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image-compression
Commit cd29faff authored by Kelly Chang's avatar Kelly Chang
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.ipynb_checkpoints/Compression_Rate_Kelly-checkpoint.ipynb
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......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 47,
"id": "8868bc30",
"metadata": {},
"outputs": [],
......@@ -24,8 +24,8 @@
},
{
"cell_type": "code",
"execution_count": 2,
"id": "76317b02",
"execution_count": 48,
"id": "0f944705",
"metadata": {},
"outputs": [],
"source": [
......@@ -33,7 +33,10 @@
" files = os.listdir(dirname)\n",
" scenes = []\n",
" for file in files:\n",
" scenes.append(os.path.join(dirname, file))\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",
......@@ -41,7 +44,12 @@
" for scene in scenes:\n",
" files = os.listdir(scene)\n",
" for file in files:\n",
" image_folder.append(os.path.join(scene, file))\n",
" #if file[-4:] == \".jp4\" or file[-7:] == \"_6.tiff\":\n",
" if file[-5:] != \".tiff\" or file[-7:] == \"_6.tiff\":\n",
" 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",
......@@ -49,8 +57,8 @@
" 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",
" 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",
" \"\"\"\n",
......@@ -79,8 +87,8 @@
},
{
"cell_type": "code",
"execution_count": 3,
"id": "be1ff8a1",
"execution_count": 49,
"id": "b18d5e38",
"metadata": {},
"outputs": [],
"source": [
......@@ -126,8 +134,8 @@
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8483903e",
"execution_count": 50,
"id": "35d4f6a0",
"metadata": {},
"outputs": [],
"source": [
......@@ -182,13 +190,11 @@
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a43f3f1c",
"execution_count": 51,
"id": "c50169ed",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"def huffman_nb(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\n",
" image = Image.open(image)\n",
......@@ -222,195 +228,32 @@
" \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"
]
},
{
"cell_type": "markdown",
"id": "eac2f456",
"metadata": {},
"source": [
"### Huffman with dividing into non-uniform bins"
]
},
{
"cell_type": "markdown",
"id": "3a3f06a5",
"id": "e34201fd",
"metadata": {},
"source": [
"### Huffman with dividing into uniform bins"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "14075c94",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"325380\n",
"325380\n"
]
},
{
"data": {
"text/plain": [
"0.4432273356119792"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def huffman_u(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\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",
" print(len(diff))\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 <= 100\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 > 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",
" 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",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 300\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 > 300\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",
" \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",
" new_error = np.ravel(new_error)\n",
" \n",
" # return the huffman dictionary\n",
" #return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, diff, boundary\n",
" print(len(diff))\n",
" return encode1, encode2, encode3, np.ravel(image), error, diff, boundary\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",
" #original = original.reshape(-1)\n",
" #error = error.reshape(-1)\n",
" o_len = 0\n",
" c_len = 0\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",
"\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",
" \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",
" \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",
" \n",
" '''if diff[i] <= 300 and diff[i] > 200:\n",
" c_len += len(encode4[str(int(error[i]))])\n",
" \n",
" if diff[i] > 300:\n",
" c_len += len(encode5[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"
