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