Upload 13 files

anyocr/cal_rec_boxes.py

@@ -0,0 +1,277 @@
+from typing import Any, List, Optional, Tuple
+import cv2
+import copy
+import math
+import numpy as np
+
+
+class CalRecBoxes:
+    """计算识别文字的汉字单字和英文单词的坐标框。代码借鉴自PaddlePaddle/PaddleOCR和fanqie03/char-detection"""
+
+    def __init__(self):
+        pass
+
+    def __call__(
+        self,
+        imgs: Optional[List[np.ndarray]],
+        dt_boxes: Optional[List[np.ndarray]],
+        rec_res: Optional[List[Any]],
+    ):
+        res = []
+        for img, box, rec_res in zip(imgs, dt_boxes, rec_res):
+            direction = self.get_box_direction(box)
+
+            rec_txt, rec_conf, rec_word_info = rec_res[0], rec_res[1], rec_res[2]
+            h, w = img.shape[:2]
+            img_box = np.array([[0, 0], [w, 0], [w, h], [0, h]])
+            word_box_content_list, word_box_list, conf_list = self.cal_ocr_word_box(
+                rec_txt, img_box, rec_word_info
+            )
+            word_box_list = self.adjust_box_overlap(copy.deepcopy(word_box_list))
+            word_box_list = self.reverse_rotate_crop_image(
+                copy.deepcopy(box), word_box_list, direction
+            )
+            res.append(
+                [rec_txt, rec_conf, word_box_list, word_box_content_list, conf_list]
+            )
+        return res
+
+    @staticmethod
+    def get_box_direction(box: np.ndarray) -> str:
+        direction = "w"
+        img_crop_width = int(
+            max(
+                np.linalg.norm(box[0] - box[1]),
+                np.linalg.norm(box[2] - box[3]),
+            )
+        )
+        img_crop_height = int(
+            max(
+                np.linalg.norm(box[0] - box[3]),
+                np.linalg.norm(box[1] - box[2]),
+            )
+        )
+        if img_crop_height * 1.0 / img_crop_width >= 1.5:
+            direction = "h"
+        return direction
+
+    @staticmethod
+    def cal_ocr_word_box(
+        rec_txt: str, box: np.ndarray, rec_word_info: List[Tuple[str, List[int]]]
+    ) -> Tuple[List[str], List[List[int]], List[float]]:
+        """Calculate the detection frame for each word based on the results of recognition and detection of ocr
+        汉字坐标是单字的
+        英语坐标是单词级别的
+        """
+
+        col_num, word_list, word_col_list, state_list, conf_list = rec_word_info
+        box = box.tolist()
+        bbox_x_start = box[0][0]
+        bbox_x_end = box[1][0]
+        bbox_y_start = box[0][1]
+        bbox_y_end = box[2][1]
+
+        cell_width = (bbox_x_end - bbox_x_start) / col_num
+        word_box_list = []
+        word_box_content_list = []
+        cn_width_list = []
+        en_width_list = []
+        cn_col_list = []
+        en_col_list = []
+
+        def cal_char_width(width_list, word_col_):
+            if len(word_col_) == 1:
+                return
+            char_total_length = (word_col_[-1] - word_col_[0]) * cell_width
+            char_width = char_total_length / (len(word_col_) - 1)
+            width_list.append(char_width)
+
+        def cal_box(col_list, width_list, word_box_list_):
+            if len(col_list) == 0:
+                return
+            if len(width_list) != 0:
+                avg_char_width = np.mean(width_list)
+            else:
+                avg_char_width = (bbox_x_end - bbox_x_start) / len(rec_txt)
+
+            for center_idx in col_list:
+                center_x = (center_idx + 0.5) * cell_width
+                cell_x_start = max(int(center_x - avg_char_width / 2), 0) + bbox_x_start
+                cell_x_end = (
+                    min(int(center_x + avg_char_width / 2), bbox_x_end - bbox_x_start)
+                    + bbox_x_start
+                )
+                cell = [
+                    [cell_x_start, bbox_y_start],
+                    [cell_x_end, bbox_y_start],
+                    [cell_x_end, bbox_y_end],
+                    [cell_x_start, bbox_y_end],
+                ]
+                word_box_list_.append(cell)
+
+        for word, word_col, state in zip(word_list, word_col_list, state_list):
+            if state == "cn":
+                cal_char_width(cn_width_list, word_col)
+                cn_col_list += word_col
+                word_box_content_list += word
+            else:
+                cal_char_width(en_width_list, word_col)
+                en_col_list += word_col
+                word_box_content_list += word
+
+        cal_box(cn_col_list, cn_width_list, word_box_list)
+        cal_box(en_col_list, en_width_list, word_box_list)
+        sorted_word_box_list = sorted(word_box_list, key=lambda box: box[0][0])
+        return word_box_content_list, sorted_word_box_list, conf_list
+
+    @staticmethod
+    def adjust_box_overlap(
+        word_box_list: List[List[List[int]]],
+    ) -> List[List[List[int]]]:
+        # 调整bbox有重叠的地方
+        for i in range(len(word_box_list) - 1):
+            cur, nxt = word_box_list[i], word_box_list[i + 1]
+            if cur[1][0] > nxt[0][0]:  # 有交集
+                distance = abs(cur[1][0] - nxt[0][0])
+                cur[1][0] -= distance / 2
+                cur[2][0] -= distance / 2
+                nxt[0][0] += distance - distance / 2
+                nxt[3][0] += distance - distance / 2
+        return word_box_list
+
+    def reverse_rotate_crop_image(
+        self,
+        bbox_points: np.ndarray,
+        word_points_list: List[List[List[int]]],
+        direction: str = "w",
+    ) -> List[List[List[int]]]:
+        """
+        get_rotate_crop_image的逆操作
+        img为原图
+        part_img为crop后的图
+        bbox_points为part_img中对应在原图的bbox, 四个点，左上，右上，右下，左下
+        part_points为在part_img中的点[(x, y), (x, y)]
+        """
+        bbox_points = np.float32(bbox_points)
+
+        left = int(np.min(bbox_points[:, 0]))
+        top = int(np.min(bbox_points[:, 1]))
+        bbox_points[:, 0] = bbox_points[:, 0] - left
+        bbox_points[:, 1] = bbox_points[:, 1] - top
+
+        img_crop_width = int(np.linalg.norm(bbox_points[0] - bbox_points[1]))
+        img_crop_height = int(np.linalg.norm(bbox_points[0] - bbox_points[3]))
+
+        pts_std = np.array(
+            [
+                [0, 0],
+                [img_crop_width, 0],
+                [img_crop_width, img_crop_height],
+                [0, img_crop_height],
+            ]
+        ).astype(np.float32)
+        M = cv2.getPerspectiveTransform(bbox_points, pts_std)
+        _, IM = cv2.invert(M)
+
+        new_word_points_list = []
+        for word_points in word_points_list:
+            new_word_points = []
+            for point in word_points:
+                new_point = point
+                if direction == "h":
+                    new_point = self.s_rotate(
+                        math.radians(-90), new_point[0], new_point[1], 0, 0
+                    )
+                    new_point[0] = new_point[0] + img_crop_width
+
+                p = np.float32(new_point + [1])
+                x, y, z = np.dot(IM, p)
+                new_point = [x / z, y / z]
+
+                new_point = [int(new_point[0] + left), int(new_point[1] + top)]
+                new_word_points.append(new_point)
+            new_word_points = self.order_points(new_word_points)
+            new_word_points_list.append(new_word_points)
+        return new_word_points_list
+
+    @staticmethod
+    def s_rotate(angle, valuex, valuey, pointx, pointy):
+        """绕pointx,pointy顺时针旋转
+        https://blog.csdn.net/qq_38826019/article/details/84233397
+        """
+        valuex = np.array(valuex)
+        valuey = np.array(valuey)
+        sRotatex = (
+            (valuex - pointx) * math.cos(angle)
+            + (valuey - pointy) * math.sin(angle)
+            + pointx
+        )
+        sRotatey = (
+            (valuey - pointy) * math.cos(angle)
+            - (valuex - pointx) * math.sin(angle)
+            + pointy
+        )
+        return [sRotatex, sRotatey]
+
+    @staticmethod
+    def order_points(box: List[List[int]]) -> List[List[int]]:
+        """矩形框顺序排列"""
+
+        def convert_to_1x2(p):
+            if p.shape == (2,):
+                return p.reshape((1, 2))
+            elif p.shape == (1, 2):
+                return p
+            else:
+                return p[:1, :]
+
+        box = np.array(box).reshape((-1, 2))
+        center_x, center_y = np.mean(box[:, 0]), np.mean(box[:, 1])
+        if np.any(box[:, 0] == center_x) and np.any(
+            box[:, 1] == center_y
+        ):  # 有两点横坐标相等，有两点纵坐标相等，菱形
+            p1 = box[np.where(box[:, 0] == np.min(box[:, 0]))]
+            p2 = box[np.where(box[:, 1] == np.min(box[:, 1]))]
+            p3 = box[np.where(box[:, 0] == np.max(box[:, 0]))]
+            p4 = box[np.where(box[:, 1] == np.max(box[:, 1]))]
+        elif np.all(box[:, 0] == center_x):  # 四个点的横坐标都相同
+            y_sort = np.argsort(box[:, 1])
+            p1 = box[y_sort[0]]
+            p2 = box[y_sort[1]]
+            p3 = box[y_sort[2]]
+            p4 = box[y_sort[3]]
+        elif np.any(box[:, 0] == center_x) and np.all(
+            box[:, 1] != center_y
+        ):  # 只有两点横坐标相等，先上下再左右
+            p12, p34 = (
+                box[np.where(box[:, 1] < center_y)],
+                box[np.where(box[:, 1] > center_y)],
+            )
+            p1, p2 = (
+                p12[np.where(p12[:, 0] == np.min(p12[:, 0]))],
+                p12[np.where(p12[:, 0] == np.max(p12[:, 0]))],
+            )
+            p3, p4 = (
+                p34[np.where(p34[:, 0] == np.max(p34[:, 0]))],
+                p34[np.where(p34[:, 0] == np.min(p34[:, 0]))],
+            )
+        else:  # 只有两点纵坐标相等，或者是没有相等的，先左右再上下
+            p14, p23 = (
+                box[np.where(box[:, 0] < center_x)],
+                box[np.where(box[:, 0] > center_x)],
+            )
+            p1, p4 = (
+                p14[np.where(p14[:, 1] == np.min(p14[:, 1]))],
+                p14[np.where(p14[:, 1] == np.max(p14[:, 1]))],
+            )
+            p2, p3 = (
+                p23[np.where(p23[:, 1] == np.min(p23[:, 1]))],
+                p23[np.where(p23[:, 1] == np.max(p23[:, 1]))],
+            )
+
+        # 解决单字切割后横坐标完全相同的shape错误
+        p1 = convert_to_1x2(p1)
+        p2 = convert_to_1x2(p2)
+        p3 = convert_to_1x2(p3)
+        p4 = convert_to_1x2(p4)
+        return np.array([p1, p2, p3, p4]).reshape((-1, 2)).tolist()

anyocr/infer_engine.py

@@ -0,0 +1,162 @@
+from typing import Any, Dict, List, Tuple, Union
+import os
+import platform
+import traceback
+from enum import Enum
+from pathlib import Path
+import numpy as np
+from onnxruntime import (
+    GraphOptimizationLevel,
+    InferenceSession,
+    SessionOptions,
+    get_available_providers,
+    get_device,
+)
+
+
+class EP(Enum):
+    CPU_EP = "CPUExecutionProvider"
+    CUDA_EP = "CUDAExecutionProvider"
+    DIRECTML_EP = "DmlExecutionProvider"
+
+
+class OrtInferSession:
+    def __init__(self, config: Dict[str, Any]):
+        self.model_path = config.get("model_path", None)
+        self._verify_model(self.model_path)
+        self.config = config
+        self.cfg_use_cuda = config.get("use_cuda", None)
+        self.cfg_use_dml = config.get("use_dml", None)
+
+        self.had_providers: List[str] = get_available_providers()
+        self.EP_list = self._get_ep_list()
+
+        self.sess_opt = self._init_sess_opts(self.config)
+        self.session = InferenceSession(
+            self.model_path,
+            sess_options=self.sess_opt,
+            providers=self.EP_list,
+        )
+        self._verify_providers()
+
+    @staticmethod
+    def _init_sess_opts(config: Dict[str, Any]) -> SessionOptions:
+        sess_opt = SessionOptions()
+        sess_opt.log_severity_level = 4
+        sess_opt.enable_cpu_mem_arena = False
+        sess_opt.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
+
+        cpu_nums = os.cpu_count()
+        intra_op_num_threads = config.get("intra_op_num_threads", -1)
+        if intra_op_num_threads != -1 and 1 <= intra_op_num_threads <= cpu_nums:
+            sess_opt.intra_op_num_threads = intra_op_num_threads
+
+        inter_op_num_threads = config.get("inter_op_num_threads", -1)
+        if inter_op_num_threads != -1 and 1 <= inter_op_num_threads <= cpu_nums:
+            sess_opt.inter_op_num_threads = inter_op_num_threads
+
+        return sess_opt
+
+    def _get_ep_list(self) -> List[Tuple[str, Dict[str, Any]]]:
+        cpu_provider_opts = {
+            "arena_extend_strategy": "kSameAsRequested",
+        }
+        EP_list = [(EP.CPU_EP.value, cpu_provider_opts)]
+
+        cuda_provider_opts = {
+            "device_id": 0,
+            "arena_extend_strategy": "kNextPowerOfTwo",
+            "cudnn_conv_algo_search": "EXHAUSTIVE",
+            "do_copy_in_default_stream": True,
+        }
+        self.use_cuda = self._check_cuda()
+        if self.use_cuda:
+            EP_list.insert(0, (EP.CUDA_EP.value, cuda_provider_opts))
+
+        self.use_directml = self._check_dml()
+        if self.use_directml:
+            directml_options = (
+                cuda_provider_opts if self.use_cuda else cpu_provider_opts
+            )
+            EP_list.insert(0, (EP.DIRECTML_EP.value, directml_options))
+        return EP_list
+
+    def _check_cuda(self) -> bool:
+        if not self.cfg_use_cuda:
+            return False
+
+        cur_device = get_device()
+        if cur_device == "GPU" and EP.CUDA_EP.value in self.had_providers:
+            return True
+        return False
+
+    def _check_dml(self) -> bool:
+        if not self.cfg_use_dml:
+            return False
+
+        cur_os = platform.system()
+        if cur_os != "Windows":
+            return False
+
+        cur_window_version = int(platform.release().split(".")[0])
+        if cur_window_version < 10:
+            return False
+
+        if EP.DIRECTML_EP.value in self.had_providers:
+            return True
+        return False
+
+    def _verify_providers(self):
+        session_providers = self.session.get_providers()
+        first_provider = session_providers[0]
+
+    def __call__(self, input_content: np.ndarray) -> np.ndarray:
+        try:
+            if not self.session:
+                self.session = InferenceSession(
+                    self.model_path,
+                    sess_options=self.sess_opt,
+                    providers=self.EP_list,
+                )
+                self._verify_providers()                
+            input_dict = dict(zip(self.get_input_names(), [input_content]))
+            res = self.session.run(self.get_output_names(), input_dict)
+            return res
+        except Exception as e:
+            error_info = traceback.format_exc()
+            raise ONNXRuntimeError(error_info) from e
+        finally:
+            del input_dict
+            self.session = None
+
+    def get_input_names(self) -> List[str]:
+        return [v.name for v in self.session.get_inputs()]
+
+    def get_output_names(self) -> List[str]:
+        return [v.name for v in self.session.get_outputs()]
+
+    def get_character_list(self, key: str = "character") -> List[str]:
+        meta_dict = self.session.get_modelmeta().custom_metadata_map
+        return meta_dict[key].splitlines()
+
+    def have_key(self, key: str = "character") -> bool:
+        meta_dict = self.session.get_modelmeta().custom_metadata_map
+        if key in meta_dict.keys():
+            return True
+        return False
+
+    @staticmethod
+    def _verify_model(model_path: Union[str, Path, None]):
+        if model_path is None:
+            raise ValueError("model_path is None!")
+
+        model_path = Path(model_path)
+        if not model_path.exists():
+            raise FileNotFoundError(f"{model_path} does not exists.")
+
+        if not model_path.is_file():
+            raise FileExistsError(f"{model_path} is not a file.")
+
+
+class ONNXRuntimeError(Exception):
+    pass

