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image-compression
Commit ad92c63a authored by Bryce Hepner's avatar Bryce Hepner
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Still has bins doesn't work. Has bin counting code

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testpyfiles/NoBinsDemo.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"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",
"from sklearn.neighbors import KernelDensity\n",
"from collections import Counter\n",
"import numpy.linalg as la"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def file_extractor(dirname=\"images\"):\n",
" files = os.listdir(dirname)\n",
" scenes = []\n",
" for file in files:\n",
" if file == '.DS_Store':\n",
" continue\n",
" else:\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",
" if file[-5:] != \".tiff\" or file[-7:] == \"_6.tiff\":\n",
" continue\n",
" else:\n",
" image_folder.append(os.path.join(scene, file))\n",
" return image_folder #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": 4,
"metadata": {},
"outputs": [],
"source": [
"def predict_pix(tiff_image_path, difference = True):\n",
" \"\"\"\n",
" This function predict the pixel values excluding the boundary.\n",
" Using the 4 neighbor pixel values and MSE to predict the next pixel value\n",
" (-1,1) (0,1) (1,1) => relative position of the 4 other given values\n",
" (-1,0) (0,0) => (0,0) is the one we want to predict\n",
" take the derivative of mean square error to solve for the system of equation \n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
" A @ [a, b, c] = [-z0+z2-z3, z0+z1+z2, -z0-z1-z2-z3] where z0 = (-1,1), z1 = (0,1), z2 = (1,1), z3 = (-1,0)\n",
" and the predicted pixel value is c.\n",
" \n",
" Input:\n",
" tiff_image_path (string): path to the tiff file\n",
" \n",
" Return:\n",
" image ndarray(512 X 640): original image \n",
" predict ndarray(325380,): predicted image excluding the boundary\n",
" diff. ndarray(325380,): IF difference = TRUE, difference between the min and max of four neighbors exclude the boundary\n",
" ELSE: the residuals of the four nearest pixels to a fitted hyperplane\n",
" error ndarray(325380,): difference between the original image and predicted image\n",
" A ndarray(3 X 3): system of equation\n",
" \"\"\"\n",
" image_obj = Image.open(tiff_image_path) #Open the image and read it as an Image object\n",
" image_array = np.array(image_obj)[1:,:].astype(int) #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
" # image_array = image_array.astype(int) \n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
" # where z0 = (-1,1), z1 = (0,1), z2 = (1,1), z3 = (-1,0)\n",
" z0 = image_array[0:-2,0:-2] # get all the first pixel for the entire image\n",
" z1 = image_array[0:-2,1:-1] # get all the second pixel for the entire image\n",
" z2 = image_array[0:-2,2::] # get all the third pixel for the entire image\n",
" z3 = image_array[1:-1,0:-2] # get all the forth pixel for the entire image\n",
" \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",
" \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",
" \n",
" #Matrix system of points that will be used to solve the least squares fitting hyperplane\n",
" points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])\n",
" \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",
" \n",
" if difference:\n",
" # calculate the difference\n",
" diff = np.max(neighbor,axis = 1) - np.min(neighbor, axis=1)\n",
" \n",
" else:\n",
" #Compute the best fitting hyperplane using least squares\n",
" #The res is the residuals of the four points used to fit the hyperplane (summed distance of each of the \n",
" #points to the hyperplane), it is a measure of gradient\n",
" f, diff, rank, s = la.lstsq(points, neighbor.T, rcond=None)\n",
" diff = diff.astype(int)\n",
" \n",
" # calculate the error\n",
" error = np.ravel(image_array[1:-1,1:-1])-predict\n",
" \n",
" return image_array, predict, diff, error"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"this huffman encoding code is found online\n",
"https://favtutor.com/blogs/huffman-coding\n",
"\"\"\"\n",
"\n",
"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",
" #reverse True, decending order\n",
" nodes = sorted(nodes, key=lambda x: x[1], reverse=True)\n",
" return nodes[0][0]\n",
