mirror of
https://github.com/RapidAI/RapidOCR.git
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320 lines
11 KiB
Python
320 lines
11 KiB
Python
# -*- encoding: utf-8 -*-
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# @Author: SWHL
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# @Contact: liekkaskono@163.com
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from dataclasses import dataclass
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from typing import List, Optional, Tuple
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import cv2
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import numpy as np
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import pyclipper
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from shapely.geometry import Polygon
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from ..utils.log import logger
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from ..utils.utils import save_img
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from ..utils.vis_res import VisRes
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@dataclass
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class TextDetOutput:
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img: Optional[np.ndarray] = None
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boxes: Optional[np.ndarray] = None
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scores: Optional[Tuple[float]] = None
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elapse: float = 0.0
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def __len__(self):
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if self.boxes is None:
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return 0
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return len(self.boxes)
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def vis(self, save_path: Optional[str] = None) -> Optional[np.ndarray]:
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if self.img is None or self.boxes is None or self.scores is None:
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logger.warning("No image or boxes to visualize.")
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return None
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vis = VisRes()
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vis_img = vis.draw_dt_boxes(self.img, self.boxes, self.scores)
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if save_path is not None:
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save_img(save_path, vis_img)
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logger.info("Visualization saved as %s", save_path)
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return vis_img
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class DetPreProcess:
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def __init__(
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self, limit_side_len: int = 736, limit_type: str = "min", mean=None, std=None
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):
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if mean is None:
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mean = [0.5, 0.5, 0.5]
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if std is None:
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std = [0.5, 0.5, 0.5]
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self.mean = np.array(mean)
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self.std = np.array(std)
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self.scale = 1 / 255.0
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self.limit_side_len = limit_side_len
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self.limit_type = limit_type
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def __call__(self, img: np.ndarray) -> Optional[np.ndarray]:
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resized_img = self.resize(img)
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if resized_img is None:
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return None
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img = self.normalize(resized_img)
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img = self.permute(img)
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img = np.expand_dims(img, axis=0).astype(np.float32)
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return img
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def normalize(self, img: np.ndarray) -> np.ndarray:
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return (img.astype("float32") * self.scale - self.mean) / self.std
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def permute(self, img: np.ndarray) -> np.ndarray:
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return img.transpose((2, 0, 1))
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def resize(self, img: np.ndarray) -> Optional[np.ndarray]:
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"""resize image to a size multiple of 32 which is required by the network"""
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h, w = img.shape[:2]
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if self.limit_type == "max":
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if max(h, w) > self.limit_side_len:
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if h > w:
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ratio = float(self.limit_side_len) / h
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else:
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ratio = float(self.limit_side_len) / w
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else:
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ratio = 1.0
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else:
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if min(h, w) < self.limit_side_len:
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if h < w:
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ratio = float(self.limit_side_len) / h
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else:
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ratio = float(self.limit_side_len) / w
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else:
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ratio = 1.0
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resize_h = int(h * ratio)
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resize_w = int(w * ratio)
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resize_h = int(round(resize_h / 32) * 32)
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resize_w = int(round(resize_w / 32) * 32)
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try:
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if int(resize_w) <= 0 or int(resize_h) <= 0:
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return None
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img = cv2.resize(img, (int(resize_w), int(resize_h)))
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except Exception as exc:
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raise ResizeImgError from exc
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return img
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class ResizeImgError(Exception):
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pass
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class DBPostProcess:
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"""The post process for Differentiable Binarization (DB)."""
