cleanup
This commit is contained in:
Adam Wojdyla
parent
28c8b93256
commit
27a998e774
242
jupyter.ipynb
242
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": 1,
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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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@ -13,18 +13,14 @@
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/t3/dwnz0lh916ng4w7bzf0z56ym0000gn/T/ipykernel_26663/17056051.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/t3/dwnz0lh916ng4w7bzf0z56ym0000gn/T/ipykernel_32034/3285565865.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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" from IPython.core.display import display, HTML\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<style>.container { width:80% !important; }</style>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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"text/plain": "<IPython.core.display.HTML object>",
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"text/html": "<style>.container { width:80% !important; }</style>"
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},
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"metadata": {},
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"output_type": "display_data"
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@ -47,7 +43,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 19,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -86,7 +82,7 @@
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"\n",
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"SVD polega na rekonstrukcji oryginalnej macierzy jako kombinacji liniowej kilku macierzy rangi jeden. Macierz rangi jeden można wyrazić jako iloczyn zewnętrzny dwóch wektorów kolumnowych. \n",
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"\n",
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"$M=\\sigma_1u_1v_1^T+\\sigma_2u_2v_2^T+\\sigma_3u_3v_3^T+\\sigma_3u_3v_3^T+....$ .\n",
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"$M=\\sigma_1u_1v_1^T+\\sigma_2u_2v_2^T+\\sigma_3u_3v_3^T+\\sigma_4u_4v_4^T+....$ .\n",
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"\n",
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"$rank M=r$ .\n",
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"$M=\\sum_{i=1}^{r} \\sigma_iu_iv_i^T$\n",
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@ -113,7 +109,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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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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@ -156,7 +152,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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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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@ -166,10 +162,7 @@
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"source": [
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"def compress_svd(image,k):\n",
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" \"\"\"\n",
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" Perform svd decomposition and truncated (using k singular values/vectors) reconstruction\n",
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" returns\n",
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" --------\n",
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" reconstructed matrix reconst_matrix, array of singular values s\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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" reconst_matrix = np.dot(U[:,:k],np.dot(np.diag(s[:k]),V[:k,:]))\n",
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@ -191,13 +184,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 22,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"def compress_show_gray_images(img_name,k):\n",
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" \"\"\"\n",
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" compresses gray scale images and display the reconstructed image.\n",
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" Also displays a plot of singular values\n",
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" Kompresuje obrazy w skali szarości i wyświetla zrekonstruowany obraz.\n",
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" Wyświetla również wykres wartości singularnych\n",
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" \"\"\"\n",
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" image=gray_images[img_name]\n",
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" original_shape = image.shape\n",
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@ -205,45 +203,42 @@
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" fig,axes = plt.subplots(1,2,figsize=(8,5))\n",
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" axes[0].plot(s)\n",
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" compression_ratio =100.0* (k*(original_shape[0] + original_shape[1])+k)/(original_shape[0]*original_shape[1])\n",
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" axes[1].set_title(\"compression ratio={:.2f}\".format(compression_ratio)+\"%\")\n",
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" axes[1].set_title(\"compression ratio={:.2f}\".format(100 - compression_ratio)+\"%\")\n",
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" axes[1].imshow(reconst_img,cmap='gray')\n",
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" axes[1].axis('off')\n",
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" fig.tight_layout()\n",
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" # compression rate = 100% * (k * (height + width + k)) / (height + width)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"W celu zbadania, jak jakość zrekonstruowanego obrazu zmienia się wraz z $k$ należy użyć poniższego interaktywnego widżetu."
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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"source": [
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"W celu zbadania, jak jakość zrekonstruowanego obrazu zmienia się wraz z $k$ należy użyć poniższego interaktywnego widżetu."
