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image-compression
Commit b428fbbd authored by Nathaniel Callens's avatar Nathaniel Callens
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basic fixes

parents 093ce109 829e9198
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Showing with 1256 additions and 1056 deletions
.DS_Store
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.ipynb_checkpoints/Compression_Rate_Kelly-checkpoint.ipynb
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Compression_Rate_Kelly.ipynb
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Encoding_Kelly.ipynb
View file @ b428fbbd
......@@ -203,7 +203,6 @@
"metadata": {},
"outputs": [],
"source": [
"\n",
"def huffman_nb(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\n",
" image = Image.open(image)\n",
......@@ -237,15 +236,14 @@
" \n",
" \n",
"def compress_rate_nb(image, error, encoding):\n",
" #original = original.reshape(-1)\n",
" #error = error.reshape(-1)\n",
" original = image.reshape(-1)\n",
" error = error.reshape(-1)\n",
" o_len = 0\n",
" c_len = 0\n",
" for i in range(0, len(original)):\n",
" o_len += len(bin(original[i])[2:])\n",
" c_len += len(encoding[str(int(error[i]))])\n",
"\n",
" \n",
" return c_len/o_len\n",
"\n"
]
......@@ -410,7 +408,8 @@
" 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",
" bins = np.linspace(min(diff),max(diff),5)[1:-1]\n",
" \n",
" boundary = np.hstack((image[0,:],image[-1,:],image[1:-1,0],image[1:-1,-1]))\n",
" boundary = boundary - image[0,0]\n",
......@@ -423,7 +422,7 @@
" encode1 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff <= 100\n",
" mask = diff <= bins[0]\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",
......@@ -431,20 +430,20 @@
" encode2 = huffman_code_tree(node)\n",
"\n",
" \n",
" mask = diff > 100\n",
" #new_error = error[mask]\n",
" #mask2 = diff[mask] <= 200\n",
" #string = [str(i) for i in new_error[mask2].astype(int)]\n",
" string = [str(i) for i in error[mask].astype(int)]\n",
" mask = diff > bins[0]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= bins[1]\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\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",
" encode3 = huffman_code_tree(node)\n",
" \n",
"\n",
" '''mask = diff > 200\n",
" mask = diff > bins[1]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 300\n",
" mask2 = diff[mask] <= bins[2]\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",
......@@ -452,12 +451,13 @@
" encode4 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff > 300\n",
" mask = diff > bins[2]\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",
" encode5 = huffman_code_tree(node)\n",
" \n",
"\n",
" \n",
" \n",
......@@ -474,10 +474,10 @@
" \n",
" # return the huffman dictionary\n",
" #return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, diff, boundary\n",
" return encode1, encode2, encode3, np.ravel(image), error, diff, boundary\n",
" return [encode1, encode2, encode3, encode4, encode5], np.ravel(image), error, diff, boundary, bins\n",
"\n",
"#def compress_rate_u(image, error, diff, bound, encode1, encode2, encode3, encode4, encode5):\n",
"def compress_rate_u(image, error, diff, bound, encode1, encode2, encode3):\n",
"def compress_rate_u(image, error, diff, bound, list_dic, bins):\n",
" #original = original.reshape(-1)\n",
" #error = error.reshape(-1)\n",
" o_len = 0\n",
......@@ -488,30 +488,23 @@
"\n",
" for i in range(0,len(bound)):\n",
" o_len += len(bin(real_b[i])[2:])\n",
" c_len += len(encode1[str(bound[i])])\n",
" c_len += len(list_dic[0][str(bound[i])])\n",
" \n",
" for i in range(0, len(original)):\n",
" o_len += len(bin(original[i])[2:])\n",
" if diff[i] <= 100:\n",
" c_len += len(encode2[str(int(error[i]))])\n",
" if diff[i] <= bins[0]:\n",
" c_len += len(list_dic[1][str(int(error[i]))])\n",
" \n",
" if diff[i] > 100:\n",
" c_len += len(encode3[str(int(error[i]))])\n",
"\n",
" '''if diff[i] <= 200 and diff[i] > 100:\n",
" c_len += len(encode3[str(int(error[i]))])'''\n",
" if diff[i] <= bins[1] and diff[i] > bins[0]:\n",
" c_len += len(list_dic[2][str(int(error[i]))])\n",
" \n",
" '''if diff[i] <= 300 and diff[i] > 200:\n",
" c_len += len(encode4[str(int(error[i]))])\n",
" if diff[i] <= bins[2] and diff[i] > bins[1]:\n",
" c_len += len(list_dic[3][str(int(error[i]))])\n",
" \n",
" if diff[i] > 300:\n",
" c_len += len(encode5[str(int(error[i]))])'''\n",
" if diff[i] > bins[2]:\n",
" c_len += len(list_dic[4][str(int(error[i]))])\n",
" \n",
" return c_len/o_len\n",
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"encode1, encode2, encode3, image, error, diff, boundary = huffman_u(images[0])\n",
"compress_rate_u(image, error, diff, boundary, encode1, encode2, encode3)\n"
" return c_len/o_len\n"
]
},
{
......
