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Runtime error
| import gradio as gr | |
| import numpy as np | |
| import sys | |
| sys.path.insert(0,'../.') | |
| from utils import Generator as netG | |
| import torch.nn as nn | |
| import torch | |
| import PIL.Image as Image | |
| import PIL | |
| from torchvision import transforms as tfs | |
| from skimage import measure | |
| import matplotlib | |
| matplotlib.use('Agg') | |
| available_device = 'cpu' | |
| #transformation initial | |
| transformations = [ | |
| tfs.Grayscale(), | |
| tfs.Resize((128, 128)), | |
| tfs.Lambda(lambda x: PIL.ImageOps.invert(x)), | |
| tfs.ToTensor() | |
| ] | |
| trans = tfs.Compose(transformations) | |
| # Model Initial | |
| model_G = netG.Generator(nc_input=2, nc_output=1).to(available_device) | |
| checkpoint = torch.load("generador_v9_current_5000.pkl", map_location = torch.device(available_device)) | |
| model_G.load_state_dict(checkpoint) | |
| model_G = model_G.eval() | |
| def sketch_recognition(input_img): | |
| img = input_img['mask'][:,:,[0,1,2]] | |
| img[img!=0] = 1 | |
| img = np.abs(img - 1) | |
| input_img = Image.fromarray(np.uint8(img)).convert('L') | |
| image = trans(input_img).reshape(-1, 1, 128, 128) | |
| image = image.to(available_device) | |
| blacks = torch.zeros_like(image).to(available_device) | |
| origin_A = torch.cat((image, blacks), 1) | |
| predicted_A = model_G(origin_A) | |
| predicted_A = predicted_A + image | |
| result = predicted_A.detach().cpu().numpy().reshape(128, 128) | |
| result = result * 255 | |
| img = np.zeros_like(result) | |
| mean = np.mean(result) | |
| img[result>mean] = 255 | |
| img[img==0] = -255 | |
| img[img==255] = 0 | |
| img = np.abs(img) | |
| return np.array(img, dtype=np.int32) | |
| img = gr.Image(tool="sketch", source="upload", label="Mask", value="CC_02_7.png", invert_colors=True, shape=(128, 128)) | |
| gr.Interface(fn=sketch_recognition, inputs=img, outputs="image").launch() | |