Hugging Face's logo

Spaces:
nateraw
/
background-remover
Runtime error

App Files Community
1
background-remover
File size: 4,108 Bytes 
f63e6a9
 
 
 
d93c5fc
f63e6a9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eabc299
f63e6a9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eabc299
 
 
 
 
 
 
 
 
 
 
f63e6a9
 
 
 
 
 
d93c5fc
 
8c3253d
 
 
 
 
 
f63e6a9
 
eabc299
 
 
 
f63e6a9
8c3253d
f63e6a9
 
 
 
 
 
 
 
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
 
import cv2
import gradio as gr
import numpy as np
import onnxruntime
import requests
from huggingface_hub import hf_hub_download
from PIL import Image


# Get x_scale_factor & y_scale_factor to resize image
def get_scale_factor(im_h, im_w, ref_size=512):

    if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
        if im_w >= im_h:
            im_rh = ref_size
            im_rw = int(im_w / im_h * ref_size)
        elif im_w < im_h:
            im_rw = ref_size
            im_rh = int(im_h / im_w * ref_size)
    else:
        im_rh = im_h
        im_rw = im_w

    im_rw = im_rw - im_rw % 32
    im_rh = im_rh - im_rh % 32

    x_scale_factor = im_rw / im_w
    y_scale_factor = im_rh / im_h

    return x_scale_factor, y_scale_factor


MODEL_PATH = hf_hub_download('nateraw/background-remover-files', 'modnet.onnx', repo_type='dataset')


def main(image_path, threshold):

    # read image
    im = cv2.imread(image_path)
    im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)

    # unify image channels to 3
    if len(im.shape) == 2:
        im = im[:, :, None]
    if im.shape[2] == 1:
        im = np.repeat(im, 3, axis=2)
    elif im.shape[2] == 4:
        im = im[:, :, 0:3]

    # normalize values to scale it between -1 to 1
    im = (im - 127.5) / 127.5

    im_h, im_w, im_c = im.shape
    x, y = get_scale_factor(im_h, im_w)

    # resize image
    im = cv2.resize(im, None, fx=x, fy=y, interpolation=cv2.INTER_AREA)

    # prepare input shape
    im = np.transpose(im)
    im = np.swapaxes(im, 1, 2)
    im = np.expand_dims(im, axis=0).astype('float32')

    # Initialize session and get prediction
    session = onnxruntime.InferenceSession(MODEL_PATH, None)
    input_name = session.get_inputs()[0].name
    output_name = session.get_outputs()[0].name
    result = session.run([output_name], {input_name: im})

    # refine matte
    matte = (np.squeeze(result[0]) * 255).astype('uint8')
    matte = cv2.resize(matte, dsize=(im_w, im_h), interpolation=cv2.INTER_AREA)

    # HACK - Could probably just convert this to PIL instead of writing
    cv2.imwrite('out.png', matte)

    image = Image.open(image_path)
    matte = Image.open('out.png')

    # obtain predicted foreground
    image = np.asarray(image)
    if len(image.shape) == 2:
        image = image[:, :, None]
    if image.shape[2] == 1:
        image = np.repeat(image, 3, axis=2)
    elif image.shape[2] == 4:
        image = image[:, :, 0:3]

    b, g, r = cv2.split(image)

    mask = np.asarray(matte)
    a = np.ones(mask.shape, dtype='uint8') * 255
    alpha_im = cv2.merge([b, g, r, a], 4)
    bg = np.zeros(alpha_im.shape)
    new_mask = np.stack([mask, mask, mask, mask], axis=2)
    foreground = np.where(new_mask > threshold, alpha_im, bg).astype(np.uint8)

    return Image.fromarray(foreground)


title = "MODNet Background Remover"
description = "Gradio demo for MODNet, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<div style='text-align: center;'> <a href='https://github.com/ZHKKKe/MODNet' target='_blank'>Github Repo</a> | <a href='https://arxiv.org/abs/2011.11961' target='_blank'>MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition</a> </div>"

url = "https://huggingface.co/datasets/nateraw/background-remover-files/resolve/main/twitter_profile_pic.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
image.save('twitter_profile_pic.jpg')

url = "https://upload.wikimedia.org/wikipedia/commons/8/8d/President_Barack_Obama.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image.save('obama.jpg')

interface = gr.Interface(
    fn=main,
    inputs=[
        gr.inputs.Image(type='filepath'),
        gr.inputs.Slider(minimum=0, maximum=250, default=100, step=5, label='Mask Cutoff Threshold'),
    ],
    outputs='image',
    examples=[['twitter_profile_pic.jpg', 120], ['obama.jpg', 155]],
    title=title,
    description=description,
    article=article,
)

if __name__ == '__main__':
    interface.launch(debug=True)