anyocr/models/anyocr_cls_v4.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e263cad54b85e6dd6ce5aed66154785682a221be2216300ce321e58a9d022296
+size 571423

anyocr/models/anyocr_det_ch_v4_lite.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69ce850fec741a2a4568c7c924bb025c9d4f1129e5f96ab428c799ccc5ef2275
+size 4729474

anyocr/models/anyocr_keys_v4.txt

@@ -0,0 +1,6623 @@
+'
+疗
+绚
+诚
+娇
+溜
+题
+贿
+者
+廖
+更
+纳
+加
+奉
+公
+一
+就
+汴
+计
+与
+路
+房
+原
+妇
+2
+0
+8
+-
+7
+其
+>
+:
+]
+,
+，
+骑
+刈
+全
+消
+昏
+傈
+安
+久
+钟
+嗅
+不
+影
+处
+驽
+蜿
+资
+关
+椤
+地
+瘸
+专
+问
+忖
+票
+嫉
+炎
+韵
+要
+月
+田
+节
+陂
+鄙
+捌
+备
+拳
+伺
+眼
+网
+盎
+大
+傍
+心
+东
+愉
+汇
+蹿
+科
+每
+业
+里
+航
+晏
+字
+平
+录
+先
+1
+3
+彤
+鲶
+产
+稍
+督
+腴
+有
+象
+岳
+注
+绍
+在
+泺
+文
+定
+核
+名
+水
+过
+理
+让
+偷
+率
+等
+这
+发
+”
+为
+含
+肥
+酉
+相
+鄱
+七
+编
+猥
+锛
+日
+镀
+蒂
+掰
+倒
+辆
+栾
+栗
+综
+涩
+州
+雌
+滑
+馀
+了
+机
+块
+司
+宰
+甙
+兴
+矽
+抚
+保
+用
+沧
+秩
+如
+收
+息
+滥
+页
+疑
+埠
+!
+！
+姥
+异
+橹
+钇
+向
+下
+跄
+的
+椴
+沫
+国
+绥
+獠
+报
+开
+民
+蜇
+何
+分
+凇
+长
+讥
+藏
+掏
+施
+羽
+中
+讲
+派
+嘟
+人
+提
+浼
+间
+世
+而
+古
+多
+倪
+唇
+饯
+控
+庚
+首
+赛
+蜓
+味
+断
+制
+觉
+技
+替
+艰
+溢
+潮
+夕
+钺
+外
+摘
+枋
+动
+双
+单
+啮
+户
+枇
+确
+锦
+曜
+杜
+或
+能
+效
+霜
+盒
+然
+侗
+电
+晁
+放
+步
+鹃
+新
+杖
+蜂
+吒
+濂
+瞬
+评
+总
+隍
+对
+独
+合
+也
+是
+府
+青
+天
+诲
+墙
+组
+滴
+级
+邀
+帘
+示
+已
+时
+骸
+仄
+泅
+和
+遨
+店
+雇
+疫
+持
+巍
+踮
+境
+只
+亨
+目
+鉴
+崤
+闲
+体
+泄
+杂
+作
+般
+轰
+化
+解
+迂
+诿
+蛭
+璀
+腾
+告
+版
+服
+省
+师
+小
+规
+程
+线
+海
+办
+引
+二
+桧
+牌
+砺
+洄
+裴
+修
+图
+痫
+胡
+许
+犊
+事
+郛
+基
+柴
+呼
+食
+研
+奶
+律
+蛋
+因
+葆
+察
+戏
+褒
+戒
+再
+李
+骁
+工
+貂
+油
+鹅
+章
+啄
+休
+场
+给
+睡
+纷
+豆
+器
+捎
+说
+敏
+学
+会
+浒
+设
+诊
+格
+廓
+查
+来
+霓
+室
+溆
+￠
+诡
+寥
+焕
+舜
+柒
+狐
+回
+戟
+砾
+厄
+实
+翩
+尿
+五
+入
+径
+惭
+喹
+股
+宇
+篝
+|
+;
+美
+期
+云
+九
+祺
+扮
+靠
+锝
+槌
+系
+企
+酰
+阊
+暂
+蚕
+忻
+豁
+本
+羹
+执
+条
+钦
+H
+獒
+限
+进
+季
+楦
+于
+芘
+玖
+铋
+茯
+未
+答
+粘
+括
+样
+精
+欠
+矢
+甥
+帷
+嵩
+扣
+令
+仔
+风
+皈
+行
+支
+部
+蓉
+刮
+站
+蜡
+救
+钊
+汗
+松
+嫌
+成
+可
+.
+鹤
+院
+从
+交
+政
+怕
+活
+调
+球
+局
+验
+髌
+第
+韫
+谗
+串
+到
+圆
+年
+米
+/
+*
+友
+忿
+检
+区
+看
+自
+敢
+刃
+个
+兹
+弄
+流
+留
+同
+没
+齿
+星
+聆
+轼
+湖
+什
+三
+建
+蛔
+儿
+椋
+汕
+震
+颧
+鲤
+跟
+力
+情
+璺
+铨
+陪
+务
+指
+族
+训
+滦
+鄣
+濮
+扒
+商
+箱
+十
+召
+慷
+辗
+所
+莞
+管
+护
+臭
+横
+硒
+嗓
+接
+侦
+六
+露
+党
+馋
+驾
+剖
+高
+侬
+妪
+幂
+猗
+绺
+骐
+央
+酐
+孝
+筝
+课
+徇
+缰
+门
+男
+西
+项
+句
+谙
+瞒
+秃
+篇
+教
+碲
+罚
+声
+呐
+景
+前
+富
+嘴
+鳌
+稀
+免
+朋
+啬
+睐
+去
+赈
+鱼
+住
+肩
+愕
+速
+旁
+波
+厅
+健
+茼
+厥
+鲟
+谅
+投
+攸
+炔
+数
+方
+击
+呋
+谈
+绩
+别
+愫
+僚
+躬
+鹧
+胪
+炳
+招
+喇
+膨
+泵
+蹦
+毛
+结
+5
+4
+谱
+识
+陕
+粽
+婚
+拟
+构
+且
+搜
+任
+潘
+比
+郢
+妨
+醪
+陀
+桔
+碘
+扎
+选
+哈
+骷
+楷
+亿
+明
+缆
+脯
+监
+睫
+逻
+婵
+共
+赴
+淝
+凡
+惦
+及
+达
+揖
+谩
+澹
+减
+焰
+蛹
+番
+祁
+柏
+员
+禄
+怡
+峤
+龙
+白
+叽
+生
+闯
+起
+细
+装
+谕
+竟
+聚
+钙
+上
+导
+渊
+按
+艾
+辘
+挡
+耒
+盹
+饪
+臀
+记
+邮
+蕙
+受
+各
+医
+搂
+普
+滇
+朗
+茸
+带
+翻
+酚
+(
+光
+堤
+墟
+蔷
+万
+幻
+〓
+瑙
+辈
+昧
+盏
+亘
+蛀
+吉
+铰
+请
+子
+假
+闻
+税
+井
+诩
+哨
+嫂
+好
+面
+琐
+校
+馊
+鬣
+缂
+营
+访
+炖
+占
+农
+缀
+否
+经
+钚
+棵
+趟
+张
+亟
+吏
+茶
+谨
+捻
+论
+迸
+堂
+玉
+信
+吧
+瞠
+乡
+姬
+寺
+咬
+溏
+苄
+皿
+意
+赉
+宝
+尔
+钰
+艺
+特
+唳
+踉
+都
+荣
+倚
+登
+荐
+丧
+奇
+涵
+批
+炭
+近
+符
+傩
+感
+道
+着
+菊
+虹
+仲
+众
+懈
+濯
+颞
+眺
+南
+释
+北
+缝
+标
+既
+茗
+整
+撼
+迤
+贲
+挎
+耱
+拒
+某
+妍
+卫
+哇
+英
+矶
+藩
+治
+他
+元
+领
+膜
+遮
+穗
+蛾
+飞
+荒
+棺
+劫
+么
+市
+火
+温
+拈
+棚
+洼
+转
+果
+奕
+卸
+迪
+伸
+泳
+斗
+邡
+侄
+涨
+屯
+萋
+胭
+氡
+崮
+枞
+惧
+冒
+彩
+斜
+手
+豚
+随
+旭
+淑
+妞
+形
+菌
+吲
+沱
+争
+驯
+歹
+挟
+兆
+柱
+传
+至
+包
+内
+响
+临
+红
+功
+弩
+衡
+寂
+禁
+老
+棍
+耆
+渍
+织
+害
+氵
+渑
+布
+载
+靥
+嗬
+虽
+苹
+咨
+娄
+库
+雉
+榜
+帜
+嘲
+套
+瑚
+亲
+簸
+欧
+边
+6
+腿
+旮
+抛
+吹
+瞳
+得
+镓
+梗
+厨
+继
+漾
+愣
+憨
+士
+策
+窑
+抑
+躯
+襟
+脏
+参
+贸
+言
+干
+绸
+鳄
+穷
+藜
+音
+折
+详
+)
+举
+悍
+甸
+癌
+黎
+谴
+死
+罩
+迁
+寒
+驷
+袖
+媒
+蒋
+掘
+模
+纠
+恣
+观
+祖
+蛆
+碍
+位
+稿
+主
+澧
+跌
+筏
+京
+锏
+帝
+贴
+证
+糠
+才
+黄
+鲸
+略
+炯
+饱
+四
+出
+园
+犀
+牧
+容
+汉
+杆
+浈
+汰
+瑷
+造
+虫
+瘩
+怪
+驴
+济
+应
+花
+沣
+谔
+夙
+旅
+价
+矿
+以
+考
+s
+u
+呦
+晒
+巡
+茅
+准
+肟
+瓴
+詹
+仟
+褂
+译
+桌
+混
+宁
+怦
+郑
+抿
+些
+余
+鄂
+饴
+攒
+珑
+群
+阖
+岔
+琨
+藓
+预
+环
+洮
+岌
+宀
+杲
+瀵
+最
+常
+囡
+周
+踊
+女
+鼓
+袭
+喉
+简
+范
+薯
+遐
+疏
+粱
+黜
+禧
+法
+箔
+斤
+遥
+汝
+奥
+直
+贞
+撑
+置
+绱
+集
+她
+馅
+逗
+钧
+橱
+魉
+[
+恙
+躁
+唤
+9
+旺
+膘
+待
+脾
+惫
+购
+吗
+依
+盲
+度
+瘿
+蠖
+俾
+之
+镗
+拇
+鲵
+厝
+簧
+续
+款
+展
+啃
+表
+剔
+品
+钻
+腭
+损
+清
+锶
+统
+涌
+寸
+滨
+贪
+链
+吠
+冈
+伎
+迥
+咏
+吁
+览
+防
+迅
+失
+汾
+阔
+逵
+绀
+蔑
+列
+川
+凭
+努
+熨
+揪
+利
+俱
+绉
+抢
+鸨
+我
+即
+责
+膦
+易
+毓
+鹊
+刹
+玷
+岿
+空
+嘞
+绊
+排
+术
+估
+锷
+违
+们
+苟
+铜
+播
+肘
+件
+烫
+审
+鲂
+广
+像
+铌
+惰
+铟
+巳
+胍
+鲍
+康
+憧
+色
+恢
+想
+拷
+尤
+疳
+知
+S
+Y
+F
+D
+A
+峄
+裕
+帮
+握
+搔
+氐
+氘
+难
+墒
+沮
+雨
+叁
+缥
+悴
+藐
+湫
+娟
+苑
+稠
+颛
+簇
+后
+阕
+闭
+蕤
+缚
+怎
+佞
+码
+嘤
+蔡
+痊
+舱
+螯
+帕
+赫
+昵
+升
+烬
+岫
+、
+疵
+蜻
+髁
+蕨
+隶
+烛
+械
+丑
+盂
+梁
+强
+鲛
+由
+拘
+揉
+劭
+龟
+撤
+钩
+呕
+孛
+费
+妻
+漂
+求
+阑
+崖
+秤
+甘
+通
+深
+补
+赃
+坎
+床
+啪
+承
+吼
+量
+暇
+钼
+烨
+阂
+擎
+脱
+逮
+称
+P
+神
+属
+矗
+华
+届
+狍
+葑
+汹
+育
+患
+窒
+蛰
+佼
+静
+槎
+运
+鳗
+庆
+逝
+曼
+疱
+克
+代
+官
+此
+麸
+耧
+蚌
+晟
+例
+础
+榛
+副
+测
+唰
+缢
+迹
+灬
+霁
+身
+岁
+赭
+扛
+又
+菡
+乜
+雾
+板
+读
+陷
+徉
+贯
+郁
+虑
+变
+钓
+菜
+圾
+现
+琢
+式
+乐
+维
+渔
+浜
+左
+吾
+脑
+钡
+警
+T
+啵
+拴
+偌
+漱
+湿
+硕
+止
+骼
+魄
+积
+燥
+联
+踢
+玛
+则
+窿
+见
+振
+畿
+送
+班
+钽
+您
+赵
+刨
+印
+讨
+踝
+籍
+谡
+舌
+崧
+汽
+蔽
+沪
+酥
+绒
+怖
+财
+帖
+肱
+私
+莎
+勋
+羔
+霸
+励
+哼
+帐
+将
+帅
+渠
+纪
+婴
+娩
+岭
+厘
+滕
+吻
+伤
+坝
+冠
+戊
+隆
+瘁
+介
+涧
+物
+黍
+并
+姗
+奢
+蹑
+掣
+垸
+锴
+命
+箍
+捉
+病
+辖
+琰
+眭
+迩
+艘
+绌
+繁
+寅
+若
+毋
+思
+诉
+类
+诈
+燮
+轲
+酮
+狂
+重
+反
+职
+筱
+县
+委
+磕
+绣
+奖
+晋
+濉
+志
+徽
+肠
+呈
+獐
+坻
+口
+片
+碰
+几
+村
+柿
+劳
+料
+获
+亩
+惕
+晕
+厌
+号
+罢
+池
+正
+鏖
+煨
+家
+棕
+复
+尝
+懋
+蜥
+锅
+岛
+扰
+队
+坠
+瘾
+钬
+@
+卧
+疣
+镇
+譬
+冰
+彷
+频
+黯
+据
+垄
+采
+八
+缪
+瘫
+型
+熹
+砰
+楠
+襁
+箐
+但
+嘶
+绳
+啤
+拍
+盥
+穆
+傲
+洗
+盯
+塘
