57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
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import os
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import time
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import cv2
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import torch
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import numpy as np
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from lama_cleaner.helper import pad_img_to_modulo, download_model
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LAMA_MODEL_URL = os.environ.get(
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"LAMA_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
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)
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class LaMa:
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def __init__(self, device):
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self.device = device
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if os.environ.get("LAMA_MODEL"):
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model_path = os.environ.get("LAMA_MODEL")
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if not os.path.exists(model_path):
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raise FileNotFoundError(f"lama torchscript model not found: {model_path}")
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else:
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model_path = download_model(LAMA_MODEL_URL)
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model = torch.jit.load(model_path, map_location="cpu")
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model = model.to(device)
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model.eval()
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self.model = model
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@torch.no_grad()
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def __call__(self, image, mask):
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"""
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image: [C, H, W] RGB
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mask: [1, H, W]
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return: BGR IMAGE
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"""
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device = self.device
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origin_height, origin_width = image.shape[1:]
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image = pad_img_to_modulo(image, mod=8)
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mask = pad_img_to_modulo(mask, mod=8)
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mask = (mask > 0) * 1
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image = torch.from_numpy(image).unsqueeze(0).to(device)
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mask = torch.from_numpy(mask).unsqueeze(0).to(device)
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start = time.time()
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inpainted_image = self.model(image, mask)
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print(f"process time: {(time.time() - start) * 1000}ms")
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cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
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cur_res = cur_res[0:origin_height, 0:origin_width, :]
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cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
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cur_res = cv2.cvtColor(cur_res, cv2.COLOR_BGR2RGB)
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return cur_res
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