"def decode_string(huffman_string, the_keys, the_values):\n",
" for i in range(len(huffman_string)):\n",
" try:\n",
" return (int(the_keys[the_values.index(huffman_string[:i+1])]),huffman_string[i+1:])\n",
" except:\n",
" pass\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def make_dictionary(tiff_image_path_list, num_bins=4, difference = True):\n",
" \"\"\"\n",
" This function is used to encode the error based on the difference\n",
" and split the difference into different bins\n",
" \n",
" Input:\n",
" tiff_image_path (string): path to the tiff file\n",
" num_bins (int): number of bins\n",
" \n",
" Return:\n",
" huffman_encoding_list list (num_bins + 1): a list of dictionary\n",
" image_array ndarray (512, 640): original image\n",
" new_error ndarray (512, 640): error that includes the boundary\n",
" diff ndarray (510, 638): difference of min and max of the 4 neighbors\n",
" boundary ndarray (2300,): the boundary values after subtracting the very first pixel value\n",
" predict ndarray (325380,): the list of predicted values\n",
" bins list (num_bins - 1,): a list of threshold to cut the bins\n",
" A ndarray (3 X 3): system of equation\n",
" \n",
" \"\"\"\n",
" list_of_all_vals = []\n",
" huffman_encoding_list = []\n",
" for i in range(num_bins+1):\n",
" list_of_all_vals.append([])\n",
" for i, tiff_image_path in enumerate(tiff_image_path_list):\n",
" # get the image_array, etc\n",
" image_array, predict, diff, error= predict_pix(tiff_image_path, difference)\n",
" \n",
" # calculate the number of points that will go in each bin\n",
" data_points_per_bin = diff.size // num_bins\n",
"\n",
" # sort the difference and create the bins\n",
" sorted_diff = np.sort(diff.copy())\n",
" # bins = [12,60,180]\n",
" bins = [21,31,48]\n",
" # get the boundary \n",
" boundary = np.hstack((image_array[0,:],image_array[-1,:],image_array[1:-1,0],image_array[1:-1,-1]))\n",
" \n",
" # take the difference of the boundary with the very first pixel\n",
" boundary = boundary - image_array[0,0]\n",
" \n",
" #boundary is 1dim, so boundary[0] is just the first element\n",
" boundary[0] = image_array[0,0]\n",
" \n",
" # huffman encode the boundary\n",
" for j in boundary:\n",
" list_of_all_vals[0].append(str(j))\n",
" \n",
" # create a list of huffman table\n",
" n = len(bins)\n",
" \n",
" # loop through different bins\n",
" for k in range (0,n):\n",
" # the first bin\n",
" if k == 0 :\n",
" # get the point within the bin and huffman huffman_encoding_dict\n",
" mask = diff <= bins[k]\n",
" for j in error[mask].astype(int):\n",
" list_of_all_vals[k+1].append(str(j))\n",
"\n",
" \n",
" # the middle bins\n",
" else:\n",
" # get the point within the bin and huffman huffman_encoding_dict\n",
" mask = diff > bins[k-1]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= bins[k]\n",
" for j in new_error[mask2].astype(int):\n",
" list_of_all_vals[k+1].append(str(j))\n",
"\n",
" \n",
" # the last bin \n",
" # get the point within the bin and huffman huffman_encoding_dict\n",
" mask = diff > bins[-1]\n",
" for j in error[mask].astype(int):\n",
" list_of_all_vals[-1].append(str(j))\n",
" for item in list_of_all_vals:\n",
" freq = dict(Counter(item))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" huffman_encoding_list.append(huffman_code_tree(node))\n",
" # create a error matrix that includes the boundary (used in encoding matrix)\n",
" new_error = np.copy(image_array)\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",
" # huffman_encoding_list = list(set(huffman_encoding_list))\n",
" diff = np.reshape(diff,(510,638))\n",
" # return the huffman dictionary\n",
" return huffman_encoding_list,bins\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def huffman(tiff_image_path, num_bins=4, difference = True):\n",
" \"\"\"\n",
" This function is used to encode the error based on the difference\n",
" and split the difference into different bins\n",
" \n",
" Input:\n",
" tiff_image_path (string): path to the tiff file\n",
" num_bins (int): number of bins\n",
" \n",
" Return:\n",
" huffman_encoding_list list (num_bins + 1): a list of dictionary\n",
" image_array ndarray (512, 640): original image\n",
" new_error ndarray (512, 640): error that includes the boundary\n",
" diff ndarray (510, 638): difference of min and max of the 4 neighbors\n",
