app.py

import gradio as gr
from matplotlib import gridspec
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import torch
from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
import time

MODEL_ID = "nvidia/segformer-b2-finetuned-cityscapes-1024-1024"
processor = AutoImageProcessor.from_pretrained(MODEL_ID)
model = AutoModelForSemanticSegmentation.from_pretrained(MODEL_ID)

def ade_palette():
    return [
        [128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156], [190, 153, 153],
        [153, 153, 153], [250, 170, 30], [220, 220, 0], [107, 142, 35], [152, 251, 152],
        [70, 130, 180], [220, 20, 60], [255, 0, 0], [0, 0, 142], [0, 0, 70],
        [0, 60, 100], [0, 80, 100], [0, 0, 230], [119, 11, 32]
    ]

labels_list = []
with open("labels.txt", "r", encoding="utf-8") as fp:
    for line in fp:
        labels_list.append(line.rstrip("\n"))

colormap = np.asarray(ade_palette(), dtype=np.uint8)

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_np):
    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_np.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 run_inference(input_img, alpha=0.5):
    start_time = time.time()
    img = Image.fromarray(input_img.astype(np.uint8)) if isinstance(input_img, np.ndarray) else input_img
    if img.mode != "RGB":
        img = img.convert("RGB")

    inputs = processor(images=img, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits

    upsampled = torch.nn.functional.interpolate(
        logits, size=img.size[::-1], mode="bilinear", align_corners=False
    )
    seg = upsampled.argmax(dim=1)[0].cpu().numpy().astype(np.uint8)

    color_seg = colormap[seg]

    # alpha 변수를 사용해 투명도 조절
    image_weight = 1.0 - alpha
    overlay_weight = alpha
    pred_img = (np.array(img) * image_weight + color_seg * overlay_weight).astype(np.uint8)

    fig = draw_plot(pred_img, seg)
    return fig

custom_theme = gr.themes.Soft(
    primary_hue="emerald", secondary_hue="teal", neutral_hue="slate"
).set(
    body_background_fill="#f9fafb",
    body_text_color="#1f2937",
    button_primary_background_fill="#10b981",
    button_primary_text_color="#ffffff",
    block_background_fill="#ffffff"
)

demo = gr.Interface(
    fn=run_inference,
    inputs=[
        gr.Image(type="numpy", label="이미지 입력"),
        gr.Slider(0.0, 1.0, value=0.5, step=0.05, label="투명도 조절")
    ],
    outputs=gr.Plot(label="결과"),
    examples=[
        ["city1.png", 0.5],
        ["city2.png", 0.5],
        ["city3.jpg", 0.5],
        ["city4.jpeg", 0.5],
        ["city5.jpg", 0.5]
    ],

    flagging_mode="never",
    cache_examples=False,
    theme=custom_theme
)

title = "City Segment"
description = ("""segformer-b2모델을 이용 도시 이미지 분할 시각.<br>
              이미지를 업로드하면 도로, 건물, 차량, 사람 등 객체별로 색상으로 구분해줍니다.""")

if __name__ == "__main__":
    demo.launch()