Hugging Face's logo

Spaces:
mugdha99
/
Image-to-Line-Drawings
Running

App Files Community
1
Image-to-Line-Drawings
File size: 4,536 Bytes 
5caf9f6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8797ea9
5caf9f6
 
 
 
 
 
 
cc6ab29
8797ea9
f8e3643
 
 
 
 
 
 
 
8797ea9
5caf9f6
 
8797ea9
585df91
5caf9f6
 
 
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
 
import numpy as np
import torch
import torch.nn as nn
import gradio as gr
from PIL import Image
import torchvision.transforms as transforms

norm_layer = nn.InstanceNorm2d

class ResidualBlock(nn.Module):
    def __init__(self, in_features):
        super(ResidualBlock, self).__init__()

        conv_block = [  nn.ReflectionPad2d(1),
                        nn.Conv2d(in_features, in_features, 3),
                        norm_layer(in_features),
                        nn.ReLU(inplace=True),
                        nn.ReflectionPad2d(1),
                        nn.Conv2d(in_features, in_features, 3),
                        norm_layer(in_features)
                        ]

        self.conv_block = nn.Sequential(*conv_block)

    def forward(self, x):
        return x + self.conv_block(x)


class Generator(nn.Module):
    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
        super(Generator, self).__init__()

        # Initial convolution block
        model0 = [   nn.ReflectionPad2d(3),
                    nn.Conv2d(input_nc, 64, 7),
                    norm_layer(64),
                    nn.ReLU(inplace=True) ]
        self.model0 = nn.Sequential(*model0)

        # Downsampling
        model1 = []
        in_features = 64
        out_features = in_features*2
        for _ in range(2):
            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
                        norm_layer(out_features),
                        nn.ReLU(inplace=True) ]
            in_features = out_features
            out_features = in_features*2
        self.model1 = nn.Sequential(*model1)

        model2 = []
        # Residual blocks
        for _ in range(n_residual_blocks):
            model2 += [ResidualBlock(in_features)]
        self.model2 = nn.Sequential(*model2)

        # Upsampling
        model3 = []
        out_features = in_features//2
        for _ in range(2):
            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
                        norm_layer(out_features),
                        nn.ReLU(inplace=True) ]
            in_features = out_features
            out_features = in_features//2
        self.model3 = nn.Sequential(*model3)

        # Output layer
        model4 = [  nn.ReflectionPad2d(3),
                        nn.Conv2d(64, output_nc, 7)]
        if sigmoid:
            model4 += [nn.Sigmoid()]

        self.model4 = nn.Sequential(*model4)

    def forward(self, x, cond=None):
        out = self.model0(x)
        out = self.model1(out)
        out = self.model2(out)
        out = self.model3(out)
        out = self.model4(out)

        return out

model1 = Generator(3, 1, 3)
model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
model1.eval()

model2 = Generator(3, 1, 3)
model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu')))
model2.eval()

def predict(input_img, ver):
    input_img = Image.open(input_img)
    transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()])
    input_img = transform(input_img)
    input_img = torch.unsqueeze(input_img, 0)

    drawing = 0
    with torch.no_grad():
        if ver == 'Simple Lines':
            drawing = model2(input_img)[0].detach()
        else:
            drawing = model1(input_img)[0].detach()
    
    drawing = transforms.ToPILImage()(drawing)
    return drawing

title="Image to Line Drawings - Complex and Simple Portraits and Landscapes"
examples=[
['01.jpeg', 'Simple Lines'], ['02.jpeg', 'Simple Lines'], ['03.jpeg', 'Simple Lines'],
['04.jpeg', 'Simple Lines'], ['05.jpeg', 'Simple Lines'], ['06.jpeg', 'Simple Lines'],
['01.jpeg', 'Complex Lines'], ['02.jpeg', 'Complex Lines'], ['03.jpeg', 'Complex Lines'],
['04.jpeg', 'Complex Lines'], ['05.jpeg', 'Complex Lines'], ['06.jpeg', 'Complex Lines'],
['07.jpeg', 'Simple Lines'], ['08.jpeg', 'Simple Lines'], ['09.jpeg', 'Simple Lines'],
['10.jpeg', 'Simple Lines'], ['11.jpeg', 'Simple Lines'], ['12.jpeg', 'Simple Lines'],
['07.jpeg', 'Complex Lines'], ['08.jpeg', 'Complex Lines'], ['09.jpeg', 'Complex Lines'],
['10.jpeg', 'Complex Lines'], ['11.jpeg', 'Complex Lines'], ['12.jpeg', 'Complex Lines']
]

iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
    gr.inputs.Radio(['Complex Lines','Simple Lines'], type="value", default='Simple Lines', label='version')],
    gr.outputs.Image(type="pil"), title=title,examples=examples)

iface.launch()