{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import os\n",
"import torch\n",
"import matplotlib.pyplot as plt\n",
"\n",
"rootdir = os.path.abspath(sys.path[0] + '/../')\n",
"sys.path.append(rootdir)\n",
"torch.cuda.set_device(2)\n",
"print(\"Set CUDA:%d as current device.\" % torch.cuda.current_device())\n",
"\n",
"from data.spherical_view_syn import *\n",
"from configs.spherical_view_syn import SphericalViewSynConfig\n",
"from utils import netio\n",
"from utils import misc\n",
"from utils import img\n",
"from utils import device\n",
"from utils import view\n",
"from components.gen_final import GenFinal\n",
"\n",
"\n",
"def load_net(path):\n",
" config = SphericalViewSynConfig()\n",
" config.from_id(path[:-4])\n",
" config.sa['perturb_sample'] = False\n",
" config.print()\n",
" net = config.create_net().to(device.default())\n",
" netio.load(path, net)\n",
" return net\n",
"\n",
"\n",
"def find_file(prefix):\n",
" for path in os.listdir():\n",
" if path.startswith(prefix):\n",
" return path\n",
" return None\n",
"\n",
"\n",
"def load_views(data_desc_file) -> view.Trans:\n",
" with open(data_desc_file, 'r', encoding='utf-8') as file:\n",
" data_desc = json.loads(file.read())\n",
" samples = data_desc['samples'] if 'samples' in data_desc else [-1]\n",
" view_centers = torch.tensor(\n",
" data_desc['view_centers'], device=device.default()).view(samples + [3])\n",
" view_rots = torch.tensor(\n",
" data_desc['view_rots'], device=device.default()).view(samples + [3, 3])\n",
" return view.Trans(view_centers, view_rots)\n",
"\n",
"\n",
"def plot_cross(center, res):\n",
" plt.plot(\n",
" [\n",
" (res[1] - 1) / 2 + center[0] - 5,\n",
" (res[1] - 1) / 2 + center[0] + 5\n",
" ],\n",
" [\n",
" (res[0] - 1) / 2 + center[1],\n",
" (res[0] - 1) / 2 + center[1]\n",
" ],\n",
" color=[0, 1, 0])\n",
" plt.plot(\n",
" [\n",
" (res[1] - 1) / 2 + center[0],\n",
" (res[1] - 1) / 2 + center[0]\n",
" ],\n",
" [\n",
" (res[0] - 1) / 2 + center[1] - 5,\n",
" (res[0] - 1) / 2 + center[1] + 5\n",
" ],\n",
" color=[0, 1, 0])\n",
"\n",
"\n",
"def plot_figures(left_images, right_images, left_center, right_center):\n",
" # Plot Fovea raw\n",
" plt.figure(figsize=(8, 4))\n",
" plt.subplot(121)\n",
" img.plot(left_images['fovea_raw'])\n",
" plt.subplot(122)\n",
" img.plot(right_images['fovea_raw'])\n",
"\n",
" # Plot Fovea\n",
" plt.figure(figsize=(8, 4))\n",
" plt.subplot(121)\n",
" img.plot(left_images['fovea'])\n",
" fovea_res = left_images['fovea'].size()[-2:]\n",
" plot_cross((0, 0), fovea_res)\n",
" plt.subplot(122)\n",
" img.plot(right_images['fovea'])\n",
" plot_cross((0, 0), fovea_res)\n",
"\n",
" #plt.subplot(1, 4, 2)\n",
" # img.plot(fovea_refined)\n",
"\n",
" # Plot Mid\n",
" plt.figure(figsize=(8, 4))\n",
" plt.subplot(121)\n",
" img.plot(left_images['mid'])\n",
" plt.subplot(122)\n",
" img.plot(right_images['mid'])\n",
"\n",
" # Plot Periph\n",
" plt.figure(figsize=(8, 4))\n",
" plt.subplot(121)\n",
" img.plot(left_images['periph'])\n",
" plt.subplot(122)\n",
" img.plot(right_images['periph'])\n",
"\n",
" # Plot Blended\n",
" plt.figure(figsize=(12, 6))\n",
" plt.subplot(121)\n",
" img.plot(left_images['blended'])\n",
" full_res = left_images['blended'].size()[-2:]\n",
" plot_cross(left_center, full_res)\n",
" plt.subplot(122)\n",
" img.plot(right_images['blended'])\n",
" plot_cross(right_center, full_res)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_gas_all_in_one'))\n",
"os.chdir(os.path.join(rootdir, 'data/__0_user_study/us_mc_all_in_one'))\n",
"#os.chdir(os.path.join(rootdir, 'data/__0_user_study/bedroom_all_in_one'))\n",
"print('Change working directory to ', os.getcwd())\n",
"torch.autograd.set_grad_enabled(False)\n",
"\n",
"fovea_net = load_net(find_file('fovea'))\n",
"periph_net = load_net(find_file('periph'))\n",
"\n",
"# Load Dataset\n",
"views = load_views('views.json')\n",
"#ref_dataset = SphericalViewSynDataset('ref.json', load_images=False, calculate_rays=False)\n",
"print('Dataset loaded.')\n",
"\n",
"print('views:', views.size())\n",
"#print('ref views:', ref_dataset.samples)\n",
"\n",
"fov_list = [20, 45, 110]\n",
"res_list = [(128, 128), (256, 256), (256, 230)] # (192,256)]\n",
"res_full = (1600, 1440)\n",
"\n",
"gen = GenFinal(fov_list, res_list, res_full, fovea_net, periph_net, device.default())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"centers = [\n",
" # ==gas==\n",
" [(-137, 64), (-142, 64)],\n",
" [(133, -44), (130, -44)],\n",
" [(-20, -5), (-25, -5)],\n",
" # ==mc==\n",
" [(-107, 80), (-112, 80)],\n",
" [(-17, -90), (-22, -90)],\n",
" [(95, 30), (91, 30)]\n",
"]\n",
"set_id = 5\n",
"\n",
"view_coord = [0, 0, 0, 0, 0]\n",
"for i, val in enumerate(views.size()):\n",
" view_coord[i] += val // 2\n",
"print('view_coord:', view_coord)\n",
"test_view = views.get(*view_coord)\n",
"\n",
"left_images = gen(centers[set_id][0], view.Trans(\n",
" test_view.trans_point(\n",
" torch.tensor([-0.03, 0, 0], device=device.default())\n",
" ), test_view.r), mono_trans=test_view, ret_raw=True)\n",
"right_images = gen(centers[set_id][1], view.Trans(\n",
" test_view.trans_point(\n",
" torch.tensor([0.03, 0, 0], device=device.default())\n",
" ), test_view.r), mono_trans=test_view, ret_raw=True)\n",
"\n",
"#plot_figures(left_images, right_images, centers[set_id][0], centers[set_id][1])\n",
"\n",
"os.makedirs('output', exist_ok=True)\n",
"for key in left_images:\n",
" img.save(\n",
" left_images[key], 'output/set%d_%s_l.png' % (set_id, key))\n",
"for key in right_images:\n",
" img.save(\n",
" right_images[key], 'output/set%d_%s_r.png' % (set_id, key))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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