update pdserving for 2.1

deploy/pdserving/win/__init__.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

deploy/pdserving/win/ocr_reader.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import cv2
-import copy
-import numpy as np
-import math
-import re
-import sys
-import argparse
-import string
-from copy import deepcopy
-
-
-class DetResizeForTest(object):
-    def __init__(self, **kwargs):
-        super(DetResizeForTest, self).__init__()
-        self.resize_type = 0
-        if 'image_shape' in kwargs:
-            self.image_shape = kwargs['image_shape']
-            self.resize_type = 1
-        elif 'limit_side_len' in kwargs:
-            self.limit_side_len = kwargs['limit_side_len']
-            self.limit_type = kwargs.get('limit_type', 'min')
-        elif 'resize_short' in kwargs:
-            self.limit_side_len = 736
-            self.limit_type = 'min'
-        else:
-            self.resize_type = 2
-            self.resize_long = kwargs.get('resize_long', 960)
-
-    def __call__(self, data):
-        img = deepcopy(data)
-        src_h, src_w, _ = img.shape
-
-        if self.resize_type == 0:
-            img, [ratio_h, ratio_w] = self.resize_image_type0(img)
-        elif self.resize_type == 2:
-            img, [ratio_h, ratio_w] = self.resize_image_type2(img)
-        else:
-            img, [ratio_h, ratio_w] = self.resize_image_type1(img)
-
-        return img
-
-    def resize_image_type1(self, img):
-        resize_h, resize_w = self.image_shape
-        ori_h, ori_w = img.shape[:2]  # (h, w, c)
-        ratio_h = float(resize_h) / ori_h
-        ratio_w = float(resize_w) / ori_w
-        img = cv2.resize(img, (int(resize_w), int(resize_h)))
-        return img, [ratio_h, ratio_w]
-
-    def resize_image_type0(self, img):
-        """
-        resize image to a size multiple of 32 which is required by the network
-        args:
-            img(array): array with shape [h, w, c]
-        return(tuple):
-            img, (ratio_h, ratio_w)
-        """
-        limit_side_len = self.limit_side_len
-        h, w, _ = img.shape
-
-        # limit the max side
-        if self.limit_type == 'max':
-            if max(h, w) > limit_side_len:
-                if h > w:
-                    ratio = float(limit_side_len) / h
-                else:
-                    ratio = float(limit_side_len) / w
-            else:
-                ratio = 1.
-        else:
-            if min(h, w) < limit_side_len:
-                if h < w:
-                    ratio = float(limit_side_len) / h
-                else:
-                    ratio = float(limit_side_len) / w
-            else:
-                ratio = 1.
-        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, (None, None)
-            img = cv2.resize(img, (int(resize_w), int(resize_h)))
-        except:
-            print(img.shape, resize_w, resize_h)
-            sys.exit(0)
-        ratio_h = resize_h / float(h)
-        ratio_w = resize_w / float(w)
-        # return img, np.array([h, w])
-        return img, [ratio_h, ratio_w]
-
-    def resize_image_type2(self, img):
-        h, w, _ = img.shape
-
-        resize_w = w
-        resize_h = h
-
-        # Fix the longer side
-        if resize_h > resize_w:
-            ratio = float(self.resize_long) / resize_h
-        else:
-            ratio = float(self.resize_long) / resize_w
-
-        resize_h = int(resize_h * ratio)
-        resize_w = int(resize_w * ratio)
-
-        max_stride = 128
-        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
-        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
-        img = cv2.resize(img, (int(resize_w), int(resize_h)))
-        ratio_h = resize_h / float(h)
-        ratio_w = resize_w / float(w)
-
-        return img, [ratio_h, ratio_w]
-
-
-class BaseRecLabelDecode(object):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, config):
-        support_character_type = [
-            'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
-            'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
-            'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
-            'ne', 'EN'
-        ]
-        character_type = config['character_type']
-        character_dict_path = config['character_dict_path']
-        use_space_char = True
-        assert character_type in support_character_type, "Only {} are supported now but get {}".format(
-            support_character_type, character_type)
-
-        self.beg_str = "sos"
-        self.end_str = "eos"
-
-        if character_type == "en":
-            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
-            dict_character = list(self.character_str)
-        elif character_type == "EN_symbol":
-            # same with ASTER setting (use 94 char).