+怔
+筛
+丿
+台
+恒
+喂
+葛
+永
+￥
+烟
+酒
+桦
+书
+砂
+蚝
+缉
+态
+瀚
+袄
+圳
+轻
+蛛
+超
+榧
+遛
+姒
+奘
+铮
+右
+荽
+望
+偻
+卡
+丶
+氰
+附
+做
+革
+索
+戚
+坨
+桷
+唁
+垅
+榻
+岐
+偎
+坛
+莨
+山
+殊
+微
+骇
+陈
+爨
+推
+嗝
+驹
+澡
+藁
+呤
+卤
+嘻
+糅
+逛
+侵
+郓
+酌
+德
+摇
+※
+鬃
+被
+慨
+殡
+羸
+昌
+泡
+戛
+鞋
+河
+宪
+沿
+玲
+鲨
+翅
+哽
+源
+铅
+语
+照
+邯
+址
+荃
+佬
+顺
+鸳
+町
+霭
+睾
+瓢
+夸
+椁
+晓
+酿
+痈
+咔
+侏
+券
+噎
+湍
+签
+嚷
+离
+午
+尚
+社
+锤
+背
+孟
+使
+浪
+缦
+潍
+鞅
+军
+姹
+驶
+笑
+鳟
+鲁
+》
+孽
+钜
+绿
+洱
+礴
+焯
+椰
+颖
+囔
+乌
+孔
+巴
+互
+性
+椽
+哞
+聘
+昨
+早
+暮
+胶
+炀
+隧
+低
+彗
+昝
+铁
+呓
+氽
+藉
+喔
+癖
+瑗
+姨
+权
+胱
+韦
+堑
+蜜
+酋
+楝
+砝
+毁
+靓
+歙
+锲
+究
+屋
+喳
+骨
+辨
+碑
+武
+鸠
+宫
+辜
+烊
+适
+坡
+殃
+培
+佩
+供
+走
+蜈
+迟
+翼
+况
+姣
+凛
+浔
+吃
+飘
+债
+犟
+金
+促
+苛
+崇
+坂
+莳
+畔
+绂
+兵
+蠕
+斋
+根
+砍
+亢
+欢
+恬
+崔
+剁
+餐
+榫
+快
+扶
+‖
+濒
+缠
+鳜
+当
+彭
+驭
+浦
+篮
+昀
+锆
+秸
+钳
+弋
+娣
+瞑
+夷
+龛
+苫
+拱
+致
+%
+嵊
+障
+隐
+弑
+初
+娓
+抉
+汩
+累
+蓖
+"
+唬
+助
+苓
+昙
+押
+毙
+破
+城
+郧
+逢
+嚏
+獭
+瞻
+溱
+婿
+赊
+跨
+恼
+璧
+萃
+姻
+貉
+灵
+炉
+密
+氛
+陶
+砸
+谬
+衔
+点
+琛
+沛
+枳
+层
+岱
+诺
+脍
+榈
+埂
+征
+冷
+裁
+打
+蹴
+素
+瘘
+逞
+蛐
+聊
+激
+腱
+萘
+踵
+飒
+蓟
+吆
+取
+咙
+簋
+涓
+矩
+曝
+挺
+揣
+座
+你
+史
+舵
+焱
+尘
+苏
+笈
+脚
+溉
+榨
+诵
+樊
+邓
+焊
+义
+庶
+儋
+蟋
+蒲
+赦
+呷
+杞
+诠
+豪
+还
+试
+颓
+茉
+太
+除
+紫
+逃
+痴
+草
+充
+鳕
+珉
+祗
+墨
+渭
+烩
+蘸
+慕
+璇
+镶
+穴
+嵘
+恶
+骂
+险
+绋
+幕
+碉
+肺
+戳
+刘
+潞
+秣
+纾
+潜
+銮
+洛
+须
+罘
+销
+瘪
+汞
+兮
+屉
+r
+林
+厕
+质
+探
+划
+狸
+殚
+善
+煊
+烹
+〒
+锈
+逯
+宸
+辍
+泱
+柚
+袍
+远
+蹋
+嶙
+绝
+峥
+娥
+缍
+雀
+徵
+认
+镱
+谷
+=
+贩
+勉
+撩
+鄯
+斐
+洋
+非
+祚
+泾
+诒
+饿
+撬
+威
+晷
+搭
+芍
+锥
+笺
+蓦
+候
+琊
+档
+礁
+沼
+卵
+荠
+忑
+朝
+凹
+瑞
+头
+仪
+弧
+孵
+畏
+铆
+突
+衲
+车
+浩
+气
+茂
+悖
+厢
+枕
+酝
+戴
+湾
+邹
+飚
+攘
+锂
+写
+宵
+翁
+岷
+无
+喜
+丈
+挑
+嗟
+绛
+殉
+议
+槽
+具
+醇
+淞
+笃
+郴
+阅
+饼
+底
+壕
+砚
+弈
+询
+缕
+庹
+翟
+零
+筷
+暨
+舟
+闺
+甯
+撞
+麂
+茌
+蔼
+很
+珲
+捕
+棠
+角
+阉
+媛
+娲
+诽
+剿
+尉
+爵
+睬
+韩
+诰
+匣
+危
+糍
+镯
+立
+浏
+阳
+少
+盆
+舔
+擘
+匪
+申
+尬
+铣
+旯
+抖
+赘
+瓯
+居
+ˇ
+哮
+游
+锭
+茏
+歌
+坏
+甚
+秒
+舞
+沙
+仗
+劲
+潺
+阿
+燧
+郭
+嗖
+霏
+忠
+材
+奂
+耐
+跺
+砀
+输
+岖
+媳
+氟
+极
+摆
+灿
+今
+扔
+腻
+枝
+奎
+药
+熄
+吨
+话
+q
+额
+慑
+嘌
+协
+喀
+壳
+埭
+视
+著
+於
+愧
+陲
+翌
+峁
+颅
+佛
+腹
+聋
+侯
+咎
+叟
+秀
+颇
+存
+较
+罪
+哄
+岗
+扫
+栏
+钾
+羌
+己
+璨
+枭
+霉
+煌
+涸
+衿
+键
+镝
+益
+岢
+奏
+连
+夯
+睿
+冥
+均
+糖
+狞
+蹊
+稻
+爸
+刿
+胥
+煜
+丽
+肿
+璃
+掸
+跚
+灾
+垂
+樾
+濑
+乎
+莲
+窄
+犹
+撮
+战
+馄
+软
+络
+显
+鸢
+胸
+宾
+妲
+恕
+埔
+蝌
+份
+遇
+巧
+瞟
+粒
+恰
+剥
+桡
+博
+讯
+凯
+堇
+阶
+滤
+卖
+斌
+骚
+彬
+兑
+磺
+樱
+舷
+两
+娱
+福
+仃
+差
+找
+桁
+÷
+净
+把
+阴
+污
+戬
+雷
+碓
+蕲
+楚
+罡
+焖
+抽
+妫
+咒
+仑
+闱
+尽
+邑
+菁
+爱
+贷
+沥
+鞑
+牡
+嗉
+崴
+骤
+塌
+嗦
+订
+拮
+滓
+捡
+锻
+次
+坪
+杩
+臃
+箬
+融
+珂
+鹗
+宗
+枚
+降
+鸬
+妯
+阄
+堰
+盐
+毅
+必
+杨
+崃
+俺
+甬
+状
+莘
+货
+耸
+菱
+腼
+铸
+唏
+痤
+孚
+澳
+懒
+溅
+翘
+疙
+杷
+淼
+缙
+骰
+喊
+悉
+砻
+坷
+艇
+赁
+界
+谤
+纣
+宴
+晃
+茹
+归
+饭
+梢
+铡
+街
+抄
+肼
+鬟
+苯
+颂
+撷
+戈
+炒
+咆
+茭
+瘙
+负
+仰
+客
+琉
+铢
+封
+卑
+珥
+椿
+镧
+窨
+鬲
+寿
+御
+袤
+铃
+萎
+砖
+餮
+脒
+裳
+肪
+孕
+嫣
+馗
+嵇
+恳
+氯
+江
+石
+褶
+冢
+祸
+阻
+狈
+羞
+银
+靳
+透
+咳
+叼
+敷
+芷
+啥
+它
+瓤
+兰
+痘
+懊
+逑
+肌
+往
+捺
+坊
+甩
+呻
+〃
+沦
+忘
+膻
+祟
+菅
+剧
+崆
+智
+坯
+臧
+霍
+墅
+攻
+眯
+倘
+拢
+骠
+铐
+庭
+岙
+瓠
+′
+缺
+泥
+迢
+捶
+?
+？
+郏
+喙
+掷
+沌
+纯
+秘
+种
+听
+绘
+固
+螨
+团
+香
+盗
+妒
+埚
+蓝
+拖
+旱
+荞
+铀
+血
+遏
+汲
+辰
+叩
+拽
+幅
+硬
+惶
+桀
+漠
+措
+泼
+唑
+齐
+肾
+念
+酱
+虚
+屁
+耶
+旗
+砦
+闵
+婉
+馆
+拭
+绅
+韧
+忏
+窝
+醋
+葺
+顾
+辞
+倜
+堆
+辋
+逆
+玟
+贱
+疾
+董
+惘
+倌
+锕
+淘
+嘀
+莽
+俭
+笏
+绑
+鲷
+杈
+择
+蟀
+粥
+嗯
+驰
+逾
+案
+谪
+褓
+胫
+哩
+昕
+颚
+鲢
+绠
+躺
+鹄
+崂
+儒
+俨
+丝
+尕
+泌
+啊
+萸
+彰
+幺
+吟
+骄
+苣
+弦
+脊
+瑰
+〈
+诛
+镁
+析
+闪
+剪
+侧
+哟
+框
+螃
+守
+嬗
+燕
+狭
+铈
+缮
+概
+迳
+痧
+鲲
+俯
+售
+笼
+痣
+扉
+挖
+满
+咋
+援
+邱
+扇
+歪
+便
+玑
+绦
+峡
+蛇
+叨
+〖
+泽
+胃
+斓
+喋
+怂
+坟
+猪
+该
+蚬
+炕
+弥
+赞
+棣
+晔
+娠
+挲
+狡
+创
+疖
+铕
+镭
+稷
+挫
+弭
+啾
+翔
+粉
+履
+苘
+哦
+楼
+秕
+铂
+土
+锣
+瘟
+挣
+栉
+习
+享
+桢
+袅
+磨
+桂
+谦
+延
+坚
+蔚
+噗
+署
+谟
+猬
+钎
+恐
+嬉
+雒
+倦
+衅
+亏
+璩
+睹
+刻
+殿
+王
+算
+雕
+麻
+丘
+柯
+骆
+丸
+塍
+谚
+添
+鲈
+垓
+桎
+蚯
+芥
+予
+飕
+镦
+谌
+窗
+醚
+菀
+亮
+搪
+莺
+蒿
+羁
+足
+J
+真
+轶
+悬
+衷
+靛
+翊
+掩
+哒
+炅
+掐
+冼
+妮
+l
+谐
+稚
+荆
+擒
+犯
+陵
+虏
+浓
+崽
+刍
+陌
+傻
+孜
+千
+靖
+演
+矜
+钕
+煽
+杰
+酗
+渗
+伞
+栋
+俗
+泫
+戍
+罕
+沾
+疽
+灏
+煦
+芬
+磴
+叱
+阱
+榉
+湃
+蜀
+叉
+醒
+彪
+租
+郡
+篷
+屎
+良
+垢
+隗
+弱
+陨
+峪
+砷
+掴
+颁
+胎
+雯
+绵
+贬
+沐
+撵
+隘
+篙
+暖
+曹
+陡
+栓
+填
+臼
+彦
+瓶
+琪
+潼
+哪
+鸡
+摩
+啦
+俟
+锋
+域
+耻
+蔫
+疯
+纹
+撇
+毒
+绶
+痛
+酯
+忍
+爪
+赳
+歆
+嘹
+辕
+烈
+册
+朴
+钱
+吮
+毯
+癜
+娃
+谀
+邵
+厮
+炽
+璞
+邃
+丐
+追
+词
+瓒
+忆
+轧
+芫
+谯
+喷
+弟
+半
+冕
+裙
+掖
+墉
+绮
+寝
+苔
+势
+顷
+褥
+切
+衮
+君
+佳
+嫒
+蚩
+霞
+佚
+洙
+逊
+镖
+暹
+唛
+&
+殒
+顶
+碗
+獗
+轭
+铺
+蛊
+废
+恹
+汨
+崩
+珍
+那
+杵
+曲
+纺
+夏
+薰
+傀
+闳
+淬
+姘
+舀
+拧
+卷
+楂
+恍
+讪
+厩
+寮
+篪
+赓
+乘
+灭
+盅
+鞣
+沟
+慎
+挂
+饺
+鼾
+杳
+树
+缨
+丛
+絮
+娌
+臻
+嗳
+篡
+侩
+述
+衰
+矛
+圈
+蚜
+匕
+筹
+匿
+濞
+晨
+叶
+骋
+郝
+挚
+蚴
+滞
+增
+侍
+描
+瓣
+吖
+嫦
+蟒
+匾
+圣
+赌
+毡
+癞
+恺
+百
+曳
+需
+篓
+肮
+庖
+帏
+卿
+驿
+遗
+蹬
+鬓
+骡
+歉
+芎
+胳
+屐
+禽
+烦
+晌
+寄
+媾
+狄
+翡
+苒
+船
+廉
+终
+痞
+殇
+々
+畦
+饶
+改
+拆
+悻
+萄
+￡
+瓿
+乃
+訾
+桅
+匮
+溧
+拥
+纱
+铍
+骗
+蕃
+龋
+缬
+父
+佐
+疚
+栎
+醍
+掳
+蓄
+x
+惆
+颜
+鲆
+榆
+〔
+猎
+敌
+暴
+谥
+鲫
+贾
+罗
+玻
+缄
+扦
+芪
+癣
+落
+徒
+臾
+恿
+猩
+托
+邴
+肄
+牵
+春
+陛
+耀
+刊
+拓
+蓓
+邳
+堕
+寇
+枉
+淌
+啡
+湄
+兽
+酷
+萼
+碚
+濠
+萤
+夹
+旬
+戮
+梭
+琥
+椭
+昔
+勺
+蜊
+绐
+晚
+孺
+僵
+宣
+摄
+冽
+旨
+萌
+忙
+蚤
+眉
+噼
+蟑
+付
+契
+瓜
+悼
+颡
+壁
+曾
+窕
+颢
+澎
+仿
+俑
+浑
+嵌
+浣
+乍
+碌
+褪
+乱
+蔟
+隙
+玩
+剐
+葫
+箫
+纲
+围
+伐
+决
+伙
+漩
+瑟
+刑
+肓
+镳
+缓
+蹭
+氨
+皓
+典
+畲
+坍
+铑
+檐
+塑
+洞
+倬
+储
+胴
+淳
+戾
+吐
+灼
+惺
+妙
+毕
+珐
+缈
+虱
+盖
+羰
+鸿
+磅
+谓
+髅
+娴
+苴
+唷
+蚣
+霹
+抨
+贤
+唠
+犬
+誓
+逍
+庠
+逼
+麓
+籼
+釉
+呜
+碧
+秧
+氩
+摔
+霄
+穸
+纨
+辟
+妈
+映
+完
+牛
+缴
+嗷
+炊
+恩
+荔
+茆
+掉
+紊
+慌
+莓
+羟
+阙
+萁
+磐
+另
+蕹
+辱
+鳐
+湮
+吡
+吩
+唐
+睦
+垠
+舒
+圜
+冗
+瞿
+溺
+芾
+囱
+匠
+僳
+汐
+菩
+饬
+漓
+黑
+霰
+浸
+濡
+窥
+毂
+蒡
+兢
+驻
+鹉
+芮
+诙
+迫
+雳
+厂
+忐
+臆
+猴
+鸣
+蚪
+栈
+箕
+羡
+渐
+莆
+捍
+眈
+哓
+趴
+蹼
+埕
+嚣
+骛
+宏
+淄
+斑
+噜
+严
+瑛
+垃
+椎
+诱
+压
+庾
+绞
+焘
+廿
+抡
+迄
+棘
+夫
+纬
+锹
+眨
+瞌
+侠
