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import glob |
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import logging |
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import os |
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from pathlib import Path |
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import cv2 |
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import numpy as np |
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import onnxruntime as rt |
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import torch |
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from PIL import Image |
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from rembg import new_session, remove |
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from tqdm.rich import tqdm |
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logger = logging.getLogger(__name__) |
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def animseg_create_fg(frame_dir, output_dir, output_mask_dir, masked_area_list, |
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bg_color=(0,255,0), |
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mask_padding=0, |
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): |
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frame_list = sorted(glob.glob( os.path.join(frame_dir, "[0-9]*.png"), recursive=False)) |
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if mask_padding != 0: |
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kernel = np.ones((abs(mask_padding),abs(mask_padding)),np.uint8) |
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kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) |
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] |
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rmbg_model = rt.InferenceSession("data/models/anime_seg/isnetis.onnx", providers=providers) |
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def get_mask(img, s=1024): |
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img = (img / 255).astype(np.float32) |
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h, w = h0, w0 = img.shape[:-1] |
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h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) |
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ph, pw = s - h, s - w |
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img_input = np.zeros([s, s, 3], dtype=np.float32) |
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img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h)) |
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img_input = np.transpose(img_input, (2, 0, 1)) |
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img_input = img_input[np.newaxis, :] |
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mask = rmbg_model.run(None, {'img': img_input})[0][0] |
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mask = np.transpose(mask, (1, 2, 0)) |
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mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] |
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mask = cv2.resize(mask, (w0, h0)) |
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mask = (mask * 255).astype(np.uint8) |
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return mask |
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for i, frame in tqdm(enumerate(frame_list),total=len(frame_list), desc=f"creating mask"): |
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frame = Path(frame) |
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file_name = frame.name |
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cur_frame_no = int(frame.stem) |
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img = Image.open(frame) |
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img_array = np.asarray(img) |
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mask_array = get_mask(img_array) |
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if mask_padding < 0: |
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mask_array = cv2.erode(mask_array.astype(np.uint8),kernel,iterations = 1) |
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elif mask_padding > 0: |
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mask_array = cv2.dilate(mask_array.astype(np.uint8),kernel,iterations = 1) |
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mask_array = cv2.morphologyEx(mask_array, cv2.MORPH_OPEN, kernel2) |
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mask_array = cv2.GaussianBlur(mask_array, (7, 7), sigmaX=3, sigmaY=3, borderType=cv2.BORDER_DEFAULT) |
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if masked_area_list[cur_frame_no] is not None: |
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masked_area_list[cur_frame_no] = np.where(masked_area_list[cur_frame_no] > mask_array[None,...], masked_area_list[cur_frame_no], mask_array[None,...]) |
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else: |
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masked_area_list[cur_frame_no] = mask_array[None,...] |
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if output_mask_dir: |
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Image.fromarray(mask_array).save( output_mask_dir / file_name ) |
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img_array = np.asarray(img).copy() |
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if bg_color is not None: |
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img_array[mask_array == 0] = bg_color |
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img = Image.fromarray(img_array) |
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img.save( output_dir / file_name ) |
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return masked_area_list |
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