
"id": "9ed20f84" "id": "9ed20f84"
...@@ -193,16 +193,18 @@ ...@@ -193,16 +193,18 @@
"id": "5d290a0c", "id": "5d290a0c",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\calle\\AppData\\Local\\Temp/ipykernel_23384/389333.py:1: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.\n",
" im == new_error\n"
]
},
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array([[ True, True, True, ..., True, True, True],\n", "False"
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" ...,\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True],\n",
" [ True, True, True, ..., True, True, True]])"
] ]
}, },
"execution_count": 8, "execution_count": 8,
...@@ -333,17 +335,30 @@ ...@@ -333,17 +335,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 62, "execution_count": 11,
"id": "ffa858e8", "id": "ffa858e8",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "ValueError",
"evalue": "could not broadcast input array from shape (508,636) into shape (510,638)",
"output_type": "error",
"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;31mValueError\u001b[0m: could not broadcast input array from shape (508,636) into shape (510,638)"
]
}
],
"source": [ "source": [
"encode_dict, encoding, error, orig_image = encoder(images, 2, plot=False)" "encode_dict, encoding, error, orig_image = encoder(images, 2, plot=False)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 121, "execution_count": 12,
"id": "825cc48c", "id": "825cc48c",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
...@@ -376,20 +391,44 @@ ...@@ -376,20 +391,44 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 122, "execution_count": 13,
"id": "ba1d2c2c", "id": "ba1d2c2c",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'encoding' 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_23384/3979147550.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'encoding' is not defined"
]
}
],
"source": [ "source": [
"em = decoder(A, encoding, encode_dict)" "em = decoder(A, encoding, encode_dict)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 123, "execution_count": 14,
"id": "b2cdce6d", "id": "b2cdce6d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'em' 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_23384/2268978435.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhopefully\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreconstruct\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mA\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 2\u001b[0m \u001b[1;31m#22487 22483 22521 22464\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 'em' is not defined"
]
}
],
"source": [ "source": [
"hopefully = reconstruct(em, A)\n", "hopefully = reconstruct(em, A)\n",
"#22487 22483 22521 22464" "#22487 22483 22521 22464"
...@@ -436,19 +475,39 @@ ...@@ -436,19 +475,39 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 125, "execution_count": 18,
"id": "4c268907", "id": "4c268907",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'read'",
"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'"
]
}
],
"source": [ "source": [
"def huffman(image, i):\n", "def huffman(image):\n",
" pred, diff, origin, error, A = predict(image, i)\n", " origin, predicty, diff, error, A = predict(image,0)\n",
" pred = np.ravel(pred[1:-1, 1:-1])\n", " \n",
" error = np.ravel(error)\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", " \n",
" boundary = np.hstack((origin[0,:],origin[-1,:],origin[1:-1,0],origin[1:-1,-1]))\n", " boundary = np.hstack((image[0,:],image[-1,:],image[1:-1,0],image[1:-1,-1]))\n",
" boundary = boundary - origin[0,0]\n", " boundary = boundary - image[0,0]\n",
" boundary[0] = origin[0,0]\n", " boundary[0] = image[0,0]\n",
"\n", "\n",
" string = [str(i) for i in boundary]\n", " string = [str(i) for i in boundary]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
...@@ -457,7 +516,7 @@ ...@@ -457,7 +516,7 @@
" encode1 = huffman_code_tree(node)\n", " encode1 = huffman_code_tree(node)\n",
" \n", " \n",
" \n", " \n",
" mask = diff <= 10\n", " mask = diff <= 25\n",
" string = [str(i) for i in error[mask].astype(int)]\n", " string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
...@@ -465,9 +524,9 @@ ...@@ -465,9 +524,9 @@
" encode2 = huffman_code_tree(node)\n", " encode2 = huffman_code_tree(node)\n",
"\n", "\n",
" \n", " \n",
" mask = diff > 10\n", " mask = diff > 25\n",
" new_error = error[mask]\n", " new_error = error[mask]\n",
" mask2 = diff[mask] <= 25\n", " mask2 = diff[mask] <= 40\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n", " string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
...@@ -475,9 +534,9 @@ ...@@ -475,9 +534,9 @@
" encode3 = huffman_code_tree(node)\n", " encode3 = huffman_code_tree(node)\n",
" \n", " \n",
"\n", "\n",
" mask = diff > 25\n", " mask = diff > 40\n",
" new_error = error[mask]\n", " new_error = error[mask]\n",
" mask2 = diff[mask] <= 45\n", " mask2 = diff[mask] <= 70\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n", " string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
...@@ -485,17 +544,15 @@ ...@@ -485,17 +544,15 @@
" encode4 = huffman_code_tree(node)\n", " encode4 = huffman_code_tree(node)\n",
" \n", " \n",
" \n", " \n",
" mask = diff > 45\n", " mask = diff > 70\n",
" string = [str(i) for i in error[mask].astype(int)]\n", " string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(string))\n", " freq = dict(Counter(string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n", " freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n", " node = make_tree(freq)\n",
" encode5 = huffman_code_tree(node)\n", " encode5 = huffman_code_tree(node)\n",
"\n", "\n",
" \n",
" \n",
"\n", "\n",
" new_error = np.copy(origin)\n", " new_error = np.copy(image)\n",
" new_error[1:-1,1:-1] = np.reshape(error,(510, 638))\n", " new_error[1:-1,1:-1] = np.reshape(error,(510, 638))\n",
" keep = new_error[0,0]\n", " keep = new_error[0,0]\n",
" new_error[0,:] = new_error[0,:] - keep\n", " new_error[0,:] = new_error[0,:] - keep\n",
...@@ -503,13 +560,18 @@ ...@@ -503,13 +560,18 @@
" new_error[1:-1,0] = new_error[1:-1,0] - 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[1:-1,-1] = new_error[1:-1,-1] - keep\n",
" new_error[0,0] = keep\n", " new_error[0,0] = keep\n",
" \n",
" \n",
" #new_error = np.ravel(new_error)\n", " #new_error = np.ravel(new_error)\n",
" \n", " \n",
" bins = [25,40,70]\n",
" \n",
" # return the huffman dictionary\n", " # return the huffman dictionary\n",
" return encode1, encode2, encode3, encode4, encode5, np.ravel(origin), error, diff, boundary\n", " return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, new_error, diff, boundary, bins\n",
"\n", " \n",
"\n", "scenes = file_extractor()\n",
"encode1, encode2, encode3, encode4, encode5, origin, error, diff, boundary = huffman(images, 0)" "images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, new_error, diff, boundary, bins = huffman(images)"
] ]
}, },
{ {
...@@ -705,7 +767,7 @@ ...@@ -705,7 +767,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 204, "execution_count": 72,
"id": "64832ca7", "id": "64832ca7",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
...@@ -713,13 +775,56 @@ ...@@ -713,13 +775,56 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"True\n" "6.736892561802416\n"
] ]
} }
], ],
"source": [ "source": [
"\n", "\n",
"print('101011' in enc_mat)" "\"\"\"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(np.ravel(o)))))\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))"
] ]
} }
], ],
......
multiple_compression_kelly.ipynb
View file @ 903776f0
...@@ -816,7 +816,7 @@ ...@@ -816,7 +816,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.9.1" "version": "3.8.11"
} }
}, },
"nbformat": 4, "nbformat": 4,
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