"### Huffman dividing into bins"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "207b0bd2",
"execution_count": 52,
"id": "205c4731",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"325380\n"
]
},
{
"data": {
"text/plain": [
"0.44205322265625"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"def huffman(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\n",
......@@ -479,13 +322,11 @@
" #new_error = np.ravel(new_error)\n",
" \n",
" bins = [25,40,70]\n",
" \n",
" list_dic = [encode1, encode2, encode3, encode4, encode5]\n",
" # return the huffman dictionary\n",
" return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, new_error, diff, boundary, bins, predict\n",
" \n",
"\n",
" return list_dic, np.ravel(image), error, new_error, diff, boundary, bins, predict\n",
"\n",
"def compress_rate(image, error, diff, bound, encode1, encode2, encode3, encode4, encode5):\n",
"def compress_rate(image, error, diff, bound, list_dic, bins):\n",
" #original = original.reshape(-1)\n",
" #error = error.reshape(-1)\n",
"\n",
......@@ -494,64 +335,53 @@
" 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",
"\n",
" diff = diff.reshape(-1)\n",
" \n",
" # calculate the bit for boundary\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",
" \n",
" for i in range(0,len(original)):\n",
" o_len += len(bin(original[i])[2:])\n",
" if diff[i] <= 25:\n",
" c_len += len(encode2[str(int(error[i]))])\n",
"\n",
" if diff[i] <= 40 and diff[i] > 25:\n",
" c_len += len(encode3[str(int(error[i]))])\n",
" \n",
" if diff[i] <= 70 and diff[i] > 40:\n",
" c_len += len(encode4[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] > 70:\n",
" c_len += len(encode5[str(int(error[i]))])\n",
" \n",
" return c_len/o_len\n",
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"encode1, encode2, encode3, encode4, encode5, image, error, diff, boundary, bins = huffman(images[0])\n",
"compress_rate(image, error, diff, boundary, encode1, encode2, encode3, encode4, encode5)\n"
" elif diff[i] <= bins[1] and diff[i] > bins[0]:\n",
" c_len += len(list_dic[2][str(int(error[i]))])\n",
" \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",
"\n",
" return c_len/o_len\n"
]
},
{
"cell_type": "markdown",
"id": "816764c9",
"id": "2e84c206",
"metadata": {},
"source": [
"## Huffman Divide into 6 bins"
"### Huffman dividing into uniform bins"
]
},
{
"cell_type": "code",
"execution_count": 430,
"id": "15eecad3",
"execution_count": 64,
"id": "18e44483",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4421759033203125"
]
},
"execution_count": 430,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"def huffman6(image):\n",
"def huffman_u(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\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",
" 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",
......@@ -564,7 +394,7 @@
" encode1 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff <= 5\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",
......@@ -572,19 +402,20 @@
" encode2 = huffman_code_tree(node)\n",
"\n",
" \n",
" mask = diff > 5\n",
" mask = diff > bins[0]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 15\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 > 15\n",
" mask = diff > bins[1]\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 30\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",
......@@ -592,22 +423,13 @@
" encode4 = huffman_code_tree(node)\n",
" \n",
" \n",
" mask = diff > 30\n",
" new_error = error[mask]\n",
" mask2 = diff[mask] <= 50\n",
" string = [str(i) for i in new_error[mask2].astype(int)]\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",
" \n",
" \n",
" mask = diff > 50\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",
" encode6 = huffman_code_tree(node)\n",
"\n",
" \n",
" \n",
......@@ -623,9 +445,11 @@
" new_error = np.ravel(new_error)\n",
" \n",
" # return the huffman dictionary\n",
" return encode1, encode2, encode3, encode4, encode5, encode6, np.ravel(image), error, diff, boundary\n",
" #return encode1, encode2, encode3, encode4, encode5, np.ravel(image), error, diff, boundary\n",
