111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
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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# only paste masked area result
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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) # [0, 1]
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image = image * 2 - 1 # [0, 1] -> [-1, 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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