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import os |
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import cv2 |
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import torch |
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from iopaint.helper import ( |
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load_jit_model, |
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download_model, |
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get_cache_path_by_url, |
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boxes_from_mask, |
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resize_max_size, |
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norm_img, |
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) |
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from .base import InpaintModel |
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from iopaint.schema import InpaintRequest |
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MIGAN_MODEL_URL = os.environ.get( |
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"MIGAN_MODEL_URL", |
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"https://github.com/Sanster/models/releases/download/migan/migan_traced.pt", |
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) |
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MIGAN_MODEL_MD5 = os.environ.get("MIGAN_MODEL_MD5", "76eb3b1a71c400ee3290524f7a11b89c") |
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class MIGAN(InpaintModel): |
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name = "migan" |
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min_size = 512 |
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pad_mod = 512 |
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pad_to_square = True |
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is_erase_model = True |
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def init_model(self, device, **kwargs): |
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self.model = load_jit_model(MIGAN_MODEL_URL, device, MIGAN_MODEL_MD5).eval() |
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@staticmethod |
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def download(): |
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download_model(MIGAN_MODEL_URL, MIGAN_MODEL_MD5) |
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@staticmethod |
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def is_downloaded() -> bool: |
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return os.path.exists(get_cache_path_by_url(MIGAN_MODEL_URL)) |
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@torch.no_grad() |
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def __call__(self, image, mask, config: InpaintRequest): |
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""" |
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images: [H, W, C] RGB, not normalized |
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masks: [H, W] |
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return: BGR IMAGE |
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""" |
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if image.shape[0] == 512 and image.shape[1] == 512: |
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return self._pad_forward(image, mask, config) |
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boxes = boxes_from_mask(mask) |
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crop_result = [] |
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config.hd_strategy_crop_margin = 128 |
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for box in boxes: |
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crop_image, crop_mask, crop_box = self._crop_box(image, mask, box, config) |
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origin_size = crop_image.shape[:2] |
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resize_image = resize_max_size(crop_image, size_limit=512) |
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resize_mask = resize_max_size(crop_mask, size_limit=512) |
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inpaint_result = self._pad_forward(resize_image, resize_mask, config) |
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inpaint_result = cv2.resize( |
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inpaint_result, |
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(origin_size[1], origin_size[0]), |
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interpolation=cv2.INTER_CUBIC, |
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) |
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original_pixel_indices = crop_mask < 127 |
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inpaint_result[original_pixel_indices] = crop_image[:, :, ::-1][ |
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original_pixel_indices |
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] |
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crop_result.append((inpaint_result, crop_box)) |
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inpaint_result = image[:, :, ::-1].copy() |
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for crop_image, crop_box in crop_result: |
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x1, y1, x2, y2 = crop_box |
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inpaint_result[y1:y2, x1:x2, :] = crop_image |
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return inpaint_result |
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def forward(self, image, mask, config: InpaintRequest): |
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"""Input images and output images have same size |
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images: [H, W, C] RGB |
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masks: [H, W] mask area == 255 |
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return: BGR IMAGE |
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""" |
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image = norm_img(image) |
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image = image * 2 - 1 |
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mask = (mask > 120) * 255 |
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mask = norm_img(mask) |
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image = torch.from_numpy(image).unsqueeze(0).to(self.device) |
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mask = torch.from_numpy(mask).unsqueeze(0).to(self.device) |
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erased_img = image * (1 - mask) |
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input_image = torch.cat([0.5 - mask, erased_img], dim=1) |
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output = self.model(input_image) |
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output = ( |
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(output.permute(0, 2, 3, 1) * 127.5 + 127.5) |
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.round() |
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.clamp(0, 255) |
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.to(torch.uint8) |
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) |
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output = output[0].cpu().numpy() |
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cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) |
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return cur_res |
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