优化代码，替换cpython\python\gpunms 为opencv dnn.nms;

README.md

@@ -4,11 +4,14 @@
 - [x]  文字方向检测 0、90、180、270度检测（支持dnn/tensorflow） 
 - [x]  支持(darknet/opencv dnn /keras)文字检测,支持darknet/keras训练
 - [x]  不定长OCR训练(英文、中英文) crnn\dense ocr 识别及训练 ,新增pytorch转keras模型代码(tools/pytorch_to_keras.py)
-- [x] 支持darknet 转keras, keras转darknet, pytorch 转keras模型
-- [x]  新增对身份证/火车票结构化数据识别
-- [ ]  新增语音模型修正OCR识别结果   
-- [ ]  新增CNN+ctc模型，支持DNN模块调用OCR，单行图像平均时间为0.02秒以下     
-- [ ]  优化CPU调用，识别速度与GPU接近(近期更新)      
+- [x]  支持darknet 转keras, keras转darknet, pytorch 转keras模型
+- [x]  身份证/火车票结构化数据识别 
+- [x]  新增CNN+ctc模型，支持DNN模块调用OCR，单行图像平均时间为0.02秒以下     
+- [ ]  CPU版本加速    
+- [ ]  支持基于用户字典OCR识别    
+- [ ]  新增语言模型修正OCR识别结果  
+- [ ]  支持树莓派实时识别方案  
+
 
 ## 环境部署
 
@@ -36,7 +39,6 @@ lib = CDLL(root+"chineseocr/darknet/libdarknet.so", RTLD_GLOBAL)
 ## 下载模型文件   
 模型文件地址:
 * [baidu pan](https://pan.baidu.com/s/1gTW9gwJR6hlwTuyB6nCkzQ)
-* [google drive](https://drive.google.com/drive/folders/1XiT1FLFvokAdwfE9WSUSS1PnZA34WBzy?usp=sharing)
 
 复制文件夹中的所有文件到models目录
 
@@ -65,11 +67,10 @@ pip install .
 wget https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm
 mv zh_giga.no_cna_cmn.prune01244.klm chineseocr/models/
 ```
-## web服务启动
+## 模型选择  
 ``` Bash
-cd chineseocr## 进入chineseocr目录
-ipython app.py 8080 ##8080端口号，可以设置任意端口
-```
+参考config.py文件
+```  
 
 ## 构建docker镜像 
 ``` Bash
@@ -81,6 +82,18 @@ docker run -d -p 8080:8080 chineseocr /root/anaconda3/bin/python app.py
 
 ```
 
+## web服务启动
+``` Bash
+cd chineseocr## 进入chineseocr目录
+python app.py 8080 ##8080端口号，可以设置任意端口
+```
+
+## 访问服务
+http://127.0.0.1:8080/ocr
+
+<img width="500" height="300" src="https://github.com/chineseocr/chineseocr/blob/master/test/demo.png"/>
+
+
 
 ## 识别结果展示
 
@@ -90,11 +103,6 @@ docker run -d -p 8080:8080 chineseocr /root/anaconda3/bin/python app.py
 <img width="500" height="300" src="https://github.com/chineseocr/chineseocr/blob/master/test/line-demo.png"/>
 
 
-## 访问服务
-http://127.0.0.1:8080/ocr
-
-<img width="500" height="300" src="https://github.com/chineseocr/chineseocr/blob/master/test/demo.png"/>
-
 
 ## 参考
 1. yolo3 https://github.com/pjreddie/darknet.git   

app.py

@@ -3,20 +3,103 @@
 @author: lywen
 """
 import os
-import cv2
 import json
 import time
-import uuid
-import base64
 import web
+import numpy as np
 from PIL import Image
 web.config.debug  = True
-import model
+
 render = web.template.render('templates', base='base')
-from config import DETECTANGLE
-from apphelper.image import union_rbox,adjust_box_to_origin
+from config import *
+from apphelper.image import union_rbox,adjust_box_to_origin,base64_to_PIL
 from application import trainTicket,idcard 
+if yoloTextFlag =='keras' or AngleModelFlag=='tf' or ocrFlag=='keras':
+    if GPU:
+        os.environ["CUDA_VISIBLE_DEVICES"] = str(GPUID)
+        import tensorflow as tf
+        from keras import backend as K
+        config = tf.ConfigProto()
+        config.gpu_options.allocator_type = 'BFC'
+        config.gpu_options.per_process_gpu_memory_fraction = 0.3## GPU最大占用量
+        config.gpu_options.allow_growth = True##GPU是否可动态增加
+        K.set_session(tf.Session(config=config))
+        K.get_session().run(tf.global_variables_initializer())
+
+    else:
+      ##CPU启动
+      os.environ["CUDA_VISIBLE_DEVICES"] = ''
 