Encoding_decoding.ipynb
View file @ b428fbbd
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 63,
"id": "14f74f21",
"metadata": {},
"outputs": [],
......@@ -24,7 +24,7 @@
},
{
"cell_type": "code",
"execution_count": 80,
"execution_count": 64,
"id": "c16af61f",
"metadata": {},
"outputs": [],
......@@ -49,15 +49,6 @@
" continue\n",
" else:\n",
" image_folder.append(os.path.join(scene, file))\n",
" '''print(image_folder)\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 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",
......@@ -87,7 +78,7 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 65,
"id": "aceba613",
"metadata": {},
"outputs": [],
......@@ -124,17 +115,15 @@
" 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",
" # calculate the error\n",
" error = np.ravel(image[1:-1,1:-1])-predict\n",
" \n",
" return image, predict, diff, error, A"
]
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 66,
"id": "6b965751",
"metadata": {},
"outputs": [],
......@@ -182,65 +171,78 @@
},
{
"cell_type": "code",
"execution_count": 111,
"execution_count": 67,
"id": "b7561883",
"metadata": {},
"outputs": [],
"source": [
"def huffman(image, bins):\n",
" origin, predict, diff, error, A = plot_hist(image)\n",
"def huffman(image, num_bins):\n",
" # get the prediction error and difference\n",
" image, 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",
" # get the number of points in each bins\n",
" data_points_per_bin = len(diff) // num_bins\n",
" \n",
" # sort the difference and create the bins\n",
" sorted_diff = diff.copy()\n",
" sorted_diff.sort()\n",
" bins = [sorted_diff[i*data_points_per_bin] for i in range(1,num_bins)]\n",
" \n",
" # get the boundary \n",
" boundary = np.hstack((image[0,:],image[-1,:],image[1:-1,0],image[1:-1,-1]))\n",
" \n",
" # take the difference of the boundary with the very first pixel\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 <= bins[0]\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 > bins[0]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= bins[1]\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 > bins[1]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= bins[2]\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\n",
" # huffman encode the boundary\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",
" encode4 = huffman_code_tree(node)\n",
" encode = huffman_code_tree(node)\n",
" \n",
" # create a list of huffman table\n",
" list_dic = [encode]\n",
" n = len(bins)\n",
" \n",
" mask = diff > bins[2]\n",
" # loop through different bins\n",
" for i in range (0,n):\n",
" # the fisrt bin\n",
" if i == 0 :\n",
" # get the point within the bin and huffman encode\n",
" mask = diff <= bins[i]\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",
" encode = huffman_code_tree(node)\n",
" list_dic.append(encode)\n",
" \n",
" # the middle bins\n",
" else:\n",
" # get the point within the bin and huffman encode\n",
" mask = diff > bins[i-1]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= bins[i]\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",
" encode = huffman_code_tree(node)\n",
" list_dic.append(encode)\n",
" \n",
" # the last bin \n",
" # get the point within the bin and huffman encode\n",
" mask = diff > bins[-1]\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",
" encode = huffman_code_tree(node)\n",
" list_dic.append(encode)\n",
"\n",
" # create a error matrix that includes the boundary (used in encoding matrix)\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",
......@@ -250,28 +252,23 @@
" 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",
" \n",
" list_dic = [encode1, encode2, encode3, encode4, encode5]\n",
" # return the huffman dictionary\n",
" return list_dic, np.ravel(image), error, new_error, diff, boundary, predict\n",
" return list_dic, np.ravel(image), error, new_error, diff, boundary, predict, bins\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 112,
"execution_count": 68,
"id": "2eb774d2",
"metadata": {},
"outputs": [],
"source": [
"def encoder(error, list_dic, diff, bound, bins):\n",
" # copy the error matrix (including the boundary)\n",
" encoded = np.copy(error).astype(int).astype(str).astype(object)\n",
" \n",
" diff = np.reshape(diff,(510,638))\n",
" \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",
......@@ -285,13 +282,12 @@
" else: \n",
" encoded[i][j] = list_dic[4][encoded[i][j]]\n",
"\n",
" \n",
" return encoded"
]
},
{
"cell_type": "code",
