From bb2a0273dde438f5705007908fb0852cab26933a Mon Sep 17 00:00:00 2001 From: LDOUBLEV Date: Mon, 22 Mar 2021 16:15:02 +0800 Subject: [PATCH] add pdserver demo to dygraph --- deploy/README_CN.md | 132 ++++++++++ deploy/__init__.py | 13 + deploy/config.yml | 71 ++++++ deploy/imgs/results.png | Bin 0 -> 122294 bytes deploy/imgs/start_server.png | Bin 0 -> 199244 bytes deploy/ocr_reader.py | 438 +++++++++++++++++++++++++++++++++ deploy/pipeline_http_client.py | 40 +++ deploy/web_service.py | 127 ++++++++++ 8 files changed, 821 insertions(+) create mode 100644 deploy/README_CN.md create mode 100644 deploy/__init__.py create mode 100644 deploy/config.yml create mode 100644 deploy/imgs/results.png create mode 100644 deploy/imgs/start_server.png create mode 100644 deploy/ocr_reader.py create mode 100644 deploy/pipeline_http_client.py create mode 100644 deploy/web_service.py diff --git a/deploy/README_CN.md b/deploy/README_CN.md new file mode 100644 index 00000000..9f503310 --- /dev/null +++ b/deploy/README_CN.md @@ -0,0 +1,132 @@ +# PPOCR 服务化部署 + +([English](./README.md)|简体中文) + +本文档将介绍如何使用[PaddleServing](https://github.com/PaddlePaddle/Serving/blob/develop/README_CN.md)工具部署PPOCR +动态图模型的pipeline在线服务。 + +**note**: Paddle Serving服务化部署框架介绍和使用教程参考[文档](https://aistudio.baidu.com/aistudio/projectdetail/1550674)。 + +## 目录 +- 环境准备 +- 模型转换 +- Paddle Serving pipeline部署 +- FAQ + + +## 环境准备 + +需要准备PaddleOCR的运行环境和Paddle Serving的运行环境。 + +1. 准备PaddleOCR的运行环境参考[链接](../../doc/doc_ch/installation.md) + +2. 准备PaddleServing的运行环境,步骤如下 + +安装serving,用于启动服务 +``` +pip3 install paddle-serving-server==0.5.0 # for CPU +pip3 install paddle-serving-server-gpu==0.5.0 # for GPU +# 其他GPU环境需要确认环境再选择执行如下命令 +pip3 install paddle-serving-server-gpu==0.5.0.post9 # GPU with CUDA9.0 +pip3 install paddle-serving-server-gpu==0.5.0.post10 # GPU with CUDA10.0 +pip3 install paddle-serving-server-gpu==0.5.0.post101 # GPU with CUDA10.1 + TensorRT6 +pip3 install paddle-serving-server-gpu==0.5.0.post11 # GPU with CUDA10.1 + TensorRT7 +``` + +2. 安装client,用于向服务发送请求 +``` +pip3 install paddle-serving-client==0.5.0 # for CPU + +pip3 install paddle-serving-client-gpu==0.5.0 # for GPU +``` + +3. 安装serving-app +``` +pip3 install paddle-serving-app==0.3.0 +``` + +**note:** 如果要安装最新版本的PaddleServing参考[链接](https://github.com/PaddlePaddle/Serving/blob/develop/doc/LATEST_PACKAGES.md)。 + +## 模型转换 +使用PaddleServing做服务化部署时,需要将保存的inference模型转换为serving易于部署的模型。 + +首先,下载PPOCR的[inference模型](https://github.com/PaddlePaddle/PaddleOCR#pp-ocr-20-series-model-listupdate-on-dec-15) +``` +# 下载并解压 OCR 文本检测模型 +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar && tar xf ch_ppocr_server_v2.0_det_infer.tar +# 下载并解压 OCR 文本识别模型 +wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar && tar xf ch_ppocr_server_v2.0_rec_infer.tar + +# 转换检测模型 +python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_det_infer/ \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --serving_server ./ppocr_det_server_2.0_serving/ \ + --serving_client ./ppocr_det_server_2.0_client/ + +# 转换识别模型 +python3 -m paddle_serving_client.convert --dirname ./ch_ppocr_server_v2.0_rec_infer/ \ + --model_filename