" boundary ndarray (2300,): the boundary values after subtracting the very first pixel value\n",
" predict ndarray (325380,): the list of predicted values\n",
" bins list (num_bins - 1,): a list of threshold to cut the bins\n",
" A ndarray (3 X 3): system of equation\n",
" \"\"\"\n",
" # get the image_array, etc\n",
" image_array, predict, diff, error= predict_pix(tiff_image_path, difference)\n",
" \n",
" # calculate the number of points that will go in each bin\n",
" data_points_per_bin = diff.size // num_bins\n",
"\n",
" # sort the difference and create the bins\n",
" sorted_diff = np.sort(diff.copy())\n",
" # bins = [sorted_diff[i*data_points_per_bin] for i in range(1,num_bins)]\n",
" # bins = [12,60,180]\n",
" bins = [21,31,48]\n",
" # get the boundary \n",
" boundary = np.hstack((image_array[0,:],image_array[-1,:],image_array[1:-1,0],image_array[1:-1,-1]))\n",
" \n",
" # take the difference of the boundary with the very first pixel\n",
" boundary = boundary - image_array[0,0]\n",
" \n",
" #boundary is 1dim, so boundary[0] is just the first element\n",
" boundary[0] = image_array[0,0]\n",
" \n",
" # huffman encode the boundary\n",
" bound_vals_as_string = [str(i) for i in boundary]\n",
" freq = dict(Counter(bound_vals_as_string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" huffman_encoding_dict = huffman_code_tree(node)\n",
" \n",
" # create a list of huffman table\n",
" huffman_encoding_list = [huffman_encoding_dict]\n",
" n = len(bins)\n",
" \n",
" # loop through different bins\n",
" for i in range (0,n):\n",
" # the first bin\n",
" if i == 0 :\n",
" # get the point within the bin and huffman huffman_encoding_dict\n",
" mask = diff <= bins[i]\n",
" line_as_string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(line_as_string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" huffman_encoding_dict = huffman_code_tree(node)\n",
" huffman_encoding_list.append(huffman_encoding_dict)\n",
" \n",
" # the middle bins\n",
" else:\n",
" # get the point within the bin and huffman huffman_encoding_dict\n",
" mask = diff > bins[i-1]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= bins[i]\n",
" line_as_string = [str(i) for i in new_error[mask2].astype(int)]\n",
" freq = dict(Counter(line_as_string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" huffman_encoding_dict = huffman_code_tree(node)\n",
" huffman_encoding_list.append(huffman_encoding_dict)\n",
" \n",
" # the last bin \n",
" # get the point within the bin and huffman huffman_encoding_dict\n",
" mask = diff > bins[-1]\n",
" line_as_string = [str(i) for i in error[mask].astype(int)]\n",
" freq = dict(Counter(line_as_string))\n",
" freq = sorted(freq.items(), key=lambda x: x[1], reverse=True)\n",
" node = make_tree(freq)\n",
" huffman_encoding_dict = huffman_code_tree(node)\n",
" huffman_encoding_list.append(huffman_encoding_dict)\n",
"\n",
" # create a error matrix that includes the boundary (used in encoding matrix)\n",
" new_error = np.copy(image_array)\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",
" # huffman_encoding_list = list(set(huffman_encoding_list))\n",
" diff = np.reshape(diff,(510,638))\n",
" # return the huffman dictionary\n",
"\n",
" return huffman_encoding_list, image_array, new_error, diff, boundary, predict, bins\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def encoder(error, list_dic, diff, bound, bins):\n",
" \"\"\"\n",
" This function encode the matrix with huffman coding tables\n",
" \n",
" Input:\n",
" error (512, 640): a matrix with all the errors\n",
" list_dic (num_dic + 1,): a list of huffman coding table \n",
" bound (2300,): the boundary values after subtracting the very first pixel value\n",
" bins (num_bins - 1,): a list of threshold to cut the bins\n",
" \n",
" Return:\n",
" encoded (512, 640): encoded matrix\n",
" \"\"\"\n",
" returnable_encode = \"\"\n",
" # copy the error matrix (including the boundary)\n",
" encoded = np.copy(error).astype(int).astype(str).astype(object)\n",
" #diff = np.reshape(diff,(510,638))\n",
" # loop through all the pixel to encode\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",
" returnable_encode += list_dic[0][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[0]:\n",
