update

5 years ago · a1652cd69b
--- a/HyperLPRLite.py
+++ b/HyperLPRLite.py
@@ -1,155 +0,0 @@
 #coding=utf-8
 import cv2
 import numpy as np
 from keras import backend as K
 from keras.models import *
 from keras.layers import *

 chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
             u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
             u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
             u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
             ]

 class LPR():
    def __init__(self,model_detection,model_finemapping,model_seq_rec):
        self.watch_cascade = cv2.CascadeClassifier(model_detection)
        self.modelFineMapping = self.model_finemapping()
        self.modelFineMapping.load_weights(model_finemapping)
        self.modelSeqRec = self.model_seq_rec(model_seq_rec)

    def computeSafeRegion(self,shape,bounding_rect):
        top = bounding_rect[1] # y
        bottom  = bounding_rect[1] + bounding_rect[3] # y +  h
        left = bounding_rect[0] # x
        right =   bounding_rect[0] + bounding_rect[2] # x +  w
        min_top = 0
        max_bottom = shape[0]
        min_left = 0
        max_right = shape[1]
        if top < min_top:
            top = min_top
        if left < min_left:
            left = min_left
        if bottom > max_bottom:
            bottom = max_bottom
        if right > max_right:
            right = max_right
        return [left,top,right-left,bottom-top]

    def cropImage(self,image,rect):
        x, y, w, h = self.computeSafeRegion(image.shape,rect)
        return image[y:y+h,x:x+w]

    def detectPlateRough(self,image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
        if top_bottom_padding_rate>0.2:
            print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate)
            exit(1)
        height = image_gray.shape[0]
        padding =    int(height*top_bottom_padding_rate)
        scale = image_gray.shape[1]/float(image_gray.shape[0])
        image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))
        image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]
        image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)
        watches = self.watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))
        cropped_images = []
        for (x, y, w, h) in watches:
            x -= w * 0.14
            w += w * 0.28
            y -= h * 0.15
            h += h * 0.3
            cropped = self.cropImage(image_color_cropped, (int(x), int(y), int(w), int(h)))
            cropped_images.append([cropped,[x, y+padding, w, h]])
        return cropped_images

    def fastdecode(self,y_pred):
        results = ""
        confidence = 0.0
        table_pred = y_pred.reshape(-1, len(chars)+1)
        res = table_pred.argmax(axis=1)
        for i,one in enumerate(res):
            if one<len(chars) and (i==0 or (one!=res[i-1])):
                results+= chars[one]
                confidence+=table_pred[i][one]
        confidence/= len(results)
        return results,confidence

    def model_seq_rec(self,model_path):
        width, height, n_len, n_class = 164, 48, 7, len(chars)+ 1
        rnn_size = 256
        input_tensor = Input((164, 48, 3))
        x = input_tensor
        base_conv = 32
        for i in range(3):
            x = Conv2D(base_conv * (2 ** (i)), (3, 3))(x)
            x = BatchNormalization()(x)
            x = Activation('relu')(x)
            x = MaxPooling2D(pool_size=(2, 2))(x)
        conv_shape = x.get_shape()
        x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2] * conv_shape[3])))(x)
        x = Dense(32)(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(x)
        gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(x)
        gru1_merged = add([gru_1, gru_1b])
        gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged)
        gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged)
        x = concatenate([gru_2, gru_2b])
        x = Dropout(0.25)(x)
        x = Dense(n_class, kernel_initializer='he_normal', activation='softmax')(x)
        base_model = Model(inputs=input_tensor, outputs=x)
        base_model.load_weights(model_path)
        return base_model

    def model_finemapping(self):
        input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
        x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
        x = Activation("relu", name='relu1')(x)
        x = MaxPool2D(pool_size=2)(x)
        x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
        x = Activation("relu", name='relu2')(x)
        x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
        x = Activation("relu", name='relu3')(x)
        x = Flatten()(x)
        output = Dense(2,name = "dense")(x)
        output = Activation("relu", name='relu4')(output)
        model = Model([input], [output])
        return model

