Merge pull request #924 from WenmuZhou/dygraph

Dygraph

deploy/cpp_infer/tools/build.sh

+OPENCV_DIR=your_opencv_dir
+LIB_DIR=your_paddle_inference_dir
+CUDA_LIB_DIR=your_cuda_lib_dir
+CUDNN_LIB_DIR=your_cudnn_lib_dir
+
+BUILD_DIR=build
+rm -rf ${BUILD_DIR}
+mkdir ${BUILD_DIR}
+cd ${BUILD_DIR}
+cmake .. \
+    -DPADDLE_LIB=${LIB_DIR} \
+    -DWITH_MKL=ON \
+    -DWITH_GPU=OFF \
+    -DWITH_STATIC_LIB=OFF \
+    -DUSE_TENSORRT=OFF \
+    -DOPENCV_DIR=${OPENCV_DIR} \
+    -DCUDNN_LIB=${CUDNN_LIB_DIR} \
+    -DCUDA_LIB=${CUDA_LIB_DIR} \
+
+make -j

deploy/cpp_infer/tools/config.txt

+# model load config
+use_gpu  0
+gpu_id  0
+gpu_mem  4000
+cpu_math_library_num_threads  10
+use_mkldnn 0
+use_zero_copy_run 1
+
+# det config
+max_side_len  960
+det_db_thresh  0.3
+det_db_box_thresh  0.5
+det_db_unclip_ratio  2.0
+det_model_dir  ./inference/det_db
+
+# rec config
+rec_model_dir  ./inference/rec_crnn
+char_list_file ../../ppocr/utils/ppocr_keys_v1.txt
+
+# show the detection results
+visualize 1
+

deploy/cpp_infer/tools/run.sh

+
+./build/ocr_system ./tools/config.txt ../../doc/imgs/12.jpg

deploy/hubserving/ocr_det/config.json

+{
+    "modules_info": {
+        "ocr_det": {
+            "init_args": {
+                "version": "1.0.0",
+                "use_gpu": true
+            },
+            "predict_args": {
+            }
+        }
+    },
+    "port": 8866,
+    "use_multiprocess": false,
+    "workers": 2    
+}

deploy/hubserving/ocr_det/module.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import copy
+import math
+import os
+import time
+
+from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
+from paddlehub.common.logger import logger
+from paddlehub.module.module import moduleinfo, runnable, serving
+from PIL import Image
+import cv2
+import numpy as np
+import paddle.fluid as fluid
+import paddlehub as hub
+
+from tools.infer.utility import base64_to_cv2
+from tools.infer.predict_det import TextDetector
+
+
+@moduleinfo(
+    name="ocr_det",
+    version="1.0.0",
+    summary="ocr detection service",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class OCRDet(hub.Module):
+    def _initialize(self, use_gpu=False, enable_mkldnn=False):
+        """
+        initialize with the necessary elements
+        """
+        from ocr_det.params import read_params
+        cfg = read_params()
+
+        cfg.use_gpu = use_gpu
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+                print("use gpu: ", use_gpu)
+                print("CUDA_VISIBLE_DEVICES: ", _places)
+                cfg.gpu_mem = 8000
+            except:
+                raise RuntimeError(
+                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
+                )
+        cfg.ir_optim = True
+        cfg.enable_mkldnn = enable_mkldnn
+
+        self.text_detector = TextDetector(cfg)
+
+    def read_images(self, paths=[]):
+        images = []
+        for img_path in paths:
+            assert os.path.isfile(
+                img_path), "The {} isn't a valid file.".format(img_path)
+            img = cv2.imread(img_path)
+            if img is None:
+                logger.info("error in loading image:{}".format(img_path))
+                continue
+            images.append(img)
+        return images
+
+    def predict(self,
+                images=[],
+                paths=[]):
+        """
+        Get the text box in the predicted images.
+        Args:
+            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
+            paths (list[str]): The paths of images. If paths not images
+        Returns:
+            res (list): The result of text detection box and save path of images.
+        """
+
+        if images != [] and isinstance(images, list) and paths == []:
+            predicted_data = images
+        elif images == [] and isinstance(paths, list) and paths != []:
+            predicted_data = self.read_images(paths)
+        else:
+            raise TypeError("The input data is inconsistent with expectations.")
+
+        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
+        
+        all_results = []
+        for img in predicted_data:
+            if img is None:
+                logger.info("error in loading image")
+                all_results.append([])
+                continue
+            dt_boxes, elapse = self.text_detector(img)
+            logger.info("Predict time : {}".format(elapse))
+
+            rec_res_final = []
+            for dno in range(len(dt_boxes)):
+                rec_res_final.append(
+                    {
+                        'text_region': dt_boxes[dno].astype(np.int).tolist()
+                    }
+                )
+            all_results.append(rec_res_final)
+        return all_results
+
+    @serving
+    def serving_method(self, images, **kwargs):
+        """
+        Run as a service.
+        """
+        images_decode = [base64_to_cv2(image) for image in images]
+        results = self.predict(images_decode, **kwargs)
+        return results
+
+   
+if __name__ == '__main__':
+    ocr = OCRDet()
+    image_path = [
+        './doc/imgs/11.jpg',
+        './doc/imgs/12.jpg',
+    ]
+    res = ocr.predict(paths=image_path)
+    print(res)
\ No newline at end of file