" return [encode1, encode2, encode3, encode4, encode5], np.ravel(image), error, diff, boundary, bins\n",
"\n",
"def compress_rate6(image, error, diff, bound, encode1, encode2, encode3, encode4, encode5, encode6):\n",
"#def compress_rate_u(image, error, diff, bound, encode1, encode2, encode3, encode4, encode5):\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",
......@@ -636,118 +460,174 @@
"\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] <= 5:\n",
" c_len += len(encode2[str(int(error[i]))])\n",
"\n",
" if diff[i] <= 15 and diff[i] > 5:\n",
" c_len += len(encode3[str(int(error[i]))])\n",
" \n",
" if diff[i] <= 30 and diff[i] > 15:\n",
" c_len += len(encode4[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] <= 50 and diff[i] > 30:\n",
" c_len += len(encode5[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] <= bins[2] and diff[i] > bins[1]:\n",
" c_len += len(list_dic[3][str(int(error[i]))])\n",
" \n",
" if diff[i] > 50:\n",
" c_len += len(encode6[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, encode4, encode5, encode6, image, error, diff, boundary = huffman(images[0])\n",
"compress_rate(image, error, diff, boundary, encode1, encode2, encode3, encode4, encode5, encode6)\n"
" return c_len/o_len\n"
]
},
{
"cell_type": "code",
"execution_count": 431,
"id": "f8a8c717",
"execution_count": null,
"id": "e1ce9912",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compression rate of huffman with different bins: 0.448723882039388\n"
]
}
],
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"num_images = im_distribution(images, \"_9\")\n",
"rate = []\n",
"\n",
"for i in range(len(num_images)):\n",
" encode1, encode2, encode3, encode4, encode5, encode6, image, error, diff, bound = huffman6(num_images[i])\n",
" r = compress_rate6(image, error, diff, bound, encode1, encode2, encode3, encode4, encode5, encode6)\n",
"rate = []\n",
"rate_nb = []\n",
"rate_u = []\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",
" encoding, error, image = huffman_nb(images[i])\n",
" r = compress_rate_nb(image, error, encoding)\n",
" rate_nb.append(r)\n",
" \n",
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n"
" list_dic, image, error, diff, bound, bins = huffman_u(images[i])\n",
" r = compress_rate_u(image, error, diff, bound, list_dic, bins)\n",
" rate_u.append(r)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6abed5da",
"execution_count": 63,
"id": "c71591f2",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'file_extractor' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/var/folders/z2/plvrsqjs023g1cmx7k19mhzr0000gn/T/ipykernel_3263/2742763429.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnum_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile_extractor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'im'\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 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mrate\u001b[0m \u001b[0;34m=\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 4\u001b[0m \u001b[0mrate_nb\u001b[0m \u001b[0;34m=\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 5\u001b[0m \u001b[0mrate_u\u001b[0m \u001b[0;34m=\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;31mNameError\u001b[0m: name 'file_extractor' is not defined"
"name": "stdout",
"output_type": "stream",
"text": [
"Compression rate of huffman with different bins: 0.40459069242931545\n",
"Compression rate of huffman with uniform bins: 0.40775704713851685\n",
"Compression rate of huffman without bins: 0.410545890687004\n"
]
}
],
"source": [
"num_images = file_extractor('im')\n",
"\n",
"rate = []\n",
"rate_nb = []\n",
"rate_u = []\n",
"for i in range(len(num_images)):\n",
" encode1, encode2, encode3, encode4, encode5, image, error, diff, bound = huffman(num_images[i])\n",
" r = compress_rate(image, error, diff, bound, encode1, encode2, encode3, encode4, encode5)\n",
" rate.append(r)\n",
" encoding, error, image = huffman_nb(num_images[i])\n",
" r = compress_rate_nb(image, error, encoding)\n",
