app.py

import tempfile
import time
from collections.abc import Sequence
from typing import Any, cast
import os
from huggingface_hub import login

import gradio as gr
import numpy as np
import pillow_heif
import spaces
import torch
from gradio_image_annotation import image_annotator
from gradio_imageslider import ImageSlider
from PIL import Image
from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
from refiners.fluxion.utils import no_grad
from refiners.solutions import BoxSegmenter
from transformers import GroundingDinoForObjectDetection, GroundingDinoProcessor
from diffusers import FluxPipeline

BoundingBox = tuple[int, int, int, int]

# 초기화 및 설정
pillow_heif.register_heif_opener()
pillow_heif.register_avif_opener()

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# HF 토큰 설정
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("Please set the HF_TOKEN environment variable")

try:
    login(token=HF_TOKEN)
except Exception as e:
    raise ValueError(f"Failed to login to Hugging Face: {str(e)}")

# 모델 초기화
segmenter = BoxSegmenter(device="cpu")
segmenter.device = device
segmenter.model = segmenter.model.to(device=segmenter.device)

gd_model_path = "IDEA-Research/grounding-dino-base"
gd_processor = GroundingDinoProcessor.from_pretrained(gd_model_path)
gd_model = GroundingDinoForObjectDetection.from_pretrained(gd_model_path, torch_dtype=torch.float32)
gd_model = gd_model.to(device=device)
assert isinstance(gd_model, GroundingDinoForObjectDetection)

# FLUX 파이프라인 초기화
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    torch_dtype=torch.bfloat16,
    use_auth_token=HF_TOKEN
)
pipe.load_lora_weights(
    hf_hub_download(
        "ByteDance/Hyper-SD",
        "Hyper-FLUX.1-dev-8steps-lora.safetensors",
        use_auth_token=HF_TOKEN
    )
)
pipe.fuse_lora(lora_scale=0.125)
pipe.to(device="cuda", dtype=torch.bfloat16)

def bbox_union(bboxes: Sequence[list[int]]) -> BoundingBox | None:
    if not bboxes:
        return None
    for bbox in bboxes:
        assert len(bbox) == 4
        assert all(isinstance(x, int) for x in bbox)
    return (
        min(bbox[0] for bbox in bboxes),
        min(bbox[1] for bbox in bboxes),
        max(bbox[2] for bbox in bboxes),
        max(bbox[3] for bbox in bboxes),
    )

def corners_to_pixels_format(bboxes: torch.Tensor, width: int, height: int) -> torch.Tensor:
    x1, y1, x2, y2 = bboxes.round().to(torch.int32).unbind(-1)
    return torch.stack((x1.clamp_(0, width), y1.clamp_(0, height), x2.clamp_(0, width), y2.clamp_(0, height)), dim=-1)

def gd_detect(img: Image.Image, prompt: str) -> BoundingBox | None:
    inputs = gd_processor(images=img, text=f"{prompt}.", return_tensors="pt").to(device=device)
    with no_grad():
        outputs = gd_model(**inputs)
    width, height = img.size
    results: dict[str, Any] = gd_processor.post_process_grounded_object_detection(
        outputs,
        inputs["input_ids"],
        target_sizes=[(height, width)],
    )[0]
    assert "boxes" in results and isinstance(results["boxes"], torch.Tensor)
    bboxes = corners_to_pixels_format(results["boxes"].cpu(), width, height)
    return bbox_union(bboxes.numpy().tolist())

def apply_mask(img: Image.Image, mask_img: Image.Image, defringe: bool = True) -> Image.Image:
    assert img.size == mask_img.size
    img = img.convert("RGB")
    mask_img = mask_img.convert("L")
    if defringe:
        rgb, alpha = np.asarray(img) / 255.0, np.asarray(mask_img) / 255.0
        foreground = cast(np.ndarray[Any, np.dtype[np.uint8]], estimate_foreground_ml(rgb, alpha))
        img = Image.fromarray((foreground * 255).astype("uint8"))
    result = Image.new("RGBA", img.size)
    result.paste(img, (0, 0), mask_img)
    return result