"execution_count": 113,
"execution_count": 69,
"id": "8eeb40d0",
"metadata": {},
"outputs": [],
......@@ -301,7 +297,7 @@
" Function that accecpts the prediction matrix A for the linear system,\n",
" the encoded matrix of error values, and the encoding dicitonary.\n",
" \"\"\"\n",
"\n",
" # change the dictionary back to list\n",
" the_keys0 = list(list_dic[0].keys())\n",
" the_values0 = list(list_dic[0].values())\n",
" \n",
......@@ -318,15 +314,18 @@
" the_values4 = list(list_dic[4].values())\n",
" \n",
" error_matrix = np.zeros((512,640))\n",
" \n",
" # loop through all the element in the matrix\n",
" for i in range(error_matrix.shape[0]):\n",
" for j in range(error_matrix.shape[1]):\n",
" # if it's the very first pixel on the image\n",
" if i == 0 and j == 0:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])])\n",
" \n",
" # if it's on the boundary\n",
" elif i == 0 or i == error_matrix.shape[0]-1 or j == 0 or j == error_matrix.shape[1]-1:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])]) + error_matrix[0][0]\n",
" # if not the boundary\n",
" else:\n",
" # predict the image with the known pixel value\n",
" z0 = error_matrix[i-1][j-1]\n",
" z1 = error_matrix[i-1][j]\n",
" z2 = error_matrix[i-1][j+1]\n",
......@@ -337,7 +336,8 @@
" y = np.vstack((y0,y1,y2))\n",
" difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)\n",
" predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))\n",
"\n",
" \n",
" # add on the difference by searching the dictionary\n",
" if difference <= bins[0]:\n",
" error_matrix[i][j] = int(the_keys1[the_values1.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[1] and difference > bins[0]:\n",
......@@ -353,17 +353,17 @@
},
{
"cell_type": "code",
"execution_count": 114,
"execution_count": 70,
"id": "f959fe93",
"metadata": {},
"outputs": [],
"source": [
"def compress_rate(image, error, diff, bound, list_dic, bins):\n",
" #original = original.reshape(-1)\n",
" #error = error.reshape(-1)\n",
"\n",
" # the bits for the original image\n",
" o_len = 0\n",
" # the bits for the compressed image\n",
" c_len = 0\n",
" # initializing the varible \n",
" im = np.reshape(image,(512, 640))\n",
" real_b = np.hstack((im[0,:],im[-1,:],im[1:-1,0],im[1:-1,-1]))\n",
" original = im[1:-1,1:-1].reshape(-1)\n",
......@@ -374,9 +374,12 @@
" o_len += len(bin(real_b[i])[2:])\n",
" c_len += len(list_dic[0][str(bound[i])])\n",
" \n",
" \n",
" # calculate the bit for the pixels inside the boundary\n",
" for i in range(0,len(original)):\n",
" # for the original image\n",
" o_len += len(bin(original[i])[2:])\n",
" \n",
" # check the difference and find the coresponding huffman table\n",
" if diff[i] <= bins[0]:\n",
" c_len += len(list_dic[1][str(int(error[i]))])\n",
" \n",
......@@ -385,247 +388,68 @@
" \n",
" elif diff[i] <= bins[2] and diff[i] > bins[1]:\n",
" c_len += len(list_dic[3][str(int(error[i]))])\n",
" else: \n",
" c_len += len(list_dic[4][str(int(error[i]))])\n",
"\n",
" else: \n",
" c_len += len(list_dic[5][str(int(error[i]))])\n",
"\n",
" return c_len/o_len"
]
},
{
"cell_type": "code",
"execution_count": 121,
"execution_count": null,
"id": "3e0e9742",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"True\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/z2/plvrsqjs023g1cmx7k19mhzr0000gn/T/ipykernel_3109/243266254.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mlist_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_error\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhuffman\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mencoded_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_error\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m640\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mreconstruct_image\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoded_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mallclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m640\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreconstruct_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/var/folders/z2/plvrsqjs023g1cmx7k19mhzr0000gn/T/ipykernel_3109/3881165701.py\u001b[0m in \u001b[0;36mdecoder\u001b[0;34m(A, encoded_matrix, list_dic, bins)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mdifference\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz3\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mpredict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msolve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdifference\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mround_\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mround_\u001b[0;34m(a, decimals, out)\u001b[0m\n\u001b[1;32m 