+脐
+竞
+瀑
+孳
+骧
+遁
+姜
+颦
+荪
+滚
+萦
+伪
+逸
+粳
+爬
+锁
+矣
+役
+趣
+洒
+颔
+诏
+逐
+奸
+甭
+惠
+攀
+蹄
+泛
+尼
+拼
+阮
+鹰
+亚
+颈
+惑
+勒
+〉
+际
+肛
+爷
+刚
+钨
+丰
+养
+冶
+鲽
+辉
+蔻
+画
+覆
+皴
+妊
+麦
+返
+醉
+皂
+擀
+〗
+酶
+凑
+粹
+悟
+诀
+硖
+港
+卜
+z
+杀
+涕
+±
+舍
+铠
+抵
+弛
+段
+敝
+镐
+奠
+拂
+轴
+跛
+袱
+e
+t
+沉
+菇
+俎
+薪
+峦
+秭
+蟹
+历
+盟
+菠
+寡
+液
+肢
+喻
+染
+裱
+悱
+抱
+氙
+赤
+捅
+猛
+跑
+氮
+谣
+仁
+尺
+辊
+窍
+烙
+衍
+架
+擦
+倏
+璐
+瑁
+币
+楞
+胖
+夔
+趸
+邛
+惴
+饕
+虔
+蝎
+§
+哉
+贝
+宽
+辫
+炮
+扩
+饲
+籽
+魏
+菟
+锰
+伍
+猝
+末
+琳
+哚
+蛎
+邂
+呀
+姿
+鄞
+却
+歧
+仙
+恸
+椐
+森
+牒
+寤
+袒
+婆
+虢
+雅
+钉
+朵
+贼
+欲
+苞
+寰
+故
+龚
+坭
+嘘
+咫
+礼
+硷
+兀
+睢
+汶
+’
+铲
+烧
+绕
+诃
+浃
+钿
+哺
+柜
+讼
+颊
+璁
+腔
+洽
+咐
+脲
+簌
+筠
+镣
+玮
+鞠
+谁
+兼
+姆
+挥
+梯
+蝴
+谘
+漕
+刷
+躏
+宦
+弼
+b
+垌
+劈
+麟
+莉
+揭
+笙
+渎
+仕
+嗤
+仓
+配
+怏
+抬
+错
+泯
+镊
+孰
+猿
+邪
+仍
+秋
+鼬
+壹
+歇
+吵
+炼
+<
+尧
+射
+柬
+廷
+胧
+霾
+凳
+隋
+肚
+浮
+梦
+祥
+株
+堵
+退
+L
+鹫
+跎
+凶
+毽
+荟
+炫
+栩
+玳
+甜
+沂
+鹿
+顽
+伯
+爹
+赔
+蛴
+徐
+匡
+欣
+狰
+缸
+雹
+蟆
+疤
+默
+沤
+啜
+痂
+衣
+禅
+w
+i
+h
+辽
+葳
+黝
+钗
+停
+沽
+棒
+馨
+颌
+肉
+吴
+硫
+悯
+劾
+娈
+马
+啧
+吊
+悌
+镑
+峭
+帆
+瀣
+涉
+咸
+疸
+滋
+泣
+翦
+拙
+癸
+钥
+蜒
++
+尾
+庄
+凝
+泉
+婢
+渴
+谊
+乞
+陆
+锉
+糊
+鸦
+淮
+I
+B
+N
+晦
+弗
+乔
+庥
+葡
+尻
+席
+橡
+傣
+渣
+拿
+惩
+麋
+斛
+缃
+矮
+蛏
+岘
+鸽
+姐
+膏
+催
+奔
+镒
+喱
+蠡
+摧
+钯
+胤
+柠
+拐
+璋
+鸥
+卢
+荡
+倾
+^
+_
+珀
+逄
+萧
+塾
+掇
+贮
+笆
+聂
+圃
+冲
+嵬
+M
+滔
+笕
+值
+炙
+偶
+蜱
+搐
+梆
+汪
+蔬
+腑
+鸯
+蹇
+敞
+绯
+仨
+祯
+谆
+梧
+糗
+鑫
+啸
+豺
+囹
+猾
+巢
+柄
+瀛
+筑
+踌
+沭
+暗
+苁
+鱿
+蹉
+脂
+蘖
+牢
+热
+木
+吸
+溃
+宠
+序
+泞
+偿
+拜
+檩
+厚
+朐
+毗
+螳
+吞
+媚
+朽
+担
+蝗
+橘
+畴
+祈
+糟
+盱
+隼
+郜
+惜
+珠
+裨
+铵
+焙
+琚
+唯
+咚
+噪
+骊
+丫
+滢
+勤
+棉
+呸
+咣
+淀
+隔
+蕾
+窈
+饨
+挨
+煅
+短
+匙
+粕
+镜
+赣
+撕
+墩
+酬
+馁
+豌
+颐
+抗
+酣
+氓
+佑
+搁
+哭
+递
+耷
+涡
+桃
+贻
+碣
+截
+瘦
+昭
+镌
+蔓
+氚
+甲
+猕
+蕴
+蓬
+散
+拾
+纛
+狼
+猷
+铎
+埋
+旖
+矾
+讳
+囊
+糜
+迈
+粟
+蚂
+紧
+鲳
+瘢
+栽
+稼
+羊
+锄
+斟
+睁
+桥
+瓮
+蹙
+祉
+醺
+鼻
+昱
+剃
+跳
+篱
+跷
+蒜
+翎
+宅
+晖
+嗑
+壑
+峻
+癫
+屏
+狠
+陋
+袜
+途
+憎
+祀
+莹
+滟
+佶
+溥
+臣
+约
+盛
+峰
+磁
+慵
+婪
+拦
+莅
+朕
+鹦
+粲
+裤
+哎
+疡
+嫖
+琵
+窟
+堪
+谛
+嘉
+儡
+鳝
+斩
+郾
+驸
+酊
+妄
+胜
+贺
+徙
+傅
+噌
+钢
+栅
+庇
+恋
+匝
+巯
+邈
+尸
+锚
+粗
+佟
+蛟
+薹
+纵
+蚊
+郅
+绢
+锐
+苗
+俞
+篆
+淆
+膀
+鲜
+煎
+诶
+秽
+寻
+涮
+刺
+怀
+噶
+巨
+褰
+魅
+灶
+灌
+桉
+藕
+谜
+舸
+薄
+搀
+恽
+借
+牯
+痉
+渥
+愿
+亓
+耘
+杠
+柩
+锔
+蚶
+钣
+珈
+喘
+蹒
+幽
+赐
+稗
+晤
+莱
+泔
+扯
+肯
+菪
+裆
+腩
+豉
+疆
+骜
+腐
+倭
+珏
+唔
+粮
+亡
+润
+慰
+伽
+橄
+玄
+誉
+醐
+胆
+龊
+粼
+塬
+陇
+彼
+削
+嗣
+绾
+芽
+妗
+垭
+瘴
+爽
+薏
+寨
+龈
+泠
+弹
+赢
+漪
+猫
+嘧
+涂
+恤
+圭
+茧
+烽
+屑
+痕
+巾
+赖
+荸
+凰
+腮
+畈
+亵
+蹲
+偃
+苇
+澜
+艮
+换
+骺
+烘
+苕
+梓
+颉
+肇
+哗
+悄
+氤
+涠
+葬
+屠
+鹭
+植
+竺
+佯
+诣
+鲇
+瘀
+鲅
+邦
+移
+滁
+冯
+耕
+癔
+戌
+茬
+沁
+巩
+悠
+湘
+洪
+痹
+锟
+循
+谋
+腕
+鳃
+钠
+捞
+焉
+迎
+碱
+伫
+急
+榷
+奈
+邝
+卯
+辄
+皲
+卟
+醛
+畹
+忧
+稳
+雄
+昼
+缩
+阈
+睑
+扌
+耗
+曦
+涅
+捏
+瞧
+邕
+淖
+漉
+铝
+耦
+禹
+湛
+喽
+莼
+琅
+诸
+苎
+纂
+硅
+始
+嗨
+傥
+燃
+臂
+赅
+嘈
+呆
+贵
+屹
+壮
+肋
+亍
+蚀
+卅
+豹
+腆
+邬
+迭
+浊
+}
+童
+螂
+捐
+圩
+勐
+触
+寞
+汊
+壤
+荫
+膺
+渌
+芳
+懿
+遴
+螈
+泰
+蓼
+蛤
+茜
+舅
+枫
+朔
+膝
+眙
+避
+梅
+判
+鹜
+璜
+牍
+缅
+垫
+藻
+黔
+侥
+惚
+懂
+踩
+腰
+腈
+札
+丞
+唾
+慈
+顿
+摹
+荻
+琬
+~
+斧
+沈
+滂
+胁
+胀
+幄
+莜
+Z
+匀
+鄄
+掌
+绰
+茎
+焚
+赋
+萱
+谑
+汁
+铒
+瞎
+夺
+蜗
+野
+娆
+冀
+弯
+篁
+懵
+灞
+隽
+芡
+脘
+俐
+辩
+芯
+掺
+喏
+膈
+蝈
+觐
+悚
+踹
+蔗
+熠
+鼠
+呵
+抓
+橼
+峨
+畜
+缔
+禾
+崭
+弃
+熊
+摒
+凸
+拗
+穹
+蒙
+抒
+祛
+劝
+闫
+扳
+阵
+醌
+踪
+喵
+侣
+搬
+仅
+荧
+赎
+蝾
+琦
+买
+婧
+瞄
+寓
+皎
+冻
+赝
+箩
+莫
+瞰
+郊
+笫
+姝
+筒
+枪
+遣
+煸
+袋
+舆
+痱
+涛
+母
+〇
+启
+践
+耙
+绲
+盘
+遂
+昊
+搞
+槿
+诬
+纰
+泓
+惨
+檬
+亻
+越
+C
+o
+憩
+熵
+祷
+钒
+暧
+塔
+阗
+胰
+咄
+娶
+魔
+琶
+钞
+邻
+扬
+杉
+殴
+咽
+弓
+〆
+髻
+】
+吭
+揽
+霆
+拄
+殖
+脆
+彻
+岩
+芝
+勃
+辣
+剌
+钝
+嘎
+甄
+佘
+皖
+伦
+授
+徕
+憔
+挪
+皇
+庞
+稔
+芜
+踏
+溴
+兖
+卒
+擢
+饥
+鳞
+煲
+‰
+账
+颗
+叻
+斯
+捧
+鳍
+琮
+讹
+蛙
+纽
+谭
+酸
+兔
+莒
+睇
+伟
+觑
+羲
+嗜
+宜
+褐
+旎
+辛
+卦
+诘
+筋
+鎏
+溪
+挛
+熔
+阜
+晰
+鳅
+丢
+奚
+灸
+呱
+献
+陉
+黛
+鸪
+甾
+萨
+疮
+拯
+洲
+疹
+辑
+叙
+恻
+谒
+允
+柔
+烂
+氏
+逅
+漆
+拎
+惋
+扈
+湟
+纭
+啕
+掬
+擞
+哥
+忽
+涤
+鸵
+靡
+郗
+瓷
+扁
+廊
+怨
+雏
+钮
+敦
+E
+懦
+憋
+汀
+拚
+啉
+腌
+岸
+f
+痼
+瞅
+尊
+咀
+眩
+飙
+忌
+仝
+迦
+熬
+毫
+胯
+篑
+茄
+腺
+凄
+舛
+碴
+锵
+诧
+羯
+後
+漏
+汤
+宓
+仞
+蚁
+壶
+谰
+皑
+铄
+棰
+罔
+辅
+晶
+苦
+牟
+闽
+\
+烃
+饮
+聿
+丙
+蛳
+朱
+煤
+涔
+鳖
+犁
+罐
+荼
+砒
+淦
+妤
+黏
+戎
+孑
+婕
+瑾
+戢
+钵
+枣
+捋
+砥
+衩
+狙
+桠
+稣
+阎
+肃
+梏
+诫
+孪
+昶
+婊
+衫
+嗔
+侃
+塞
+蜃
+樵
+峒
+貌
+屿
+欺
+缫
+阐
+栖
+诟
+珞
+荭
+吝
+萍
+嗽
+恂
+啻
+蜴
+磬
+峋
+俸
+豫
+谎
+徊
+镍
+韬
+魇
+晴
+U
+囟
+猜
+蛮
+坐
+囿
+伴
+亭
+肝
+佗
+蝠
+妃
+胞
+滩
+榴
+氖
+垩
+苋
+砣
+扪
+馏
+姓
+轩
+厉
+夥
+侈
+禀
+垒
+岑
+赏
+钛
+辐
+痔
+披
+纸
+碳
+“
+坞
+蠓
+挤
+荥
+沅
+悔
+铧
+帼
+蒌
+蝇
+a
+p
+y
+n
+g
+哀
+浆
+瑶
+凿
+桶
+馈
+皮
+奴
+苜
+佤
+伶
+晗
+铱
+炬
+优
+弊
+氢
+恃
+甫
+攥
+端
+锌
+灰
+稹
+炝
+曙
+邋
+亥
+眶
+碾
+拉
+萝
+绔
+捷
+浍
+腋
+姑
+菖
+凌
+涞
+麽
+锢
+桨
+潢
+绎
+镰
+殆
+锑
+渝
+铬
+困
+绽
+觎
+匈
+糙
+暑
+裹
+鸟
+盔
+肽
+迷
+綦
+『
+亳
+佝
+俘
+钴
+觇
+骥
+仆
+疝
+跪
+婶
+郯
+瀹
+唉
+脖
+踞
+针
+晾
+忒
+扼
+瞩
+叛
+椒
+疟
+嗡
+邗
+肆
+跆
+玫
+忡
+捣
+咧
+唆
+艄
+蘑
+潦
+笛
+阚
+沸
+泻
+掊
+菽
+贫
+斥
+髂
+孢
+镂
+赂
+麝
+鸾
+屡
+衬
+苷
+恪
+叠
+希
+粤
+爻
+喝
+茫
+惬
+郸
+绻
+庸
+撅
+碟
+宄
+妹
+膛
+叮
+饵
+崛
+嗲
+椅
+冤
+搅
+咕
+敛
+尹
+垦
+闷
+蝉
+霎
+勰
+败
+蓑
+泸
+肤
+鹌
+幌
+焦
+浠
+鞍
+刁
+舰
+乙
+竿
+裔
+。
+茵
+函
+伊
+兄
+丨
+娜
+匍
+謇
+莪
+宥
+似
+蝽
+翳
+酪
+翠
+粑
+薇
+祢
+骏
+赠
+叫
+Q
+噤
+噻
+竖
+芗
+莠
+潭
+俊
+羿
+耜
+O
+郫
+趁
+嗪
+囚
+蹶
+芒
+洁
+笋
+鹑
+敲
+硝
+啶
+堡
+渲
+揩
+』
+携
+宿
+遒
+颍
+扭
+棱
+割
+萜
+蔸
+葵
+琴
+捂
+饰
+衙
+耿
+掠
+募
+岂
+窖
+涟
+蔺
+瘤
+柞
+瞪
+怜
+匹
+距
+楔
+炜
+哆
+秦
+缎
+幼
+茁
+绪
+痨
+恨
+楸
+娅
+瓦
+桩
+雪
+嬴
+伏
+榔
+妥
+铿
+拌
+眠
+雍
+缇
+‘
+卓
+搓
+哌
+觞
+噩
+屈
+哧
+髓
+咦
+巅
+娑
+侑
+淫
+膳
+祝
+勾
+姊
+莴
+胄
+疃
+薛
+蜷
+胛
+巷
+芙
+芋
+熙
+闰
+勿
+窃
+狱
+剩
+钏
+幢
+陟
+铛
+慧
+靴
+耍
+k
+浙
+浇
+飨
+惟
+绗
+祜
+澈
+啼
+咪
+磷
+摞
+诅
+郦
+抹
+跃
+壬
+吕
+肖
+琏
+颤
+尴
+剡
+抠
+凋
+赚
+泊
+津
+宕
+殷
+倔
+氲
+漫
+邺
+涎
+怠
+$
+垮
+荬
+遵
+俏
+叹
+噢
+饽
+蜘
+孙
+筵
+疼
+鞭
+羧
+牦
+箭
+潴
+c
+眸
+祭
+髯
+啖
+坳
+愁
+芩
+驮
+倡
+巽
+穰
+沃
+胚
+怒
+凤
+槛
+剂
+趵
+嫁
+v
+邢
+灯
+鄢
+桐
+睽
+檗
+锯
+槟
+婷
+嵋
+圻
+诗
+蕈
+颠