" returnable_encode += list_dic[1][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[1] and diff[i-1][j-1] > bins[0]:\n",
" returnable_encode +=list_dic[2][encoded[i][j]]\n",
" elif diff[i-1][j-1] <= bins[2] and diff[i-1][j-1] > bins[1]:\n",
" returnable_encode +=list_dic[3][encoded[i][j]]\n",
" else:\n",
" returnable_encode += list_dic[4][encoded[i][j]]\n",
" return returnable_encode"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# def bitstring_to_bytes(s):\n",
"# v = int(s, 2)\n",
"# b = bytearray()\n",
"# while v:\n",
"# b.append(v & 0xff)\n",
"# v >>= 8\n",
"# return bytes(b[::-1])\n",
"def bitstring_to_bytes(input_string):\n",
" int_array = []\n",
" length_of_string = len(input_string)\n",
" while length_of_string >= 8:\n",
" int_array.append(int(input_string[:8],2))\n",
" input_string = input_string[8:]\n",
" length_of_string = len(input_string)\n",
" if length_of_string > 0:\n",
" zerobuffer = \"\"\n",
" for _ in range(8-length_of_string):\n",
" zerobuffer += \"0\"\n",
" int_array.append(int(input_string+zerobuffer,2))\n",
" # print(int_array[0:20])\n",
" # print(int_array[-12:])\n",
" return bytes(int_array)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def decoder(encoded_string, list_dic, bins, use_diff):\n",
" \"\"\"\n",
" This function decodes the encoded_matrix.\n",
" Input:\n",
" A (3 X 3): system of equation\n",
" list_dic (num_dic + 1,): a list of huffman coding table \n",
" encoded_matrix (512, 640): encoded matrix\n",
" bins (num_bins - 1,): a list of threshold to cut the bins\n",
" \n",
" Return:\n",
" decode_matrix (512, 640): decoded matrix\n",
" \"\"\"\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
" # change the dictionary back to list\n",
" # !!!!!WARNING!!!! has to change this part, everytime you change the number of bins\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",
" #Matrix system of points that will be used to solve the least squares fitting hyperplane\n",
" points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])\n",
" \n",
" decode_matrix = np.zeros((512,640))\n",
" # loop through all the element in the matrix\n",
" for i in range(decode_matrix.shape[0]):\n",
" for j in range(decode_matrix.shape[1]):\n",
" # if it's the very first pixel on the image\n",
" if i == 0 and j == 0:\n",
" colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys0, the_values=the_values0)\n",
" decode_matrix[i][j] = colorvalue\n",
" \n",
" # if it's on the boundary (any of the 4 edges)\n",
" elif i == 0 or i == decode_matrix.shape[0]-1 or j == 0 or j == decode_matrix.shape[1]-1:\n",
" colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys0, the_values=the_values0)\n",
" decode_matrix[i][j] = colorvalue + decode_matrix[0][0]\n",
" # if not the boundary\n",
" else:\n",
" # predict the image with the known pixel value\n",
" z0 = decode_matrix[i-1][j-1]\n",
" z1 = decode_matrix[i-1][j]\n",
" z2 = decode_matrix[i-1][j+1]\n",
" z3 = decode_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",
" if use_diff:\n",
" difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)\n",
" else:\n",
" \n",
" f, difference, rank, s = la.lstsq(points, [z0,z1,z2,z3], rcond=None) \n",
" difference = difference.astype(int)\n",
" \n",
" predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))\n",
" \n",
" # add on the difference by searching the dictionary\n",
" # !!!!!WARNING!!!! has to change this part, eveytime you change the number of bins\n",
" if difference <= bins[0]:\n",
" colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys1, the_values=the_values1)\n",
" decode_matrix[i][j] = colorvalue + int(predict)\n",
" elif difference <= bins[1] and difference > bins[0]:\n",
" colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys2, the_values=the_values2)\n",
" decode_matrix[i][j] = colorvalue + int(predict)\n",
" elif difference <= bins[2] and difference > bins[1]:\n",
" colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys3, the_values=the_values3)\n",
" decode_matrix[i][j] = colorvalue + int(predict)\n",
" else:\n",
" colorvalue, encoded_string = decode_string(encoded_string,the_keys=the_keys4, the_values=the_values4)\n",
" decode_matrix[i][j] = colorvalue + int(predict)\n",
"\n",
" return decode_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def read_from_file(filename):\n",
" with open(filename, 'rb') as file:\n",
