Create app.py

app.py

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+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
+
+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)
+
+
+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(
+    img: Image.Image,
+    prompt: str | BoundingBox | None,
+) -> tuple[tuple[Image.Image, Image.Image], gr.DownloadButton]:
+    # enforce max dimensions for pymatting performance reasons
+    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, y2 = (int(x * 2048 / orig_res) for x in prompt)
+            prompt = (x0, y0, x1, y2)
+
+    mask, bbox, time_log = _gpu_process(img, prompt)
+
+    t0 = time.time()
+    masked_alpha = apply_mask(img, mask, defringe=True)
+    time_log.append(f"crop: {time.time() - t0}")
+    print(", ".join(time_log))
+
+    masked_rgb = 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, masked_rgb), 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) -> 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;
+}
+"""
+
+
+with gr.Blocks(css=css) as demo:
+
+    with gr.Tab("By prompt", id="tab_prompt"):
+        with gr.Row():
+            with gr.Column():
+                iimg = gr.Image(type="pil", label="Input")
+                prompt = gr.Textbox(label="What should we cut?")
+                btn = gr.Button("Cut Out Object", interactive=False)  # 수정됨: ClearButton에서 Button으로 변경
+            with gr.Column():
+                oimg = ImageSlider(label="Before / After", show_download_button=False, interactive=False)
+                dlbt = gr.DownloadButton("Download Cutout", interactive=False)
+
+        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],
+            outputs=[oimg, dlbt],
+            api_name=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("By bounding box", id="tab_bb"):
+        with gr.Row():
+            with gr.Column():
+                annotator = image_annotator(
+                    image_type="pil",
+                    disable_edit_boxes=True,
+                    show_download_button=False,
+                    show_share_button=False,
+                    single_box=True,
+                    label="Input",
+                )
+                btn = gr.Button("Cut Out Object", interactive=False)  # 수정됨: ClearButton에서 Button으로 변경
+            with gr.Column():
+                oimg = ImageSlider(label="Before / After", show_download_button=False)
+                dlbt = gr.DownloadButton("Download Cutout", interactive=False)
+
+        btn.add(oimg)
+
+        annotator.change(
+            fn=on_change_bbox,
+            inputs=[annotator],
+            outputs=[btn],
+        )
+        btn.click(
+            fn=process_bbox,
+            inputs=[annotator],
+            outputs=[oimg, dlbt],
+            api_name=False,
+        )
+
+        examples = [
+            {
+                "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 = gr.Examples(
+            examples=examples,
+            inputs=[annotator],
+            outputs=[oimg, dlbt],
+            fn=process_bbox,
+            cache_examples=True,
+        )
+
+
+demo.queue(max_size=30, api_open=False)
+demo.launch(show_api=False)