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| import os | |
| import gradio as gr | |
| import sys | |
| sys.path.insert(0, 'U-2-Net') | |
| from skimage import io, transform | |
| import torch | |
| import torchvision | |
| from torch.autograd import Variable | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.utils.data import Dataset, DataLoader | |
| from torchvision import transforms#, utils | |
| # import torch.optim as optim | |
| import numpy as np | |
| from PIL import Image | |
| import glob | |
| from data_loader import RescaleT | |
| from data_loader import ToTensor | |
| from data_loader import ToTensorLab | |
| from data_loader import SalObjDataset | |
| from model import U2NET # full size version 173.6 MB | |
| from model import U2NETP # small version u2net 4.7 MB | |
| from modnet import ModNet | |
| import huggingface_hub | |
| # normalize the predicted SOD probability map | |
| def normPRED(d): | |
| ma = torch.max(d) | |
| mi = torch.min(d) | |
| dn = (d-mi)/(ma-mi) | |
| return dn | |
| def save_output(image_name,pred,d_dir): | |
| predict = pred | |
| predict = predict.squeeze() | |
| predict_np = predict.cpu().data.numpy() | |
| im = Image.fromarray(predict_np*255).convert('RGB') | |
| img_name = image_name.split(os.sep)[-1] | |
| image = io.imread(image_name) | |
| imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR) | |
| pb_np = np.array(imo) | |
| aaa = img_name.split(".") | |
| bbb = aaa[0:-1] | |
| imidx = bbb[0] | |
| for i in range(1,len(bbb)): | |
| imidx = imidx + "." + bbb[i] | |
| imo.save(d_dir+'/'+imidx+'.png') | |
| return d_dir+'/'+imidx+'.png' | |
| modnet_path = huggingface_hub.hf_hub_download('hylee/apdrawing_model', | |
| 'modnet.onnx', | |
| force_filename='modnet.onnx') | |
| modnet = ModNet(modnet_path) | |
| # --------- 1. get image path and name --------- | |
| model_name='u2net_portrait'#u2netp | |
| image_dir = 'portrait_im' | |
| prediction_dir = 'portrait_results' | |
| if(not os.path.exists(prediction_dir)): | |
| os.mkdir(prediction_dir) | |
| model_dir = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'U-2-Net/saved_models/u2net_portrait/u2net_portrait.pth') | |
| # --------- 3. model define --------- | |
| print("...load U2NET---173.6 MB") | |
| net = U2NET(3,1) | |
| net.load_state_dict(torch.load(model_dir, map_location='cpu')) | |
| # if torch.cuda.is_available(): | |
| # net.cuda() | |
| net.eval() | |
| def process(im): | |
| image = modnet.segment(im.name) | |
| im_path = os.path.abspath(os.path.basename(im.name)) | |
| Image.fromarray(np.uint8(image)).save(im_path) | |
| img_name_list = [im_path] | |
| print("Number of images: ", len(img_name_list)) | |
| # --------- 2. dataloader --------- | |
| # 1. dataloader | |
| test_salobj_dataset = SalObjDataset(img_name_list=img_name_list, | |
| lbl_name_list=[], | |
| transform=transforms.Compose([RescaleT(512), | |
| ToTensorLab(flag=0)]) | |
| ) | |
| test_salobj_dataloader = DataLoader(test_salobj_dataset, | |
| batch_size=1, | |
| shuffle=False, | |
| num_workers=1) | |
| results = [] | |
| # --------- 4. inference for each image --------- | |
| for i_test, data_test in enumerate(test_salobj_dataloader): | |
| print("inferencing:", img_name_list[i_test].split(os.sep)[-1]) | |
| inputs_test = data_test['image'] | |
| inputs_test = inputs_test.type(torch.FloatTensor) | |
| # if torch.cuda.is_available(): | |
| # inputs_test = Variable(inputs_test.cuda()) | |
| # else: | |
| inputs_test = Variable(inputs_test) | |
| d1, d2, d3, d4, d5, d6, d7 = net(inputs_test) | |
| # normalization | |
| pred = 1.0 - d1[:, 0, :, :] | |
| pred = normPRED(pred) | |
| # save results to test_results folder | |
| results.append(save_output(img_name_list[i_test], pred, prediction_dir)) | |
| del d1, d2, d3, d4, d5, d6, d7 | |
| print(results) | |
| return Image.open(results[0]) | |
| title = "U-2-Net" | |
| description = "Gradio demo for U-2-Net, https://github.com/xuebinqin/U-2-Net" | |
| article = "" | |
| gr.Interface( | |
| process, | |
| [gr.inputs.Image(type="file", label="Input") | |
| ], | |
| [gr.outputs.Image(type="pil", label="Output")], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=[], | |
| allow_flagging=False, | |
| allow_screenshot=False | |
| ).launch(enable_queue=True,cache_examples=True) | |