deploy/hubserving/ocr_det/params.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+class Config(object):
+    pass
+
+
+def read_params():
+    cfg = Config()
+    
+    #params for text detector
+    cfg.det_algorithm = "DB"
+    cfg.det_model_dir = "./inference/ch_det_mv3_db/"
+    cfg.det_max_side_len = 960
+
+    #DB parmas
+    cfg.det_db_thresh =0.3
+    cfg.det_db_box_thresh =0.5
+    cfg.det_db_unclip_ratio =2.0
+
+    # #EAST parmas
+    # cfg.det_east_score_thresh = 0.8
+    # cfg.det_east_cover_thresh = 0.1
+    # cfg.det_east_nms_thresh = 0.2
+
+    # #params for text recognizer
+    # cfg.rec_algorithm = "CRNN"
+    # cfg.rec_model_dir = "./inference/ch_det_mv3_crnn/"
+
+    # cfg.rec_image_shape = "3, 32, 320"
+    # cfg.rec_char_type = 'ch'
+    # cfg.rec_batch_num = 30
+    # cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
+    # cfg.use_space_char = True
+
+    cfg.use_zero_copy_run = False
+
+    return cfg

deploy/hubserving/ocr_rec/config.json

+{
+    "modules_info": {
+        "ocr_rec": {
+            "init_args": {
+                "version": "1.0.0",
+                "use_gpu": true
+            },
+            "predict_args": {
+            }
+        }
+    },
+    "port": 8867,
+    "use_multiprocess": false,
+    "workers": 2
+}

