app.py

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

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

# -------------------------------
# Load Labels
# -------------------------------
with open("labels.txt", "r", encoding="utf-8") as f:
    labels_list = [line.strip() for line in f]

CITYSCAPES_PALETTE = [
    (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)
]
colormap = np.array(CITYSCAPES_PALETTE, dtype=np.uint8)

def label_to_color_image(label):
    return colormap[label]

# -------------------------------
# Visualization
# -------------------------------
def draw_plot(pred_img, seg_np):
    fig = plt.figure(figsize=(20, 15))
    gs = gridspec.GridSpec(1, 2, width_ratios=[6, 1])

    # main overlay
    plt.subplot(gs[0])
    plt.imshow(pred_img)
    plt.axis("off")

    # legend
    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)
    ax = plt.subplot(gs[1])
    plt.imshow(FULL_COLOR_MAP[unique_labels], interpolation="nearest")
    ax.yaxis.tick_right()
    plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])
    plt.xticks([])
    ax.tick_params(width=0, labelsize=20)

    return fig

# -------------------------------
# Inference
# -------------------------------
def run_inference(input_img, alpha):
    img = Image.fromarray(input_img.astype(np.uint8)).convert("RGB")
    img_np = np.array(img)  # numpy array 변환

    # Model inference
    inputs = processor(images=img, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits

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

    # Overlay with alpha slider
    color_seg = colormap[seg]
    overlay = (img_np * (1 - alpha) + color_seg * alpha).astype(np.uint8)

    fig = draw_plot(overlay, seg)
    return fig

# -------------------------------
# Gradio UI
# -------------------------------
demo = gr.Interface(
    fn=run_inference,
    inputs=[
        gr.Image(type="numpy", label="Input Image"),
        gr.Slider(minimum=0, maximum=1, value=0.5, step=0.01, label="Overlay Transparency (alpha)")
    ],
    outputs=gr.Plot(label="Segmentation Result"),
    examples=[
        ["city1.jpg"],
        ["city2.jpg"],
        ["city3.jpg"],
        ["city4.jpg"],
        ["city5.jpg"]
    ],
    title="SegFormer B2 Cityscapes Segmentation Demo",
    cache_examples=False  # 캐싱 끔
)

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