|
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):
|
|
|
|
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]):
|
|
|
|
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
|
|
|
|
|
|
def preprocess_image(self,im: np.ndarray, model_input_size: list=[1024,1024]) -> torch.Tensor:
|
|
|
|
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
|
|
|
