Merge remote-tracking branch 'origin/0.3.0-doc-fix' into 0.3.0-doc-fix

sync

python/examples/ocr/README.md

+# OCR 
+
+## Get Model
+```
+python -m paddle_serving_app.package --get_model ocr_rec
+tar -xzvf ocr_rec.tar.gz
+```
+
+## RPC Service
+
+### Start Service
+
+```
+python -m paddle_serving_server.serve --model ocr_rec_model --port 9292
+```
+
+### Client Prediction
+
+```
+python test_ocr_rec_client.py
+```

python/examples/ocr/test_ocr_rec_client.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.
+
+from paddle_serving_client import Client
+from paddle_serving_app.reader import OCRReader
+import cv2
+
+client = Client()
+client.load_client_config("ocr_rec_client/serving_client_conf.prototxt")
+client.connect(["127.0.0.1:9292"])
+
+image_file_list = ["./test_rec.jpg"]
+img = cv2.imread(image_file_list[0])
+ocr_reader = OCRReader()
+feed = {"image": ocr_reader.preprocess([img])}
+fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
+fetch_map = client.predict(feed=feed, fetch=fetch)
+rec_res = ocr_reader.postprocess(fetch_map)
+print(image_file_list[0])
+print(rec_res[0][0])

python/paddle_serving_app/models/model_list.py

@@ -31,10 +31,12 @@ class ServingModels(object):
         self.model_dict["ImageClassification"] = [
             "resnet_v2_50_imagenet", "mobilenet_v2_imagenet"
         ]
+        self.model_dict["OCR"] = ["ocr_rec"]
 
         image_class_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageClassification/"
         image_seg_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ImageSegmentation/"
         object_detection_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/ObjectDetection/"
+        ocr_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/image/OCR/"
         senta_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SentimentAnalysis/"
         semantic_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/SemanticRepresentation/"
         wordseg_url = "https://paddle-serving.bj.bcebos.com/paddle_hub_models/text/LexicalAnalysis/"
@@ -52,6 +54,7 @@ class ServingModels(object):
         pack_url(self.model_dict, "ObjectDetection", object_detection_url)
         pack_url(self.model_dict, "ImageSegmentation", image_seg_url)
         pack_url(self.model_dict, "ImageClassification", image_class_url)
+        pack_url(self.model_dict, "OCR", ocr_url)
 
     def get_model_list(self):
         return self.model_dict

python/paddle_serving_app/reader/__init__.py

@@ -12,7 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from .chinese_bert_reader import ChineseBertReader
-from .image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize, CenterCrop, Resize, Transpose, Div, RGB2BGR, BGR2RGB, RCNNPostprocess, SegPostprocess, PadStride
+from .image_reader import ImageReader, File2Image, URL2Image, Sequential, Normalize
+from .image_reader import CenterCrop, Resize, Transpose, Div, RGB2BGR, BGR2RGB
+from .image_reader import RCNNPostprocess, SegPostprocess, PadStride
 from .lac_reader import LACReader
 from .senta_reader import SentaReader
 from .imdb_reader import IMDBDataset
+from .ocr_reader import OCRReader

python/paddle_serving_app/reader/ocr_reader.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.
+
+import cv2
+import copy
+import numpy as np
+import math
+import re
+import sys
+import argparse
+from paddle_serving_app.reader import Sequential, Resize, Transpose, Div, Normalize
+
+
+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):
+        args = self.parse_args()
+        image_shape = [int(v) for v in args.rec_image_shape.split(",")]
+        self.rec_image_shape = image_shape
+        self.character_type = args.rec_char_type
+        self.rec_batch_num = args.rec_batch_num
+        char_ops_params = {}
+        char_ops_params["character_type"] = args.rec_char_type
+        char_ops_params["character_dict_path"] = args.rec_char_dict_path
+        char_ops_params['loss_type'] = 'ctc'
+        self.char_ops = CharacterOps(char_ops_params)
+
+    def parse_args(self):
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--rec_algorithm", type=str, default='CRNN')
+        parser.add_argument("--rec_model_dir", type=str)
+        parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
+        parser.add_argument("--rec_char_type", type=str, default='ch')
+        parser.add_argument("--rec_batch_num", type=int, default=1)
+        parser.add_argument(
+            "--rec_char_dict_path", type=str, default="./ppocr_keys_v1.txt")
+        return parser.parse_args()
+
+    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))
+
+        seq = Sequential([
+            Resize(imgH, resized_w), Transpose((2, 0, 1)), Div(255),
+            Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5], True)
+        ])
+        resized_image = seq(img)
+        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(self, outputs):
+        rec_res = []
+        rec_idx_lod = outputs["ctc_greedy_decoder_0.tmp_0.lod"]
+        predict_lod = outputs["softmax_0.tmp_0.lod"]
+        rec_idx_batch = outputs["ctc_greedy_decoder_0.tmp_0"]
+        for rno in range(len(rec_idx_lod) - 1):
+            beg = rec_idx_lod[rno]
+            end = rec_idx_lod[rno + 1]
+            rec_idx_tmp = rec_idx_batch[beg:end, 0]
+            preds_text = self.char_ops.decode(rec_idx_tmp)
+            beg = predict_lod[rno]
+            end = predict_lod[rno + 1]
+            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])
+        return rec_res