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def __init__(
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self,
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thresh: float = 0.3,
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box_thresh: float = 0.7,
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max_candidates: int = 1000,
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unclip_ratio: float = 2.0,
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score_mode: str = "fast",
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use_dilation: bool = False,
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):
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self.thresh = thresh
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self.box_thresh = box_thresh
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self.max_candidates = max_candidates
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self.unclip_ratio = unclip_ratio
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self.min_size = 3
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self.score_mode = score_mode
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self.dilation_kernel = None
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if use_dilation:
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self.dilation_kernel = np.array([[1, 1], [1, 1]])
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def __call__(
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self, pred: np.ndarray, ori_shape: Tuple[int, int]
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) -> Tuple[np.ndarray, List[float]]:
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src_h, src_w = ori_shape
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pred = pred[:, 0, :, :]
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segmentation = pred > self.thresh
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mask = segmentation[0]
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if self.dilation_kernel is not None:
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mask = cv2.dilate(
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np.array(segmentation[0]).astype(np.uint8), self.dilation_kernel
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)
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boxes, scores = self.boxes_from_bitmap(pred[0], mask, src_w, src_h)
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boxes, scores = self.filter_det_res(boxes, scores, src_h, src_w)
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return boxes, scores
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def boxes_from_bitmap(
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self, pred: np.ndarray, bitmap: np.ndarray, dest_width: int, dest_height: int
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) -> Tuple[np.ndarray, List[float]]:
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"""
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bitmap: single map with shape (1, H, W),
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whose values are binarized as {0, 1}
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"""
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height, width = bitmap.shape
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outs = cv2.findContours(
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(bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
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)
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if len(outs) == 3:
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img, contours, _ = outs[0], outs[1], outs[2]
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elif len(outs) == 2:
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contours, _ = outs[0], outs[1]
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num_contours = min(len(contours), self.max_candidates)
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boxes, scores = [], []
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for index in range(num_contours):
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contour = contours[index]
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points, sside = self.get_mini_boxes(contour)
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if sside < self.min_size:
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continue
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if self.score_mode == "fast":
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score = self.box_score_fast(pred, points.reshape(-1, 2))
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else:
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score = self.box_score_slow(pred, contour)
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if self.box_thresh > score:
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continue
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box = self.unclip(points)
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box, sside = self.get_mini_boxes(box)
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if sside < self.min_size + 2:
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continue
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box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
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box[:, 1] = np.clip(
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np.round(box[:, 1] / height * dest_height), 0, dest_height
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)
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boxes.append(box.astype(np.int32))
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scores.append(score)
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return np.array(boxes, dtype=np.int32), scores
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def get_mini_boxes(self, contour: np.ndarray) -> Tuple[np.ndarray, float]:
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bounding_box = cv2.minAreaRect(contour)
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points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
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index_1, index_2, index_3, index_4 = 0, 1, 2, 3
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if points[1][1] > points[0][1]:
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index_1 = 0
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index_4 = 1
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else:
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index_1 = 1
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index_4 = 0
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if points[3][1] > points[2][1]:
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index_2 = 2
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index_3 = 3
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else:
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index_2 = 3
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index_3 = 2
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box = np.array(
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[points[index_1], points[index_2], points[index_3], points[index_4]]
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)
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return box, min(bounding_box[1])
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@staticmethod
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def box_score_fast(bitmap: np.ndarray, _box: np.ndarray) -> float:
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h, w = bitmap.shape[:2]
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box = _box.copy()
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xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1)
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xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
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ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
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ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1)
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
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box[:, 0] = box[:, 0] - xmin
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box[:, 1] = box[:, 1] - ymin
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cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
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return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0]
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def box_score_slow(self, bitmap: np.ndarray, contour: np.ndarray) -> float:
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"""use polyon mean score as the mean score"""
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h, w = bitmap.shape[:2]
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contour = contour.copy()
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contour = np.reshape(contour, (-1, 2))
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xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
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xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
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ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
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ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
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contour[:, 0] = contour[:, 0] - xmin
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contour[:, 1] = contour[:, 1] - ymin
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cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
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return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0]
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def unclip(self, box: np.ndarray) -> np.ndarray:
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unclip_ratio = self.unclip_ratio
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poly = Polygon(box)
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distance = poly.area * unclip_ratio / poly.length
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offset = pyclipper.PyclipperOffset()
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offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
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expanded = np.array(offset.Execute(distance)).reshape((-1, 1, 2))
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return expanded
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def filter_det_res(
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self, dt_boxes: np.ndarray, scores: List[float], img_height: int, img_width: int
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) -> Tuple[np.ndarray, List[float]]:
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dt_boxes_new, new_scores = [], []
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for box, score in zip(dt_boxes, scores):
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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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new_scores.append(score)
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return np.array(dt_boxes_new), new_scores
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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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