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import io
from pathlib import Path
from typing import Union
import numpy as np
import cv2
import torch
import torch.nn.functional as F
from PIL import Image
from rembg import remove
from internals.data.task import ModelType
import internals.util.image as ImageUtil
from carvekit.api.high import HiInterface
from internals.util.commons import download_image, read_url
import onnxruntime as rt
import huggingface_hub
class RemoveBackground:
def remove(self, image: Union[str, Image.Image]) -> Image.Image:
if type(image) is str:
image = Image.open(io.BytesIO(read_url(image)))
output = remove(image)
return output
class RemoveBackgroundV2:
def __init__(self):
model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
self.anime_rembg = rt.InferenceSession(
model_path, providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
)
self.interface = HiInterface(
object_type="object", # Can be "object" or "hairs-like".
batch_size_seg=5,
batch_size_matting=1,
device="cuda" if torch.cuda.is_available() else "cpu",
seg_mask_size=640, # Use 640 for Tracer B7 and 320 for U2Net
matting_mask_size=2048,
trimap_prob_threshold=231,
trimap_dilation=30,
trimap_erosion_iters=5,
fp16=False,
)
def remove(
self, image: Union[str, Image.Image], model_type: ModelType = ModelType.REAL
) -> Image.Image:
if type(image) is str:
image = download_image(image)
if model_type == ModelType.ANIME or model_type == ModelType.COMIC:
print("Using Anime Background remover")
_, img = self.__rmbg_fn(np.array(image))
return Image.fromarray(img)
else:
print("Using Real Background remover")
img_path = Path.home() / ".cache" / "rm_bg.png"
w, h = image.size
if max(w, h) > 1536:
image = ImageUtil.resize_image(image, dimension=1024)
image.save(img_path)
images_without_background = self.interface([img_path])
out = images_without_background[0]
return out
def __get_mask(self, img, s=1024):
img = (img / 255).astype(np.float32)
h, w = h0, w0 = img.shape[:-1]
h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
ph, pw = s - h, s - w
img_input = np.zeros([s, s, 3], dtype=np.float32)
img_input[ph // 2 : ph // 2 + h, pw // 2 : pw // 2 + w] = cv2.resize(
img, (w, h)
)
img_input = np.transpose(img_input, (2, 0, 1))
img_input = img_input[np.newaxis, :]
mask = self.anime_rembg.run(None, {"img": img_input})[0][0]
mask = np.transpose(mask, (1, 2, 0))
mask = mask[ph // 2 : ph // 2 + h, pw // 2 : pw // 2 + w]
mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
return mask
def __rmbg_fn(self, img):
mask = self.__get_mask(img)
img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
mask = (mask * 255).astype(np.uint8)
img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
mask = mask.repeat(3, axis=2)
return mask, img
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