{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Permutation tests"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-cell"
]
},
"outputs": [],
"source": [
"import re\n",
"from pathlib import Path\n",
"\n",
"#import ipywidgets as widgets\n",
"#from ipywidgets import Layout, Button, HBox, VBox, Box, Dropdown, Select, Text, Output, IntSlider, Label\n",
"from IPython.display import display, clear_output, Image, Video\n",
"import panel as pn\n",
"\n",
"\n",
"#from bokeh.plotting import figure, show, output_notebook\n",
"#output_notebook()\n",
"#pn.extension('bokeh')\n",
"pn.extension()\n",
"#pn.extension('ipywidgets')\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-cell"
]
},
"outputs": [],
"source": [
"outputs_root = Path('images_out')\n",
"#folder_prefix = 'permutations_limited_palette_2D'\n",
"#folder_prefix = 'exp_stability_modes'\n",
"folder_prefix = 'exp_video_basic_stability_modes'\n",
"folders = list(outputs_root.glob(f'{folder_prefix}_*'))\n",
"len(folders)\n",
"\n",
"def format_val(v):\n",
" try:\n",
" v = float(v)\n",
" if int(v) == v:\n",
" v = int(v)\n",
" except:\n",
" pass\n",
" return v\n",
"\n",
"def parse_folder_name(folder):\n",
" #chunks = folder.name[1+len(folder_prefix):].split('_')\n",
" #chunks = folder.name[1+len(folder_prefix):].split('-')\n",
" metadata_string = folder.name[1+len(folder_prefix):]\n",
" pattern = r\"_?([a-zA-Z_]+)-(True|False|[0-9.]+)\"\n",
" matches = re.findall(pattern, metadata_string)\n",
" d_ = {k:format_val(v) for k,v in matches}\n",
" d_['fpath'] = folder\n",
" d_['n_images'] = len(list(folder.glob('*.png')))\n",
" return d_\n",
"\n",
"#parse_folder_name(folders[0])\n",
"df_meta = pd.DataFrame([parse_folder_name(f) for f in folders])\n",
"\n",
"variant_names = [v for v in df_meta.columns.tolist() if v not in ['fpath']]\n",
"variant_ranges = {v:df_meta[v].unique() for v in variant_names}\n",
"[v.sort() for v in variant_ranges.values()]\n",
"True\n",
"\n",
"##########################################\n",
"\n",
"# to do: output and display palettes\n",
"\n",
"#kargs = {k:widgets.Dropdown(options=v, value=v[0], disabled=False, layout=Layout(width='auto')) for k,v in variant_ranges.items()}\n",
"#kargs['i'] = widgets.IntSlider(min=1, max=40, step=1, value=1, continuous_update=False, readout=True, readout_format='d')\n",
"\n",
"n_imgs_per_group = 20\n",
"\n",
"def setting_name_shorthand(setting_name):\n",
" return ''.join([tok[0] for tok in setting_name.split('_')])\n",
"\n",
"kargs = {k:pn.widgets.DiscreteSlider(name=k, options=list(v), value=v[0]) for k,v in variant_ranges.items()}\n",
"#kargs['i'] = pn.widgets.IntSlider(name='i', start=1, end=n_imgs_per_group, step=1, value=n_imgs_per_group)\n",
"kargs['i'] = pn.widgets.Player(interval=300, name='step', start=1, end=n_imgs_per_group, step=1, value=1, loop_policy='reflect')\n",
"\n",
"PRELOAD_IMAGES = False\n",
"from PIL import Image\n",
"\n",
"def read_image(fpath):\n",
" #return plt.imread(fpath)\n",
" #return pn.pane.PNG(fpath, width=700)\n",
" with Image.open(fpath) as _img:\n",
" img = _img.copy()\n",
" return img\n",
"\n",
"url_prefix = \"https://raw.githubusercontent.com/dmarx/pytti-settings-test/main/images_out/\"\n",
"#im_path = im_path.replace('images_out/', url_prefix)\n",
"\n",
"image_paths = [str(p) for p in Path('images_out').glob('**/*.png')]\n",
"#print(len(list(image_paths)))\n",
"d_image_urls = {im_path:im_path.replace('images_out/', url_prefix) for im_path in image_paths}\n",
"\n",
"if PRELOAD_IMAGES:\n",
" d_images = {}\n",
" for folder in df_meta['fpath']:\n",
" for im_path in folder.glob('*.png'):\n",