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        elif character_type in support_character_type:
-            self.character_str = ""
-            assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
-                character_type)
-            with open(character_dict_path, "rb") as fin:
-                lines = fin.readlines()
-                for line in lines:
-                    line = line.decode('utf-8').strip("\n").strip("\r\n")
-                    self.character_str += line
-            if use_space_char:
-                self.character_str += " "
-            dict_character = list(self.character_str)
-
-        else:
-            raise NotImplementedError
-        self.character_type = character_type
-        dict_character = self.add_special_char(dict_character)
-        self.dict = {}
-        for i, char in enumerate(dict_character):
-            self.dict[char] = i
-        self.character = dict_character
-
-    def add_special_char(self, dict_character):
-        return dict_character
-
-    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
-        """ 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):
-            char_list = []
-            conf_list = []
-            for idx in range(len(text_index[batch_idx])):
-                if text_index[batch_idx][idx] in ignored_tokens:
-                    continue
-                if is_remove_duplicate:
-                    # only for predict
-                    if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
-                            batch_idx][idx]:
-                        continue
-                char_list.append(self.character[int(text_index[batch_idx][
-                    idx])])
-                if text_prob is not None:
-                    conf_list.append(text_prob[batch_idx][idx])
-                else:
-                    conf_list.append(1)
-            text = ''.join(char_list)
-            result_list.append((text, np.mean(conf_list)))
-        return result_list
-
-    def get_ignored_tokens(self):
-        return [0]  # for ctc blank
-
-
-class CTCLabelDecode(BaseRecLabelDecode):
-    """ Convert between text-label and text-index """
-
-    def __init__(
-            self,
-            config,
-            #character_dict_path=None,
-            #character_type='ch',
-            #use_space_char=False,
-            **kwargs):
-        super(CTCLabelDecode, self).__init__(config)
-
-    def __call__(self, preds, label=None, *args, **kwargs):
-        preds_idx = preds.argmax(axis=2)
-        preds_prob = preds.max(axis=2)
-        text = self.decode(preds_idx, preds_prob, is_remove_duplicate=True)
-        if label is None:
-            return text
-        label = self.decode(label)
-        return text, label
-
-    def add_special_char(self, dict_character):
-        dict_character = ['blank'] + dict_character
-        return dict_character
-
-
-class CharacterOps(object):
-    """ Convert between text-label and text-index """
-
-    def __init__(self, config):
-        self.character_type = config['character_type']
-        self.loss_type = config['loss_type']
-        if self.character_type == "en":
-            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
-            dict_character = list(self.character_str)
-        elif self.character_type == "ch":
-            character_dict_path = config['character_dict_path']
-            self.character_str = ""
-            with open(character_dict_path, "rb") as fin:
-                lines = fin.readlines()
-                for line in lines:
-                    line = line.decode('utf-8').strip("\n").strip("\r\n")
-                    self.character_str += line
-            dict_character = list(self.character_str)
-        elif self.character_type == "en_sensitive":
-            # same with ASTER setting (use 94 char).
-            self.character_str = string.printable[:-6]
-            dict_character = list(self.character_str)
-        else:
-            self.character_str = None
-        assert self.character_str is not None, \
-            "Nonsupport type of the character: {}".format(self.character_str)
-        self.beg_str = "sos"
-        self.end_str = "eos"
-        if self.loss_type == "attention":
-            dict_character = [self.beg_str, self.end_str] + dict_character
-        self.dict = {}
-        for i, char in enumerate(dict_character):
-            self.dict[char] = i
-        self.character = dict_character
-
-    def encode(self, text):
-        """convert text-label into text-index.
-        input:
-            text: text labels of each image. [batch_size]
-
-        output:
-            text: concatenated text index for CTCLoss.
