mirror of
https://github.com/RapidAI/RapidOCR.git
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125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
# 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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# -*- encoding: utf-8 -*-
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# @Author: SWHL
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# @Contact: liekkaskono@163.com
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import time
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from typing import Any, Dict, Optional, Tuple
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import numpy as np
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from rapidocr_paddle.utils import PaddleInferSession
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from .utils import DBPostProcess, DetPreProcess
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class TextDetector:
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def __init__(self, config: Dict[str, Any]):
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self.limit_side_len = config.get("limit_side_len")
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self.limit_type = config.get("limit_type")
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self.mean = config.get("mean")
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self.std = config.get("std")
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self.preprocess_op = None
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post_process = {
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"thresh": config.get("thresh", 0.3),
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"box_thresh": config.get("box_thresh", 0.5),
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"max_candidates": config.get("max_candidates", 1000),
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"unclip_ratio": config.get("unclip_ratio", 1.6),
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"use_dilation": config.get("use_dilation", True),
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"score_mode": config.get("score_mode", "fast"),
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}
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self.postprocess_op = DBPostProcess(**post_process)
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self.infer = PaddleInferSession(config)
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def __call__(self, img: np.ndarray) -> Tuple[Optional[np.ndarray], float]:
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start_time = time.perf_counter()
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if img is None:
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raise ValueError("img is None")
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ori_img_shape = img.shape[0], img.shape[1]
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self.preprocess_op = self.get_preprocess(max(img.shape[0], img.shape[1]))
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prepro_img = self.preprocess_op(img)
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if prepro_img is None:
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return None, 0
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preds = self.infer(prepro_img)[0]
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dt_boxes, dt_boxes_scores = self.postprocess_op(preds, ori_img_shape)
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_img_shape)
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elapse = time.perf_counter() - start_time
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return dt_boxes, elapse
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def get_preprocess(self, max_wh):
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if self.limit_type == "min":
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limit_side_len = self.limit_side_len
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elif max_wh < 960:
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limit_side_len = 960
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elif max_wh < 1500:
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limit_side_len = 1500
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else:
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limit_side_len = 2000
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return DetPreProcess(limit_side_len, self.limit_type, self.mean, self.std)
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def filter_tag_det_res(
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self, dt_boxes: np.ndarray, image_shape: Tuple[int, int]
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) -> np.ndarray:
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img_height, img_width = image_shape
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dt_boxes_new = []
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for box in dt_boxes:
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box = self.order_points_clockwise(box)
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box = self.clip_det_res(box, img_height, img_width)
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rect_width = int(np.linalg.norm(box[0] - box[1]))
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rect_height = int(np.linalg.norm(box[0] - box[3]))
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if rect_width <= 3 or rect_height <= 3:
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continue
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dt_boxes_new.append(box)
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return np.array(dt_boxes_new)
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def order_points_clockwise(self, pts: np.ndarray) -> np.ndarray:
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"""
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reference from:
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https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
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sort the points based on their x-coordinates
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"""
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xSorted = pts[np.argsort(pts[:, 0]), :]
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# grab the left-most and right-most points from the sorted
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# x-roodinate points
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leftMost = xSorted[:2, :]
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rightMost = xSorted[2:, :]
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# now, sort the left-most coordinates according to their
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# y-coordinates so we can grab the top-left and bottom-left
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# points, respectively
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leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
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(tl, bl) = leftMost
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rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
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(tr, br) = rightMost
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rect = np.array([tl, tr, br, bl], dtype="float32")
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return rect
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def clip_det_res(
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self, points: np.ndarray, img_height: int, img_width: int
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) -> np.ndarray:
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for pno in range(points.shape[0]):
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points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
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points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
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return points
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