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| import sys, os | |
| import gradio as gr | |
| import numpy as np | |
| sys.path.append('stylegan3') | |
| import utils | |
| def to_uint8(im, ndim=2): | |
| im -= np.min(im) | |
| im /= np.max(im) | |
| im *= 255. | |
| im = np.asarray(im, dtype=np.uint8) | |
| if ndim == 3: | |
| if im.ndim == 2: | |
| im = np.expand_dims(im, axis=-1) | |
| elif im.ndim == 3: | |
| if im.shape[0] == 1: | |
| np.transpose(im, (1,2,0)) | |
| im = np.tile(im, (1,1,3)) #make fake RGB | |
| return im | |
| elif ndim ==2: | |
| if im.ndim == 2: | |
| return im | |
| if im.ndim == 3: | |
| if im.shape[0] == 1: #[1, H, W] | |
| return im[0,...] | |
| elif im.shape[2] == 1: #[H, W, 1] | |
| return im[...,0] | |
| else: | |
| raise AssertionError(f"Unexpected image passed to to_uint8 with shape: {np.shape(im)}.") | |
| in_gpu = False | |
| num_images = 1 | |
| G = utils.load_default_gen(in_gpu=in_gpu) | |
| sampler = utils.SampleFromGAN(G=G, z_shp=[num_images, G.z_dim], in_gpu=in_gpu) | |
| def sample_GAN(): | |
| im = sampler() | |
| im = im.numpy() | |
| im = np.transpose(im, (1,2,0)) | |
| im = np.squeeze(im) #if single channel (yes), drop it. | |
| # print(f"sample_linearBP: im shape: {im.shape}; min: {np.min(im)}, max: {np.max(im)}.") | |
| im = to_uint8(im, ndim=2) | |
| # print(f'1. uint image shape: {im.shape}') | |
| return im | |
| title="Generate fake linear array images" | |
| description="Generate fake linear array images." | |
| with gr.Blocks() as demo: | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(): | |
| button_gen = gr.Button("Generate fake linear image") | |
| with gr.Column(): | |
| output_im = gr.Image(type="numpy", height=256, width=256, image_mode="L", label="fake image", interactive=False) #grayscale image | |
| button_gen.click(sample_GAN, inputs=None, outputs=output_im) | |
| demo.launch(share=False) | |