-                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
-            length: length of each text. [batch_size]
-        """
-        if self.character_type == "en":
-            text = text.lower()
-
-        text_list = []
-        for char in text:
-            if char not in self.dict:
-                continue
-            text_list.append(self.dict[char])
-        text = np.array(text_list)
-        return text
-
-    def decode(self, text_index, is_remove_duplicate=False):
-        """ convert text-index into text-label. """
-        char_list = []
-        char_num = self.get_char_num()
-
-        if self.loss_type == "attention":
-            beg_idx = self.get_beg_end_flag_idx("beg")
-            end_idx = self.get_beg_end_flag_idx("end")
-            ignored_tokens = [beg_idx, end_idx]
-        else:
-            ignored_tokens = [char_num]
-
-        for idx in range(len(text_index)):
-            if text_index[idx] in ignored_tokens:
-                continue
-            if is_remove_duplicate:
-                if idx > 0 and text_index[idx - 1] == text_index[idx]:
-                    continue
-            char_list.append(self.character[text_index[idx]])
-        text = ''.join(char_list)
-        return text
-
-    def get_char_num(self):
-        return len(self.character)
-
-    def get_beg_end_flag_idx(self, beg_or_end):
-        if self.loss_type == "attention":
-            if beg_or_end == "beg":
-                idx = np.array(self.dict[self.beg_str])
-            elif beg_or_end == "end":
-                idx = np.array(self.dict[self.end_str])
-            else:
-                assert False, "Unsupport type %s in get_beg_end_flag_idx"\
-                    % beg_or_end
-            return idx
-        else:
-            err = "error in get_beg_end_flag_idx when using the loss %s"\
-                % (self.loss_type)
-            assert False, err
-
-
-class OCRReader(object):
-    def __init__(self,
-                 algorithm="CRNN",
-                 image_shape=[3, 32, 320],
-                 char_type="ch",
-                 batch_num=1,
-                 char_dict_path="./ppocr_keys_v1.txt"):
-        self.rec_image_shape = image_shape
-        self.character_type = char_type
-        self.rec_batch_num = batch_num
-        char_ops_params = {}
-        char_ops_params["character_type"] = char_type
-        char_ops_params["character_dict_path"] = char_dict_path
-        char_ops_params['loss_type'] = 'ctc'
-        self.char_ops = CharacterOps(char_ops_params)
-        self.label_ops = CTCLabelDecode(char_ops_params)
-
-    def resize_norm_img(self, img, max_wh_ratio):
-        imgC, imgH, imgW = self.rec_image_shape
-        if self.character_type == "ch":
-            imgW = int(32 * max_wh_ratio)
-        h = img.shape[0]
-        w = img.shape[1]
-        ratio = w / float(h)
-        if math.ceil(imgH * ratio) > imgW:
-            resized_w = imgW
-        else:
-            resized_w = int(math.ceil(imgH * ratio))
-        resized_image = cv2.resize(img, (resized_w, imgH))
-        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((imgC, imgH, imgW), dtype=np.float32)
-
-        padding_im[:, :, 0:resized_w] = resized_image
-        return padding_im
-
-    def preprocess(self, img_list):
-        img_num = len(img_list)
-        norm_img_batch = []
-        max_wh_ratio = 0
-        for ino in range(img_num):
-            h, w = img_list[ino].shape[0:2]
-            wh_ratio = w * 1.0 / h
-            max_wh_ratio = max(max_wh_ratio, wh_ratio)
-
-        for ino in range(img_num):
-            norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
-            norm_img = norm_img[np.newaxis, :]
-            norm_img_batch.append(norm_img)
-        norm_img_batch = np.concatenate(norm_img_batch)
-        norm_img_batch = norm_img_batch.copy()
-
-        return norm_img_batch[0]
-
-    def postprocess_old(self, outputs, with_score=False):
-        rec_res = []
-        rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
-        rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
-        if with_score:
-            predict_lod = outputs["softmax_0.tmp_0.lod"]
-        for rno in range(len(rec_idx_lod) - 1):
-            beg = rec_idx_lod[rno]
-            end = rec_idx_lod[rno + 1]
-            if isinstance(rec_idx_batch, list):
-                rec_idx_tmp = [x[0] for x in rec_idx_batch[beg:end]]
-            else:  #nd array
-                rec_idx_tmp = rec_idx_batch[beg:end, 0]
-            preds_text = self.char_ops.decode(rec_idx_tmp)
-            if with_score:
-                beg = predict_lod[rno]
-                end = predict_lod[rno + 1]
-                if isinstance(outputs["softmax_0.tmp_0"], list):
-                    outputs["softmax_0.tmp_0"] = np.array(outputs[
-                        "softmax_0.tmp_0"]).astype(np.float32)
-                probs = outputs["softmax_0.tmp_0"][beg:end, :]
-                ind = np.argmax(probs, axis=1)
-                blank = probs.shape[1]
-                valid_ind = np.where(ind != (blank - 1))[0]
-                score = np.mean(probs[valid_ind, ind[valid_ind]])
-                rec_res.append([preds_text, score])
-            else:
-                rec_res.append([preds_text])
-        return rec_res
-
-    def postprocess(self, outputs, with_score=False):
-        preds = outputs["save_infer_model/scale_0.tmp_1"]
-        try:
-            preds = preds.numpy()
-        except:
-            pass
-        preds_idx = preds.argmax(axis=2)
-        preds_prob = preds.max(axis=2)
-        text = self.label_ops.decode(
-            preds_idx, preds_prob, is_remove_duplicate=True)
-        return text

deploy/pdserving/win/ocr_web_server.py

@@ -22,6 +22,7 @@ from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
 from paddle_serving_app.reader import Div, Normalize, Transpose
 from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
 from ocr_reader import OCRReader
+from ..ocr_reader import OCRReader
 try:
     from paddle_serving_server_gpu.web_service import WebService
 except ImportError: