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Ethan F. Tseng
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Upload first version of code.
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‎.gitignore

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*.swp
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**/*.out
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**/__pycache__
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**/.ipynb_checkpoints
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**/*.pyc
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**/.DS_Store

‎Experimental/Monochromatic_Experimental.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "01f63883",
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"metadata": {},
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"source": [
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"# Monochromatic Neural Étendue Expansion Experimental Code"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac28b847",
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"metadata": {},
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"source": [
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"### This notebook can be used to produce the monochromatic étendue expanded experimental holograms shown in the manuscript and in the supplementary information.\n",
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"\n",
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"### In the cells below please select one expander type and one target image. For example, to produce a 64x étendue expanded hologram with the neural étendue expander please select 'neural_mono_64x'. To produce a 16x étendue expanded hologram with a random expander [Kuo et al. 2020] please select 'random_16x'. To produce a conventional hologram [Shi et al. 2021] please select 'conventional_16x' or 'conventional_64x'. The target images provided are labeled as '000.png', '001.png', and so on."
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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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"id": "8ef8193c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import os\n",
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"from imageio import imread"
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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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"id": "908f9ffd",
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"metadata": {},
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"outputs": [],
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"source": [
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"### --- BEGIN CONFIG --- ###\n",
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"# Choose only one of the following expanders.\n",
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"#expander_type = 'conventional_16x'\n",
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"#expander_type = 'conventional_64x'\n",
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"#expander_type = 'random_16x'\n",
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"#expander_type = 'random_64x'\n",
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"#expander_type = 'neural_mono_16x'\n",
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"expander_type = 'neural_mono_64x'\n",
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"\n",
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"# Choose only one of the following target images.\n",
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"#target_img_name = '000'\n",
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"target_img_name = '001'\n",
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"### --- END CONFIG --- ###\n",
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"\n",
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"if (expander_type == 'random_16x') or (expander_type == 'neural_mono_16x'):\n",
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" correction_factor = np.array([1.0])\n",
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" top_percentile = 99.9\n",
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" eff_corners = None\n",
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"elif (expander_type == 'conventional_16x'):\n",
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" correction_factor = np.array([1.0])\n",
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" top_percentile = 99.9\n",
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" eff_corners = [144,144,240,240]\n",
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"elif (expander_type == 'random_64x') or (expander_type == 'neural_mono_64x'):\n",
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" correction_factor = np.array([1.0])\n",
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" top_percentile = 99.0\n",
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" eff_corners = None\n",
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"elif (expander_type == 'conventional_64x'):\n",
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" correction_factor = np.array([1.0])\n",
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" top_percentile = 99.9\n",
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" eff_corners = [336,336,432,432]\n",
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"else:\n",
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" assert('Undefined expander.')\n",
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"print(eff_corners)"
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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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"id": "f1b94ccc",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_target_img(img_name):\n",
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" img = imread(img_name)\n",
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" if img.dtype == 'uint8':\n",
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" img = img.astype(np.float32) / 255.0\n",
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" elif img.dtype == 'uint16':\n",
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" img = img.astype(np.float32) / 65535.0\n",
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" elif img.dtype == 'float32':\n",
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" img = img / 1.0\n",
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" else:\n",
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" assert('Invalid image type')\n",
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" if len(img.shape) == 3:\n",
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" img = 0.299 * img[:,:,0] + 0.587 * img[:,:,1] + 0.114 * img[:,:,2] # Convert to gray scale\n",
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" return img\n",
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"\n",
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"def white_balance(cap_img, target_img, correction_factor, top_percentile, eff_corners = None):\n",
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" # Normalize so that max == 1.\n",
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" # We are only adjusting the ratios between the colors, not the overall brightness.\n",
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" if eff_corners == None:\n",
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" scale_factors = np.mean(target_img, axis=(0,1)) / np.mean(cap_img, axis=(0,1))\n",
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" else:\n",
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" cap_img_eff = cap_img[eff_corners[0]:eff_corners[2],eff_corners[1]:eff_corners[3]]\n",
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" scale_factors = np.mean(target_img, axis=(0,1)) / np.mean(cap_img_eff, axis=(0,1))\n",
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"\n",
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" scale_factors = scale_factors / np.max(scale_factors)\n",
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" cap_img = cap_img * scale_factors\n",
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"\n",
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" # Additional manual color balancing.\n",
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" cap_img = cap_img * correction_factor\n",
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"\n",
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" # Scale overall brightness so that top percentile of pixels are clipped.\n",
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" top_scale = np.percentile(cap_img, top_percentile)\n",
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" cap_img = cap_img / top_scale\n",
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" return np.clip(cap_img, 0.0, 1.0)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "41a8642c",
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"metadata": {},
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"source": [
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"### Display Intensity Scaled Experimentally Captured Hologram ###"
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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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"id": "8acda60c",
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"metadata": {},
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"outputs": [],
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"source": [
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"target_img = get_target_img(os.path.join('Target_Images',target_img_name+'.png'))\n",
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"w_cap = np.load(os.path.join('Data',expander_type,'w',target_img_name+'.npy'))\n",
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"w_cap_wb = np.fliplr(white_balance(w_cap, target_img, correction_factor, top_percentile, eff_corners))\n",
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"w_cap_wb = np.stack([w_cap_wb, w_cap_wb, w_cap_wb], axis=-1)\n",
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"\n",
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"plt.figure()\n",
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"plt.title('Experimentally Captured Hologram')\n",
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"plt.imshow(w_cap_wb)"
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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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"id": "7fc758bd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.10.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎Experimental/Trichromatic_Experimental.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "01f63883",
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"metadata": {},
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"source": [
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"# Trichromatic Neural Étendue Expansion Experimental Code"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac28b847",
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"metadata": {},
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"source": [
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"### This notebook can be used to produce the trichromatic étendue expanded experimental holograms shown in the manuscript and in the supplementary information.\n",
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"\n",
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"### In the cells below please select one expander type and one target image. For example, to produce a 64x étendue expanded hologram with the neural étendue expander please select 'neural_tri_64x'. To produce a 16x étendue expanded hologram with a random expander [Kuo et al. 2020] please select 'random_16x'. To produce a conventional hologram [Shi et al. 2021] please select 'conventional_16x' or 'conventional_64x'. The target images provided are labeled as '000.png', '001.png', and so on."
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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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"id": "8ef8193c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import os\n",
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"from imageio import imread"
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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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"id": "908f9ffd",
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"metadata": {},
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"outputs": [],
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"source": [
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"### --- BEGIN CONFIG --- ###\n",
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"# Choose only one of the following expanders.\n",
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"#expander_type = 'conventional_16x'\n",
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"#expander_type = 'conventional_64x'\n",
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"#expander_type = 'random_16x'\n",
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"#expander_type = 'random_64x'\n",
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"#expander_type = 'neural_tri_16x'\n",
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"expander_type = 'neural_tri_64x'\n",
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"\n",
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"# Choose only one of the following target images.\n",
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"#target_img_name = '000'\n",
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"target_img_name = '001'\n",
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"### --- END CONFIG --- ###\n",
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"\n",
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"if (expander_type == 'random_16x') or (expander_type == 'neural_tri_16x'):\n",
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" correction_factor = np.array([1.0,1.0,1.2])\n",
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" top_percentile = 99.9\n",
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" eff_corners = None\n",
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"elif (expander_type == 'conventional_16x'):\n",
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" correction_factor = np.array([1.0,1.0,1.2])\n",
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" top_percentile = 99.9\n",
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" eff_corners = [144,144,240,240]\n",
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"elif (expander_type == 'random_64x') or (expander_type == 'neural_tri_64x'):\n",
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" correction_factor = np.array([1.0,1.1,1.6])\n",
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" top_percentile = 99.0\n",
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" eff_corners = None\n",
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"elif (expander_type == 'conventional_64x'):\n",
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" correction_factor = np.array([1.0,1.1,1.2])\n",