deploy/hubserving/ocr_rec/module.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import copy
+import math
+import os
+import time
+
+from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
+from paddlehub.common.logger import logger
+from paddlehub.module.module import moduleinfo, runnable, serving
+from PIL import Image
+import cv2
+import numpy as np
+import paddle.fluid as fluid
+import paddlehub as hub
+
+from tools.infer.utility import base64_to_cv2
+from tools.infer.predict_rec import TextRecognizer
+
+
+@moduleinfo(
+    name="ocr_rec",
+    version="1.0.0",
+    summary="ocr recognition service",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class OCRRec(hub.Module):
+    def _initialize(self, use_gpu=False, enable_mkldnn=False):
+        """
+        initialize with the necessary elements
+        """
+        from ocr_rec.params import read_params
+        cfg = read_params()
+
+        cfg.use_gpu = use_gpu
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+                print("use gpu: ", use_gpu)
+                print("CUDA_VISIBLE_DEVICES: ", _places)
+                cfg.gpu_mem = 8000
+            except:
+                raise RuntimeError(
+                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
+                )
+        cfg.ir_optim = True
+        cfg.enable_mkldnn = enable_mkldnn
+
+        self.text_recognizer = TextRecognizer(cfg)
+
+    def read_images(self, paths=[]):
+        images = []
+        for img_path in paths:
+            assert os.path.isfile(
+                img_path), "The {} isn't a valid file.".format(img_path)
+            img = cv2.imread(img_path)
+            if img is None:
+                logger.info("error in loading image:{}".format(img_path))
+                continue
+            images.append(img)
+        return images
+
+    def predict(self,
+                images=[],
+                paths=[]):
+        """
+        Get the text box in the predicted images.
+        Args:
+            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
+            paths (list[str]): The paths of images. If paths not images
+        Returns:
+            res (list): The result of text detection box and save path of images.
+        """
+
+        if images != [] and isinstance(images, list) and paths == []:
+            predicted_data = images
+        elif images == [] and isinstance(paths, list) and paths != []:
+            predicted_data = self.read_images(paths)
+        else:
+            raise TypeError("The input data is inconsistent with expectations.")
+
+        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
+        
+        img_list = []
+        for img in predicted_data:
+            if img is None:
+                continue
+            img_list.append(img)
+            
+        rec_res_final = []
+        try:
+            rec_res, predict_time = self.text_recognizer(img_list)
+            for dno in range(len(rec_res)):
+                text, score = rec_res[dno]
+                rec_res_final.append(
+                    {
+                        'text': text,
+                        'confidence': float(score),
+                    }
+                )
+        except Exception as e:
+            print(e)
+            return [[]]
+
+        return [rec_res_final]
+
+
+    @serving
+    def serving_method(self, images, **kwargs):
+        """
+        Run as a service.
+        """
+        images_decode = [base64_to_cv2(image) for image in images]
+        results = self.predict(images_decode, **kwargs)
+        return results
+
+   
+if __name__ == '__main__':
+    ocr = OCRRec()
+    image_path = [
+        './doc/imgs_words/ch/word_1.jpg',
+        './doc/imgs_words/ch/word_2.jpg',
+        './doc/imgs_words/ch/word_3.jpg',
+    ]
+    res = ocr.predict(paths=image_path)
+    print(res)
\ No newline at end of file

deploy/hubserving/ocr_rec/params.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+class Config(object):
+    pass
+
+
+def read_params():
+    cfg = Config()
+    
+    # #params for text detector
+    # cfg.det_algorithm = "DB"
+    # cfg.det_model_dir = "./inference/ch_det_mv3_db/"
+    # cfg.det_max_side_len = 960
+
+    # #DB parmas
+    # cfg.det_db_thresh =0.3
+    # cfg.det_db_box_thresh =0.5
+    # cfg.det_db_unclip_ratio =2.0
+
+    # #EAST parmas
+    # cfg.det_east_score_thresh = 0.8
+    # cfg.det_east_cover_thresh = 0.1
+    # cfg.det_east_nms_thresh = 0.2
+
+    #params for text recognizer
+    cfg.rec_algorithm = "CRNN"
+    cfg.rec_model_dir = "./inference/ch_rec_mv3_crnn/"
+
+    cfg.rec_image_shape = "3, 32, 320"
+    cfg.rec_char_type = 'ch'
+    cfg.rec_batch_num = 30
+    cfg.max_text_length = 25
+
+    cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
+    cfg.use_space_char = True
+
+    cfg.use_zero_copy_run = False
+
+    return cfg

deploy/hubserving/ocr_system/config.json

+{
+    "modules_info": {
+        "ocr_system": {
+            "init_args": {
+                "version": "1.0.0",
+                "use_gpu": true
+            },
+            "predict_args": {
+            }
+        }
+    },
+    "port": 8868,
+    "use_multiprocess": false,
+    "workers": 2
+}
+