" rate_nb.append(r)\n",
" encode1, encode2, encode3, image, error, diff, bound = huffman_u(num_images[i])\n",
" r = compress_rate_u(image, error, diff, bound, encode1, encode2, encode3)\n",
" rate_u.append(r)\n",
" \n",
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n",
"print(f\"Compression rate of huffman without bins: {np.mean(rate_nb)}\")\n",
"print(f\"Compression rate of huffman with uniform bins: {np.mean(rate_u)}\")"
"print(f\"Compression rate of huffman with uniform bins: {np.mean(rate_u)}\")\n",
"print(f\"Compression rate of huffman without bins: {np.mean(rate_nb)}\")"
]
},
{
"cell_type": "code",
"execution_count": 238,
"id": "992dd8bb",
"execution_count": null,
"id": "9200fa53",
"metadata": {},
"outputs": [],
"source": [
"origin, predict, diff, error, A = plot_hist(images[0])"
"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\n",
"\n",
"def decoder(A, encoded_matrix, list_dic, bins):\n",
" \"\"\"\n",
" Function that accecpts the prediction matrix A for the linear system,\n",
" the encoded matrix of error values, and the encoding dicitonary.\n",
" \"\"\"\n",
"\n",
" the_keys0 = list(list_dic[0].keys())\n",
" the_values0 = list(list_dic[0].values())\n",
" \n",
" the_keys1 = list(list_dic[1].keys())\n",
" the_values1 = list(list_dic[1].values())\n",
" \n",
" the_keys2 = list(list_dic[2].keys())\n",
" the_values2 = list(list_dic[2].values())\n",
" \n",
" the_keys3 = list(list_dic[3].keys())\n",
" the_values3 = list(list_dic[3].values())\n",
" \n",
" the_keys4 = list(list_dic[4].keys())\n",
" the_values4 = list(list_dic[4].values())\n",
" \n",
" error_matrix = np.zeros((512,640))\n",
" \n",
" for i in range(error_matrix.shape[0]):\n",
" for j in range(error_matrix.shape[1]):\n",
" if i == 0 and j == 0:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])])\n",
" \n",
" elif i == 0 or i == error_matrix.shape[0]-1 or j == 0 or j == error_matrix.shape[1]-1:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])]) + error_matrix[0][0]\n",
" else:\n",
" z0 = error_matrix[i-1][j-1]\n",
" z1 = error_matrix[i-1][j]\n",
" z2 = error_matrix[i-1][j+1]\n",
" z3 = error_matrix[i][j-1]\n",
" y0 = int(-z0+z2-z3)\n",
" y1 = int(z0+z1+z2)\n",
" y2 = int(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n",
" difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)\n",
" predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))\n",
"\n",
" if difference <= bins[0]:\n",
" error_matrix[i][j] = int(the_keys1[the_values1.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[1] and difference > bins[0]:\n",
" error_matrix[i][j] = int(the_keys2[the_values2.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[2] and difference > bins[1]:\n",
" error_matrix[i][j] = int(the_keys3[the_values3.index(encoded_matrix[i,j])]) + int(predict)\n",
" else:\n",
" error_matrix[i][j] = int(the_keys4[the_values4.index(encoded_matrix[i,j])]) + int(predict)\n",
" \n",
" \n",
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9200fa53",
"id": "96a73eba",
"metadata": {},
"outputs": [],
"source": []
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"bins = [25,40,70]\n",
"A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
"list_dic, image, error, new_error, diff, bound, predict = huffman(images[0], 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))"
]
}
],
"metadata": {
......
Compression_Rate_Kelly.ipynb
View file @ cd29faff
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "8868bc30",
"metadata": {},
"outputs": [],
......@@ -19,12 +19,13 @@
"from sklearn.neighbors import KernelDensity\n",
"import pandas as pd\n",
"from collections import Counter\n",
"import time"
"import time\n",
"import numpy.linalg as la"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "0f944705",
"metadata": {},
"outputs": [],
......@@ -87,7 +88,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "b18d5e38",
"metadata": {},
"outputs": [],
......@@ -126,15 +127,75 @@
" 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",
" #image = np.ravel(image[1:-1,1:-1])\n",