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" top_percentile = 99.9\n",
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" eff_corners = [336,336,432,432]\n",
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"else:\n",
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" assert('Undefined expander.')\n",
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"print(eff_corners)"
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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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"id": "f1b94ccc",
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_target_img(img_name):\n",
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" img = imread(img_name)\n",
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" if img.dtype == 'uint8':\n",
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" img = img.astype(np.float32) / 255.0\n",
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" elif img.dtype == 'uint16':\n",
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" img = img.astype(np.float32) / 65535.0\n",
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" elif img.dtype == 'float32':\n",
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" img = img / 1.0\n",
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" else:\n",
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" assert('Invalid image type')\n",
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" if len(img.shape) == 2:\n",
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" img = np.stack([img, img, img], axis=-1)\n",
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" return img\n",
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"\n",
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"def white_balance(cap_img, target_img, correction_factor, top_percentile, eff_corners = None):\n",
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" # Normalize so that max == 1.\n",
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" # We are only adjusting the ratios between the colors, not the overall brightness.\n",
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" if eff_corners == None:\n",
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" scale_factors = np.mean(target_img, axis=(0,1)) / np.mean(cap_img, axis=(0,1))\n",
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" else:\n",
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" cap_img_eff = cap_img[eff_corners[0]:eff_corners[2],eff_corners[1]:eff_corners[3],:]\n",
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" scale_factors = np.mean(target_img, axis=(0,1)) / np.mean(cap_img_eff, axis=(0,1))\n",
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"\n",
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" scale_factors = scale_factors / np.max(scale_factors)\n",
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" cap_img = cap_img * scale_factors\n",
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"\n",
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" # Additional manual color balancing.\n",
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" cap_img = cap_img * correction_factor\n",
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"\n",
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" # Scale overall brightness so that top percentile of pixels are clipped.\n",
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" top_scale = np.percentile(cap_img, top_percentile)\n",
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" cap_img = cap_img / top_scale\n",
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" return np.clip(cap_img, 0.0, 1.0)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "41a8642c",
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"metadata": {},
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"source": [
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"### Display Intensity Scaled Experimentally Captured Hologram ###"
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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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"id": "8acda60c",
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"metadata": {},
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"outputs": [],
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"source": [
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"target_img = get_target_img(os.path.join('Target_Images',target_img_name+'.png'))\n",
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"r_cap = np.load(os.path.join('Data',expander_type,'r',target_img_name+'.npy'))\n",
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"g_cap = np.load(os.path.join('Data',expander_type,'g',target_img_name+'.npy'))\n",
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"b_cap = np.load(os.path.join('Data',expander_type,'b',target_img_name+'.npy'))\n",
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"rgb_cap = np.stack([r_cap, g_cap, b_cap], axis=-1)\n",
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"rgb_cap_wb = np.fliplr(white_balance(rgb_cap, target_img, correction_factor, top_percentile, eff_corners))\n",
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"\n",
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"plt.figure()\n",
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"plt.title('Experimentally Captured Hologram')\n",
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"plt.imshow(rgb_cap_wb)"
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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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"id": "7fc758bd",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.10.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎LICENSE

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Permission is hereby granted, free of charge, to any person or organization
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obtaining a copy of the software and accompanying documentation covered by
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this license (the "Software") to use, reproduce, display, distribute,
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execute, and transmit the Software, and to prepare derivative works of the
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Software, and to permit third-parties to whom the Software is furnished to
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do so, all subject to the following:
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The copyright notices in the Software and this entire statement, including
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the above license grant, this restriction and the following disclaimer,
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must be included in all copies of the Software, in whole or in part, and
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all derivative works of the Software, unless such copies or derivative
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works are solely in the form of machine-executable object code generated by
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a source language processor.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
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SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
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FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
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ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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DEALINGS IN THE SOFTWARE.

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