3635\u001b[0m \u001b[0maround\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mequivalent\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0msee\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdetails\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3636\u001b[0m \"\"\"\n\u001b[0;32m-> 3637\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0maround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3638\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3639\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36maround\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36maround\u001b[0;34m(a, decimals, out)\u001b[0m\n\u001b[1;32m 3260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3261\u001b[0m \"\"\"\n\u001b[0;32m-> 3262\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'round'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"bins = [25,40,70]\n",
"#bins = [25,40,70]\n",
"A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
"for image in images:\n",
" list_dic, image, error, new_error, diff, bound, predict = huffman(image, bins)\n",
" encoded_matrix = encoder(np.reshape(new_error,(512,640)), list_dic, diff, bound, bins)\n",
" reconstruct_image = decoder(A, encoded_matrix, list_dic, bins)\n",
" print(np.allclose(image.reshape(512,640), reconstruct_image))\n"
"list_dic, image, error, new_error, diff, bound, predict, bins = huffman(images[0], 4)\n",
"encoded_matrix = encoder(np.reshape(new_error,(512,640)), list_dic, diff, bound, bins)\n",
"reconstruct_image = decoder(A, encoded_matrix, list_dic, bins)\n",
"print(np.allclose(image.reshape(512,640), reconstruct_image))\n",
"print(len(list_dic))"
]
},
{
"cell_type": "code",
"execution_count": 119,
"id": "ceb0b957",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 120,
"id": "60297ad0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 79,
"execution_count": null,
"id": "f0948ab2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1626032871_679543_6.tiff\n",
"1640843156_813147\n",
"1640843156_729797\n",
"1640843156_846487\n",
"1640843156_829817\n",
"1640843156_763137\n",
"1640843156_779807\n",
"1640843156_796477\n",
"1640843156_746467\n",
"1626032832_004943_6.tiff\n",
"1626032832_004943_18.jp4\n",
"1626032832_004943_19.jp4\n",
"1626032832_004943_17.jp4\n",
"1626032832_004943_16.jp4\n",
"1626033289_579773_6.tiff\n",
"1626033496_437803_16.jp4\n",
"1626033496_437803_17.jp4\n",
"1626033496_437803_19.jp4\n",
"1626033496_437803_18.jp4\n",
"1626033496_437803_6.tiff\n",
"1626032610_393963_6.tiff\n",
"1626033067_752083_19.jp4\n",
"1626033067_752083_18.jp4\n",
"1626033067_752083_6.tiff\n",
"1626033067_752083_16.jp4\n",
"1626033067_752083_17.jp4\n",
"1626032738_736293_6.tiff\n",
"Compression rate of huffman with different bins: 0.40459069242931545\n"
]
}
],
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"rate = []\n",
"\n",
"\n",
"rate1 = []\n",
"rate2 = []\n",
"rate3 = []\n",
"bins1 = [25,40,70]\n",
"bins2 = [50,100,150]\n",
"bins3 = [30,50,100]\n",
"B = [bins1, bins2, bins3]\n",
"for i in range(len(images)):\n",
" list_dic, image, error, new_error, diff, bound, bins, predict = huffman(images[i])\n",
" r = compress_rate(image, error, diff, bound, list_dic, bins)\n",
" rate.append(r)\n",
" \n",
" \n",
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n"
" for j, bins in enumerate(B):\n",
" list_dic, image, error, new_error, diff, bound, predict = huffman(images[i], bins)\n",
" r = compress_rate(image, error, diff, bound, list_dic, bins)\n",
" if j == 0:\n",
" rate1.append(r)\n",
" elif j == 1:\n",
" rate2.append(r)\n",
" else:\n",
" rate3.append(r)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "792b7936",
"execution_count": null,
"id": "7d615dcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.41151123046875, 0.4466471354166667, 0.38627176920572914, 0.41287577311197915, 0.3977305094401042, 0.41076253255208334]\n"
]
}
],
"outputs": [],
"source": [
"print(rate)"
"print(f\"Compression rate of huffman with bins {bins1}: {np.mean(rate1)}\")\n",
"print(f\"Compression rate of huffman with bins {bins2}: {np.mean(rate2)}\")\n",
"print(f\"Compression rate of huffman with bins {bins3}: {np.mean(rate3)}\")\n"
]
},
{
......
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