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{
"cells": [
{
"cell_type": "markdown",
"id": 3.0293430767166755e+38,
"metadata": {
"id": 3.0293430767166755e+38
},
"source": [
"# Gradio Demo: blocks_flipper"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": 2.8891853944186117e+38,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 616
},
"id": 2.8891853944186117e+38,
"outputId": "b60a6d5e-045d-4b40-bfd8-6caa407a34df",
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7868\n",
"\n",
"To create a public link, set `share=True` in `launch()`.\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"http://127.0.0.1:7868/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import gradio as gr\n",
"import os\n",
"from PIL import Image\n",
"from functools import partial\n",
"\n",
"def retrieve_input_image(dataset, inputs):\n",
" img_id = inputs\n",
" img_path = os.path.join('online_demo', dataset, 'step-100_scale-6.0', img_id, 'input.png')\n",
" image = Image.open(img_path)\n",
" return image\n",
"\n",
"def retrieve_novel_view(dataset, img_id, polar, azimuth, zoom, seed):\n",
" polar = polar // 30 + 1\n",
" azimuth = azimuth // 30\n",
" zoom = int(zoom * 2 + 1)\n",
" img_path = os.path.join('online_demo', dataset, 'step-100_scale-6.0', img_id,\\\n",
" 'polar-%d_azimuth-%d_distance-%d_seed-%d.png' % (polar, azimuth, zoom, seed))\n",
" image = Image.open(img_path)\n",
" return image\n",
" \n",
"\n",
"with gr.Blocks() as demo:\n",
" gr.Markdown(\"Flip text or image files using this demo.\")\n",
" with gr.Tab(\"In-the-wild Images\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" default_input_image = Image.open( os.path.join('online_demo', 'nerf_wild', 'step-100_scale-6.0', 'car1', 'input.png'))\n",
" input_image = gr.Image(default_input_image, shape=[256, 256])\n",
" options = sorted(os.listdir('online_demo/nerf_wild/step-100_scale-6.0'))\n",
" img_id = gr.Dropdown(options, value='car1', label='options')\n",
" text_button = gr.Button(\"Load Input Image\")\n",
" retrieve_input_image_dataset = partial(retrieve_input_image, 'nerf_wild')\n",
" text_button.click(retrieve_input_image_dataset, inputs=img_id, outputs=input_image)\n",
"\n",
" with gr.Column(scale=1):\n",
" novel_view = gr.Image(shape=[256, 256])\n",
" inputs = [img_id,\n",
" gr.Slider(-30, 30, value=0, step=30, label='Polar angle (vertical rotation in degrees)'),\n",
" gr.Slider(0, 330, value=0, step=30, label='Azimuth angle (horizontal rotation in degrees)'),\n",
" gr.Slider(-0.5, 0.5, value=0, step=0.5, label='Zoom'),\n",
" gr.Slider(1, 4, value=1, step=1, label='Random seed')]\n",
" \n",
" submit_button = gr.Button(\"Get Novel View\")\n",
" retrieve_novel_view_dataset = partial(retrieve_novel_view, 'nerf_wild')\n",
" submit_button.click(retrieve_novel_view_dataset, inputs=inputs, outputs=novel_view)\n",
" \n",
" with gr.Tab(\"Google Scanned Objects\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" default_input_image = Image.open( os.path.join('online_demo', 'GSO', 'step-100_scale-6.0', 'SAMBA_HEMP', 'input.png'))\n",
" input_image = gr.Image(default_input_image, shape=[256, 256])\n",
" options = sorted(os.listdir('online_demo/GSO/step-100_scale-6.0'))\n",
" img_id = gr.Dropdown(options, value='SAMBA_HEMP', label='options')\n",
" text_button = gr.Button(\"Choose Input Image\")\n",
" retrieve_input_image_dataset = partial(retrieve_input_image, 'GSO')\n",
" text_button.click(retrieve_input_image_dataset, inputs=img_id, outputs=input_image)\n",
"\n",
" with gr.Column(scale=1):\n",
" novel_view = gr.Image(shape=[256, 256])\n",
" inputs = [img_id,\n",
" gr.Slider(-30, 30, value=0, step=30, label='Polar angle (vertical rotation in degrees)'),\n",
" gr.Slider(0, 330, value=0, step=30, label='Azimuth angle (horizontal rotation in degrees)'),\n",
" gr.Slider(-0.5, 0.5, value=0, step=0.5, label='Zoom'),\n",
" gr.Slider(1, 4, value=1, step=1, label='Random seed')]\n",
" \n",
" submit_button = gr.Button(\"Get Novel View\")\n",
" retrieve_novel_view_dataset = partial(retrieve_novel_view, 'GSO')\n",
" submit_button.click(retrieve_novel_view_dataset, inputs=inputs, outputs=novel_view)\n",