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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": null,
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"execution_count": 23,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"def compute_k_max(img_name):\n",
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" \"\"\"\n",
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" utility function for calculating max value of the slider range\n",
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" Funkcja do obliczania maksymalnej wartości zakresu suwaka \"k\"\n",
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" \"\"\"\n",
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" img = gray_images[img_name]\n",
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" m,n = img.shape\n",
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" return m*n/(m+n+1)\n",
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"\n",
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"#set up the widgets\n",
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"import ipywidgets as widgets\n",
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"\n",
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"list_widget = widgets.Dropdown(options=list(gray_images.keys()))\n",
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@ -253,23 +248,22 @@
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" img_name=list_widget.value\n",
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" int_slider_widget.max = compute_k_max(img_name)\n",
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"\n",
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"list_widget.observe(update_k_max,'value')\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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"list_widget.observe(update_k_max,'value')"
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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": null,
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"execution_count": 24,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Print matrices\n",
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"def print_matrices(img_name, k):\n",
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" if (img_name not in gray_images.keys()):\n",
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" if img_name not in gray_images.keys():\n",
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" return\n",
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" \n",
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" image=gray_images[img_name]\n",
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@ -285,57 +279,87 @@
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" print('*' * 100)\n",
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" print(f\"Shape of V matrix: {V[:k,:].shape}\")\n",
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" print(f\"V MATRIX: {V[:k,:]}\")\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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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": null,
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"outputs": [],
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"execution_count": 25,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'camera', 'coin', 'clock', 'te…",
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
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"version_minor": 0,
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"model_id": "e73d6e563a63469e9a4b9f32a01b7187"
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}
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": "<function __main__.print_matrices(img_name, k)>"
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"interact(print_matrices, img_name=list_widget, k=int_slider_widget)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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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": null,
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"outputs": [],
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"source": [
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"interact(compress_show_gray_images,img_name=list_widget,k=int_slider_widget);"
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],
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"execution_count": 26,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": "interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'camera', 'coin', 'clock', 'te…",
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
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"version_minor": 0,
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"model_id": "5a74237c02e040abb124f54625d61846"
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}
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"interact(compress_show_gray_images,img_name=list_widget,k=int_slider_widget);"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Ładowanie kolorowych obrazów"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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"source": [
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"### Ładowanie kolorowych obrazów"
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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": null,
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"execution_count": 27,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"color_images = {\n",
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@ -347,13 +371,7 @@
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" \"orange\": img_as_float(Image.open('orange.jpeg')),\n",
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" \"teacher\": img_as_float(Image.open('teacher.jpeg'))\n",
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"}\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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]
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 28,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -403,7 +421,7 @@
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"source": [
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"def compress_show_color_images_reshape(img_name,k):\n",
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" \"\"\"\n",
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" compress and display the reconstructed color image using the reshape method \n",
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" Kompresowanie i wyświetlanie zrekonstruowanego obrazu kolorowego przy użyciu metody reshape.\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,_ = compress_svd(image_reshaped,k)\n",
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" image_reconst = image_reconst.reshape(original_shape)\n",
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" compression_ratio =100.0* (k*(original_shape[0] + 3*original_shape[1])+k)/(original_shape[0]*original_shape[1]*original_shape[2])\n",
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" plt.title(\"compression ratio={:.2f}\".format(compression_ratio)+\"%\")\n",
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" plt.title(\"compression ratio={:.2f}\".format(100 - compression_ratio)+\"%\")\n",
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" plt.imshow(image_reconst)"
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]
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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": 12,
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"execution_count": 29,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 30,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -461,25 +479,21 @@
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"outputs": [
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{
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"data": {
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"text/plain": "interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'coffee', 'rocket', 'koala', '…",
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "4715b532b1d64abc9a8451cbfcfcc0e7",
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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', 'coffee', 'rocket', 'koala', '…"
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]
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"version_minor": 0,
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"model_id": "46b3c56fd85e40b299d36dd0c1630c9f"
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}
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<function __main__.print_matrices(img_name, k)>"
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]
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"text/plain": "<function __main__.print_matrices(img_name, k)>"
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},
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"execution_count": 13,
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -490,7 +504,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 31,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -499,14 +513,12 @@
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"outputs": [
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{
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"data": {
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"text/plain": "interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'coffee', 'rocket', 'koala', '…",
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f56619fa4fac4a6b9babba92ed3fb72a",
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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', 'coffee', 'rocket', 'koala', '…"
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]
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"version_minor": 0,
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"model_id": "b5123a5d03fb489699824a8415bc6701"
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}
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},
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"metadata": {},
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"output_type": "display_data"
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@ -540,7 +552,7 @@
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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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"execution_count": 32,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -550,7 +562,7 @@
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"source": [
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"def compress_show_color_images_layer(img_name,k):\n",
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" \"\"\"\n",
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" compress and display the reconstructed color image using the layer method \n",
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" Kompresowanie i wyświetlanie zrekonstruowanego obrazu kolorowego przy użyciu metody warstwowej.\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",
|
||||
@ -561,7 +573,7 @@
|
||||
" image_reconst[:,:,i] = image_reconst_layers[i]\n",
|
||||
" \n",
|
||||
" compression_ratio =100.0*3* (k*(original_shape[0] + original_shape[1])+k)/(original_shape[0]*original_shape[1]*original_shape[2])\n",
|
||||
" plt.title(\"compression ratio={:.2f}\".format(compression_ratio)+\"%\")\n",
|
||||
" plt.title(\"compression ratio={:.2f}\".format(100- compression_ratio)+\"%\")\n",
|
||||
" plt.imshow(image_reconst, vmin=0, vmax=255)\n"
|
||||
]
|
||||
},
|
||||
@ -578,7 +590,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
@ -602,7 +614,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
@ -611,14 +623,12 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'coffee', 'rocket', 'koala', '…",
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "4fa3385da3234652945d611efad33b62",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'coffee', 'rocket', 'koala', '…"
|
||||
]
|
||||
"version_minor": 0,
|
||||
"model_id": "14cf5f5b31f74c2482088989f5e87558"
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
@ -630,7 +640,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
@ -641,7 +651,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user