+遭
+痢
+芸
+怯
+馥
+竭
+锗
+徜
+恭
+遍
+籁
+剑
+嘱
+苡
+龄
+僧
+桑
+潸
+弘
+澶
+楹
+悲
+讫
+愤
+腥
+悸
+谍
+椹
+呢
+桓
+葭
+攫
+阀
+翰
+躲
+敖
+柑
+郎
+笨
+橇
+呃
+魁
+燎
+脓
+葩
+磋
+垛
+玺
+狮
+沓
+砜
+蕊
+锺
+罹
+蕉
+翱
+虐
+闾
+巫
+旦
+茱
+嬷
+枯
+鹏
+贡
+芹
+汛
+矫
+绁
+拣
+禺
+佃
+讣
+舫
+惯
+乳
+趋
+疲
+挽
+岚
+虾
+衾
+蠹
+蹂
+飓
+氦
+铖
+孩
+稞
+瑜
+壅
+掀
+勘
+妓
+畅
+髋
+W
+庐
+牲
+蓿
+榕
+练
+垣
+唱
+邸
+菲
+昆
+婺
+穿
+绡
+麒
+蚱
+掂
+愚
+泷
+涪
+漳
+妩
+娉
+榄
+讷
+觅
+旧
+藤
+煮
+呛
+柳
+腓
+叭
+庵
+烷
+阡
+罂
+蜕
+擂
+猖
+咿
+媲
+脉
+【
+沏
+貅
+黠
+熏
+哲
+烁
+坦
+酵
+兜
+×
+潇
+撒
+剽
+珩
+圹
+乾
+摸
+樟
+帽
+嗒
+襄
+魂
+轿
+憬
+锡
+〕
+喃
+皆
+咖
+隅
+脸
+残
+泮
+袂
+鹂
+珊
+囤
+捆
+咤
+误
+徨
+闹
+淙
+芊
+淋
+怆
+囗
+拨
+梳
+渤
+R
+G
+绨
+蚓
+婀
+幡
+狩
+麾
+谢
+唢
+裸
+旌
+伉
+纶
+裂
+驳
+砼
+咛
+澄
+樨
+蹈
+宙
+澍
+倍
+貔
+操
+勇
+蟠
+摈
+砧
+虬
+够
+缁
+悦
+藿
+撸
+艹
+摁
+淹
+豇
+虎
+榭
+ˉ
+吱
+d
+°
+喧
+荀
+踱
+侮
+奋
+偕
+饷
+犍
+惮
+坑
+璎
+徘
+宛
+妆
+袈
+倩
+窦
+昂
+荏
+乖
+K
+怅
+撰
+鳙
+牙
+袁
+酞
+X
+痿
+琼
+闸
+雁
+趾
+荚
+虻
+涝
+《
+杏
+韭
+偈
+烤
+绫
+鞘
+卉
+症
+遢
+蓥
+诋
+杭
+荨
+匆
+竣
+簪
+辙
+敕
+虞
+丹
+缭
+咩
+黟
+m
+淤
+瑕
+咂
+铉
+硼
+茨
+嶂
+痒
+畸
+敬
+涿
+粪
+窘
+熟
+叔
+嫔
+盾
+忱
+裘
+憾
+梵
+赡
+珙
+咯
+娘
+庙
+溯
+胺
+葱
+痪
+摊
+荷
+卞
+乒
+髦
+寐
+铭
+坩
+胗
+枷
+爆
+溟
+嚼
+羚
+砬
+轨
+惊
+挠
+罄
+竽
+菏
+氧
+浅
+楣
+盼
+枢
+炸
+阆
+杯
+谏
+噬
+淇
+渺
+俪
+秆
+墓
+泪
+跻
+砌
+痰
+垡
+渡
+耽
+釜
+讶
+鳎
+煞
+呗
+韶
+舶
+绷
+鹳
+缜
+旷
+铊
+皱
+龌
+檀
+霖
+奄
+槐
+艳
+蝶
+旋
+哝
+赶
+骞
+蚧
+腊
+盈
+丁
+`
+蜚
+矸
+蝙
+睨
+嚓
+僻
+鬼
+醴
+夜
+彝
+磊
+笔
+拔
+栀
+糕
+厦
+邰
+纫
+逭
+纤
+眦
+膊
+馍
+躇
+烯
+蘼
+冬
+诤
+暄
+骶
+哑
+瘠
+」
+臊
+丕
+愈
+咱
+螺
+擅
+跋
+搏
+硪
+谄
+笠
+淡
+嘿
+骅
+谧
+鼎
+皋
+姚
+歼
+蠢
+驼
+耳
+胬
+挝
+涯
+狗
+蒽
+孓
+犷
+凉
+芦
+箴
+铤
+孤
+嘛
+坤
+V
+茴
+朦
+挞
+尖
+橙
+诞
+搴
+碇
+洵
+浚
+帚
+蜍
+漯
+柘
+嚎
+讽
+芭
+荤
+咻
+祠
+秉
+跖
+埃
+吓
+糯
+眷
+馒
+惹
+娼
+鲑
+嫩
+讴
+轮
+瞥
+靶
+褚
+乏
+缤
+宋
+帧
+删
+驱
+碎
+扑
+俩
+俄
+偏
+涣
+竹
+噱
+皙
+佰
+渚
+唧
+斡
+#
+镉
+刀
+崎
+筐
+佣
+夭
+贰
+肴
+峙
+哔
+艿
+匐
+牺
+镛
+缘
+仡
+嫡
+劣
+枸
+堀
+梨
+簿
+鸭
+蒸
+亦
+稽
+浴
+{
+衢
+束
+槲
+j
+阁
+揍
+疥
+棋
+潋
+聪
+窜
+乓
+睛
+插
+冉
+阪
+苍
+搽
+「
+蟾
+螟
+幸
+仇
+樽
+撂
+慢
+跤
+幔
+俚
+淅
+覃
+觊
+溶
+妖
+帛
+侨
+曰
+妾
+泗
+·
+：
+瀘
+風
+Ë
+（
+）
+∶
+紅
+紗
+瑭
+雲
+頭
+鶏
+財
+許
+•
+¥
+樂
+焗
+麗
+—
+；
+滙
+東
+榮
+繪
+興
+…
+門
+業
+π
+楊
+國
+顧
+é
+盤
+寳
+Λ
+龍
+鳳
+島
+誌
+緣
+結
+銭
+萬
+勝
+祎
+璟
+優
+歡
+臨
+時
+購
+＝
+★
+藍
+昇
+鐵
+觀
+勅
+農
+聲
+畫
+兿
+術
+發
+劉
+記
+專
+耑
+園
+書
+壴
+種
+Ο
+●
+褀
+號
+銀
+匯
+敟
+锘
+葉
+橪
+廣
+進
+蒄
+鑽
+阝
+祙
+貢
+鍋
+豊
+夬
+喆
+團
+閣
+開
+燁
+賓
+館
+酡
+沔
+順
+＋
+硚
+劵
+饸
+陽
+車
+湓
+復
+萊
+氣
+軒
+華
+堃
+迮
+纟
+戶
+馬
+學
+裡
+電
+嶽
+獨
+マ
+シ
+サ
+ジ
+燘
+袪
+環
+❤
+臺
+灣
+専
+賣
+孖
+聖
+攝
+線
+▪
+α
+傢
+俬
+夢
+達
+莊
+喬
+貝
+薩
+劍
+羅
+壓
+棛
+饦
+尃
+璈
+囍
+醫
+Ｇ
+Ｉ
+Ａ
+＃
+Ｎ
+鷄
+髙
+嬰
+啓
+約
+隹
+潔
+賴
+藝
+～
+寶
+籣
+麺
+　
+嶺
+√
+義
+網
+峩
+長
+∧
+魚
+機
+構
+②
+鳯
+偉
+Ｌ
+Ｂ
+㙟
+畵
+鴿
+＇
+詩
+溝
+嚞
+屌
+藔
+佧
+玥
+蘭
+織
+１
+３
+９
+０
+７
+點
+砭
+鴨
+鋪
+銘
+廳
+弍
+‧
+創
+湯
+坶
+℃
+卩
+骝
+＆
+烜
+荘
+當
+潤
+扞
+係
+懷
+碶
+钅
+蚨
+讠
+☆
+叢
+爲
+埗
+涫
+塗
+→
+楽
+現
+鯨
+愛
+瑪
+鈺
+忄
+悶
+藥
+飾
+樓
+視
+孬
+ㆍ
+燚
+苪
+師
+①
+丼
+锽
+│
+韓
+標
+è
+兒
+閏
+匋
+張
+漢
+Ü
+髪
+會
+閑
+檔
+習
+裝
+の
+峯
+菘
+輝
+И
+雞
+釣
+億
+浐
+Ｋ
+Ｏ
+Ｒ
+８
+Ｈ
+Ｅ
+Ｐ
+Ｔ
+Ｗ
+Ｄ
+Ｓ
+Ｃ
+Ｍ
+Ｆ
+姌
+饹
+»
+晞
+廰
+ä
+嵯
+鷹
+負
+飲
+絲
+冚
+楗
+澤
+綫
+區
+❋
+←
+質
+靑
+揚
+③
+滬
+統
+産
+協
+﹑
+乸
+畐
+經
+運
+際
+洺
+岽
+為
+粵
+諾
+崋
+豐
+碁
+ɔ
+Ｖ
+２
+６
+齋
+誠
+訂
+´
+勑
+雙
+陳
+無
+í
+泩
+媄
+夌
+刂
+ｉ
+ｃ
+ｔ
+ｏ
+ｒ
+ａ
+嘢
+耄
+燴
+暃
+壽
+媽
+靈
+抻
+體
+唻
+É
+冮
+甹
+鎮
+錦
+ʌ
+蜛
+蠄
+尓
+駕
+戀
+飬
+逹
+倫
+貴
+極
+Я
+Й
+寬
+磚
+嶪
+郎
+職
+｜
+間
+ｎ
+ｄ
+剎
+伈
+課
+飛
+橋
+瘊
+№
+譜
+骓
+圗
+滘
+縣
+粿
+咅
+養
+濤
+彳
+®
+％
+Ⅱ
+啰
+㴪
+見
+矞
+薬
+糁
+邨
+鲮
+顔
+罱
+З
+選
+話
+贏
+氪
+俵
+競
+瑩
+繡
+枱
+β
+綉
+á
+獅
+爾
+™
+麵
+戋
+淩
+徳
+個
+劇
+場
+務
+簡
+寵
+ｈ
+實
+膠
+轱
+圖
+築
+嘣
+樹
+㸃
+營
+耵
+孫
+饃
+鄺
+飯
+麯
+遠
+輸
+坫
+孃
+乚
+閃
+鏢
+㎡
+題
+廠
+關
+↑
+爺
+將
+軍
+連
+篦
+覌
+參
+箸
+－
+窠
+棽
+寕
+夀
+爰
+歐
+呙
+閥
+頡
+熱
+雎
+垟
+裟
+凬
+勁
+帑
+馕
+夆
+疌
+枼
+馮
+貨
+蒤
+樸
+彧
+旸
+靜
+龢
+暢
+㐱
+鳥
+珺
+鏡
+灡
+爭
+堷
+廚
+Ó
+騰
+診
+┅
+蘇
+褔
+凱
+頂
+豕
+亞
+帥
+嘬
+⊥
+仺
+桖
+複
+饣
+絡
+穂
+顏
+棟
+納
+▏
+濟
+親
+設
+計
+攵
+埌
+烺
+ò
+頤
+燦
+蓮
+撻
+節
+講
+濱
+濃
+娽
+洳
+朿
+燈
+鈴
+護
+膚
+铔
+過
+補
+Ｚ
+Ｕ
+５
+４
+坋
+闿
+䖝
+餘
+缐
+铞
+貿
+铪
+桼
+趙
+鍊
+［
+㐂
+垚
+菓
+揸
+捲
+鐘
+滏
+𣇉
+爍
+輪
+燜
+鴻
+鮮
+動
+鹞
+鷗
+丄
+慶
+鉌
+翥
+飮
+腸
+⇋
+漁
+覺
+來
+熘
+昴
+翏
+鲱
+圧
+鄉
+萭
+頔
+爐
+嫚
+г
+貭
+類
+聯
+幛
+輕
+訓
+鑒
+夋
+锨
+芃
+珣
+䝉
+扙
+嵐
+銷
+處
+ㄱ
+���
+誘
+苝
+歸
+儀
+燒
+楿
+內
+粢
+葒
+奧
+麥
+礻
+滿
+蠔
+穵
+瞭
+態
+鱬
+榞
+硂
+鄭
+黃
+煙
+祐
+奓
+逺
+＊
+瑄
+獲
+聞
+薦
+讀
+這
+樣
+決
+問
+啟
+們
+執
+説
+轉
+單
+隨
+唘
+帶
+倉
+庫
+還
+贈
+尙
+皺
+■
+餅
+產
+○
+∈
+報
+狀
+楓
+賠
+琯
+嗮
+禮
+｀
+傳
+＞
+≤
+嗞
+Φ
+≥
+換
+咭
+∣
+↓
+曬
+ε
+応
+寫
+″
+終
+様
+純
+費
+療
+聨
+凍
+壐
+郵
+ü
+黒
+∫
+製
+塊
+調
+軽
+確
+撃
+級
+馴
+Ⅲ
+涇
+繹
+數
+碼
+證
+狒
+処
+劑
+＜
+晧
+賀
+衆
+］
+櫥
+兩
+陰
+絶
+對
+鯉
+憶
+◎
+ｐ
+ｅ
+Ｙ
+蕒
+煖
+頓
+測
+試
+鼽
+僑
+碩
+妝
+帯
+≈
+鐡
+舖
+權
+喫
+倆
+ˋ
+該
+悅
+ā
+俫
+．
+ｆ
+ｓ
+ｂ
+ｍ
+ｋ
+ｇ
+ｕ
+ｊ
+貼
+淨
+濕
+針
+適
+備
+ｌ
+／
+給
+謢
+強
+觸
+衛
+與
+⊙
+＄
+緯
+變
+⑴
+⑵
+⑶
+㎏
+殺
+∩
+幚
+─
+價
+▲
+離
+ú
+ó
+飄
+烏
+関
+閟
+﹝
+﹞
+邏
+輯
+鍵
+驗
+訣
+導
+歷
+屆
+層
+▼
+儱
+錄
+熳
+ē
+艦
+吋
+錶
+辧
+飼
+顯
+④
+禦
+販
+気
+対
+枰
+閩
+紀
+幹
+瞓
+貊
+淚
+△
+眞
+墊
+Ω
+獻
+褲
+縫
+緑
+亜
+鉅
+餠
+｛
+｝
+◆
+蘆
+薈
+█
+◇
+溫
+彈
+晳
+粧
+犸
+穩
+訊
+崬
+凖
+熥
+П
+舊
+條
+紋
+圍
+Ⅳ
+筆
+尷
+難
+雜
+錯
+綁
+識
+頰
+鎖
+艶
+□
+殁
+殼
+⑧
+├
+▕
+鵬
+ǐ
+ō
+ǒ
+糝
+綱
+▎
+μ
+盜
+饅
+醬
+籤
+蓋
+釀
+鹽
+據
+à
+ɡ
+辦
+◥
+彐
+┌
+婦
+獸
+鲩
+伱
+ī
+蒟
+蒻
+齊
+袆
+腦
+寧
+凈
+妳
+煥
+詢
+偽
+謹
+啫
+鯽
+騷
+鱸
+損
+傷
+鎻
+髮
+買
+冏
+儥
+両
+﹢
+∞
+載
+喰
+ｚ
+羙
+悵
+燙
+曉
+員
+組
+徹
+艷
+痠
+鋼
+鼙
+縮
+細
+嚒
+爯
+≠
+維
+＂
+鱻
+壇
+厍
+帰
+浥
+犇
+薡
+軎
+²
+應
+醜
+刪
+緻
+鶴
+賜
+噁
+軌
+尨
+镔
+鷺
+槗
+彌
+葚
+濛
+請
+溇
+緹
+賢
+訪
+獴
+瑅
+資
+縤
+陣
+蕟
+栢
+韻
+祼
+恁
+伢
+謝
+劃
+涑
+總
+衖
+踺
+砋
+凉
+籃
+駿
+苼
+瘋
+昽
+紡
+驊
+腎
+﹗
+響
+杋
+剛
+嚴
+禪
+歓
+槍
+傘
+檸
+檫
+炣
+勢
+鏜
+鎢
+銑
+尐
+減
+奪
+惡
+θ
+僮
+婭
+臘
+ū
+ì
+殻
+鉄
+∑
+蛲
+焼
+緖
+續
+紹
+懮

