example_inference.py
| 1 | from skimage import io |
| 2 | import torch, os |
| 3 | from PIL import Image |
| 4 | from briarmbg import BriaRMBG |
| 5 | from utilities import preprocess_image, postprocess_image |
| 6 | from huggingface_hub import hf_hub_download |
| 7 | |
| 8 | def example_inference(): |
| 9 | |
| 10 | im_path = f"{os.path.dirname(os.path.abspath(__file__))}/example_input.jpg" |
| 11 | |
| 12 | net = BriaRMBG() |
| 13 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| 14 | net = BriaRMBG.from_pretrained("briaai/RMBG-1.4") |
| 15 | net.to(device) |
| 16 | net.eval() |
| 17 | |
| 18 | # prepare input |
| 19 | model_input_size = [1024,1024] |
| 20 | orig_im = io.imread(im_path) |
| 21 | orig_im_size = orig_im.shape[0:2] |
| 22 | image = preprocess_image(orig_im, model_input_size).to(device) |
| 23 | |
| 24 | # inference |
| 25 | result=net(image) |
| 26 | |
| 27 | # post process |
| 28 | result_image = postprocess_image(result[0][0], orig_im_size) |
| 29 | |
| 30 | # save result |
| 31 | pil_mask_im = Image.fromarray(result_image) |
| 32 | orig_image = Image.open(im_path) |
| 33 | no_bg_image = orig_image.copy() |
| 34 | no_bg_image.putalpha(pil_mask_im) |
| 35 | no_bg_image.save("example_image_no_bg.png") |
| 36 | |
| 37 | |
| 38 | if __name__ == "__main__": |
| 39 | example_inference() |