deploy/hubserving/ocr_system/module.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import ast
+import copy
+import math
+import os
+import time
+
+from paddle.fluid.core import AnalysisConfig, create_paddle_predictor, PaddleTensor
+from paddlehub.common.logger import logger
+from paddlehub.module.module import moduleinfo, runnable, serving
+from PIL import Image
+import cv2
+import numpy as np
+import paddle.fluid as fluid
+import paddlehub as hub
+
+from tools.infer.utility import base64_to_cv2
+from tools.infer.predict_system import TextSystem
+
+
+@moduleinfo(
+    name="ocr_system",
+    version="1.0.0",
+    summary="ocr system service",
+    author="paddle-dev",
+    author_email="paddle-dev@baidu.com",
+    type="cv/text_recognition")
+class OCRSystem(hub.Module):
+    def _initialize(self, use_gpu=False, enable_mkldnn=False):
+        """
+        initialize with the necessary elements
+        """
+        from ocr_system.params import read_params
+        cfg = read_params()
+
+        cfg.use_gpu = use_gpu
+        if use_gpu:
+            try:
+                _places = os.environ["CUDA_VISIBLE_DEVICES"]
+                int(_places[0])
+                print("use gpu: ", use_gpu)
+                print("CUDA_VISIBLE_DEVICES: ", _places)
+                cfg.gpu_mem = 8000
+            except:
+                raise RuntimeError(
+                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
+                )
+        cfg.ir_optim = True
+        cfg.enable_mkldnn = enable_mkldnn
+        
+        self.text_sys = TextSystem(cfg)
+
+    def read_images(self, paths=[]):
+        images = []
+        for img_path in paths:
+            assert os.path.isfile(
+                img_path), "The {} isn't a valid file.".format(img_path)
+            img = cv2.imread(img_path)
+            if img is None:
+                logger.info("error in loading image:{}".format(img_path))
+                continue
+            images.append(img)
+        return images
+
+    def predict(self,
+                       images=[],
+                       paths=[]):
+        """
+        Get the chinese texts in the predicted images.
+        Args:
+            images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
+            paths (list[str]): The paths of images. If paths not images
+        Returns:
+            res (list): The result of chinese texts and save path of images.
+        """
+
+        if images != [] and isinstance(images, list) and paths == []:
+            predicted_data = images
+        elif images == [] and isinstance(paths, list) and paths != []:
+            predicted_data = self.read_images(paths)
+        else:
+            raise TypeError("The input data is inconsistent with expectations.")
+
+        assert predicted_data != [], "There is not any image to be predicted. Please check the input data."
+
+        all_results = []
+        for img in predicted_data:
+            if img is None:
+                logger.info("error in loading image")
+                all_results.append([])
+                continue
+            starttime = time.time()
+            dt_boxes, rec_res = self.text_sys(img)
+            elapse = time.time() - starttime
+            logger.info("Predict time: {}".format(elapse))
+
+            dt_num = len(dt_boxes)
+            rec_res_final = []
+
+            for dno in range(dt_num):
+                text, score = rec_res[dno]
+                rec_res_final.append(
+                    {
+                        'text': text,
+                        'confidence': float(score),
+                        'text_region': dt_boxes[dno].astype(np.int).tolist()
+                    }
+                )
+            all_results.append(rec_res_final)
+        return all_results
+
+    @serving
+    def serving_method(self, images, **kwargs):
+        """
+        Run as a service.
+        """
+        images_decode = [base64_to_cv2(image) for image in images]
+        results = self.predict(images_decode, **kwargs)
+        return results
+
+   
+if __name__ == '__main__':
+    ocr = OCRSystem()
+    image_path = [
+        './doc/imgs/11.jpg',
+        './doc/imgs/12.jpg',
+    ]
+    res = ocr.predict(paths=image_path)
+    print(res)
\ No newline at end of file

deploy/hubserving/ocr_system/params.py

+# -*- coding:utf-8 -*-
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+
+class Config(object):
+    pass
+
+
+def read_params():
+    cfg = Config()
+    
+    #params for text detector
+    cfg.det_algorithm = "DB"
+    cfg.det_model_dir = "./inference/ch_det_mv3_db/"
+    cfg.det_max_side_len = 960
+
+    #DB parmas
+    cfg.det_db_thresh =0.3
+    cfg.det_db_box_thresh =0.5
+    cfg.det_db_unclip_ratio =2.0
+
+    #EAST parmas
+    cfg.det_east_score_thresh = 0.8
+    cfg.det_east_cover_thresh = 0.1
+    cfg.det_east_nms_thresh = 0.2
+
+    #params for text recognizer
+    cfg.rec_algorithm = "CRNN"
+    cfg.rec_model_dir = "./inference/ch_rec_mv3_crnn/"
+
+    cfg.rec_image_shape = "3, 32, 320"
+    cfg.rec_char_type = 'ch'
+    cfg.rec_batch_num = 30
+    cfg.max_text_length = 25
+
+    cfg.rec_char_dict_path = "./ppocr/utils/ppocr_keys_v1.txt"
+    cfg.use_space_char = True
+
+    cfg.use_zero_copy_run = False
+
+    return cfg

