Spaces:
Build error
Build error
File size: 4,419 Bytes
de51c6d 18f931d b9f4814 18f931d b9f4814 18f931d ca61862 18f931d c388d64 18f931d de51c6d b9f4814 420ecfb 18f931d ec88f2e de51c6d 1682357 de51c6d ec88f2e de51c6d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
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)
Image.fromarray(np.uint8(image)).save(im.name)
img_name_list = [im.name]
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]), Image.open(im.name)
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"),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)
|