" image = np.ravel(image)\n",
" error = image-predict\n",
" \n",
" return image, predict, diff, error, A"
" return image, predict, diff, error, A\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b0c3eaa",
"metadata": {},
"outputs": [],
"source": [
"def plot_hist_lstsq(tiff_list):\n",
"\n",
" image = tiff_list\n",
" image = Image.open(image) #Open the image and read it as an Image object\n",
" image = np.array(image)[1:,:] #Convert to an array, leaving out the first row because the first row is just housekeeping data\n",
" image = image.astype(int)\n",
" A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]]) # the matrix for system of equation\n",
" z0 = image[0:-2,0:-2] # get all the first pixel for the entire image\n",
" z1 = image[0:-2,1:-1] # get all the second pixel for the entire image\n",
" z2 = image[0:-2,2::] # get all the third pixel for the entire image\n",
" z3 = image[1:-1,0:-2] # get all the forth pixel for the entire image\n",
" # calculate the out put of the system of equation\n",
" y0 = np.ravel(-z0+z2-z3)\n",
" y1 = np.ravel(z0+z1+z2)\n",
" y2 = np.ravel(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n",
" # use numpy solver to solve the system of equations all at once\n",
" #predict = np.floor(np.linalg.solve(A,y)[-1])\n",
" predict = np.round(np.round((np.linalg.solve(A,y)[-1]),1))\n",
" \n",
" points = np.array([[-1,-1,1], [-1,0,1], [-1,1,1], [0,-1,1]])\n",
" #fit = la.solve(A,y)\n",
" \n",
" #mse_start = (points@fit).T\n",
" \n",
" \n",
" # flatten the neighbor pixels 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",
" f, res, rank, s = la.lstsq(points, neighbor.T, rcond=None) \n",
" \n",
" #mse_finish = (neighbor-mse_start)**2\n",
" #lstsqur = np.sum(mse_finish, axis=1) / 4\n",
" \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 = np.ravel(image[1:-1,1:-1])-predict\n",
" \n",
" return image, predict, res, error, A, diff\n",
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"i, p, l, e, A, d = plot_hist_lstsq(images[0])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"id": "35d4f6a0",
"metadata": {},
"outputs": [],
......@@ -190,7 +251,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"id": "c50169ed",
"metadata": {},
"outputs": [],
......@@ -250,13 +311,25 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"id": "205c4731",
"metadata": {},
"outputs": [],
"source": [
"def huffman(image):\n",
" origin, predict, diff, error, A = plot_hist(image)\n",
"def huffman(image, res = False):\n",
" if res:\n",
" image, predict, res, error, A, diff = plot_hist_lstsq(images[0])\n",
" il = l.astype(int)\n",
" bins = []\n",
" num_bins = 5\n",
" data_points_per_bin = len(il) // num_bins #l is the list of data that you want to create bins for\n",
"\n",
" sorted_l = il.copy()\n",
" sorted_l.sort()\n",
"\n",
" bins = [sorted_l[_ * data_points_per_bin: (_+1)*data_points_per_bin] for _ in range(num_bins)]\n",
" else:\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",
......@@ -365,7 +438,7 @@
"id": "2e84c206",
"metadata": {},
"source": [
"### Huffman dividing into bins"
"### Huffman dividing into uniform bins"
]
},
{
......@@ -380,7 +453,8 @@
" 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",
......@@ -393,7 +467,7 @@
" 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",
......@@ -401,20 +475,20 @@
" 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",
......@@ -422,12 +496,13 @@
" 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",
......@@ -444,10 +519,10 @@
" \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",
......@@ -458,47 +533,31 @@
"\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"
]
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": null,
"id": "e1ce9912",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compression rate of huffman with different bins: 0.40459069242931545\n",
"Compression rate of huffman without bins: 0.410545890687004\n"
]
}
],
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
......@@ -512,29 +571,40 @@
" rate.append(r)\n",
" \n",