anyocr/models/anyocr_rec_v4_server.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3a2d9fc2362736795e44aa3b157bd65994b787decf9a4d0fcdcb14f220a498e
+size 90500640

anyocr/pipeline.py

@@ -0,0 +1,459 @@
+from typing import Any, Dict, List, Optional, Tuple, Union
+import cv2
+import copy
+import numpy as np
+from pathlib import Path
+from pydantic import BaseModel
+from anyocr.cal_rec_boxes import CalRecBoxes
+from anyocr.text_cls import TextClassifier
+from anyocr.text_det import TextDetector
+from anyocr.text_rec import TextRecognizer
+from anyocr.utils import (
+    LoadImage,
+    add_round_letterbox,
+    increase_min_side,
+    reduce_max_side
+)
+
+
+class PipelineOCR:
+    def __init__(self, **kwargs):
+        config = kwargs
+        global_config = config["Global"]
+        self.print_verbose = global_config["print_verbose"]
+        self.text_score = global_config["text_score"]
+        self.min_height = global_config["min_height"]
+        self.width_height_ratio = global_config["width_height_ratio"]
+
+        self.use_det = global_config["use_det"]
+        self.text_det = TextDetector(config["Det"])
+
+        self.use_cls = global_config["use_cls"]
+        self.text_cls = TextClassifier(config["Cls"])
+
+        self.use_rec = global_config["use_rec"]
+        self.text_rec = TextRecognizer(config["Rec"])
+
+        self.load_img = LoadImage()
+        self.max_side_len = global_config["max_side_len"]
+        self.min_side_len = global_config["min_side_len"]
+
+        self.cal_rec_boxes = CalRecBoxes()
+
+    def __call__(
+        self,
+        img_content: Union[str, np.ndarray, bytes, Path],
+        use_det: Optional[bool] = None,
+        use_cls: Optional[bool] = None,
+        use_rec: Optional[bool] = None,
+        **kwargs,
+    ) -> Tuple[Optional[List[List[Union[Any, str]]]], Optional[List[float]]]:
+        use_det = self.use_det if use_det is None else use_det
+        use_cls = self.use_cls if use_cls is None else use_cls
+        use_rec = self.use_rec if use_rec is None else use_rec
+        return_word_box = False
+        if kwargs:
+            box_thresh = kwargs.get("box_thresh", 0.5)
+            unclip_ratio = kwargs.get("unclip_ratio", 1.6)
+            text_score = kwargs.get("text_score", 0.5)
+            return_word_box = kwargs.get("return_word_box", False)
+            self.text_det.postprocess_op.box_thresh = box_thresh
+            self.text_det.postprocess_op.unclip_ratio = unclip_ratio
+            self.text_score = text_score
+
+        img = self.load_img(img_content)
+
+        raw_h, raw_w = img.shape[:2]
+        op_record = {}
+        img, ratio_h, ratio_w = self.preprocess(img)
+        op_record["preprocess"] = {"ratio_h": ratio_h, "ratio_w": ratio_w}
+
+        dt_boxes, cls_res, rec_res = None, None, None
+        det_elapse, cls_elapse, rec_elapse = 0.0, 0.0, 0.0
+
+        if use_det:
+            img, op_record = self.maybe_add_letterbox(img, op_record)
+            dt_boxes, det_elapse = self.auto_text_det(img)
+            if dt_boxes is None:
+                return None, None
+
+            img = self.get_crop_img_list(img, dt_boxes)
+            # print(img)
+        if use_cls:
+            img, cls_res, cls_elapse = self.text_cls(img)
+            # print(img)
+
+        if use_rec:
+            rec_res, rec_elapse = self.text_rec(img, return_word_box)
+
+        if dt_boxes is not None and rec_res is not None and return_word_box:
+            rec_res = self.cal_rec_boxes(img, dt_boxes, rec_res)
+            for rec_res_i in rec_res:
+                if rec_res_i[2]:
+                    rec_res_i[2] = (
+                        self._get_origin_points(rec_res_i[2], op_record, raw_h, raw_w)
+                        .astype(np.int32)
+                        .tolist()
+                    )
+
+        if dt_boxes is not None:
+            dt_boxes = self._get_origin_points(dt_boxes, op_record, raw_h, raw_w)
+
+        ocr_res = self.get_final_res(
+            dt_boxes, cls_res, rec_res, det_elapse, cls_elapse, rec_elapse
+        )
+        del img, dt_boxes, cls_res, rec_res
+        return ocr_res
+
+    def preprocess(self, img: np.ndarray) -> Tuple[np.ndarray, float, float]:
+        h, w = img.shape[:2]
+        max_value = max(h, w)
+        ratio_h = ratio_w = 1.0
+        if max_value > self.max_side_len:
+            img, ratio_h, ratio_w = reduce_max_side(img, self.max_side_len)
+
+        h, w = img.shape[:2]
+        min_value = min(h, w)
+        if min_value < self.min_side_len:
+            img, ratio_h, ratio_w = increase_min_side(img, self.min_side_len)
+        return img, ratio_h, ratio_w
+
+    def maybe_add_letterbox(
+        self, img: np.ndarray, op_record: Dict[str, Any]
+    ) -> Tuple[np.ndarray, Dict[str, Any]]:
+        h, w = img.shape[:2]
+
+        if self.width_height_ratio == -1:
+            use_limit_ratio = False
+        else:
+            use_limit_ratio = w / h > self.width_height_ratio
+
+        if h <= self.min_height or use_limit_ratio:
+            padding_h = self._get_padding_h(h, w)
+            block_img = add_round_letterbox(img, (padding_h, padding_h, 0, 0))
+            op_record["padding_1"] = {"top": padding_h, "left": 0}
+            return block_img, op_record
+
+        op_record["padding_1"] = {"top": 0, "left": 0}
+        return img, op_record
+
+    def _get_padding_h(self, h: int, w: int) -> int:
+        new_h = max(int(w / self.width_height_ratio), self.min_height) * 2
+        padding_h = int(abs(new_h - h) / 2)
+        return padding_h
+
+    def auto_text_det(
+        self, img: np.ndarray
+    ) -> Tuple[Optional[List[np.ndarray]], float]:
+        dt_boxes, det_elapse = self.text_det(img)
+        if dt_boxes is None or len(dt_boxes) < 1:
+            return None, 0.0
+
+        dt_boxes = self.sorted_boxes(dt_boxes)
+        return dt_boxes, det_elapse
+
+    def get_crop_img_list(
+        self, img: np.ndarray, dt_boxes: List[np.ndarray]
+    ) -> List[np.ndarray]:
+        def get_rotate_crop_image(img: np.ndarray, points: np.ndarray) -> np.ndarray:
+            img_crop_width = int(
+                max(
+                    np.linalg.norm(points[0] - points[1]),
+                    np.linalg.norm(points[2] - points[3]),
+                )
+            )
+            img_crop_height = int(
+                max(
+                    np.linalg.norm(points[0] - points[3]),
+                    np.linalg.norm(points[1] - points[2]),
+                )
+            )
+            pts_std = np.array(
+                [
+                    [0, 0],
+                    [img_crop_width, 0],
+                    [img_crop_width, img_crop_height],
+                    [0, img_crop_height],
+                ]
+            ).astype(np.float32)
+            M = cv2.getPerspectiveTransform(points, pts_std)
+            dst_img = cv2.warpPerspective(
+                img,
+                M,
+                (img_crop_width, img_crop_height),
+                borderMode=cv2.BORDER_REPLICATE,
+                flags=cv2.INTER_CUBIC,
+            )
+            dst_img_height, dst_img_width = dst_img.shape[0:2]
+            if dst_img_height * 1.0 / dst_img_width >= 1.5:
+                dst_img = np.rot90(dst_img)
+            return dst_img
+
+        img_crop_list = []
+        for box in dt_boxes:
+            tmp_box = copy.deepcopy(box)
+            img_crop = get_rotate_crop_image(img, tmp_box)
+            img_crop_list.append(img_crop)
+        return img_crop_list
+
+    @staticmethod
+    def sorted_boxes(dt_boxes: np.ndarray) -> List[np.ndarray]:
+        """
+        Sort text boxes in order from top to bottom, left to right
+        args:
+            dt_boxes(array):detected text boxes with shape [4, 2]
+        return:
+            sorted boxes(array) with shape [4, 2]
+        """
+        num_boxes = dt_boxes.shape[0]
+        sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
+        _boxes = list(sorted_boxes)
+
+        for i in range(num_boxes - 1):
+            for j in range(i, -1, -1):
+                if (
+                    abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10
+                    and _boxes[j + 1][0][0] < _boxes[j][0][0]
+                ):
+                    tmp = _boxes[j]
+                    _boxes[j] = _boxes[j + 1]
+                    _boxes[j + 1] = tmp
+                else:
+                    break
+        return _boxes
+
+    def _get_origin_points(
+        self,
+        dt_boxes: List[np.ndarray],
+        op_record: Dict[str, Any],
+        raw_h: int,
+        raw_w: int,
+    ) -> np.ndarray:
+        dt_boxes_array = np.array(dt_boxes).astype(np.float32)
+        for op in reversed(list(op_record.keys())):
+            v = op_record[op]
+            if "padding" in op:
+                top, left = v.get("top"), v.get("left")
+                dt_boxes_array[:, :, 0] -= left
+                dt_boxes_array[:, :, 1] -= top
+            elif "preprocess" in op:
+                ratio_h = v.get("ratio_h")
+                ratio_w = v.get("ratio_w")
+                dt_boxes_array[:, :, 0] *= ratio_w
+                dt_boxes_array[:, :, 1] *= ratio_h
+
+        dt_boxes_array = np.where(dt_boxes_array < 0, 0, dt_boxes_array)
+        dt_boxes_array[..., 0] = np.where(
+            dt_boxes_array[..., 0] > raw_w, raw_w, dt_boxes_array[..., 0]
+        )
+        dt_boxes_array[..., 1] = np.where(
+            dt_boxes_array[..., 1] > raw_h, raw_h, dt_boxes_array[..., 1]
+        )
+        return dt_boxes_array
+
+    def get_final_res(
+        self,
+        dt_boxes: Optional[List[np.ndarray]],
+        cls_res: Optional[List[List[Union[str, float]]]],
+        rec_res: Optional[List[Tuple[str, float, List[Union[str, float]]]]],
+        det_elapse: float,
+        cls_elapse: float,
+        rec_elapse: float,
+    ) -> Tuple[Optional[List[List[Union[Any, str]]]], Optional[List[float]]]:
+        if dt_boxes is None and rec_res is None and cls_res is not None:
+            return cls_res, [cls_elapse]
+
+        if dt_boxes is None and rec_res is None:
+            return None, None
+
+        if dt_boxes is None and rec_res is not None:
+            return [[res[0], res[1]] for res in rec_res], [rec_elapse]
+
+        if dt_boxes is not None and rec_res is None:
+            return [box.tolist() for box in dt_boxes], [det_elapse]
+
+        dt_boxes, rec_res = self.filter_result(dt_boxes, rec_res)
+        if not dt_boxes or not rec_res or len(dt_boxes) <= 0:
+            return None, None
+
+        ocr_res = [[box.tolist(), *res] for box, res in zip(dt_boxes, rec_res)], [
+            det_elapse,
+            cls_elapse,
+            rec_elapse,
+        ]
+        return ocr_res
+
+    def filter_result(
+        self,
+        dt_boxes: Optional[List[np.ndarray]],
+        rec_res: Optional[List[Tuple[str, float]]],
+    ) -> Tuple[Optional[List[np.ndarray]], Optional[List[Tuple[str, float]]]]:
+        if dt_boxes is None or rec_res is None:
+            return None, None
+
+        filter_boxes, filter_rec_res = [], []
+        for box, rec_reuslt in zip(dt_boxes, rec_res):
+            text, score = rec_reuslt[0], rec_reuslt[1]
+            if float(score) >= self.text_score:
+                filter_boxes.append(box)
+                filter_rec_res.append(rec_reuslt)
+
+        return filter_boxes, filter_rec_res
+    
+    
+class anyocrConfig(BaseModel):
+    text_score: float = 0.5
+    use_det: bool = True
+    use_cls: bool = True
+    use_rec: bool = True
+    print_verbose: bool = False
+    min_height: int = 30
+    width_height_ratio: float = 8
+    max_side_len: int = 2000
+    min_side_len: int = 30
+    return_word_box: bool = False
+    intra_op_num_threads: int = -1
+    inter_op_num_threads: int = -1
+    
+    det_use_cuda: bool = False
+    det_use_dml: bool = False
+    det_model_path: Optional[str] = None
+    det_limit_side_len: float = 736
+    det_limit_type: str = "min"
+    det_std: list = [ 0.5, 0.5, 0.5 ]
+    det_mean: list = [ 0.5, 0.5, 0.5 ]
+    det_max_candidates:int = 1000
+    det_thresh: float = 0.3
+    det_box_thresh: float = 0.5
+    det_unclip_ratio: float = 1.6
+    det_donot_use_dilation: bool = False
+    det_score_mode: str = "slow"  #"fast"
+    det_intra_op_num_threads: int = -1
+    det_inter_op_num_threads: int = -1
+    
+    cls_use_cuda: bool = False
+    cls_use_dml: bool = False
+    cls_model_path: Optional[str] = None
+    cls_image_shape: List[int] = [3, 48, 192]
+    cls_label_list: List[str] = ["0", "180"]
+    cls_batch_num: int = 6
+    cls_thresh: float = 0.9
+    cls_intra_op_num_threads: int = -1
+    cls_inter_op_num_threads: int = -1
+    
+    rec_use_cuda: bool = False
+    rec_use_dml: bool = False
+    rec_keys_path: Optional[str] = None
+    rec_model_path: Optional[str] = None
+    rec_img_shape: List[int] = [3, 48, 320]
+    rec_batch_num: int = 6
+    rec_intra_op_num_threads: int = -1
+    rec_inter_op_num_threads: int = -1
+
+
+class anyocr(object):
+    def __init__(self,config: dict = None):
+        ROOT_PATH = Path(__file__).resolve().parent
+        if config:
+            self.config = config
+        else:
+            self.config = anyocrConfig().model_dump()
+            self.config['det_model_path'] = ROOT_PATH.joinpath("models/anyocr_det_ch_v4_lite.onnx").as_posix()
+            self.config['rec_model_path'] = ROOT_PATH.joinpath("models/anyocr_rec_v4_server.onnx").as_posix()
+            self.config['cls_model_path'] = ROOT_PATH.joinpath("models/anyocr_cls_v4.onnx").as_posix()
+            self.config['rec_keys_path'] = ROOT_PATH.joinpath("models/anyocr_keys_v4.txt").as_posix()
+        
+        ocrconfigraw = anyocrConfig(**self.config).model_dump()
+        ocrconfig = {
+            "Global": {},
+            "Det": {},
+            "Cls": {},
+            "Rec": {}
+        }
+        for x,y in ocrconfigraw.items():
+            if not x.startswith('det_') and not x.startswith('rec_') and not x.startswith('cls_'):
+                ocrconfig["Global"][x] = y
+            elif x.startswith('det_'):
+                if x in ["det_model_path", "det_use_cuda", "det_use_dml","det_std","det_mean","det_max_candidates"]:
+                    ocrconfig["Det"][x.replace("det_","")] = y
+                elif x == "det_donot_use_dilation":
+                    ocrconfig["Det"]["use_dilation"] = not y
+                else:
+                    ocrconfig["Det"][x] = y
+            elif x.startswith('cls_'):
+                if x in ["cls_label_list", "cls_model_path", "cls_use_cuda", "cls_use_dml"]:
+                    ocrconfig["Cls"][x.replace("cls_","")] = y
+                else:
+                    ocrconfig["Cls"][x] = y
+            elif x.startswith('rec_'):
+                if x in ["rec_model_path", "rec_use_cuda", "rec_use_dml"]:
+                    ocrconfig["Rec"][x.replace("rec_","")] = y
+                else:
+                    ocrconfig["Rec"][x] = y
+        
+        self.ocrmodel = PipelineOCR(**ocrconfig)
+        
+    def cls_line_wordbox(self, wordbox):
+        res = {'1': [wordbox[0]]}
+        for item in wordbox[1:]:
+            box = item[-1]
+            x1,y1 = box[0]
+            x2,y2 = box[2]
+            reference_y = (y2 + y1) / 2
+            flag = True
+            for k in list(res.keys()):
+                start_box = res[k][0][-1]
+                start_y1 = start_box[0][-1]
+                start_y2 = start_box[2][-1]
+                start_reference_y = (start_y2 + start_y1) / 2
+                threshold = start_y2 - start_reference_y
+                if abs(start_reference_y - reference_y) < threshold:
+                    res[k].append(item)
+                    flag = False
+                    break
+            if flag:
+                item_key = f'{len(list(res.keys())) + 1}'
+                res[item_key] = [item]
+        return res   
+    
+    def completions(
+            self,
+            image,
+            use_det = True,
+            use_cls = True,
+            use_rec = True,
+            cls_line = True,
+            **kwargs
+        ):
+        res = []
+        
+        result,elapse = self.ocrmodel(image,use_det=use_det,use_cls=use_cls,use_rec=use_rec,**kwargs)
+        if not result:
+            return ""
+        for line in result:
+            if line:
+                box,text,score = line
+                res.append([text,box])      
+        if not cls_line:
+            content = ''.join([f"{x[0]}" for x in res])
+        else:
+            try:
+                content = ''
+                lines = self.cls_line_wordbox(res)
+                lines = list(sorted(lines.items(),key = lambda x: int(x[0])))
+                for line in lines:
+                    content += f'{" ".join([str(x[0]) for x in line[-1]])}\n'
+            except:
+                content = ''.join([f"{x[0]}" for x in res])
+        return content 
+    
+    def raw_completions(
+            self,
+            image,
+            use_det = True,
+            use_cls = True,
+            use_rec = True,
+            **kwargs
+        ):
+        result,elapse = self.ocrmodel(image,use_det=use_det,use_cls=use_cls,use_rec=use_rec,**kwargs)
+        return result,elapse

