Update rembg/bg.py

rembg/bg.py

@@ -1,125 +1,26 @@
 import io
+from typing import Any, Union
 import numpy as np
-from PIL import Image, ImageOps
-from typing import Any, List, Optional, Tuple, Union
+from PIL import Image
 from enum import Enum
-from pymatting.alpha.estimate_alpha_cf import estimate_alpha_cf
-from pymatting.foreground.estimate_foreground_ml import estimate_foreground_ml
-from scipy.ndimage import binary_erosion
 
-# Definir los valores por defecto para los parámetros
-DEFAULT_ALPHA_INFLUENCE = 0.5
-DEFAULT_SEGMENTATION_STRENGTH = 0.5
-DEFAULT_SMOOTHING = 0.5
+from rembg import new_session, remove
+from rembg.sessions.base import BaseSession
+from rembg.util.util import fix_image_orientation
 
 class ReturnType(Enum):
     BYTES = 0
     PILLOW = 1
     NDARRAY = 2
 
-def alpha_matting_cutout(
-    img: PILImage,
-    mask: PILImage,
-    alpha_influence: float,
-    segmentation_strength: float,
-    erode_structure_size: int,
-) -> PILImage:
-    if img.mode == "RGBA" or img.mode == "CMYK":
-        img = img.convert("RGB")
-
-    img = np.asarray(img)
-    mask = np.asarray(mask)
-
-    is_foreground = mask > alpha_influence
-    is_background = mask < (1.0 - alpha_influence)
-
-    structure = None
-    if erode_structure_size > 0:
-        structure = np.ones((erode_structure_size, erode_structure_size), dtype=np.uint8)
-
-    is_foreground = binary_erosion(is_foreground, structure=structure)
-    is_background = binary_erosion(is_background, structure=structure, border_value=1)
-
-    trimap = np.full(mask.shape, dtype=np.uint8, fill_value=128)
-    trimap[is_foreground] = 255
-    trimap[is_background] = 0
-
-    img_normalized = img / 255.0
-    trimap_normalized = trimap / 255.0
-
-    alpha = estimate_alpha_cf(img_normalized, trimap_normalized)
-    foreground = estimate_foreground_ml(img_normalized, alpha)
-    cutout = stack_images(foreground, alpha)
-
-    cutout = np.clip(cutout * 255, 0, 255).astype(np.uint8)
-    cutout = Image.fromarray(cutout)
-
-    return cutout
-#aca termina la modificacion
-def naive_cutout(img: PILImage, mask: PILImage) -> PILImage:
-    empty = Image.new("RGBA", (img.size), 0)
-    cutout = Image.composite(img, empty, mask)
-    return cutout
-
-
-def get_concat_v_multi(imgs: List[PILImage]) -> PILImage:
-    pivot = imgs.pop(0)
-    for im in imgs:
-        pivot = get_concat_v(pivot, im)
-    return pivot
-
-
-def get_concat_v(img1: PILImage, img2: PILImage) -> PILImage:
-    dst = Image.new("RGBA", (img1.width, img1.height + img2.height))
-    dst.paste(img1, (0, 0))
-    dst.paste(img2, (0, img1.height))
-    return dst
-
-
-def post_process(mask: np.ndarray) -> np.ndarray:
-    """
-    Post Process the mask for a smooth boundary by applying Morphological Operations
-    Research based on paper: https://www.sciencedirect.com/science/article/pii/S2352914821000757
-    args:
-        mask: Binary Numpy Mask
-    """
-    mask = morphologyEx(mask, MORPH_OPEN, kernel)
-    mask = GaussianBlur(mask, (5, 5), sigmaX=2, sigmaY=2, borderType=BORDER_DEFAULT)
-    mask = np.where(mask < 127, 0, 255).astype(np.uint8)  # convert again to binary
-    return mask
-
-
-def apply_background_color(img: PILImage, color: Tuple[int, int, int, int]) -> PILImage:
-    r, g, b, a = color
-    colored_image = Image.new("RGBA", img.size, (r, g, b, a))
-    colored_image.paste(img, mask=img)
-
-    return colored_image
-
-
-def fix_image_orientation(img: PILImage) -> PILImage:
-    return ImageOps.exif_transpose(img)
-
-
-def download_models() -> None:
-    for session in sessions_class:
-        session.download_models()
-
-
-def remove(
-    data: Union[bytes, PILImage, np.ndarray],
-    alpha_matting: bool = False,
-    alpha_matting_foreground_threshold: int = 240,
-    alpha_matting_background_threshold: int = 10,
-    alpha_matting_erode_size: int = 10,
-    session: Optional[BaseSession] = None,
-    only_mask: bool = False,
-    post_process_mask: bool = False,
-    bgcolor: Optional[Tuple[int, int, int, int]] = None,
-    *args: Optional[Any],
-    **kwargs: Optional[Any]
-) -> Union[bytes, PILImage, np.ndarray]:
-    if isinstance(data, PILImage):
+def remove_background(
+    data: Union[bytes, Image.Image, np.ndarray],
+    alpha_influence: float = 0.5,
+    segmentation_strength: float = 0.5,
+    smoothing: float = 0.5,
+    model: str = "u2net",
+) -> Union[bytes, Image.Image, np.ndarray]:
+    if isinstance(data, Image.Image):
         return_type = ReturnType.PILLOW
         img = data
     elif isinstance(data, bytes):
@@ -129,57 +30,28 @@ def remove(
         return_type = ReturnType.NDARRAY
         img = Image.fromarray(data)
     else:
-        raise ValueError("Input type {} is not supported.".format(type(data)))
+        raise ValueError("Input type {} is not supported.".format(type(data))
 
