app.py

import gradio as gr
import random

from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import tensorflow as tf
from transformers import SegformerFeatureExtractor, TFSegformerForSemanticSegmentation

feature_extractor = SegformerFeatureExtractor.from_pretrained(
    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
)
model = TFSegformerForSemanticSegmentation.from_pretrained(
    "nvidia/segformer-b5-finetuned-cityscapes-1024-1024"
)

def palette():

    return [
        [204, 87, 92],  # road (Reddish)
        [112, 185, 212],  # sidewalk (Blue)
        [196, 160, 122],  # building (Brown)
        [106, 135, 242],  # wall (Light Blue)
        [91, 192, 222],  # fence (Turquoise)
        [255, 192, 203],  # pole (Pink)
        [176, 224, 230],  # traffic light (Light Blue)
        [222, 49, 99],  # traffic sign (Red)
        [139, 69, 19],  # vegetation (Brown)
        [255, 0, 0],  # terrain (Red)
        [0, 0, 255],  # sky (Blue)
        [255, 228, 181],  # person (Peach)
        [128, 0, 0],  # rider (Maroon)
        [0, 128, 0],  # car (Green)
        [255, 99, 71],  # truck (Tomato)
        [0, 255, 0],  # bus (Lime)
        [128, 0, 128],  # train (Purple)
        [255, 255, 0],  # motorcycle (Yellow)
        [128, 0, 128]  # bicycle (Purple)

    ]

labels_list = []

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

# Gradio 인터페이스 정의
iface = gr.Interface(
    fn=lambda image: predict_segmentation(image, model),
    inputs="image",
    outputs="image",
    examples=["city1.jpg","city2.jpg","city3.jpg"],
)
iface.launch()

# 이미지 세그멘테이션 함수 정의
def predict_segmentation(image, model):
    # 이미지 변환
    image = Image.fromarray(image.astype('uint8'), 'RGB')
    image = image.resize((1024, 1024))  # 모델의 입력 크기에 맞게 조절
    image_array = tf.keras.preprocessing.image.img_to_array(image)
    image_array = tf.expand_dims(image_array, 0)

    # 모델 추론
    predictions = model(image_array)["output_0"]

    # 레이블별 색상 매핑
    segmented_image = tf.zeros_like(predictions)
    for label, color in label_colors.items():
        mask = tf.reduce_all(tf.equal(predictions, color), axis=-1, keepdims=True)
        for i in range(3):
            segmented_image += tf.cast(mask, tf.float32) * tf.constant(color[i], dtype=tf.float32)

    # 이미지 리턴
    segmented_image = tf.cast(segmented_image, tf.uint8)
    segmented_image = tf.image.resize(segmented_image, [image.height, image.width])
    return segmented_image.numpy()