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ppocr/utils/utility.py
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216
ppocr/utils/utility.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import cv2
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import random
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import numpy as np
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import paddle
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import importlib.util
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import sys
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import subprocess
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def print_dict(d, logger, delimiter=0):
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"""
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Recursively visualize a dict and
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indenting acrrording by the relationship of keys.
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"""
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for k, v in sorted(d.items()):
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if isinstance(v, dict):
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logger.info("{}{} : ".format(delimiter * " ", str(k)))
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print_dict(v, logger, delimiter + 4)
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elif isinstance(v, list) and len(v) >= 1 and isinstance(v[0], dict):
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logger.info("{}{} : ".format(delimiter * " ", str(k)))
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for value in v:
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print_dict(value, logger, delimiter + 4)
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else:
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logger.info("{}{} : {}".format(delimiter * " ", k, v))
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def get_check_global_params(mode):
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check_params = [
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"use_gpu",
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"max_text_length",
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"image_shape",
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"image_shape",
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"character_type",
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"loss_type",
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]
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if mode == "train_eval":
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check_params = check_params + [
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"train_batch_size_per_card",
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"test_batch_size_per_card",
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]
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elif mode == "test":
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check_params = check_params + ["test_batch_size_per_card"]
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return check_params
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def _check_image_file(path):
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img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif", "pdf"}
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return any([path.lower().endswith(e) for e in img_end])
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def get_image_file_list(img_file, infer_list=None):
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imgs_lists = []
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if infer_list and not os.path.exists(infer_list):
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raise Exception("not found infer list {}".format(infer_list))
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if infer_list:
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with open(infer_list, "r") as f:
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lines = f.readlines()
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for line in lines:
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image_path = line.strip().split("\t")[0]
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image_path = os.path.join(img_file, image_path)
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imgs_lists.append(image_path)
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else:
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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img_end = {"jpg", "bmp", "png", "jpeg", "rgb", "tif", "tiff", "gif", "pdf"}
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if os.path.isfile(img_file) and _check_image_file(img_file):
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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file_path = os.path.join(img_file, single_file)
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if os.path.isfile(file_path) and _check_image_file(file_path):
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imgs_lists.append(file_path)
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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imgs_lists = sorted(imgs_lists)
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return imgs_lists
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def binarize_img(img):
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if len(img.shape) == 3 and img.shape[2] == 3:
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # conversion to grayscale image
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# use cv2 threshold binarization
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_, gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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img = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)
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return img
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def alpha_to_color(img, alpha_color=(255, 255, 255)):
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if len(img.shape) == 3 and img.shape[2] == 4:
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B, G, R, A = cv2.split(img)
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alpha = A / 255
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R = (alpha_color[0] * (1 - alpha) + R * alpha).astype(np.uint8)
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G = (alpha_color[1] * (1 - alpha) + G * alpha).astype(np.uint8)
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B = (alpha_color[2] * (1 - alpha) + B * alpha).astype(np.uint8)
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img = cv2.merge((B, G, R))
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return img
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def check_and_read(img_path):
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if os.path.basename(img_path)[-3:].lower() == "gif":
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gif = cv2.VideoCapture(img_path)
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ret, frame = gif.read()
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if not ret:
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logger = logging.getLogger("ppocr")
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logger.info("Cannot read {}. This gif image maybe corrupted.")
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return None, False
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if len(frame.shape) == 2 or frame.shape[-1] == 1:
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frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
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imgvalue = frame[:, :, ::-1]
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return imgvalue, True, False
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elif os.path.basename(img_path)[-3:].lower() == "pdf":
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from paddle.utils import try_import
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fitz = try_import("fitz")
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from PIL import Image
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imgs = []
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with fitz.open(img_path) as pdf:
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for pg in range(0, pdf.page_count):
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page = pdf[pg]
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mat = fitz.Matrix(2, 2)
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pm = page.get_pixmap(matrix=mat, alpha=False)
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# if width or height > 2000 pixels, don't enlarge the image
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if pm.width > 2000 or pm.height > 2000:
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pm = page.get_pixmap(matrix=fitz.Matrix(1, 1), alpha=False)
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img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
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img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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imgs.append(img)
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return imgs, False, True
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return None, False, False
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def load_vqa_bio_label_maps(label_map_path):
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with open(label_map_path, "r", encoding="utf-8") as fin:
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lines = fin.readlines()
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old_lines = [line.strip() for line in lines]
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lines = ["O"]
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for line in old_lines:
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# "O" has already been in lines
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if line.upper() in ["OTHER", "OTHERS", "IGNORE"]:
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continue
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lines.append(line)
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labels = ["O"]
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for line in lines[1:]:
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labels.append("B-" + line)
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labels.append("I-" + line)
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label2id_map = {label.upper(): idx for idx, label in enumerate(labels)}
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id2label_map = {idx: label.upper() for idx, label in enumerate(labels)}
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return label2id_map, id2label_map
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def set_seed(seed=1024):
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random.seed(seed)
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np.random.seed(seed)
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paddle.seed(seed)
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def check_install(module_name, install_name):
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spec = importlib.util.find_spec(module_name)
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if spec is None:
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print(f"Warning! The {module_name} module is NOT installed")
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print(
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f"Try install {module_name} module automatically. You can also try to install manually by pip install {install_name}."
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)
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python = sys.executable
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try:
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subprocess.check_call(
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[python, "-m", "pip", "install", install_name],
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stdout=subprocess.DEVNULL,
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)
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print(f"The {module_name} module is now installed")
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except subprocess.CalledProcessError as exc:
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raise Exception(f"Install {module_name} failed, please install manually")
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else:
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print(f"{module_name} has been installed.")
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class AverageMeter:
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def __init__(self):
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self.reset()
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def reset(self):
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"""reset"""
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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"""update"""
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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