+if yoloTextFlag=='opencv':
+    scale,maxScale = IMGSIZE
+    from text.opencv_dnn_detect import text_detect
+elif yoloTextFlag=='darknet':
+    scale,maxScale = IMGSIZE
+    from text.darknet_detect import text_detect
+elif yoloTextFlag=='keras':
+    scale,maxScale = IMGSIZE[0],2048
+    from text.keras_detect import  text_detect
+else:
+     print( "err,text engine in keras\opencv\darknet")
+
+from text.opencv_dnn_detect import angle_detect
+
+if ocr_redis:
+    ##多任务并发识别
+    from apphelper.redisbase import redisDataBase
+    ocr = redisDataBase().put_values
+else:   
+    from crnn.keys import alphabetChinese,alphabetEnglish
+    if ocrFlag=='keras':
+        from crnn.network_keras import CRNN
+        if chineseModel:
+            alphabet = alphabetChinese
+            if LSTMFLAG:
+                ocrModel = ocrModelKerasLstm
+            else:
+                ocrModel = ocrModelKerasDense
+        else:
+            ocrModel = ocrModelKerasEng
+            alphabet = alphabetEnglish
+            LSTMFLAG = True
+
+    elif ocrFlag=='torch':
+        from crnn.network_torch import CRNN
+        if chineseModel:
+            alphabet = alphabetChinese
+            if LSTMFLAG:
+                ocrModel = ocrModelTorchLstm
+            else:
+                ocrModel = ocrModelTorchDense
+
+        else:
+            ocrModel = ocrModelTorchEng
+            alphabet = alphabetEnglish
+            LSTMFLAG = True
+    elif ocrFlag=='opencv':
+        from crnn.network_dnn import CRNN
+        ocrModel = ocrModelOpencv
+        alphabet = alphabetChinese
+    else:
+        print( "err,ocr engine in keras\opencv\darknet")
+
+    nclass = len(alphabet)+1   
+    if ocrFlag=='opencv':
+        crnn = CRNN(alphabet=alphabet)
+    else:
+        crnn = CRNN( 32, 1, nclass, 256, leakyRelu=False,lstmFlag=LSTMFLAG,GPU=GPU,alphabet=alphabet)
+    if os.path.exists(ocrModel):
+        crnn.load_weights(ocrModel)
+    else:
+        print("download model or tranform model with tools!")
+
+    ocr = crnn.predict_job
+
+
+from main import TextOcrModel
+
+model =  TextOcrModel(ocr,text_detect,angle_detect)
+
 
 billList = ['通用OCR','火车票','身份证']
 
@@ -30,7 +113,6 @@ def GET(self):
         post['H'] = 1000
         post['width'] = 600
         post['W'] = 600
-        post['uuid'] = uuid.uuid1().__str__()
         post['billList'] = billList
         return render.ocr(post)
 
@@ -42,33 +124,32 @@ def POST(self):
         textLine = data.get('textLine',False)##只进行单行识别
 
         imgString = data['imgString'].encode().split(b';base64,')[-1]
-        imgString = base64.b64decode(imgString)
-        jobid = uuid.uuid1().__str__()
-        path = 'test/{}.jpg'.format(jobid)
-        with open(path,'wb') as f:
-            f.write(imgString)
-        img = cv2.imread(path)##GBR
+        img = base64_to_PIL(imgString)
+        if img is not None:
+            img = np.array(img)
+
         H,W = img.shape[:2]
         timeTake = time.time()
         if textLine:
             ##单行识别
             partImg = Image.fromarray(img)
-            text = model.crnnOcr(partImg.convert('L'))
+            text    = ocr.predict(partImg.convert('L'))
             res =[ {'text':text,'name':'0','box':[0,0,W,0,W,H,0,H]} ]
         else:
             detectAngle = textAngle
-            _,result,angle= model.model(img,
+            result,angle= model.model(img,
+                                        scale=scale,
+                                        maxScale=maxScale,
                                         detectAngle=detectAngle,##是否进行文字方向检测，通过web传参控制
-                                        config=dict(MAX_HORIZONTAL_GAP=50,##字符之间的最大间隔，用于文本行的合并
+                                        MAX_HORIZONTAL_GAP=100,##字符之间的最大间隔，用于文本行的合并
                                         MIN_V_OVERLAPS=0.6,
                                         MIN_SIZE_SIM=0.6,
                                         TEXT_PROPOSALS_MIN_SCORE=0.1,
                                         TEXT_PROPOSALS_NMS_THRESH=0.3,
-                                        TEXT_LINE_NMS_THRESH = 0.7,##文本行之间测iou值
-                                                ),
-                                        leftAdjust=True,##对检测的文本行进行向左延伸
-                                        rightAdjust=True,##对检测的文本行进行向右延伸
-                                        alph=0.01,##对检测的文本行进行向右、左延伸的倍数
+                                        TEXT_LINE_NMS_THRESH = 0.99,##文本行之间测iou值
+                                        LINE_MIN_SCORE=0.1,
+                                        leftAdjustAlph=0.01,##对检测的文本行进行向左延伸
+                                        rightAdjustAlph=0.01,##对检测的文本行进行向右延伸
                                        )
 
 
@@ -101,8 +182,6 @@ def POST(self):
 
         timeTake = time.time()-timeTake
 
-
-        os.remove(path)
         return json.dumps({'res':res,'timeTake':round(timeTake,4)},ensure_ascii=False)