" encoding, error, image = huffman_nb(images[i])\n",
" \n",
" r = compress_rate_nb(image, error, encoding)\n",
" rate_nb.append(r)\n",
" \n",
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n",
"print(f\"Compression rate of huffman without bins: {np.mean(rate_nb)}\")\n"
" list_dic, image, error, diff, bound, bins = huffman_u(images[i])\n",
" r = compress_rate_u(image, error, diff, bound, list_dic, bins)\n",
" rate_u.append(r)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a9a31f6",
"id": "c71591f2",
"metadata": {},
"outputs": [],
"source": []
"source": [
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n",
"print(f\"Compression rate of huffman with uniform bins: {np.mean(rate_u)}\")\n",
"print(f\"Compression rate of huffman without bins: {np.mean(rate_nb)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c71591f2",
"id": "d3e6d819",
"metadata": {},
"outputs": [],
"source": []
"source": [
"mask = np.array(rate) < np.array(rate_u)\n",
"print(sum(mask)/len(mask))\n",
"\n",
"mask = np.array(rate) < np.array(rate_nb)\n",
"print(sum(mask)/len(mask))"
]
},
{
"cell_type": "code",
......@@ -542,6 +612,416 @@
"id": "9200fa53",
"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\n",
"\n",
"def decoder(A, encoded_matrix, list_dic, bins):\n",
" \"\"\"\n",
" Function that accecpts the prediction matrix A for the linear system,\n",
" the encoded matrix of error values, and the encoding dicitonary.\n",
" \"\"\"\n",
"\n",
" the_keys0 = list(list_dic[0].keys())\n",
" the_values0 = list(list_dic[0].values())\n",
" \n",
" the_keys1 = list(list_dic[1].keys())\n",
" the_values1 = list(list_dic[1].values())\n",
" \n",
" the_keys2 = list(list_dic[2].keys())\n",
" the_values2 = list(list_dic[2].values())\n",
" \n",
" the_keys3 = list(list_dic[3].keys())\n",
" the_values3 = list(list_dic[3].values())\n",
" \n",
" the_keys4 = list(list_dic[4].keys())\n",
" the_values4 = list(list_dic[4].values())\n",
" \n",
" error_matrix = np.zeros((512,640))\n",
" \n",
" for i in range(error_matrix.shape[0]):\n",
" for j in range(error_matrix.shape[1]):\n",
" if i == 0 and j == 0:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])])\n",
" \n",
" elif i == 0 or i == error_matrix.shape[0]-1 or j == 0 or j == error_matrix.shape[1]-1:\n",
" error_matrix[i][j] = int(the_keys0[the_values0.index(encoded_matrix[i,j])]) + error_matrix[0][0]\n",
" else:\n",
" z0 = error_matrix[i-1][j-1]\n",
" z1 = error_matrix[i-1][j]\n",
" z2 = error_matrix[i-1][j+1]\n",
" z3 = error_matrix[i][j-1]\n",
" y0 = int(-z0+z2-z3)\n",
" y1 = int(z0+z1+z2)\n",
" y2 = int(-z0-z1-z2-z3)\n",
" y = np.vstack((y0,y1,y2))\n",
" difference = max(z0,z1,z2,z3) - min(z0,z1,z2,z3)\n",
" predict = np.round(np.round(np.linalg.solve(A,y)[-1][0],1))\n",
"\n",
" if difference <= bins[0]:\n",
" error_matrix[i][j] = int(the_keys1[the_values1.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[1] and difference > bins[0]:\n",
" error_matrix[i][j] = int(the_keys2[the_values2.index(encoded_matrix[i,j])]) + int(predict)\n",
" elif difference <= bins[2] and difference > bins[1]:\n",
" error_matrix[i][j] = int(the_keys3[the_values3.index(encoded_matrix[i,j])]) + int(predict)\n",
" else:\n",
" error_matrix[i][j] = int(the_keys4[the_values4.index(encoded_matrix[i,j])]) + int(predict)\n",
" \n",
" \n",
" return error_matrix.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23a39f8b",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"bins = [25,40,70]\n",
"A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
"list_dic, image, error, new_error, diff, bound, predict = huffman(images[0], 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))"
]
},
{
"cell_type": "markdown",
"id": "d0e4be69",
"metadata": {},
"source": [
"## use res"
]
},
{
"cell_type": "code",
"execution_count": 83,
"id": "bd7e39d7",
"metadata": {},
"outputs": [],
"source": [
"def huffman(image, num_bins, use_res = False):\n",
" if use_res:\n",
" image, predict, res, error, A, diff = plot_hist_lstsq(image)\n",
" il = res.astype(int)\n",