    def finemappingVertical(self,image,rect):
        resized = cv2.resize(image,(66,16))
        resized = resized.astype(np.float)/255
        res_raw= self.modelFineMapping.predict(np.array([resized]))[0]
        res  =res_raw*image.shape[1]
        res = res.astype(np.int)
        H,T = res
        H-=3
        if H<0:
            H=0
        T+=2;
        if T>= image.shape[1]-1:
            T= image.shape[1]-1
        rect[2] -=  rect[2]*(1-res_raw[1] + res_raw[0])
        rect[0]+=res[0]
        image = image[:,H:T+2]
        image = cv2.resize(image, (int(136), int(36)))
        return image,rect

    def recognizeOne(self,src):
        x_tempx = src
        x_temp = cv2.resize(x_tempx,( 164,48))
        x_temp = x_temp.transpose(1, 0, 2)
        y_pred = self.modelSeqRec.predict(np.array([x_temp]))
        y_pred = y_pred[:,2:,:]
        return self.fastdecode(y_pred)

    def SimpleRecognizePlateByE2E(self,image):
        images = self.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
        res_set = []
        for j,plate in enumerate(images):
            plate, rect  =plate
            image_rgb,rect_refine = self.finemappingVertical(plate,rect)
            res,confidence = self.recognizeOne(image_rgb)
            res_set.append([res,confidence,rect_refine])
        return res_set
--- a/HyperLprGUI.py
+++ b/HyperLprGUI.py
@@ -1,794 +0,0 @@
 """
 Author: youngorsu
 Email : zhiyongsu@qq.com
 Last edited: 2018.1.29
 """
 # coding=utf-8


 import sys
 import os
 from PyQt5.QtWidgets import (
    QMainWindow,
    QLabel,
    QLineEdit,
    QPushButton,
    QHBoxLayout,
    QVBoxLayout,
    QGridLayout,
    QTableWidget,
    QWidget,
    QAbstractItemView,
    QHeaderView,
    QGraphicsView,
    QGraphicsScene,
    QGraphicsPixmapItem,
    QSplitter,
    QFileDialog,
    QTableWidgetItem,
    QGraphicsRectItem,
    QCheckBox,
    QMessageBox,
    QGroupBox,
    QGraphicsSimpleTextItem,
    qApp,
    QAction,
    QApplication)
 from PyQt5.QtGui import QIcon, QColor, QPainter, QImage, QPixmap, QPen, QBrush, QFont, QPalette, QKeySequence
 from PyQt5.QtCore import Qt, QDir, QSize, QEventLoop, QThread, pyqtSignal

 from hyperlpr_py3 import pipline as pp

 import cv2

 import numpy as np

 import time

 import shutil

 draw_plate_in_image_enable = 1

 plateTypeName = ["蓝", "黄", "绿", "白", "黑 "]


 def SimpleRecognizePlateWithGui(image):
    t0 = time.time()

    images = pp.detect.detectPlateRough(
        image, image.shape[0], top_bottom_padding_rate=0.1)

    res_set = []
    y_offset = 32
    for j, plate in enumerate(images):
        plate, rect, origin_plate = plate

        plate = cv2.resize(plate, (136, 36 * 2))
        t1 = time.time()

        plate_type = pp.td.SimplePredict(plate)
        plate_color = plateTypeName[plate_type]

        if (plate_type > 0) and (plate_type < 5):
            plate = cv2.bitwise_not(plate)

        if draw_plate_in_image_enable == 1:
            image[y_offset:y_offset + plate.shape[0], 0:plate.shape[1]] = plate
            y_offset = y_offset + plate.shape[0] + 4

        image_rgb = pp.fm.findContoursAndDrawBoundingBox(plate)

        if draw_plate_in_image_enable == 1:
            image[y_offset:y_offset + image_rgb.shape[0],
                  0:image_rgb.shape[1]] = image_rgb
            y_offset = y_offset + image_rgb.shape[0] + 4

        image_rgb = pp.fv.finemappingVertical(image_rgb)

        if draw_plate_in_image_enable == 1:
            image[y_offset:y_offset + image_rgb.shape[0],
                  0:image_rgb.shape[1]] = image_rgb
            y_offset = y_offset + image_rgb.shape[0] + 4

        pp.cache.verticalMappingToFolder(image_rgb)

        if draw_plate_in_image_enable == 1:
            image[y_offset:y_offset + image_rgb.shape[0],
                  0:image_rgb.shape[1]] = image_rgb
            y_offset = y_offset + image_rgb.shape[0] + 4

        e2e_plate, e2e_confidence = pp.e2e.recognizeOne(image_rgb)
        print("e2e:", e2e_plate, e2e_confidence)

        image_gray = cv2.cvtColor(image_rgb, cv2.COLOR_RGB2GRAY)

