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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 transformers.image_utils import load_image
from skimage import io
from PIL import Image
class RMBGPipe(Pipeline):
def __init__(self,**kwargs):
Pipeline.__init__(self,**kwargs)
self.device = torch.device("cuda:0" 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 "return_mask" in kwargs:
postprocess_kwargs["return_mask"] = kwargs["return_mask"]
return preprocess_kwargs, {}, postprocess_kwargs
def preprocess(self,input_image,model_input_size: list=[1024,1024]):
# preprocess the input
orig_im = load_image(input_image)
orig_im = np.array(orig_im)
orig_im_size = orig_im.shape[0:2]
preprocessed_image = self.preprocess_image(orig_im, model_input_size).to(self.device)
inputs = {
"preprocessed_image":preprocessed_image,
"orig_im_size":orig_im_size,
"input_image" : input_image
}
return inputs
def _forward(self,inputs):
result = self.model(inputs.pop("preprocessed_image"))
inputs["result"] = result
return inputs
def postprocess(self,inputs,return_mask:bool=False ):
result = inputs.pop("result")
orig_im_size = inputs.pop("orig_im_size")
input_image = inputs.pop("input_image")
result_image = self.postprocess_image(result[0][0], orig_im_size)
pil_im = Image.fromarray(result_image)
if return_mask ==True :
return pil_im
no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
input_image = load_image(input_image)
no_bg_image.paste(input_image, mask=pil_im)
return no_bg_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]
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')
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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