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import os
import torch
import argparse
import numpy as np
from PIL import Image
from skimage import io
from models.ormbg import ORMBG
import torch.nn.functional as F
def parse_args():
parser = argparse.ArgumentParser(
description="Remove background from images using ORMBG model."
)
parser.add_argument(
"--image",
type=str,
default=os.path.join("examples", "image", "example01.jpeg"),
help="Path to the input image file.",
)
parser.add_argument(
"--output",
type=str,
default=os.path.join("example01_no_background.png"),
help="Path to the output image file.",
)
parser.add_argument(
"--model-path",
type=str,
default=os.path.join("models", "ormbg.pth"),
help="Path to the model file.",
)
parser.add_argument(
"--compare",
action="store_false",
help="Flag to save the original and processed images side by side.",
)
return parser.parse_args()
def preprocess_image(im: np.ndarray, model_input_size: list) -> 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"
).type(torch.uint8)
image = torch.divide(im_tensor, 255.0)
return image
def postprocess_image(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
def inference(args):
image_path = args.image
result_name = args.output
model_path = args.model_path
compare = args.compare
net = ORMBG()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_path))
net = net.cuda()
else:
net.load_state_dict(torch.load(model_path, map_location="cpu"))
net.eval()
model_input_size = [1024, 1024]
orig_im = io.imread(image_path)
orig_im_size = orig_im.shape[0:2]
image = preprocess_image(orig_im, model_input_size).to(device)
result = net(image)
# post process
result_image = postprocess_image(result[0][0], orig_im_size)
# save result
pil_im = Image.fromarray(result_image)
if pil_im.mode == "RGBA":
pil_im = pil_im.convert("RGB")
no_bg_image = Image.new("RGBA", pil_im.size, (0, 0, 0, 0))
orig_image = Image.open(image_path)
no_bg_image.paste(orig_image, mask=pil_im)
if compare:
combined_width = orig_image.width + no_bg_image.width
combined_image = Image.new("RGBA", (combined_width, orig_image.height))
combined_image.paste(orig_image, (0, 0))
combined_image.paste(no_bg_image, (orig_image.width, 0))
stacked_output_path = os.path.splitext(result_name)[0] + ".png"
combined_image.save(stacked_output_path)
else:
no_bg_image.save(result_name)
if __name__ == "__main__":
inference(parse_args())
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