-    # Fix image orientation
     img = fix_image_orientation(img)
+    session = new_session(model)
+    output = remove(
+        img,
+        alpha_matting=True,
+        alpha_matting_foreground_threshold=alpha_influence * 255,
+        alpha_matting_background_threshold=(1 - alpha_influence) * 255,
+        alpha_matting_erode_size=int(segmentation_strength * 20),
+        alpha_matting_smoothing=smoothing,
+        session=session
+    )
+
+    if return_type == ReturnType.PILLOW:
+        return output
+    elif return_type == ReturnType.NDARRAY:
+        return np.array(output)
+    else:
+        bio = io.BytesIO()
+        output.save(bio, "PNG")
+        bio.seek(0)
+        return bio.read()
 
-    if session is None:
-        session = new_session("u2net", *args, **kwargs)
-
-    masks = session.predict(img, *args, **kwargs)
-    cutouts = []
-
-    for mask in masks:
-        if post_process_mask:
-            mask = Image.fromarray(post_process(np.array(mask)))
-
-        if only_mask:
-            cutout = mask
-
-        elif alpha_matting:
-            try:
-                cutout = alpha_matting_cutout(
-                    img,
-                    mask,
-                    alpha_matting_foreground_threshold,
-                    alpha_matting_background_threshold,
-                    alpha_matting_erode_size,
-                )
-            except ValueError:
-                cutout = naive_cutout(img, mask)
-
-        else:
-            cutout = naive_cutout(img, mask)
-
-        cutouts.append(cutout)
-
-    cutout = img
-    if len(cutouts) > 0:
-        cutout = get_concat_v_multi(cutouts)
-
-    if bgcolor is not None and not only_mask:
-        cutout = apply_background_color(cutout, bgcolor)
-
-    if ReturnType.PILLOW == return_type:
-        return cutout
-
-    if ReturnType.NDARRAY == return_type:
-        return np.asarray(cutout)
-
-    bio = io.BytesIO()
-    cutout.save(bio, "PNG")
-    bio.seek(0)
-
-    return bio.read()