IOPaint/lama_cleaner/model/mi_gan.py

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2023-11-20 06:05:28 +01:00
import os
import cv2
import torch
from lama_cleaner.const import Config
from lama_cleaner.helper import (
load_jit_model,
download_model,
get_cache_path_by_url,
boxes_from_mask,
resize_max_size,
norm_img,
)
from lama_cleaner.model.base import InpaintModel
MIGAN_MODEL_URL = os.environ.get(
"MIGAN_MODEL_URL",
"/Users/cwq/code/github/MI-GAN/exported_models/migan_places512/models/migan_traced.pt",
)
MIGAN_MODEL_MD5 = os.environ.get("MIGAN_MODEL_MD5", "76eb3b1a71c400ee3290524f7a11b89c")
class MIGAN(InpaintModel):
name = "migan"
min_size = 512
pad_mod = 512
pad_to_square = True
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is_erase_model = True
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def init_model(self, device, **kwargs):
self.model = load_jit_model(MIGAN_MODEL_URL, device, MIGAN_MODEL_MD5).eval()
@staticmethod
def download():
download_model(MIGAN_MODEL_URL, MIGAN_MODEL_MD5)
@staticmethod
def is_downloaded() -> bool:
return os.path.exists(get_cache_path_by_url(MIGAN_MODEL_URL))
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
images: [H, W, C] RGB, not normalized
masks: [H, W]
return: BGR IMAGE
"""
if image.shape[0] == 512 and image.shape[1] == 512:
return self._pad_forward(image, mask, config)
boxes = boxes_from_mask(mask)
crop_result = []
config.hd_strategy_crop_margin = 128
for box in boxes:
crop_image, crop_mask, crop_box = self._crop_box(image, mask, box, config)
origin_size = crop_image.shape[:2]
resize_image = resize_max_size(crop_image, size_limit=512)
resize_mask = resize_max_size(crop_mask, size_limit=512)
inpaint_result = self._pad_forward(resize_image, resize_mask, config)
# only paste masked area result
inpaint_result = cv2.resize(
inpaint_result,
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC,
)
original_pixel_indices = crop_mask < 127
inpaint_result[original_pixel_indices] = crop_image[:, :, ::-1][
original_pixel_indices
]
crop_result.append((inpaint_result, crop_box))
inpaint_result = image[:, :, ::-1]
for crop_image, crop_box in crop_result:
x1, y1, x2, y2 = crop_box
inpaint_result[y1:y2, x1:x2, :] = crop_image
return inpaint_result
def forward(self, image, mask, config: Config):
"""Input images and output images have same size
images: [H, W, C] RGB
masks: [H, W] mask area == 255
return: BGR IMAGE
"""
image = norm_img(image) # [0, 1]
image = image * 2 - 1 # [0, 1] -> [-1, 1]
mask = (mask > 120) * 255
mask = norm_img(mask)
image = torch.from_numpy(image).unsqueeze(0).to(self.device)
mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
erased_img = image * (1 - mask)
input_image = torch.cat([0.5 - mask, erased_img], dim=1)
output = self.model(input_image)
output = (
(output.permute(0, 2, 3, 1) * 127.5 + 127.5)
.round()
.clamp(0, 255)
.to(torch.uint8)
)
output = output[0].cpu().numpy()
cur_res = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return cur_res