" d_images[str(im_path)] = read_image(im_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"variant_names"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide-input"
]
},
"outputs": [],
"source": [
"\n",
"\n",
"#@widgets.interact(\n",
"@pn.interact(\n",
" **kargs\n",
")\n",
"#@pn.interact\n",
"def display_images(\n",
" palettes,\n",
" palette_size,\n",
" gamma,\n",
" hdr_weight,\n",
" smoothing_weight,\n",
" palette_normalization_weight,\n",
" i,\n",
"):\n",
" folder = df_meta[\n",
" (palettes == df_meta['palettes']) &\n",
" (palette_size == df_meta['palette_size']) &\n",
" (gamma == df_meta['gamma']) &\n",
" (hdr_weight == df_meta['hdr_weight']) &\n",
" (smoothing_weight == df_meta['smoothing_weight']) &\n",
" (palette_normalization_weight == df_meta['palette_normalization_weight'])\n",
" ]['fpath'].values[0]\n",
" im_path = str(folder / f\"{folder.name}_{i}.png\")\n",
" im_url = d_image_urls[im_path]\n",
" #return Image(im_path, width=700)\n",
" #print(type(im_path))\n",
" #im = im_path\n",
" #url_prefix = \"https://raw.githubusercontent.com/dmarx/pytti-settings-test/main/images_out/\"\n",
" #im_path = im_path.replace('images_out/', url_prefix)\n",
" #print(im_path)\n",
" #if PRELOAD_IMAGES:\n",
" # im = d_images[im_path]\n",
" #else:\n",
" # im = im_path\n",
" #return pn.pane.PNG(im, width=700)\n",
" #return im\n",
" #return pn.pane.PNG(im_url, width=700)\n",
" return pn.pane.HTML(f'<img src=\"{im_url}\" width=\"700\">', width=700, height=350, sizing_mode='fixed')\n",
"\n",
"# embedding this makes the page nearly a gigabyte in size.\n",
"# need to use a CDN of something like that.\n",
"pn.panel(display_images, height=1000).embed(max_opts=n_imgs_per_group, max_states=999999999)\n",
"#pn.panel(display_images)\n",
"#display_images "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"\n",
"#@widgets.interact(\n",
"@pn.interact(\n",
" **kargs\n",
")\n",
"#@pn.interact\n",
"def display_images(\n",
" ref,\n",
" dsw,\n",
" ssw,\n",
" i,\n",
"):\n",
" folder = df_meta[\n",
" #(reencode_each_frame == df_meta['ref']) &\n",
" #(direct_stabilization_weight == df_meta['dsw']) &\n",
" #(semantic_stabilization_weight == df_meta['ssw'])\n",
" (ref == df_meta['ref']) &\n",
" (dsw == df_meta['dsw']) &\n",
" (ssw == df_meta['ssw'])\n",
" ]['fpath'].values[0]\n",
" im_path = str(folder / f\"{folder.name}_{i}.png\")\n",
" #im_url = d_image_urls[im_path]\n",
" im_url = im_path\n",
" #return Image(im_path, width=700)\n",
" #print(type(im_path))\n",
" #im = im_path\n",
" #url_prefix = \"https://raw.githubusercontent.com/dmarx/pytti-settings-test/main/images_out/\"\n",
" #im_path = im_path.replace('images_out/', url_prefix)\n",
" #print(im_path)\n",
" #if PRELOAD_IMAGES:\n",
" # im = d_images[im_path]\n",
" #else:\n",
" # im = im_path\n",
" #return pn.pane.PNG(im, width=700)\n",
" #return im\n",
" #return pn.pane.PNG(im_url, width=700)\n",
" return pn.pane.HTML(f'<img src=\"{im_url}\" width=\"700\">', width=700, height=350, sizing_mode='fixed')\n",
"\n",
"# embedding this makes the page nearly a gigabyte in size.\n",
"# need to use a CDN of something like that.\n",
"pn.panel(display_images, height=1000)#.embed(max_opts=n_imgs_per_group, max_states=999999999)"
]
}
],
"metadata": {
"interpreter": {
"hash": "3eff1e1332ed0784bebe5613522d192d113df675730803c3b8984f113f4e15fd"
},
"kernelspec": {
"display_name": "Python 3.9.7 ('pytti-book-l72HEyWC')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
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"orig_nbformat": 4
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"nbformat": 4,
"nbformat_minor": 2
}