" \n",
" with gr.Tab(\"RTMV\"):\n",
" with gr.Row():\n",
" with gr.Column(scale=1):\n",
" default_input_image = Image.open( os.path.join('online_demo', 'RTMV', 'step-100_scale-6.0', '00000', 'input.png'))\n",
" input_image = gr.Image(default_input_image, shape=[256, 256])\n",
" options = sorted(os.listdir('online_demo/RTMV/step-100_scale-6.0'))\n",
" img_id = gr.Dropdown(options, value='00000', label='options')\n",
" text_button = gr.Button(\"Choose Input Image\")\n",
" retrieve_input_image_dataset = partial(retrieve_input_image, 'RTMV')\n",
" text_button.click(retrieve_input_image_dataset, inputs=img_id, outputs=input_image)\n",
"\n",
" with gr.Column(scale=1):\n",
" novel_view = gr.Image(shape=[256, 256])\n",
" inputs = [img_id,\n",
" gr.Slider(-30, 30, value=0, step=30, label='Polar angle (vertical rotation in degrees)'),\n",
" gr.Slider(0, 330, value=0, step=30, label='Azimuth angle (horizontal rotation in degrees)'),\n",
" gr.Slider(-0.5, 0.5, value=0, step=0.5, label='Zoom'),\n",
" gr.Slider(1, 4, value=1, step=1, label='Random seed')]\n",
" \n",
" submit_button = gr.Button(\"Get Novel View\")\n",
" retrieve_novel_view_dataset = partial(retrieve_novel_view, 'RTMV')\n",
" submit_button.click(retrieve_novel_view_dataset, inputs=inputs, outputs=novel_view)\n",
" \n",
" \n",
"\n",
"if __name__ == \"__main__\":\n",
" demo.launch()\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bk8_q39r_iGt",
"metadata": {
"id": "bk8_q39r_iGt"
},
"outputs": [],
"source": [
"img_id = gr.Dropdown(options, value='car1')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e91860b3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/rliu/anaconda3/lib/python3.9/site-packages/gradio/utils.py:805: UserWarning: Expected 2 arguments for function <function retrieve_input_image at 0x7ffabc3310d0>, received 0.\n",
" warnings.warn(\n",
"/home/rliu/anaconda3/lib/python3.9/site-packages/gradio/utils.py:809: UserWarning: Expected at least 2 arguments for function <function retrieve_input_image at 0x7ffabc3310d0>, received 0.\n",
" warnings.warn(\n"
]
},
{
"ename": "AttributeError",
"evalue": "change() and other events can only be called within a Blocks context.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_2769604/1401407332.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mimg_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretrieve_input_image\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/gradio/events.py\u001b[0m in \u001b[0;36mchange\u001b[0;34m(self, fn, inputs, outputs, api_name, status_tracker, scroll_to_output, show_progress, queue, batch, max_batch_size, preprocess, postprocess, cancels, every, _js)\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;34m\"The 'status_tracker' parameter has been deprecated and has no effect.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m )\n\u001b[0;32m---> 81\u001b[0;31m dep = self.set_event_trigger(\n\u001b[0m\u001b[1;32m 82\u001b[0m \u001b[0;34m\"change\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/gradio/blocks.py\u001b[0m in \u001b[0;36mset_event_trigger\u001b[0;34m(self, event_name, fn, inputs, outputs, preprocess, postprocess, scroll_to_output, show_progress, api_name, js, no_target, queue, batch, max_batch_size, cancels, every)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mContext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot_block\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m raise AttributeError(\n\u001b[0m\u001b[1;32m 195\u001b[0m \u001b[0;34mf\"{event_name}() and other events can only be called within a Blocks context.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 196\u001b[0m )\n",
"\u001b[0;31mAttributeError\u001b[0m: change() and other events can only be called within a Blocks context."
]
}
],
"source": [
"img_id.change(retrieve_input_image)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ab10a43f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'car1'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"img_id.value"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2febec07",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|