IOPaint/lama_cleaner/lama/__init__.py
2022-04-06 23:01:18 +08:00

122 lines
3.5 KiB
Python

import os
from typing import List
import cv2
import torch
import numpy as np
from lama_cleaner.helper import pad_img_to_modulo, download_model, boxes_from_mask
LAMA_MODEL_URL = os.environ.get(
"LAMA_MODEL_URL",
"https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
)
class LaMa:
def __init__(self, crop_trigger_size: List[int], crop_margin: int, device):
"""
Args:
crop_trigger_size: h, w
crop_margin:
device:
"""
self.crop_trigger_size = crop_trigger_size
self.crop_margin = crop_margin
self.device = device
if os.environ.get("LAMA_MODEL"):
model_path = os.environ.get("LAMA_MODEL")
if not os.path.exists(model_path):
raise FileNotFoundError(
f"lama torchscript model not found: {model_path}"
)
else:
model_path = download_model(LAMA_MODEL_URL)
print(f"Load LaMa model from: {model_path}")
model = torch.jit.load(model_path, map_location="cpu")
model = model.to(device)
model.eval()
self.model = model
@torch.no_grad()
def __call__(self, image, mask):
"""
image: [C, H, W] RGB
mask: [1, H, W]
return: BGR IMAGE
"""
area = image.shape[1] * image.shape[2]
if area < self.crop_trigger_size[0] * self.crop_trigger_size[1]:
return self._run(image, mask)
print("Trigger crop image")
boxes = boxes_from_mask(mask)
crop_result = []
for box in boxes:
crop_image, crop_box = self._run_box(image, mask, box)
crop_result.append((crop_image, crop_box))
image = (image.transpose(1, 2, 0) * 255).astype(np.uint8)[:, :, ::-1]
for crop_image, crop_box in crop_result:
x1, y1, x2, y2 = crop_box
image[y1:y2, x1:x2, :] = crop_image
return image
def _run_box(self, image, mask, box):
"""
Args:
image: [C, H, W] RGB
mask: [1, H, W]
box: [left,top,right,bottom]
Returns:
BGR IMAGE
"""
box_h = box[3] - box[1]
box_w = box[2] - box[0]
cx = (box[0] + box[2]) // 2
cy = (box[1] + box[3]) // 2
img_h, img_w = image.shape[1:]
w = box_w + self.crop_margin * 2
h = box_h + self.crop_margin * 2
l = max(cx - w // 2, 0)
t = max(cy - h // 2, 0)
r = min(cx + w // 2, img_w)
b = min(cy + h // 2, img_h)
crop_img = image[:, t:b, l:r]
crop_mask = mask[:, t:b, l:r]
print(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
return self._run(crop_img, crop_mask), [l, t, r, b]
def _run(self, image, mask):
"""
image: [C, H, W] RGB
mask: [1, H, W]
return: BGR IMAGE
"""
device = self.device
origin_height, origin_width = image.shape[1:]
image = pad_img_to_modulo(image, mod=8)
mask = pad_img_to_modulo(mask, mod=8)
mask = (mask > 0) * 1
image = torch.from_numpy(image).unsqueeze(0).to(device)
mask = torch.from_numpy(mask).unsqueeze(0).to(device)
inpainted_image = self.model(image, mask)
cur_res = inpainted_image[0].permute(1, 2, 0).detach().cpu().numpy()
cur_res = cur_res[0:origin_height, 0:origin_width, :]
cur_res = np.clip(cur_res * 255, 0, 255).astype("uint8")
cur_res = cv2.cvtColor(cur_res, cv2.COLOR_RGB2BGR)
return cur_res