app.py

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]

    # Return segmentation label image instead of Matplotlib Figure
    return seg.numpy()

# Gradio Interface 설정
demo = gr.Interface(fn=sepia,
                    inputs=gr.Image(shape=(800, 600)),
                    outputs=['label'],  # 'plot'에서 'label'로 변경
                    examples=["cityoutdoor-1.jpg", "cityoutdoor-2.jpg", "cityoutdoor-3.jpg"],
                    allow_flagging='never')

# Gradio 실행
demo.launch()