Hugging Face's logo

Spaces:
dakkoong
/
homework3
Sleeping

App Files Community
homework3
File size: 3,069 Bytes 
3a298ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c6c6ea
3a298ab
 
067eb76
 
3c6c6ea
067eb76
 
3a298ab
3c6c6ea
 
ca2787d
3c6c6ea
3a298ab
498dbc4
3c6c6ea
3a298ab
 
 
3c6c6ea
 
 
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
 
import gradio as gr
import numpy as np
from PIL import Image
import tensorflow as tf
from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation

feature_extractor = SegformerFeatureExtractor.from_pretrained(
    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"
)

def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [204, 166, 62],
        [188, 229, 92],
        [47, 157,39],
        [178, 235, 244],
        [0, 51, 153],
        [181, 178, 255],
        [128, 65, 217],
        [255, 178, 245],
        [153, 0, 76],
        [25, 186, 52],
        [81, 162, 235],
        [255, 255, 0],
        [62, 57, 159],
        [91, 189, 203],
        [0, 0, 255],
        [0, 255, 255],
        [12, 168, 0],
        [255, 0, 0],
        [231, 32, 65]
    ]

labels_list = []

with open(r'labels.txt', 'r') as fp:
    for line in fp:
        labels_list.append(line[:-1])

colormap = np.asarray(ade_palette())

def label_to_color_image(label):
    if label.ndim != 2:
        raise ValueError("Expect 2-D input label")

    if np.max(label) >= len(colormap):
        raise ValueError("label value too large.")
    return colormap[label]

def draw_plot(pred_img, seg):
    fig = plt.figure(figsize=(20, 15))

    grid_spec = gridspec.GridSpec(1, 2, width_ratios=[6, 1])

    plt.subplot(grid_spec[0])
    plt.imshow(pred_img)
    plt.axis('off')
    LABEL_NAMES = np.asarray(labels_list)
    FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
    FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)

    unique_labels = np.unique(seg.numpy().astype("uint8"))
    ax = plt.subplot(grid_spec[1])
    plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation="nearest")
    ax.yaxis.tick_right()
    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
    plt.xticks([], [])
    ax.tick_params(width=0.0, labelsize=25)
    return fig

def sepia(input_img):
    input_img = Image.fromarray(input_img)

    inputs = feature_extractor(images=input_img, return_tensors="tf")
    outputs = model(**inputs)
    logits = outputs.logits

    logits = tf.transpose(logits, [0, 2, 3, 1])
    logits = tf.image.resize(
        logits, input_img.size[::-1]
    )
    seg = tf.math.argmax(logits, axis=-1)[0]

    color_seg = np.zeros(
        (seg.shape[0], seg.shape[1], 3), dtype=np.uint8
    )
    for label, color in enumerate(colormap):
        color_seg[seg.numpy() == label, :] = color

    # Return segmentation label image instead of Matplotlib Figure
    return color_seg

# Gradio Interface μ„€μ •
demo = gr.Interface(fn=sepia,
                    inputs=gr.Image(shape=(800, 600)),
                    outputs=['label'],  # 'plot'μ—μ„œ 'label'둜 λ³€κ²½
                    examples=["cityoutdoor-1.jpg", "cityoutdoor-2.jpg", "cityoutdoor-3.jpg"],
                    allow_flagging='never')

# Gradio μ‹€ν–‰
demo.launch()