" data_points_per_bin = len(il) // num_bins #l is the list of data that you want to create bins for\n",
"\n",
" sorted_l = il.copy()\n",
" sorted_l.sort()\n",
" bins = [sorted_l[i*data_points_per_bin] for i in range(1,num_bins)]\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",
" encode = huffman_code_tree(node)\n",
"\n",
" list_dic = [encode]\n",
" n = len(bins)\n",
" \n",
" for i in range (0,n):\n",
" if i == 0 :\n",
" mask = res <= 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",
" else:\n",
" mask = res > bins[i-1]\n",
" new_error = error[mask]\n",
" mask2 = res[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",
" mask = res > 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",
" encode = huffman_code_tree(node)\n",
" list_dic.append(encode)\n",
" else:\n",
" image, predict, res, error, A, diff = plot_hist_lstsq(image)\n",
" data_points_per_bin = len(diff) // num_bins #l is the list of data that you want to create bins for\n",
"\n",
" sorted_l = diff.copy()\n",
" sorted_l.sort()\n",
" bins = [sorted_l[i*data_points_per_bin] for i in range(1,num_bins)]\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",
" encode = huffman_code_tree(node)\n",
" \n",
" list_dic = [encode]\n",
" n = len(bins)\n",
" for i in range (0,n):\n",
" if i == 0 :\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",
" else:\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",
" 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",
" encode = huffman_code_tree(node)\n",
" list_dic.append(encode)\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",
" # return the huffman dictionary\n",
" return list_dic, np.ravel(image), error, new_error, diff, boundary, predict, bins, res\n",
" \n",
"\n",
"\n",
"def compress_rate(image, error, diff, bound, list_dic, bins, res, use_res = False):\n",
" \n",
" o_len = 0\n",
" c_len = 0\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",
" diff = diff.reshape(-1)\n",
" \n",
" # calculate the bit for boundary\n",
" for i in range(0,len(bound)):\n",
" o_len += len(bin(real_b[i])[2:])\n",
" c_len += len(list_dic[0][str(bound[i])])\n",
" if use_res:\n",
" for i in range(0,len(original)):\n",
" o_len += len(bin(original[i])[2:])\n",
" if res[i] <= bins[0]:\n",
" c_len += len(list_dic[1][str(int(error[i]))])\n",
"\n",
" elif res[i] <= bins[1] and res[i] > bins[0]:\n",
" c_len += len(list_dic[2][str(int(error[i]))])\n",
"\n",
" elif res[i] <= bins[2] and res[i] > bins[1]:\n",
" c_len += len(list_dic[3][str(int(error[i]))])\n",
"\n",
" elif res[i] <= bins[3] and res[i] > bins[2]:\n",
" c_len += len(list_dic[4][str(int(error[i]))])\n",
" \n",
" elif res[i] <= bins[4] and res[i] > bins[3]:\n",
" c_len += len(list_dic[5][str(int(error[i]))])\n",
" \n",
" else: \n",
" c_len += len(list_dic[6][str(int(error[i]))])\n",
" \n",
" else:\n",
" for i in range(0, len(original)):\n",
" o_len += len(bin(original[i])[2:])\n",
" if diff[i] <= bins[0]:\n",
" c_len += len(list_dic[1][str(int(error[i]))])\n",
" \n",
" elif diff[i] <= bins[1] and diff[i] > bins[0]:\n",
" c_len += len(list_dic[2][str(int(error[i]))])\n",
"\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",
" elif diff[i] <= bins[3] and diff[i] > bins[2]:\n",
" c_len += len(list_dic[4][str(int(error[i]))])'''\n",
" \n",
" \n",
" '''elif diff[i] <= bins[4] and diff[i] > bins[3]:\n",
" c_len += len(list_dic[5][str(int(error[i]))])\n",
" \n",
" else: \n",
" c_len += len(list_dic[6][str(int(error[i]))])'''\n",
" \n",
"\n",
"\n",
" return c_len/o_len\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1b2e63e2",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"rate = []\n",
"A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
"for i in range(len(images)):\n",
" list_dic, image, error, new_error, diff, bound, predict, bins, res = huffman(images[i], 6, True)\n",
" r = compress_rate(image, error, diff, bound, list_dic, bins, res, use_res = True)\n",
" rate.append(r)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2eaf807",
"metadata": {},
"outputs": [],
"source": [