anyocr/text_cls.py

@@ -0,0 +1,93 @@
+from typing import Any, Dict, List, Tuple, Union
+import copy
+import math
+import time
+import cv2
+import numpy as np
+from anyocr.infer_engine import OrtInferSession
+
+
+class ClsPostProcess:
+    def __init__(self, label_list: List[str]):
+        self.label_list = label_list
+
+    def __call__(self, preds: np.ndarray) -> List[Tuple[str, float]]:
+        pred_idxs = preds.argmax(axis=1)
+        decode_out = [
+            (self.label_list[idx], preds[i, idx]) for i, idx in enumerate(pred_idxs)
+        ]
+        return decode_out
+    
+    
+class TextClassifier:
+    def __init__(self, config: Dict[str, Any]):
+        self.cls_image_shape = config["cls_image_shape"]
+        self.cls_batch_num = config["cls_batch_num"]
+        self.cls_thresh = config["cls_thresh"]
+        self.postprocess_op = ClsPostProcess(config["label_list"])
+
+        self.infer = OrtInferSession(config)
+
+    def __call__(
+        self, img_list: Union[np.ndarray, List[np.ndarray]]
+    ) -> Tuple[List[np.ndarray], List[List[Union[str, float]]], float]:
+        if isinstance(img_list, np.ndarray):
+            img_list = [img_list]
+
+        img_list = copy.deepcopy(img_list)
+
+        # Calculate the aspect ratio of all text bars
+        width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
+
+        # Sorting can speed up the cls process
+        indices = np.argsort(np.array(width_list))
+
+        img_num = len(img_list)
+        cls_res = [["", 0.0]] * img_num
+        batch_num = self.cls_batch_num
+        elapse = 0
+        for beg_img_no in range(0, img_num, batch_num):
+            end_img_no = min(img_num, beg_img_no + batch_num)
+
+            norm_img_batch = []
+            for ino in range(beg_img_no, end_img_no):
+                norm_img = self.resize_norm_img(img_list[indices[ino]])
+                norm_img = norm_img[np.newaxis, :]
+                norm_img_batch.append(norm_img)
+            norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32)
+
+            starttime = time.time()
+            prob_out = self.infer(norm_img_batch)[0]
+            cls_result = self.postprocess_op(prob_out)
+            elapse += time.time() - starttime
+
+            for rno, (label, score) in enumerate(cls_result):
+                cls_res[indices[beg_img_no + rno]] = [label, score]
+                if "180" in label and score > self.cls_thresh:
+                    img_list[indices[beg_img_no + rno]] = cv2.rotate(
+                        img_list[indices[beg_img_no + rno]], 1
+                    )
+        return img_list, cls_res, elapse
+
+    def resize_norm_img(self, img: np.ndarray) -> np.ndarray:
+        img_c, img_h, img_w = self.cls_image_shape
+        h, w = img.shape[:2]
+        ratio = w / float(h)
+        if math.ceil(img_h * ratio) > img_w:
+            resized_w = img_w
+        else:
+            resized_w = int(math.ceil(img_h * ratio))
+
+        resized_image = cv2.resize(img, (resized_w, img_h))
+        resized_image = resized_image.astype("float32")
+        if img_c == 1:
+            resized_image = resized_image / 255
+            resized_image = resized_image[np.newaxis, :]
+        else:
+            resized_image = resized_image.transpose((2, 0, 1)) / 255
+
+        resized_image -= 0.5
+        resized_image /= 0.5
+        padding_im = np.zeros((img_c, img_h, img_w), dtype=np.float32)
+        padding_im[:, :, :resized_w] = resized_image
+        return padding_im

anyocr/text_det.py

@@ -0,0 +1,336 @@
+from typing import Any, List, Dict, Optional, Tuple
+import cv2
+import time
+import numpy as np
+import pyclipper
+from shapely.geometry import Polygon
+from anyocr.infer_engine import OrtInferSession
+
+
+class DetPreProcess:
+    def __init__(
+        self, limit_side_len: int = 736, limit_type: str = "min", mean=None, std=None
+    ):
+        if mean is None:
+            mean = [0.5, 0.5, 0.5]
+
+        if std is None:
+            std = [0.5, 0.5, 0.5]
+
+        self.mean = np.array(mean)
+        self.std = np.array(std)
+        self.scale = 1 / 255.0
+
+        self.limit_side_len = limit_side_len
+        self.limit_type = limit_type
+
+    def __call__(self, img: np.ndarray) -> Optional[np.ndarray]:
+        resized_img = self.resize(img)
+        if resized_img is None:
+            return None
+
+        img = self.normalize(resized_img)
+        img = self.permute(img)
+        img = np.expand_dims(img, axis=0).astype(np.float32)
+        return img
+
+    def normalize(self, img: np.ndarray) -> np.ndarray:
+        return (img.astype("float32") * self.scale - self.mean) / self.std
+
+    def permute(self, img: np.ndarray) -> np.ndarray:
+        return img.transpose((2, 0, 1))
+
+    def resize(self, img: np.ndarray) -> Optional[np.ndarray]:
+        """resize image to a size multiple of 32 which is required by the network"""
+        h, w = img.shape[:2]
+
+        if self.limit_type == "max":
+            if max(h, w) > self.limit_side_len:
+                if h > w:
+                    ratio = float(self.limit_side_len) / h
+                else:
+                    ratio = float(self.limit_side_len) / w
+            else:
+                ratio = 1.0
+        else:
+            if min(h, w) < self.limit_side_len:
+                if h < w:
+                    ratio = float(self.limit_side_len) / h
+                else:
+                    ratio = float(self.limit_side_len) / w
+            else:
+                ratio = 1.0
+
+        resize_h = int(h * ratio)
+        resize_w = int(w * ratio)
+
+        resize_h = int(round(resize_h / 32) * 32)
+        resize_w = int(round(resize_w / 32) * 32)
+
+        try:
+            if int(resize_w) <= 0 or int(resize_h) <= 0:
+                return None
+            img = cv2.resize(img, (int(resize_w), int(resize_h)))
+        except Exception as exc:
+            raise ResizeImgError from exc
+
+        return img
+
+
+class ResizeImgError(Exception):
+    pass
+
+
+class DBPostProcess:
+    """The post process for Differentiable Binarization (DB)."""
+
+    def __init__(
+        self,
+        thresh: float = 0.3,
+        box_thresh: float = 0.7,
+        max_candidates: int = 1000,
+        unclip_ratio: float = 2.0,
+        score_mode: str = "fast",
+        use_dilation: bool = False,
+    ):
+        self.thresh = thresh
+        self.box_thresh = box_thresh
+        self.max_candidates = max_candidates
+        self.unclip_ratio = unclip_ratio
+        self.min_size = 3
+        self.score_mode = score_mode
+
+        self.dilation_kernel = None
+        if use_dilation:
+            self.dilation_kernel = np.array([[1, 1], [1, 1]])
+
+    def __call__(
+        self, pred: np.ndarray, ori_shape: Tuple[int, int]
+    ) -> Tuple[np.ndarray, List[float]]:
+        src_h, src_w = ori_shape
+        pred = pred[:, 0, :, :]
+        segmentation = pred > self.thresh
+
+        mask = segmentation[0]
+        if self.dilation_kernel is not None:
+            mask = cv2.dilate(
+                np.array(segmentation[0]).astype(np.uint8), self.dilation_kernel
+            )
+        boxes, scores = self.boxes_from_bitmap(pred[0], mask, src_w, src_h)
+        return boxes, scores
+
+    def boxes_from_bitmap(
+        self, pred: np.ndarray, bitmap: np.ndarray, dest_width: int, dest_height: int
+    ) -> Tuple[np.ndarray, List[float]]:
+        """
+        bitmap: single map with shape (1, H, W),
+                whose values are binarized as {0, 1}
+        """
+
+        height, width = bitmap.shape
+
+        outs = cv2.findContours(
+            (bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
+        )
+        if len(outs) == 3:
+            img, contours, _ = outs[0], outs[1], outs[2]
+        elif len(outs) == 2:
+            contours, _ = outs[0], outs[1]
+
+        num_contours = min(len(contours), self.max_candidates)
+
+        boxes, scores = [], []
+        for index in range(num_contours):
+            contour = contours[index]
+            points, sside = self.get_mini_boxes(contour)
+            if sside < self.min_size:
+                continue
+
+            if self.score_mode == "fast":
+                score = self.box_score_fast(pred, points.reshape(-1, 2))
+            else:
+                score = self.box_score_slow(pred, contour)
+
+            if self.box_thresh > score:
+                continue
+
+            box = self.unclip(points)
+            box, sside = self.get_mini_boxes(box)
+            if sside < self.min_size + 2:
+                continue
+
+            box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0, dest_width)
+            box[:, 1] = np.clip(
+                np.round(box[:, 1] / height * dest_height), 0, dest_height
+            )
+            boxes.append(box.astype(np.int32))
+            scores.append(score)
+        return np.array(boxes, dtype=np.int32), scores
+
+    def get_mini_boxes(self, contour: np.ndarray) -> Tuple[np.ndarray, float]:
+        bounding_box = cv2.minAreaRect(contour)
+        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])
+
+        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
+        if points[1][1] > points[0][1]:
+            index_1 = 0
+            index_4 = 1
+        else:
+            index_1 = 1
+            index_4 = 0
+
+        if points[3][1] > points[2][1]:
+            index_2 = 2
+            index_3 = 3
+        else:
+            index_2 = 3
+            index_3 = 2
+
+        box = np.array(
+            [points[index_1], points[index_2], points[index_3], points[index_4]]
+        )
+        return box, min(bounding_box[1])
+
+    @staticmethod
+    def box_score_fast(bitmap: np.ndarray, _box: np.ndarray) -> float:
+        h, w = bitmap.shape[:2]
+        box = _box.copy()
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+        box[:, 0] = box[:, 0] - xmin
+        box[:, 1] = box[:, 1] - ymin
+        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0]
+
+    def box_score_slow(self, bitmap: np.ndarray, contour: np.ndarray) -> float:
+        """use polyon mean score as the mean score"""
+        h, w = bitmap.shape[:2]
+        contour = contour.copy()
+        contour = np.reshape(contour, (-1, 2))
+
+        xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
+        xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
+        ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
+        ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)
+
+        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
+
+        contour[:, 0] = contour[:, 0] - xmin
+        contour[:, 1] = contour[:, 1] - ymin
+
+        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype(np.int32), 1)
+        return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0]
+
+    def unclip(self, box: np.ndarray) -> np.ndarray:
+        unclip_ratio = self.unclip_ratio
+        poly = Polygon(box)
+        distance = poly.area * unclip_ratio / poly.length
+        offset = pyclipper.PyclipperOffset()
+        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
+        expanded = np.array(offset.Execute(distance)).reshape((-1, 1, 2))
+        return expanded
+
+
+class TextDetector:
+    def __init__(self, config: Dict[str, Any]):
+        self.limit_side_len = config.get("limit_side_len")
+        self.limit_type = config.get("limit_type")
+        self.mean = config.get("mean")
+        self.std = config.get("std")
+        self.preprocess_op = None
+
+        post_process = {
+            "thresh": config.get("thresh", 0.3),
+            "box_thresh": config.get("box_thresh", 0.5),
+            "max_candidates": config.get("max_candidates", 1000),
+            "unclip_ratio": config.get("unclip_ratio", 1.6),
+            "use_dilation": config.get("use_dilation", True),
+            "score_mode": config.get("score_mode", "fast"),
+        }
+        # print(config)
+        self.postprocess_op = DBPostProcess(**post_process)
+
+        self.infer = OrtInferSession(config)
+
+    def __call__(self, img: np.ndarray) -> Tuple[Optional[np.ndarray], float]:
+        start_time = time.perf_counter()
+
+        if img is None:
+            raise ValueError("img is None")
+
+        ori_img_shape = img.shape[0], img.shape[1]
+        self.preprocess_op = self.get_preprocess(max(img.shape[0], img.shape[1]))
+        prepro_img = self.preprocess_op(img)
+        if prepro_img is None:
+            return None, 0
+
+        preds = self.infer(prepro_img)[0]
+        dt_boxes, dt_boxes_scores = self.postprocess_op(preds, ori_img_shape)
+        dt_boxes = self.filter_tag_det_res(dt_boxes, ori_img_shape)
+        elapse = time.perf_counter() - start_time
+        return dt_boxes, elapse
+
+    def get_preprocess(self, max_wh):
+        if self.limit_type == "min":
+            limit_side_len = self.limit_side_len
+        elif max_wh < 960:
+            limit_side_len = 960
+        elif max_wh < 1500:
+            limit_side_len = 1500
+        else:
+            limit_side_len = 2000
+        return DetPreProcess(limit_side_len, self.limit_type, self.mean, self.std)
+
+    def filter_tag_det_res(
+        self, dt_boxes: np.ndarray, image_shape: Tuple[int, int]
+    ) -> np.ndarray:
+        img_height, img_width = image_shape
+        dt_boxes_new = []
+        for box in dt_boxes:
+            box = self.order_points_clockwise(box)
+            box = self.clip_det_res(box, img_height, img_width)
+
+            rect_width = int(np.linalg.norm(box[0] - box[1]))
+            rect_height = int(np.linalg.norm(box[0] - box[3]))
+            if rect_width <= 3 or rect_height <= 3:
+                continue
+
+            dt_boxes_new.append(box)
+        return np.array(dt_boxes_new)
+
+    def order_points_clockwise(self, pts: np.ndarray) -> np.ndarray:
+        """
+        reference from:
+        https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
+        sort the points based on their x-coordinates
+        """
+        xSorted = pts[np.argsort(pts[:, 0]), :]
+
+        # grab the left-most and right-most points from the sorted
+        # x-roodinate points
+        leftMost = xSorted[:2, :]
+        rightMost = xSorted[2:, :]
+
+        # now, sort the left-most coordinates according to their
+        # y-coordinates so we can grab the top-left and bottom-left
+        # points, respectively
+        leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
+        (tl, bl) = leftMost
+
+        rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
+        (tr, br) = rightMost
+
+        rect = np.array([tl, tr, br, bl], dtype="float32")
+        return rect
+
+    def clip_det_res(
+        self, points: np.ndarray, img_height: int, img_width: int
+    ) -> np.ndarray:
+        for pno in range(points.shape[0]):
+            points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
+            points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
+        return points