inference.pdmodel \ + --params_filename inference.pdiparams \ + --serving_server ./ppocr_rec_server_2.0_serving/ \ + --serving_client ./ppocr_rec_server_2.0_client/ +``` + +检测模型转换完成后,会在当前文件夹多出`ppocr_det_server_2.0_serving` 和`ppocr_det_server_2.0_client`的文件夹,具备如下格式: +``` +|- ppocr_det_server_2.0_serving/ + |- __model__ + |- __params__ + |- serving_server_conf.prototxt + |- serving_server_conf.stream.prototxt + +|- ppocr_det_server_2.0_client + |- serving_client_conf.prototxt + |- serving_client_conf.stream.prototxt + +``` +识别模型同理。 + +## Paddle Serving pipeline部署 + +``` +# 下载PaddleOCR代码,若已下载可跳过此步骤 +git clone https://github.com/PaddlePaddle/PaddleOCR + +# 进入到工作目录 +cd PaddleOCR/deploy/pdserver/ +``` +pdserver目录包含启动pipeline服务和发送预测请求的代码,包括: +``` +__init__.py +config.yml # 启动服务的配置文件 +ocr_reader.py # OCR模型预处理和后处理的代码实现 +pipeline_http_client.py # 发送pipeline预测请求的脚本 +web_service.py # 启动pipeline服务端的脚本 +``` + +启动服务可运行如下命令 +``` +# 启动服务,运行日志保存在log.txt +python3 web_service.py &>log.txt & +``` +成功启动服务后,log.txt中会打印类似如下日志 +![](./imgs/start_server.png) + + +发送服务请求 +``` +python3 pipeline_http_client.py +``` +成功运行后,模型预测的结果会打印在cmd窗口中,结果示例为: +![](./imgs/results.png) + + + +## FAQ +** Q1**: 发送请求后得不到结果 +** A1**: 启动服务和发送请求时不要设置代理,可以在启动服务前和发送请求前关闭代理,关闭代理的命令是: +``` +unset https_proxy +unset http_proxy +``` diff --git a/deploy/__init__.py b/deploy/__init__.py new file mode 100644 index 00000000..185a92b8 --- /dev/null +++ b/deploy/__init__.py @@ -0,0 +1,13 @@ +# 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. diff --git a/deploy/config.yml b/deploy/config.yml new file mode 100644 index 00000000..aef735db --- /dev/null +++ b/deploy/config.yml @@ -0,0 +1,71 @@ +#rpc端口, rpc_port和http_port不允许同时为空。当rpc_port为空且http_port不为空时,会自动将rpc_port设置为http_port+1 +rpc_port: 18090 + +#http端口, rpc_port和http_port不允许同时为空。当rpc_port可用且http_port为空时,不自动生成http_port +http_port: 9999 + +#worker_num, 最大并发数。当build_dag_each_worker=True时, 框架会创建worker_num个进程,每个进程内构建grpcSever和DAG +##当build_dag_each_worker=False时,框架会设置主线程grpc线程池的max_workers=worker_num +worker_num: 20 + +#build_dag_each_worker, False,框架在进程内创建一条DAG;True,框架会每个进程内创建多个独立的DAG +build_dag_each_worker: false + +dag: + #op资源类型, True, 为线程模型;False,为进程模型 + is_thread_op: False + + #重试次数 + retry: 1 + + #使用性能分析, True,生成Timeline性能数据,对性能有一定影响;False为不使用 + use_profile: False + + tracer: + interval_s: 10 +op: + det: + #并发数,is_thread_op=True时,为线程并发;否则为进程并发 + concurrency: 4 + + #当op配置没有server_endpoints时,从local_service_conf读取本地服务配置 + local_service_conf: + #client类型,包括brpc, grpc和local_predictor.local_predictor不启动Serving服务,进程内预测 + client_type: local_predictor + + #det模型路径 + model_config: /paddle/serving/models/det_serving_server/ #ocr_det_model + + #Fetch结果列表,以client_config中fetch_var的alias_name为准 + fetch_list: ["save_infer_model/scale_0.tmp_1"] + + #计算硬件ID,当devices为""或不写时为CPU预测;当devices为"0", "0,1,2"时为GPU预测,表示使用的GPU卡 + devices: "2" + + ir_optim: True + rec: + #并发数,is_thread_op=True时,为线程并发;否则为进程并发 + concurrency: 1 + + #超时时间, 单位ms + timeout: -1 + + #Serving交互重试次数,默认不重试 + retry: 1 + + #当op配置没有server_endpoints时,从local_service_conf读取本地服务配置 + local_service_conf: + + #client类型,包括brpc, grpc和local_predictor。