def generate_background(prompt: str, width: int, height: int) -> Image.Image:
    """배경 이미지 생성 함수"""
    try:
        with timer("Background generation"):
            image = pipe(
                prompt=prompt,
                width=width,
                height=height,
                num_inference_steps=8,
                guidance_scale=4.0,
            ).images[0]
        return image
    except Exception as e:
        raise gr.Error(f"Background generation failed: {str(e)}")

def combine_with_background(foreground: Image.Image, background: Image.Image) -> Image.Image:
    """전경과 배경 합성 함수"""
    background = background.resize(foreground.size)
    return Image.alpha_composite(background.convert('RGBA'), foreground)

@spaces.GPU
def _gpu_process(img: Image.Image, prompt: str | BoundingBox | None) -> tuple[Image.Image, BoundingBox | None, list[str]]:
    time_log: list[str] = []
    if isinstance(prompt, str):
        t0 = time.time()
        bbox = gd_detect(img, prompt)
        time_log.append(f"detect: {time.time() - t0}")
        if not bbox:
            print(time_log[0])
            raise gr.Error("No object detected")
    else:
        bbox = prompt
    t0 = time.time()
    mask = segmenter(img, bbox)
    time_log.append(f"segment: {time.time() - t0}")
    return mask, bbox, time_log

def _process(img: Image.Image, prompt: str | BoundingBox | None, bg_prompt: str | None = None) -> tuple[tuple[Image.Image, Image.Image, Image.Image], gr.DownloadButton]:
    if img.width > 2048 or img.height > 2048:
        orig_res = max(img.width, img.height)
        img.thumbnail((2048, 2048))
        if isinstance(prompt, tuple):
            x0, y0, x1, y1 = (int(x * 2048 / orig_res) for x in prompt)
            prompt = (x0, y0, x1, y1)

    mask, bbox, time_log = _gpu_process(img, prompt)
    masked_alpha = apply_mask(img, mask, defringe=True)

    if bg_prompt:
        try:
            background = generate_background(bg_prompt, img.width, img.height)
            combined = combine_with_background(masked_alpha, background)
        except Exception as e:
            raise gr.Error(f"Background processing failed: {str(e)}")
    else:
        combined = Image.alpha_composite(Image.new("RGBA", masked_alpha.size, "white"), masked_alpha)

    thresholded = mask.point(lambda p: 255 if p > 10 else 0)
    bbox = thresholded.getbbox()
    to_dl = masked_alpha.crop(bbox)

    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
    to_dl.save(temp, format="PNG")
    temp.close()

    return (img, combined, masked_alpha), gr.DownloadButton(value=temp.name, interactive=True)

def process_bbox(prompts: dict[str, Any]) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
    assert isinstance(img := prompts["image"], Image.Image)
    assert isinstance(boxes := prompts["boxes"], list)
    if len(boxes) == 1:
        assert isinstance(box := boxes[0], dict)
        bbox = tuple(box[k] for k in ["xmin", "ymin", "xmax", "ymax"])
    else:
        assert len(boxes) == 0
        bbox = None
    return _process(img, bbox)

def on_change_bbox(prompts: dict[str, Any] | None):
    return gr.update(interactive=prompts is not None)

def process_prompt(img: Image.Image, prompt: str, bg_prompt: str | None = None) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
    return _process(img, prompt, bg_prompt)

def on_change_prompt(img: Image.Image | None, prompt: str | None, bg_prompt: str | None = None):
    return gr.update(interactive=bool(img and prompt))

# CSS 스타일 정의
css = """
footer {display: none}
.main-title {
    text-align: center;
    margin: 2em 0;
}
.main-title h1 {
    color: #2196F3;
    font-size: 2.5em;
}
.container {
    max-width: 1200px;
    margin: auto;
    padding: 20px;
}
"""

