add calculate function
This commit is contained in:
parent
a4de07ea79
commit
85ba70d8b3
150
jupyter.ipynb
150
jupyter.ipynb
@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 1,
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"metadata": {
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"pycharm": {
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"is_executing": true,
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@ -14,7 +14,7 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/8w/3c34c7kd2n144tvm764_pdbw0000gq/T/ipykernel_56318/3079151100.py:11: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
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"/var/folders/tq/jq5nwbnj7v10tls99x99qbh40000gn/T/ipykernel_57719/1134982733.py:12: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
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" from IPython.core.display import display, HTML\n"
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]
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},
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@ -38,6 +38,7 @@
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"from skimage.color import rgb2gray\n",
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"from skimage import img_as_ubyte,img_as_float\n",
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"from numpy.linalg import svd\n",
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"import scipy.linalg as la\n",
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"from PIL import Image\n",
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"from ipywidgets import interact\n",
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"\n",
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@ -48,7 +49,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"execution_count": 2,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -114,7 +115,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": 3,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -157,7 +158,51 @@
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"def calculate(A):\n",
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" m = A.shape[0]\n",
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" n = A.shape[1]\n",
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" S = np.zeros(n)\n",
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"\n",
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" # finding eigenvectors with biggest eigenvalues of A*transpose(A)\n",
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" helper = np.matmul(A, np.transpose(A))\n",
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" eigenvalues, eigenvectors = la.eigh(helper)\n",
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" # descending sort of all the eigenvectors according to their eigenvalues\n",
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" index = eigenvalues.argsort()[::-1]\n",
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" eigenvalues = eigenvalues[index]\n",
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" eigenvectors = eigenvectors[:, index]\n",
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" U = eigenvectors\n",
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"\n",
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" # S is a diagonal matrix that keeps square root of eigenvalues\n",
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" j = 0\n",
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" for i in eigenvalues:\n",
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" if j == S.size:\n",
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" break\n",
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" elif i >= 0:\n",
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" S[j] = np.sqrt(i)\n",
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" j += 1\n",
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" # same finding process for transpose(A)*A\n",
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" helper = np.matmul(np.transpose(A), A)\n",
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" eigenvalues, eigenvectors = la.eigh(helper)\n",
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" # descending sort of all the eigenvectors according to their eigenvalues\n",
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" index = eigenvalues.argsort()[::-1]\n",
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" eigenvalues = eigenvalues[index]\n",
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" eigenvectors = eigenvectors[:, index]\n",
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" V = np.transpose(eigenvectors)\n",
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"\n",
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" # sorting S in descending order\n",
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" S[::-1].sort()\n",
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" # print_to_file(S)\n",
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"\n",
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" return U, S, V"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -169,7 +214,8 @@
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" \"\"\"\n",
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" Wykonaj dekompozycję SVD, a następnie okrojoną rekonstrukcję (przy użyciu k wartości/wektorów osobliwych)\n",
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" \"\"\"\n",
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" U,s,V = svd(image,full_matrices=False)\n",
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"# U,s,V = svd(image,full_matrices=False)\n",
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" U,s,V = calculate(image)\n",
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" reconst_matrix = np.dot(U[:,:k],np.dot(np.diag(s[:k]),V[:k,:]))\n",
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"\n",
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" return reconst_matrix,s"
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@ -189,7 +235,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 6,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -228,7 +274,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 7,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -258,7 +304,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 21,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -278,7 +324,8 @@
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" original_shape = image.shape\n",
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" print(f\"Input image dimensions. Width:{original_shape[1]} Height:{original_shape[0]}\")\n",
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"\n",
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" U,s,V = svd(image,full_matrices=False)\n",
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"# U,s,V = svd(image,full_matrices=False)\n",
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" U,s,V = calculate(image)\n",
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" print(f\"Shape of U matrix: {U[:,:k].shape}\")\n",
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" print(f\"U MATRIX: {U[:,:k]}\")\n",
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" print('*' * 100)\n",
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@ -291,9 +338,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 22,
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"metadata": {
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"collapsed": true,
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"pycharm": {
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"name": "#%%\n"
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}
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@ -302,12 +348,12 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "aca9f364d54e4861a5e74c0d8f1c90a4",
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"model_id": "7c4a40e6f857407586d21ab113128a97",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'camera', 'coin', 'clock', 'te…"
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"interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'coffee', 'rocket', 'koala', '…"
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]
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},
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"metadata": {},
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@ -319,7 +365,7 @@
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"<function __main__.print_matrices(img_name, k)>"
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]
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},
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"execution_count": 29,
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -330,9 +376,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 10,
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"metadata": {
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"collapsed": true,
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"pycharm": {
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"name": "#%%\n"
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}
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@ -341,7 +386,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "4182f0c1306b4ad0906069edd805e512",
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"model_id": "3533923ce918489eab6fc7de25234078",
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"version_major": 2,
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"version_minor": 0
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},
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@ -370,7 +415,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 11,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -427,7 +472,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 12,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -462,7 +507,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"execution_count": 13,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -486,7 +531,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"def print_color_matrices(img_name,k):\n",
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" \"\"\"\n",
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" Wyświetlanie macierzy U V S wraz z wymiarami.\n",
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" \"\"\"\n",
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" image = color_images[img_name]\n",
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" original_shape = image.shape\n",
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" image_reconst_layers = [compress_svd(image[:,:,i],k)[0] for i in range(3)]\n",
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" print(image_reconst_layers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -496,12 +557,12 @@
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{
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='img_name', index=5, options=('cat', 'astro', 'coffee', 'rocket', '…"
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"interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'coffee', 'rocket', 'koala', '…"
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]
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},
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"metadata": {},
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@ -513,7 +574,7 @@
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"<function __main__.print_color_matrices(img_name, k)>"
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]
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},
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"execution_count": 48,
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -524,9 +585,8 @@
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 16,
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"metadata": {
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"collapsed": true,
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"pycharm": {
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"name": "#%%\n"
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}
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@ -535,7 +595,7 @@
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{
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"data": {
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"model_id": "86f500d175554478ac9805075a809d28",
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"version_major": 2,
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"version_minor": 0
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},
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@ -575,7 +635,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 17,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -600,22 +660,6 @@
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" plt.imshow(image_reconst, vmin=0, vmax=255)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"outputs": [],
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"source": [
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"def print_color_matrices(img_name,k):\n",
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" \"\"\"\n",
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" Wyświetlanie macierzy U V S wraz z wymiarami.\n",
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" \"\"\"\n",
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" image = color_images[img_name]\n",
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" original_shape = image.shape\n",
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" image_reconst_layers = [compress_svd(image[:,:,i],k)[0] for i in range(3)]\n",
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" print(image_reconst_layers)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@ -629,7 +673,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 18,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -653,15 +697,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {
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"collapsed": true
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},
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "280c9df9b5c64f9a937c79974567b4f8",
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"model_id": "e97ca096507f42b49f98dbb87ac5a2ec",
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"version_major": 2,
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"version_minor": 0
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},
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@ -679,7 +721,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 20,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -689,7 +731,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "71fea739144848d69afb1662172190ad",
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"version_major": 2,
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"version_minor": 0
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},
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@ -729,7 +771,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.9"
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"version": "3.8.13"
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}
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},
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"nbformat": 4,
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