" return file.read()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"newnamesforlater = []\n",
"images = image_extractor(scenes)\n",
"oglist_dic, ogbins = make_dictionary(images[:10], 1, False)\n",
"file_size_ratios = []\n",
"np.save(\"first_dic.npy\", oglist_dic)\n",
"for i in range(10):\n",
" list_dic, image, new_error, diff, bound, predict, bins = huffman(images[i], 1, False)\n",
" encoded_string1 = encoder(new_error, oglist_dic, diff, bound, ogbins)\n",
" # reconstruct_image = decoder(A, encoded_string, list_dic, bins, False)\n",
" # print(np.allclose(image, reconstruct_image))\n",
" inletters = bitstring_to_bytes(encoded_string1)\n",
" if images[i][:-5] == \".tiff\":\n",
" newname = images[i][:-5]\n",
" else:\n",
" newname = images[i][:-4]\n",
" newnamesforlater.append(newname + \"_Compressed.txt\")\n",
" with open(newname + \"_Compressed.txt\", 'wb') as f:\n",
" f.write(inletters)\n",
" file_size_ratios.append((os.path.getsize(newname + \"_Compressed.txt\"))/os.path.getsize('images/1626032610_393963/1626032610_393963_0.tiff'))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def check_bin_size(tiff_image_path_list, num_bins=4, difference = True):\n",
" \"\"\"\n",
" This function is used to encode the error based on the difference\n",
" and split the difference into different bins\n",
" \n",
" Input:\n",
" tiff_image_path (string): path to the tiff file\n",
" num_bins (int): number of bins\n",
" \n",
" Return:\n",
" huffman_encoding_list list (num_bins + 1): a list of dictionary\n",
" image_array ndarray (512, 640): original image\n",
" new_error ndarray (512, 640): error that includes the boundary\n",
" diff ndarray (510, 638): difference of min and max of the 4 neighbors\n",
" boundary ndarray (2300,): the boundary values after subtracting the very first pixel value\n",
" predict ndarray (325380,): the list of predicted values\n",
" bins list (num_bins - 1,): a list of threshold to cut the bins\n",
" A ndarray (3 X 3): system of equation\n",
" \n",
" \"\"\"\n",
" all_bins = []\n",
" for i, tiff_image_path in enumerate(tiff_image_path_list):\n",
" # get the image_array, etc\n",
" image_array, predict, diff, error= predict_pix(tiff_image_path, difference)\n",
" \n",
" # calculate the number of points that will go in each bin\n",
" data_points_per_bin = diff.size // num_bins\n",
"\n",
" # sort the difference and create the bins\n",
" sorted_diff = np.sort(diff.copy())\n",
" bins = [sorted_diff[i*data_points_per_bin] for i in range(1,num_bins)]\n",
" all_bins.append(bins)\n",
" return np.mean(all_bins,axis = 0), np.min(all_bins,axis = 0), np.max(all_bins,axis=0)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(array([21.00404858, 31.92712551, 48.06477733]), array([11, 16, 22]), array([ 30, 70, 141]))\n"
]
}
],
"source": [
"scenes = file_extractor()\n",
"newnamesforlater = []\n",
"images = image_extractor(scenes)\n",
"print(check_bin_size(images))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"newnamesforlater = []\n",
"images = image_extractor(scenes)\n",
"oglist_dic, ogbins = make_dictionary(images[:10], 1, False)\n",
"file_size_ratios = []\n",
"np.save(\"first_dic.npy\", oglist_dic)\n",
"for i in range(10):\n",
" list_dic, image, new_error, diff, bound, predict, bins = huffman(images[i], 1, False)\n",
" encoded_string1 = encoder(new_error, oglist_dic, diff, bound, ogbins)\n",
" # reconstruct_image = decoder(A, encoded_string, list_dic, bins, False)\n",
" # print(np.allclose(image, reconstruct_image))\n",
" inletters = bitstring_to_bytes(encoded_string1)\n",
" if images[i][:-5] == \".tiff\":\n",
" newname = images[i][:-5]\n",
" else:\n",
" newname = images[i][:-4]\n",
" newnamesforlater.append(newname + \"_Compressed.txt\")\n",
" with open(newname + \"_Compressed.txt\", 'wb') as f:\n",
" f.write(inletters)\n",
" file_size_ratios.append((os.path.getsize(newname + \"_Compressed.txt\"))/os.path.getsize('images/1626032610_393963/1626032610_393963_0.tiff'))"
]
}
],
"metadata": {
"interpreter": {
"hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
},
"kernelspec": {
"display_name": "Python 3.8.10 64-bit",
"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": "3.8.10"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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