anyocr/text_rec.py

@@ -0,0 +1,281 @@
+from typing import Any, Optional,Dict, List, Tuple, Union
+import math
+import time
+import cv2
+import numpy as np
+from pathlib import Path
+from anyocr.infer_engine import OrtInferSession
+
+
+class CTCLabelDecode:
+    def __init__(
+        self,
+        character: Optional[List[str]] = None,
+        character_path: Union[str, Path, None] = None,
+    ):
+        self.character = self.get_character(character, character_path)
+        self.dict = {char: i for i, char in enumerate(self.character)}
+
+    def __call__(
+        self, preds: np.ndarray, return_word_box: bool = False, **kwargs
+    ) -> List[Tuple[str, float]]:
+        preds_idx = preds.argmax(axis=2)
+        preds_prob = preds.max(axis=2)
+        text = self.decode(
+            preds_idx, preds_prob, return_word_box, is_remove_duplicate=True
+        )
+        if return_word_box:
+            for rec_idx, rec in enumerate(text):
+                wh_ratio = kwargs["wh_ratio_list"][rec_idx]
+                max_wh_ratio = kwargs["max_wh_ratio"]
+                rec[2][0] = rec[2][0] * (wh_ratio / max_wh_ratio)
+        return text
+
+    def get_character(
+        self,
+        character: Optional[List[str]] = None,
+        character_path: Union[str, Path, None] = None,
+    ) -> List[str]:
+        if character is None and character_path is None:
+            raise ValueError("character must not be None")
+
+        character_list = None
+        if character:
+            character_list = character
+
+        if character_path:
+            character_list = self.read_character_file(character_path)
+
+        if character_list is None:
+            raise ValueError("character must not be None")
+
+        character_list = self.insert_special_char(
+            character_list, " ", len(character_list)
+        )
+        character_list = self.insert_special_char(character_list, "blank", 0)
+        return character_list
+
+    @staticmethod
+    def read_character_file(character_path: Union[str, Path]) -> List[str]:
+        character_list = []
+        with open(character_path, "rb") as f:
+            lines = f.readlines()
+            for line in lines:
+                line = line.decode("utf-8").strip("\n").strip("\r\n")
+                character_list.append(line)
+        return character_list
+
+    @staticmethod
+    def insert_special_char(
+        character_list: List[str], special_char: str, loc: int = -1
+    ) -> List[str]:
+        character_list.insert(loc, special_char)
+        return character_list
+
+    def decode(
+        self,
+        text_index: np.ndarray,
+        text_prob: Optional[np.ndarray] = None,
+        return_word_box: bool = False,
+        is_remove_duplicate: bool = False,
+    ) -> List[Tuple[str, float]]:
+        """convert text-index into text-label."""
+        result_list = []
+        ignored_tokens = self.get_ignored_tokens()
+        batch_size = len(text_index)
+        for batch_idx in range(batch_size):
+            selection = np.ones(len(text_index[batch_idx]), dtype=bool)
+            if is_remove_duplicate:
+                selection[1:] = text_index[batch_idx][1:] != text_index[batch_idx][:-1]
+
+            for ignored_token in ignored_tokens:
+                selection &= text_index[batch_idx] != ignored_token
+
+            if text_prob is not None:
+                conf_list = np.array(text_prob[batch_idx][selection]).tolist()
+            else:
+                conf_list = [1] * len(selection)
+
+            if len(conf_list) == 0:
+                conf_list = [0]
+
+            char_list = [
+                self.character[text_id] for text_id in text_index[batch_idx][selection]
+            ]
+            text = "".join(char_list)
+            if return_word_box:
+                word_list, word_col_list, state_list = self.get_word_info(
+                    text, selection
+                )
+                result_list.append(
+                    (
+                        text,
+                        np.mean(conf_list).tolist(),
+                        [
+                            len(text_index[batch_idx]),
+                            word_list,
+                            word_col_list,
+                            state_list,
+                            conf_list,
+                        ],
+                    )
+                )
+            else:
+                result_list.append((text, np.mean(conf_list).tolist()))
+        return result_list
+
+    @staticmethod
+    def get_word_info(
+        text: str, selection: np.ndarray
+    ) -> Tuple[List[List[str]], List[List[int]], List[str]]:
+        """
+        Group the decoded characters and record the corresponding decoded positions.
+        from https://github.com/PaddlePaddle/PaddleOCR/blob/fbba2178d7093f1dffca65a5b963ec277f1a6125/ppocr/postprocess/rec_postprocess.py#L70
+
+        Args:
+            text: the decoded text
+            selection: the bool array that identifies which columns of features are decoded as non-separated characters
+        Returns:
+            word_list: list of the grouped words
+            word_col_list: list of decoding positions corresponding to each character in the grouped word
+            state_list: list of marker to identify the type of grouping words, including two types of grouping words:
+                        - 'cn': continous chinese characters (e.g., 你好啊)
+                        - 'en&num': continous english characters (e.g., hello), number (e.g., 123, 1.123), or mixed of them connected by '-' (e.g., VGG-16)
+        """
+        state = None
+        word_content = []
+        word_col_content = []
+        word_list = []
+        word_col_list = []
+        state_list = []
+        valid_col = np.where(selection)[0]
+        col_width = np.zeros(valid_col.shape)
+        if len(valid_col) > 0:
+            col_width[1:] = valid_col[1:] - valid_col[:-1]
+            col_width[0] = min(
+                3 if "\u4e00" <= text[0] <= "\u9fff" else 2, int(valid_col[0])
+            )
+
+        for c_i, char in enumerate(text):
+            if "\u4e00" <= char <= "\u9fff":
+                c_state = "cn"
+            else:
+                c_state = "en&num"
+
+            if state is None:
+                state = c_state
+
+            if state != c_state or col_width[c_i] > 4:
+                if len(word_content) != 0:
+                    word_list.append(word_content)
+                    word_col_list.append(word_col_content)
+                    state_list.append(state)
+                    word_content = []
+                    word_col_content = []
+                state = c_state
+
+            word_content.append(char)
+            word_col_content.append(int(valid_col[c_i]))
+
+        if len(word_content) != 0:
+            word_list.append(word_content)
+            word_col_list.append(word_col_content)
+            state_list.append(state)
+
+        return word_list, word_col_list, state_list
+
+    @staticmethod
+    def get_ignored_tokens() -> List[int]:
+        return [0]  # for ctc blank
+
+
+class TextRecognizer:
+    def __init__(self, config: Dict[str, Any]):
+        self.session = OrtInferSession(config)
+
+        character = None
+        if self.session.have_key():
+            character = self.session.get_character_list()
+
+        character_path = config.get("rec_keys_path", None)
+        self.postprocess_op = CTCLabelDecode(
+            character=character, character_path=character_path
+        )
+
+        self.rec_batch_num = config["rec_batch_num"]
+        self.rec_image_shape = config["rec_img_shape"]
+
+    def __call__(
+        self,
+        img_list: Union[np.ndarray, List[np.ndarray]],
+        return_word_box: bool = False,
+    ) -> Tuple[List[Tuple[str, float]], float]:
+        if isinstance(img_list, np.ndarray):
+            img_list = [img_list]
+
+        # Calculate the aspect ratio of all text bars
+        width_list = [img.shape[1] / float(img.shape[0]) for img in img_list]
+
+        # Sorting can speed up the recognition process
+        indices = np.argsort(np.array(width_list))
+
+        img_num = len(img_list)
+        rec_res = [("", 0.0)] * img_num
+
+        batch_num = self.rec_batch_num
+        elapse = 0
+        for beg_img_no in range(0, img_num, batch_num):
+            end_img_no = min(img_num, beg_img_no + batch_num)
+
+            # Parameter Alignment for PaddleOCR
+            imgC, imgH, imgW = self.rec_image_shape[:3]
+            max_wh_ratio = imgW / imgH
+            wh_ratio_list = []
+            for ino in range(beg_img_no, end_img_no):
+                h, w = img_list[indices[ino]].shape[0:2]
+                wh_ratio = w * 1.0 / h
+                max_wh_ratio = max(max_wh_ratio, wh_ratio)
+                wh_ratio_list.append(wh_ratio)
+
+            norm_img_batch = []
+            for ino in range(beg_img_no, end_img_no):
+                norm_img = self.resize_norm_img(img_list[indices[ino]], max_wh_ratio)
+                norm_img_batch.append(norm_img[np.newaxis, :])
+            norm_img_batch = np.concatenate(norm_img_batch).astype(np.float32)
+
+            starttime = time.time()
+            preds = self.session(norm_img_batch)[0]
+            rec_result = self.postprocess_op(
+                preds,
+                return_word_box,
+                wh_ratio_list=wh_ratio_list,
+                max_wh_ratio=max_wh_ratio,
+            )
+
+            for rno, one_res in enumerate(rec_result):
+                rec_res[indices[beg_img_no + rno]] = one_res
+            elapse += time.time() - starttime
+        return rec_res, elapse
+
+    def resize_norm_img(self, img: np.ndarray, max_wh_ratio: float) -> np.ndarray:
+        img_channel, img_height, img_width = self.rec_image_shape
+        assert img_channel == img.shape[2]
+
+        img_width = int(img_height * max_wh_ratio)
+
+        h, w = img.shape[:2]
+        ratio = w / float(h)
+        if math.ceil(img_height * ratio) > img_width:
+            resized_w = img_width
+        else:
+            resized_w = int(math.ceil(img_height * ratio))
+
+        resized_image = cv2.resize(img, (resized_w, img_height))
+        resized_image = resized_image.astype("float32")
+        resized_image = resized_image.transpose((2, 0, 1)) / 255
+        resized_image -= 0.5
+        resized_image /= 0.5
+
+        padding_im = np.zeros((img_channel, img_height, img_width), dtype=np.float32)
+        padding_im[:, :, 0:resized_w] = resized_image
+        return padding_im

anyocr/utils.py

@@ -0,0 +1,198 @@
+from typing import Any, Tuple,Dict, List, Optional, Union
+import cv2
+import numpy as np
+from pathlib import Path
+from io import BytesIO
+from PIL import Image, UnidentifiedImageError
+
+
+
+def reduce_max_side(
+    img: np.ndarray, max_side_len: int = 2000
+) -> Tuple[np.ndarray, float, float]:
+    h, w = img.shape[:2]
+
+    ratio = 1.0
+    if max(h, w) > max_side_len:
+        if h > w:
+            ratio = float(max_side_len) / h
+        else:
+            ratio = float(max_side_len) / w
+
+    resize_h = int(h * ratio)
+    resize_w = int(w * ratio)
+
+    resize_h = int(round(resize_h / 32) * 32)
+    resize_w = int(round(resize_w / 32) * 32)
+
+    try:
+        if int(resize_w) <= 0 or int(resize_h) <= 0:
+            raise ResizeImgError("resize_w or resize_h is less than or equal to 0")
+        img = cv2.resize(img, (resize_w, resize_h))
+    except Exception as exc:
+        raise ResizeImgError() from exc
+
+    ratio_h = h / resize_h
+    ratio_w = w / resize_w
+    return img, ratio_h, ratio_w
+
+
+def increase_min_side(
+    img: np.ndarray, min_side_len: int = 30
+) -> Tuple[np.ndarray, float, float]:
+    h, w = img.shape[:2]
+
+    ratio = 1.0
+    if min(h, w) < min_side_len:
+        if h < w:
+            ratio = float(min_side_len) / h
+        else:
+            ratio = float(min_side_len) / w
+
+    resize_h = int(h * ratio)
+    resize_w = int(w * ratio)
+
+    resize_h = int(round(resize_h / 32) * 32)
+    resize_w = int(round(resize_w / 32) * 32)
+
+    try:
+        if int(resize_w) <= 0 or int(resize_h) <= 0:
+            raise ResizeImgError("resize_w or resize_h is less than or equal to 0")
+        img = cv2.resize(img, (resize_w, resize_h))
+    except Exception as exc:
+        raise ResizeImgError() from exc
+
+    ratio_h = h / resize_h
+    ratio_w = w / resize_w
+    return img, ratio_h, ratio_w
+
+
+def add_round_letterbox(
+    img: np.ndarray,
+    padding_tuple: Tuple[int, int, int, int],
+) -> np.ndarray:
+    padded_img = cv2.copyMakeBorder(
+        img,
+        padding_tuple[0],
+        padding_tuple[1],
+        padding_tuple[2],
+        padding_tuple[3],
+        cv2.BORDER_CONSTANT,
+        value=(0, 0, 0),
+    )
+    return padded_img
+
+
+class ResizeImgError(Exception):
+    pass
+
+
+InputType = Union[str, np.ndarray, bytes, Path, Image.Image]
+class LoadImage:
+    def __init__(self):
+        pass
+
+    def __call__(self, img: InputType) -> np.ndarray:
+        if not isinstance(img, InputType.__args__):
+            raise LoadImageError(
+                f"The img type {type(img)} does not in {InputType.__args__}"
+            )
+
+        origin_img_type = type(img)
+        img = self.load_img(img)
+        img = self.convert_img(img, origin_img_type)
+        return img
+
+    def load_img(self, img: InputType) -> np.ndarray:
+        if isinstance(img, (str, Path)):
+            self.verify_exist(img)
+            try:
+                img = self.img_to_ndarray(Image.open(img))
+            except UnidentifiedImageError as e:
+                raise LoadImageError(f"cannot identify image file {img}") from e
+            return img
+
+        if isinstance(img, bytes):
+            img = self.img_to_ndarray(Image.open(BytesIO(img)))
+            return img
+
+        if isinstance(img, np.ndarray):
+            return img
+
+        if isinstance(img, Image.Image):
+            return self.img_to_ndarray(img)
+
+        raise LoadImageError(f"{type(img)} is not supported!")
+
+    def img_to_ndarray(self, img: Image.Image) -> np.ndarray:
+        if img.mode == "1":
+            img = img.convert("L")
+            return np.array(img)
+        return np.array(img)
+
+    def convert_img(self, img: np.ndarray, origin_img_type: Any) -> np.ndarray:
+        if img.ndim == 2:
+            return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+        if img.ndim == 3:
+            channel = img.shape[2]
+            if channel == 1:
+                return cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+            if channel == 2:
+                return self.cvt_two_to_three(img)
+
+            if channel == 3:
+                if issubclass(origin_img_type, (str, Path, bytes, Image.Image)):
+                    return cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+                return img
+
+            if channel == 4:
+                return self.cvt_four_to_three(img)
+
+            raise LoadImageError(
+                f"The channel({channel}) of the img is not in [1, 2, 3, 4]"
+            )
+
+        raise LoadImageError(f"The ndim({img.ndim}) of the img is not in [2, 3]")
+
+    @staticmethod
+    def cvt_two_to_three(img: np.ndarray) -> np.ndarray:
+        """gray + alpha → BGR"""
+        img_gray = img[..., 0]
+        img_bgr = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)
+
+        img_alpha = img[..., 1]
+        not_a = cv2.bitwise_not(img_alpha)
+        not_a = cv2.cvtColor(not_a, cv2.COLOR_GRAY2BGR)
+
+        new_img = cv2.bitwise_and(img_bgr, img_bgr, mask=img_alpha)
+        new_img = cv2.add(new_img, not_a)
+        return new_img
+
+    @staticmethod
+    def cvt_four_to_three(img: np.ndarray) -> np.ndarray:
+        """RGBA → BGR"""
+        r, g, b, a = cv2.split(img)
+        new_img = cv2.merge((b, g, r))
+
+        not_a = cv2.bitwise_not(a)
+        not_a = cv2.cvtColor(not_a, cv2.COLOR_GRAY2BGR)
+
+        new_img = cv2.bitwise_and(new_img, new_img, mask=a)
+
+        mean_color = np.mean(new_img)
+        if mean_color <= 0.0:
+            new_img = cv2.add(new_img, not_a)
+        else:
+            new_img = cv2.bitwise_not(new_img)
+        return new_img
+
+    @staticmethod
+    def verify_exist(file_path: Union[str, Path]):
+        if not Path(file_path).exists():
+            raise LoadImageError(f"{file_path} does not exist.")
+
+
+class LoadImageError(Exception):
+    pass

anyocr/version.py

@@ -0,0 +1,2 @@
+__version__ = "4.0.0"
+__author__ = "AnyOCR"