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| import numpy as np | |
| import gradio as gr | |
| import os | |
| from PIL import Image | |
| from functools import partial | |
| def retrieve_input_image_wild(dataset, inputs): | |
| img_id = inputs | |
| img_path = os.path.join('online_demo', dataset, 'step-100_scale-6.0') | |
| try: | |
| image = Image.open(os.path.join(img_path, '%s.jpg' % img_id)) | |
| except: | |
| image = Image.open(os.path.join(img_path, '%s.png' % img_id)) | |
| image.thumbnail([256, 256], Image.Resampling.LANCZOS) | |
| return image | |
| def retrieve_input_image(dataset, inputs): | |
| img_id = inputs | |
| img_path = os.path.join('online_demo', dataset, 'step-100_scale-6.0', img_id, 'input.png') | |
| image = Image.open(img_path) | |
| return image | |
| def retrieve_novel_view(dataset, img_id, polar, azimuth, zoom, seed): | |
| polar = polar // 30 + 1 | |
| azimuth = azimuth // 30 | |
| zoom = int(zoom * 2 + 1) | |
| img_path = os.path.join('online_demo', dataset, 'step-100_scale-6.0', img_id,\ | |
| 'polar-%d_azimuth-%d_distance-%d_seed-%d.png' % (polar, azimuth, zoom, seed)) | |
| image = Image.open(img_path) | |
| return image | |
| with gr.Blocks() as demo: | |
| # gr.Markdown("Stable Diffusion Novel View Synthesis (Precomputed Results)") | |
| with gr.Tab("In-the-wild Images"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| default_input_image = Image.open( os.path.join('online_demo', 'nerf_wild', 'step-100_scale-6.0', 'car1.png')) | |
| default_input_image.thumbnail([256, 256], Image.Resampling.LANCZOS) | |
| input_image = gr.Image(default_input_image, shape=[256, 256]) | |
| options = sorted(next(os.walk('online_demo/nerf_wild/step-100_scale-6.0'))[1]) | |
| img_id = gr.Dropdown(options, value='car1', label='options') | |
| text_button = gr.Button("Load Input Image") | |
| retrieve_input_image_dataset = partial(retrieve_input_image_wild, 'nerf_wild') | |
| text_button.click(retrieve_input_image_dataset, inputs=img_id, outputs=input_image) | |
| with gr.Column(scale=1): | |
| novel_view = gr.Image(shape=[256, 256]) | |
| inputs = [img_id, | |
| gr.Slider(-30, 30, value=0, step=30, label='Polar angle (vertical rotation in degrees)'), | |
| gr.Slider(0, 330, value=0, step=30, label='Azimuth angle (horizontal rotation in degrees)'), | |
| gr.Slider(-0.5, 0.5, value=0, step=0.5, label='Zoom'), | |
| gr.Slider(0, 3, value=1, step=1, label='Random seed')] | |
| submit_button = gr.Button("Generate Novel View") | |
| retrieve_novel_view_dataset = partial(retrieve_novel_view, 'nerf_wild') | |
| submit_button.click(retrieve_novel_view_dataset, inputs=inputs, outputs=novel_view) | |
| with gr.Tab("Google Scanned Objects"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| default_input_image = Image.open( os.path.join('online_demo', 'GSO', 'step-100_scale-6.0', 'SAMBA_HEMP', 'input.png')) | |
| default_input_image.thumbnail([256, 256], Image.Resampling.LANCZOS) | |
| input_image = gr.Image(default_input_image, shape=[256, 256]) | |
| options = sorted(os.listdir('online_demo/GSO/step-100_scale-6.0')) | |
| img_id = gr.Dropdown(options, value='SAMBA_HEMP', label='options') | |
| text_button = gr.Button("Load Input Image") | |
| retrieve_input_image_dataset = partial(retrieve_input_image, 'GSO') | |
| text_button.click(retrieve_input_image_dataset, inputs=img_id, outputs=input_image) | |
| with gr.Column(scale=1): | |
| novel_view = gr.Image(shape=[256, 256]) | |
| inputs = [img_id, | |
| gr.Slider(-30, 30, value=0, step=30, label='Polar angle (vertical rotation in degrees)'), | |
| gr.Slider(0, 330, value=0, step=30, label='Azimuth angle (horizontal rotation in degrees)'), | |
| gr.Slider(-0.5, 0.5, value=0, step=0.5, label='Zoom'), | |
| gr.Slider(0, 3, value=1, step=1, label='Random seed')] | |
| submit_button = gr.Button("Generate Novel View") | |
| retrieve_novel_view_dataset = partial(retrieve_novel_view, 'GSO') | |
| submit_button.click(retrieve_novel_view_dataset, inputs=inputs, outputs=novel_view) | |
| with gr.Tab("RTMV"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| default_input_image = Image.open( os.path.join('online_demo', 'RTMV', 'step-100_scale-6.0', '00000', 'input.png')) | |
| default_input_image.thumbnail([256, 256], Image.Resampling.LANCZOS) | |
| input_image = gr.Image(default_input_image, shape=[256, 256]) | |
| options = sorted(os.listdir('online_demo/RTMV/step-100_scale-6.0')) | |
| img_id = gr.Dropdown(options, value='00000', label='options') | |
| text_button = gr.Button("Load Input Image") | |
| retrieve_input_image_dataset = partial(retrieve_input_image, 'RTMV') | |
| text_button.click(retrieve_input_image_dataset, inputs=img_id, outputs=input_image) | |
| with gr.Column(scale=1): | |
| novel_view = gr.Image(shape=[256, 256]) | |
| inputs = [img_id, | |
| gr.Slider(-30, 30, value=0, step=30, label='Polar angle (vertical rotation in degrees)'), | |
| gr.Slider(0, 330, value=0, step=30, label='Azimuth angle (horizontal rotation in degrees)'), | |
| gr.Slider(-0.5, 0.5, value=0, step=0.5, label='Zoom'), | |
| gr.Slider(0, 3, value=1, step=1, label='Random seed')] | |
| submit_button = gr.Button("Generate Novel View") | |
| retrieve_novel_view_dataset = partial(retrieve_novel_view, 'RTMV') | |
| submit_button.click(retrieve_novel_view_dataset, inputs=inputs, outputs=novel_view) | |
| if __name__ == "__main__": | |
| demo.launch() | |