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
    gr.HTML("""
        <div class="main-title">
            <h1>🎨 Advanced Image Object Extractor</h1>
            <p>Extract objects from images using text prompts or bounding boxes</p>
        </div>
    """)

    with gr.Tabs() as tabs:
        with gr.Tab("✨ Extract by Text", id="tab_prompt"):
            with gr.Row(equal_height=True):
                with gr.Column(scale=1, min_width=400):
                    gr.HTML("<h3>📥 Input Section</h3>")
                    iimg = gr.Image(
                        type="pil",
                        label="Upload Image"
                    )
                    with gr.Group():
                        prompt = gr.Textbox(
                            label="🎯 Object to Extract",
                            placeholder="Enter what you want to extract..."
                        )
                        bg_prompt = gr.Textbox(
                            label="🖼️ Background Generation Prompt (optional)",
                            placeholder="Describe the background you want..."
                        )
                    btn = gr.Button(
                        "🚀 Process Image",
                        variant="primary",
                        interactive=False
                    )

                with gr.Column(scale=1, min_width=400):
                    gr.HTML("<h3>📤 Output Section</h3>")
                    oimg = ImageSlider(
                        label="Results Preview",
                        show_download_button=False
                    )
                    dlbt = gr.DownloadButton(
                        "💾 Download Result",
                        interactive=False
                    )

            with gr.Accordion("📚 Examples", open=False):
                examples = [
                    ["examples/text.jpg", "text", "white background"],
                    ["examples/black-lamp.jpg", "black lamp", "minimalist interior"]
                ]
                ex = gr.Examples(
                    examples=examples,
                    inputs=[iimg, prompt, bg_prompt],
                    outputs=[oimg, dlbt],
                    fn=process_prompt,
                    cache_examples=True
                )

        with gr.Tab("📏 Extract by Box", id="tab_bb"):
            with gr.Row(equal_height=True):
                with gr.Column(scale=1, min_width=400):
                    gr.HTML("<h3>📥 Input Section</h3>")
                    annotator = image_annotator(
                        image_type="pil",
                        disable_edit_boxes=True,
                        show_download_button=False,
                        show_share_button=False,
                        single_box=True,
                        label="Draw Box Around Object"
                    )
                    btn_bb = gr.Button(
                        "✂️ Extract Selection",
                        variant="primary",
                        interactive=False
                    )

                with gr.Column(scale=1, min_width=400):
                    gr.HTML("<h3>📤 Output Section</h3>")
                    oimg_bb = ImageSlider(
                        label="Results Preview",
                        show_download_button=False
                    )
                    dlbt_bb = gr.DownloadButton(
                        "💾 Download Result",
                        interactive=False
                    )

            with gr.Accordion("📚 Examples", open=False):
                examples_bb = [
                    ["examples/text.jpg", [{"xmin": 51, "ymin": 511, "xmax": 639, "ymax": 1255}]],
                    ["examples/black-lamp.jpg", [{"xmin": 88, "ymin": 148, "xmax": 700, "ymax": 1414}]]
                ]
                ex_bb = gr.Examples(
                    examples=examples_bb,
                    inputs=[annotator],
                    outputs=[oimg_bb, dlbt_bb],
                    fn=process_bbox,
                    cache_examples=True
                )

    # Event handlers
    btn.add(oimg)
    for inp in [iimg, prompt]:
        inp.change(
            fn=on_change_prompt,
            inputs=[iimg, prompt, bg_prompt],
            outputs=[btn],
        )
    btn.click(
        fn=process_prompt,
        inputs=[iimg, prompt, bg_prompt],
        outputs=[oimg, dlbt],
        api_name=False,
    )

    btn_bb.add(oimg_bb)
    annotator.change(
        fn=on_change_bbox,
        inputs=[annotator],
        outputs=[btn_bb],
    )
    btn_bb.click(
        fn=process_bbox,
        inputs=[annotator],
        outputs=[oimg_bb, dlbt_bb],
        api_name=False,
    )

demo.queue(max_size=30, api_open=False)
demo.launch(
    show_api=False,
    share=False,
    server_name="0.0.0.0",
    server_port=7860,
    show_error=True
)