Delete src/app.py

src/app.py

@@ -1,453 +0,0 @@
-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
-)