src_app (1).py

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

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

import spaces
import argparse
import os
from os import path
import shutil
from datetime import datetime
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download
import gradio as gr
from diffusers import FluxPipeline
from PIL import Image
from huggingface_hub import login

# HF 토큰 인증 처리
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)}")

# FLUX 파이프라인 초기화 수정
def initialize_pipeline():
    try:
        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)
        return pipe
    except Exception as e:
        raise ValueError(f"Failed to initialize pipeline: {str(e)}")
# 파이프라인 초기화
try:
    pipe = initialize_pipeline()
except Exception as e:
    raise RuntimeError(f"Failed to setup the model: {str(e)}")
    
BoundingBox = tuple[int, int, int, int]

pillow_heif.register_heif_opener()
pillow_heif.register_avif_opener()

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

# weird dance because ZeroGPU
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)  # type: ignore
assert isinstance(gd_model, GroundingDinoForObjectDetection)

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

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)}") # 괄호 닫기 수정


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)

def _process(
    img: Image.Image,
    prompt: str | BoundingBox | None,
    bg_prompt: str | None,
) -> tuple[tuple[Image.Image, Image.Image, Image.Image], gr.DownloadButton]:
    try:
        # 기존 객체 추출 로직
        mask, bbox, time_log = _gpu_process(img, prompt)
        masked_alpha = apply_mask(img, mask, defringe=True)
        
        # 배경 생성 및 합성
        if bg_prompt:
            background = generate_background(bg_prompt, img.width, img.height)
            combined = combine_with_background(masked_alpha, background)
        else:
            combined = Image.alpha_composite(Image.new("RGBA", masked_alpha.size, "white"), masked_alpha)
        
        # 저장 로직
        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)
    except Exception as e:
        raise gr.Error(f"Processing failed: {str(e)}")

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:
    assert isinstance(gd_processor, GroundingDinoProcessor)

    # Grounding Dino expects a dot after each category.
    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:
        # Mitigate edge halo effects via color decontamination
        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


@spaces.GPU
def _gpu_process(
    img: Image.Image,
    prompt: str | BoundingBox | None,
) -> tuple[Image.Image, BoundingBox | None, list[str]]:
    # Because of ZeroGPU shenanigans, we need a *single* function with the
    # `spaces.GPU` decorator that *does not* contain postprocessing.

    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_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) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
    return _process(img, prompt)


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


css = """
footer {
    visibility: hidden;
}
"""

# 스타일 정의 추가
css = """
footer {visibility: hidden}
.container {max-width: 1200px; margin: auto; padding: 20px;}
.main-title {text-align: center; color: #2a2a2a; margin-bottom: 2em;}
.tabs {background: #f7f7f7; border-radius: 15px; padding: 20px;}
.input-column {background: white; padding: 20px; border-radius: 10px; box-shadow: 0 2px 6px rgba(0,0,0,0.1);}
.output-column {background: white; padding: 20px; border-radius: 10px; box-shadow: 0 2px 6px rgba(0,0,0,0.1);}
.custom-button {background: #2196F3; color: white; border: none; border-radius: 5px; padding: 10px 20px;}
.custom-button:hover {background: #1976D2;}
.example-region {margin-top: 2em; padding: 20px; background: #f0f0f0; border-radius: 10px;}
"""

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"],            
                    ["examples/potted-plant.jpg", "potted plant"],
                    ["examples/chair.jpg", "chair"],
                    ["examples/black-lamp.jpg", "black lamp"],
                ]
                ex = gr.Examples(
                    examples=examples,
                    inputs=[iimg, 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 = [
                    {
                        "image": "examples/text.jpg",
                        "boxes": [{"xmin": 51, "ymin": 511, "xmax": 639, "ymax": 1255}],
                    },            
                    {
                        "image": "examples/potted-plant.jpg",
                        "boxes": [{"xmin": 51, "ymin": 511, "xmax": 639, "ymax": 1255}],
                    },
                    {
                        "image": "examples/chair.jpg",
                        "boxes": [{"xmin": 98, "ymin": 330, "xmax": 973, "ymax": 1468}],
                    },
                    {
                        "image": "examples/black-lamp.jpg",
                        "boxes": [{"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],
            outputs=[btn],
        )
    btn.click(
        fn=process_prompt,
        inputs=[iimg, prompt, bg_prompt],  # bg_prompt 추가
        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,
    )

# 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;
}
"""

# Launch settings
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
)

# Launch settings
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
)