"print(f\"Compression rate of huffman with different bins in res: {np.mean(rate)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "faece884",
"metadata": {},
"outputs": [],
"source": [
"scenes = file_extractor()\n",
"images = image_extractor(scenes)\n",
"rate = []\n",
"A = np.array([[3,0,-1],[0,3,3],[1,-3,-4]])\n",
"start = time.time()\n",
"for i in range(len(images)):\n",
" list_dic, image, error, new_error, diff, bound, predict, bins, res = huffman(images[i], 6, False)\n",
" r = compress_rate(image, error, diff, bound, list_dic, bins, res, False)\n",
" rate.append(r)\n",
"end = time.time()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7ca02b7",
"metadata": {},
"outputs": [],
"source": [
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n",
"print(f\"time: {end-start}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6cf44d7c",
"metadata": {},
"outputs": [],
"source": [
"start = time.time()\n",
"for i in range(len(images)):\n",
" list_dic, image, error, new_error, diff, bound, predict, bins, res = huffman(images[i], 5, False)\n",
" r = compress_rate(image, error, diff, bound, list_dic, bins, res, False)\n",
" rate.append(r)\n",
"end = time.time()\n",
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n",
"print(f\"time: {end-start}\")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "d4051c85",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Compression rate of huffman with different bins: 0.4045967177402825\n",
"time: 109.86948299407959\n"
]
}
],
"source": [
"start = time.time()\n",
"for i in range(len(images)):\n",
" list_dic, image, error, new_error, diff, bound, predict, bins, res = huffman(images[i], 4, False)\n",
" r = compress_rate(image, error, diff, bound, list_dic, bins, res, False)\n",
" rate.append(r)\n",
"end = time.time()\n",
"print(f\"Compression rate of huffman with different bins: {np.mean(rate)}\")\n",
"print(f\"time: {end-start}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46f62de1",
"metadata": {},
"outputs": [],
"source": [
"# [15,25,45,75] 0.40429\n",
"# [15,25,35,45,55,65,75] 0.404036\n",
"# [25,40,70] 0.40459\n",
"# [50, 100, 150] 0.40698\n",
"# [30, 50, 100] 0.40497\n",
"# [5,15,25,35,45,55,65,75] 0.4040099826388888\n",
"# [5,15,30,45,75] 0.4043133622426835\n",
"# res with four dividing 0.4080680784195189\n",
"# diff with four dividing 0.40421\n",
"# diff with 3 bins 0.40459"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3e3d266",
"metadata": {},
"outputs": [],
"source": []
}
],
......
Encoding_decoding.ipynb
View file @ cd29faff
......@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 54,
"id": "14f74f21",
"metadata": {},
"outputs": [],
......@@ -24,7 +24,7 @@
},
{
"cell_type": "code",
"execution_count": 80,
"execution_count": 55,
"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": 56,
"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": 57,
"id": "6b965751",
"metadata": {},
"outputs": [],
......@@ -182,65 +171,78 @@
},
{
"cell_type": "code",
"execution_count": 111,
"execution_count": 58,
"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": 59,
"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": 60,
"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": 61,
"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": [
"True\n",
"True\n",
"True\n",
"True\n",
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"True\n",
"True\n",
"True\n",
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"True\n",
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"True\n",
"True\n",
"True\n",
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"True\n",
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"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
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"True\n",
"True\n",
"True\n",
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"True\n",
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"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"True\n",
"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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