deploy/ios_demo/download_dependencies.sh

+#!/bin/bash
+set -e
+
+OCR_MODEL_URL="https://paddleocr.bj.bcebos.com/deploy/lite/ocr_v1_for_cpu.tar.gz"
+PADDLE_LITE_LIB_URL="https://paddlelite-demo.bj.bcebos.com/libs/ios/paddle_lite_libs_v2_6_0.tar.gz"
+OPENCV3_FRAMEWORK_URL="https://paddlelite-demo.bj.bcebos.com/libs/ios/opencv3.framework.tar.gz"
+
+download_and_extract() {
+    local url="$1"
+    local dst_dir="$2"
+    local tempdir=$(mktemp -d)
+
+    echo "Downloading ${url} ..."
+    curl -L ${url} > ${tempdir}/temp.tar.gz
+    echo "Download ${url} done "
+
+    if [ ! -d ${dst_dir} ];then
+        mkdir -p ${dst_dir}
+    fi
+
+    echo "Extracting ..."
+    tar -zxvf ${tempdir}/temp.tar.gz -C ${dst_dir}
+    echo "Extract done "
+
+    rm -rf ${tempdir}
+}
+
+echo -e "[Download ios ocr demo denpendancy]\n"
+download_and_extract "${OCR_MODEL_URL}" "./ocr_demo/models"
+download_and_extract "${PADDLE_LITE_LIB_URL}" "./ocr_demo"
+download_and_extract "${OPENCV3_FRAMEWORK_URL}" "./ocr_demo"
+echo -e "[done]\n"

deploy/ios_demo/ocr_demo.xcodeproj/project.pbxproj

+// !$*UTF8*$!
+{
+	archiveVersion = 1;
+	classes = {
+	};
+	objectVersion = 50;
+	objects = {
+
+/* Begin PBXBuildFile section */
+		A98EA8B624CD8E85B9EFADA1 /* OcrData.m in Sources */ = {isa = PBXBuildFile; fileRef = A98EAD74A71FE136D084F392 /* OcrData.m */; };
+		E0A53559219A832A005A6056 /* AppDelegate.m in Sources */ = {isa = PBXBuildFile; fileRef = E0A53558219A832A005A6056 /* AppDelegate.m */; };
+		E0A5355C219A832A005A6056 /* ViewController.mm in Sources */ = {isa = PBXBuildFile; fileRef = E0A5355B219A832A005A6056 /* ViewController.mm */; };
+		E0A5355F219A832A005A6056 /* Main.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = E0A5355D219A832A005A6056 /* Main.storyboard */; };
+		E0A53561219A832E005A6056 /* Assets.xcassets in Resources */ = {isa = PBXBuildFile; fileRef = E0A53560219A832E005A6056 /* Assets.xcassets */; };
+		E0A53564219A832E005A6056 /* LaunchScreen.storyboard in Resources */ = {isa = PBXBuildFile; fileRef = E0A53562219A832E005A6056 /* LaunchScreen.storyboard */; };
+		E0A53567219A832E005A6056 /* main.m in Sources */ = {isa = PBXBuildFile; fileRef = E0A53566219A832E005A6056 /* main.m */; };
+		E0A53576219A89FF005A6056 /* lib in Resources */ = {isa = PBXBuildFile; fileRef = E0A53574219A89FF005A6056 /* lib */; };
+		E0A5357B219AA3D1005A6056 /* AVFoundation.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = E0A5357A219AA3D1005A6056 /* AVFoundation.framework */; };
+		E0A5357D219AA3DF005A6056 /* AssetsLibrary.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = E0A5357C219AA3DE005A6056 /* AssetsLibrary.framework */; };
+		E0A5357F219AA3E7005A6056 /* CoreMedia.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = E0A5357E219AA3E7005A6056 /* CoreMedia.framework */; };
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+}

deploy/ios_demo/ocr_demo/AppDelegate.h

+//
+//  AppDelegate.h
+//  seg_demo
+//
+//  Created by Li,Xiaoyang(SYS) on 2018/11/13.
+//  Copyright © 2018年 Li,Xiaoyang(SYS). All rights reserved.
+//
+
+#import <UIKit/UIKit.h>
+
+@interface AppDelegate : UIResponder <UIApplicationDelegate>
+
+@property (strong, nonatomic) UIWindow *window;
+
+
+@end
+