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import torch, os
import torch.nn.functional as F
from torchvision.transforms.functional import normalize
import numpy as np
from transformers import Pipeline
from skimage import io
from PIL import Image
class RMBGPipe(Pipeline):
def __init__(self, **kwargs):
Pipeline.__init__(self, **kwargs)
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
self.model.eval()
def _sanitize_parameters(self, **kwargs):
# parse parameters
preprocess_kwargs = {}
postprocess_kwargs = {}
if "model_input_size" in kwargs:
preprocess_kwargs["model_input_size"] = kwargs["model_input_size"]
if "out_name" in kwargs:
postprocess_kwargs["out_name"] = kwargs["out_name"]
return preprocess_kwargs, {}, postprocess_kwargs
def preprocess(self, orig_im: Image, model_input_size: list = [1024, 1024]):
# preprocess the input
orig_im_size = orig_im.shape[0:2]
image = self.preprocess_image(orig_im, model_input_size).to(self.device)
inputs = {
"orig_im": orig_im,
"image": image,
"orig_im_size": orig_im_size,
}
return inputs
def _forward(self, inputs):
result = self.model(inputs.pop("image"))
inputs["result"] = result
return inputs
def postprocess(self, inputs, out_name=""):
result = inputs.pop("result")
orig_im_size = inputs.pop("orig_im_size")
orig_image = inputs.pop("orig_im")
result_image = self.postprocess_image(result[0][0], orig_im_size)
if out_name != "":
# if out_name is specified we save the image using that name
pil_im = Image.fromarray(result_image)
no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
no_bg_image.paste(orig_image, mask=pil_im)
no_bg_image.save(out_name)
else:
return result_image
# utilities functions
def preprocess_image(
self, im: np.ndarray, model_input_size: list = [1024, 1024]
) -> torch.Tensor:
# same as utilities.py with minor modification
if len(im.shape) < 3:
im = im[:, :, np.newaxis]
# orig_im_size=im.shape[0:2]
im_tensor = torch.tensor(im, dtype=torch.float32).permute(2, 0, 1)
im_tensor = F.interpolate(
torch.unsqueeze(im_tensor, 0), size=model_input_size, mode="bilinear"
).type(torch.uint8)
image = torch.divide(im_tensor, 255.0)
image = normalize(image, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0])
return image
def postprocess_image(self, result: torch.Tensor, im_size: list) -> np.ndarray:
result = torch.squeeze(F.interpolate(result, size=im_size, mode="bilinear"), 0)
ma = torch.max(result)
mi = torch.min(result)
result = (result - mi) / (ma - mi)
im_array = (result * 255).permute(1, 2, 0).cpu().data.numpy().astype(np.uint8)
im_array = np.squeeze(im_array)
return im_array
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