local_predictor不启动Serving服务,进程内预测 + client_type: local_predictor + + #rec模型路径 + model_config: /paddle/serving/models/rec_serving_server/ #ocr_rec_model + + #Fetch结果列表,以client_config中fetch_var的alias_name为准 + fetch_list: ["save_infer_model/scale_0.tmp_1"] #["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"] + + #计算硬件ID,当devices为""或不写时为CPU预测;当devices为"0", "0,1,2"时为GPU预测,表示使用的GPU卡 + devices: "2" + + ir_optim: True diff --git a/deploy/imgs/results.png b/deploy/imgs/results.png new file mode 100644 index 0000000000000000000000000000000000000000..35322bf9462c859bb2d158dd1d50ab52f35bad41 GIT binary patch literal 122294 zcmZ^q1yoy2xA&12r$C`dacFUO3GPseTX85>Boq%0#fp{^ihFS@#UXfc_ZAE85S$R~ zOYgn!y6^q&bJwhuGiO%LBxm-X*?Z3U{Xc8IRl>)4iGzZIg0G@1uZ@C&-j0HTs)2=p 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zgl~+0`olL-IR@Ytj5TxYb8Z5BRBGq@PWaN5sSU09*YTVCH!Z5wG@j}yN;Kci;PpA{Ki2Z2Z4}BwZg&ARP_e T*uDD;@N?P3%DB?VHR68&7b|-~ literal 0 HcmV?d00001 diff --git a/deploy/ocr_reader.py b/deploy/ocr_reader.py new file mode 100644 index 00000000..95110706 --- /dev/null +++ b/deploy/ocr_reader.py @@ -0,0 +1,438 @@ +# 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 +import paddle + + +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_long' in kwargs: + self.resize_type = 2 + self.resize_long = kwargs.get('resize_long', 960) + else: + self.limit_side_len = 736 + self.limit_type = 'min' + + 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): + if isinstance(preds, paddle.Tensor): + preds = preds.numpy() + 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 diff --git a/deploy/pipeline_http_client.py b/deploy/pipeline_http_client.py new file mode 100644 index 00000000..88c4a81e --- /dev/null +++ b/deploy/pipeline_http_client.py @@ -0,0 +1,40 @@ +# 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 numpy as np +import requests +import json +import base64 +import os + + +def cv2_to_base64(image): + return base64.b64encode(image).decode('utf8') + + +url = "http://127.0.0.1:9999/ocr/prediction" +test_img_dir = "../doc/imgs/" +for idx, img_file in enumerate(os.listdir(test_img_dir)): + with open(os.path.join(test_img_dir, img_file), 'rb') as file: + image_data1 = file.read() + + image = cv2_to_base64(image_data1) + + for i in range(1): + data = {"key": ["image"], "value": [image]} + r = requests.post(url=url, data=json.dumps(data)) + print(r.json()) + +test_img_dir = "../doc/imgs/" +print("==> total number of test imgs: ", len(os.listdir(test_img_dir))) diff --git a/deploy/web_service.py b/deploy/web_service.py new file mode 100644 index 00000000..b47ef65d --- /dev/null +++ b/deploy/web_service.py @@ -0,0 +1,127 @@ +# 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. +try: + from paddle_serving_server_gpu.web_service import WebService, Op +except ImportError: + from paddle_serving_server.web_service import WebService, Op + +import logging +import numpy as np +import cv2 +import base64 +# from paddle_serving_app.reader import OCRReader +from ocr_reader import OCRReader, DetResizeForTest +from paddle_serving_app.reader import Sequential, ResizeByFactor +from paddle_serving_app.reader import Div, Normalize, Transpose +from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes + +_LOGGER = logging.getLogger() + + +class DetOp(Op): + def init_op(self): + self.det_preprocess = Sequential([ + DetResizeForTest(), Div(255), + Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), Transpose( + (2, 0, 1)) + ]) + self.filter_func = FilterBoxes(10, 10) + self.post_func = DBPostProcess({ + "thresh": 0.3, + "box_thresh": 0.5, + "max_candidates": 1000, + "unclip_ratio": 1.5, + "min_size": 3 + }) + + def preprocess(self, input_dicts, data_id, log_id): + (_, input_dict), = input_dicts.items() + data = base64.b64decode(input_dict["image"].encode('utf8')) + data = np.fromstring(data, np.uint8) + # Note: class variables(self.var) can only be used in process op mode + im = cv2.imdecode(data, cv2.IMREAD_COLOR) + self.im = im + self.ori_h, self.ori_w, _ = im.shape + + det_img = self.det_preprocess(self.im) + _, self.new_h, self.new_w = det_img.shape + print("det image shape", det_img.shape) + return {"x": det_img[np.newaxis, :].copy()}, False, None, "" + + def postprocess(self, input_dicts, fetch_dict, log_id): + print("input_dicts: ", input_dicts) + det_out = fetch_dict["save_infer_model/scale_0.tmp_1"] + ratio_list = [ + float(self.new_h) / self.ori_h, float(self.new_w) / self.ori_w + ] + dt_boxes_list = self.post_func(det_out, [ratio_list]) + dt_boxes = self.filter_func(dt_boxes_list[0], [self.ori_h, self.ori_w]) + out_dict = {"dt_boxes": dt_boxes, "image": self.im} + + print("out dict", out_dict["dt_boxes"]) + return out_dict, None, "" + + +class RecOp(Op): + def init_op(self): + self.ocr_reader = OCRReader( + char_dict_path="../../ppocr/utils/ppocr_keys_v1.txt") + + self.get_rotate_crop_image = GetRotateCropImage() + self.sorted_boxes = SortedBoxes() + + def preprocess(self, input_dicts, data_id, log_id): + (_, input_dict), = input_dicts.items() + im = input_dict["image"] + dt_boxes = input_dict["dt_boxes"] + dt_boxes = self.sorted_boxes(dt_boxes) + feed_list = [] + img_list = [] + max_wh_ratio = 0 + for i, dtbox in enumerate(dt_boxes): + boximg = self.get_rotate_crop_image(im, dt_boxes[i]) + img_list.append(boximg) + h, w = boximg.shape[0:2] + wh_ratio = w * 1.0 / h + max_wh_ratio = max(max_wh_ratio, wh_ratio) + _, w, h = self.ocr_reader.resize_norm_img(img_list[0], + max_wh_ratio).shape + + imgs = np.zeros((len(img_list), 3, w, h)).astype('float32') + for id, img in enumerate(img_list): + norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio) + imgs[id] = norm_img + print("rec image shape", imgs.shape) + feed = {"x": imgs.copy()} + return feed, False, None, "" + + def postprocess(self, input_dicts, fetch_dict, log_id): + rec_res = self.ocr_reader.postprocess(fetch_dict, with_score=True) + res_lst = [] + for res in rec_res: + res_lst.append(res[0]) + res = {"res": str(res_lst)} + return res, None, "" + + +class OcrService(WebService): + def get_pipeline_response(self, read_op): + det_op = DetOp(name="det", input_ops=[read_op]) + rec_op = RecOp(name="rec", input_ops=[det_op]) + return rec_op + + +uci_service = OcrService(name="ocr") +uci_service.prepare_pipeline_config("config.yml") +uci_service.run_service() -- GitLab