mirror of
https://github.com/vladmandic/sdnext.git
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103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
import os
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from urllib.parse import urlparse
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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 torch.hub import download_url_to_file, get_dir
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from PIL import Image
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from modules import devices
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from installer import log
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LAMA_MODEL_URL = "https://github.com/enesmsahin/simple-lama-inpainting/releases/download/v0.1.0/big-lama.pt"
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def prepare_img_and_mask(image, mask, device, pad_out_to_modulo=8, scale_factor=None):
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def ceil_modulo(x, mod):
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if x % mod == 0:
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return x
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return (x // mod + 1) * mod
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def get_image(img):
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if isinstance(img, Image.Image):
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img = np.array(img)
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if img.ndim == 3:
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img = np.transpose(img, (2, 0, 1)) # chw
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elif img.ndim == 2:
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img = img[np.newaxis, ...]
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img = img.astype(np.float32) / 255
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return img
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def pad_img_to_modulo(img, mod):
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_channels, height, width = img.shape
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out_height = ceil_modulo(height, mod)
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out_width = ceil_modulo(width, mod)
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return np.pad(
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img,
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((0, 0), (0, out_height - height), (0, out_width - width)),
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mode="symmetric",
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)
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def scale_image(img, factor, interpolation=cv2.INTER_LANCZOS4):
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if img.shape[0] == 1:
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img = img[0]
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else:
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img = np.transpose(img, (1, 2, 0))
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img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation)
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if img.ndim == 2:
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img = img[None, ...]
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else:
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img = np.transpose(img, (2, 0, 1))
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return img
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out_image = get_image(image)
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out_mask = get_image(mask)
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if scale_factor is not None:
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out_image = scale_image(out_image, scale_factor)
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out_mask = scale_image(out_mask, scale_factor, interpolation=cv2.INTER_LANCZOS4)
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if pad_out_to_modulo is not None and pad_out_to_modulo > 1:
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out_image = pad_img_to_modulo(out_image, pad_out_to_modulo)
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out_mask = pad_img_to_modulo(out_mask, pad_out_to_modulo)
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out_image = torch.from_numpy(out_image).unsqueeze(0).to(device)
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out_mask = torch.from_numpy(out_mask).unsqueeze(0).to(device)
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out_mask = (out_mask > 0) * 1
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return out_image, out_mask
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def download_model():
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parts = urlparse(LAMA_MODEL_URL)
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hub_dir = get_dir()
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model_dir = os.path.join(hub_dir, "checkpoints")
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os.makedirs(os.path.join(model_dir, "hub", "checkpoints"), exist_ok=True)
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filename = os.path.basename(parts.path)
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cached_file = os.path.join(model_dir, filename)
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if not os.path.exists(cached_file):
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log.info(f'LaMa download: url="{LAMA_MODEL_URL}" file="{cached_file}"')
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hash_prefix = None
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download_url_to_file(LAMA_MODEL_URL, cached_file, hash_prefix, progress=True)
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return cached_file
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class SimpleLama:
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def __init__(self):
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self.device = devices.device
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model_path = download_model()
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self.model = torch.jit.load(model_path, map_location=self.device)
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self.model.eval()
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self.model.to(self.device)
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def __call__(self, image: Image.Image, mask: Image.Image):
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if image is None:
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log.warning('LaMa: image is none')
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return None
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if mask is None:
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mask = Image.new('L', image.size, 0)
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return None
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image, mask = prepare_img_and_mask(image, mask, self.device)
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with devices.inference_context():
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inpainted = self.model(image, mask)
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cur_res = inpainted[0].permute(1, 2, 0).detach().float().cpu().numpy()
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cur_res = np.clip(cur_res * 255, 0, 255).astype(np.uint8)
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cur_res = Image.fromarray(cur_res)
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return cur_res
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