        #print("校正", time.time() - t1, "s")

        t2 = time.time()
        val = pp.segmentation.slidingWindowsEval(image_gray)
        # print val
        #print("分割和识别", time.time() - t2, "s")

        res=""
        confidence = 0
        if len(val) == 3:
            blocks, res, confidence = val
            if confidence / 7 > 0.7:

                if draw_plate_in_image_enable == 1:
                    image = pp.drawRectBox(image, rect, res)
                    for i, block in enumerate(blocks):
                        block_ = cv2.resize(block, (24, 24))
                        block_ = cv2.cvtColor(block_, cv2.COLOR_GRAY2BGR)
                        image[j * 24:(j * 24) + 24, i *
                              24:(i * 24) + 24] = block_
                        if image[j * 24:(j * 24) + 24,
                                 i * 24:(i * 24) + 24].shape == block_.shape:
                            pass

        res_set.append([res,
                        confidence / 7,
                        rect,
                        plate_color,
                        e2e_plate,
                        e2e_confidence,
                        len(blocks)])
        print("seg:",res,confidence/7)
    #print(time.time() - t0, "s")

    print("---------------------------------")
    return image, res_set


 class LicenseRecognizationThread(QThread):

    recognization_done_signal = pyqtSignal(list)

    def __init__(self, parent=None):
        super().__init__(parent)
        self.hyperlpr_dir_path = ""
        self.filenames = []

    def set_parameter(self, filename_list, path):
        self.hyperlpr_dir_path = path
        self.filenames = filename_list

    def run(self):
        while True:
            time.sleep(1)
            if len(self.hyperlpr_dir_path) > 0:
                for i in range(0, len(self.filenames)):
                    path = os.path.join(
                        self.hyperlpr_dir_path, self.filenames[i])
                    image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
                    image, res_set = SimpleRecognizePlateWithGui(image)
                    self.recognization_done_signal.emit([i, res_set])

                self.hyperlpr_dir_path = ""


 class HyperLprImageView(QGraphicsView):

    def __init__(self):

        super().__init__()

        self.init_ui()

    def init_ui(self):

        scene = QGraphicsScene()
        scene.setBackgroundBrush(QColor(100, 100, 100))
        scene.setItemIndexMethod(QGraphicsScene.BspTreeIndex)

        scene.setSceneRect(scene.itemsBoundingRect())

        self.setDragMode(QGraphicsView.RubberBandDrag)
        self.setViewportUpdateMode(QGraphicsView.FullViewportUpdate)
        self.setRenderHints(QPainter.Antialiasing | QPainter.TextAntialiasing)

        self.frame_item = QGraphicsPixmapItem()

        self.text_item_offset = 0
        self.rect_item_array = []
        self.text_item_array = []
        for i in range(0, 5):
            rect_item = QGraphicsRectItem()
            rect_item.setVisible(False)
            rect_item.setZValue(20.0)
            rect_item.setPen(QPen(Qt.red, 5))
            rect_item.setRect(20, 20, 20, 20)
            scene.addItem(rect_item)
            self.rect_item_array.append(rect_item)
            text_item = QGraphicsSimpleTextItem("")
            text_item.setBrush(QBrush(Qt.red))
            text_item.setZValue(20.0)
            text_item.setPos(10, 50)
            text_item.setFont(QFont("黑体", 24))
            text_item.setVisible(False)
            scene.addItem(text_item)
            self.text_item_array.append(text_item)

        scene.addItem(self.frame_item)

        self.curr_factor = 1.0

        self.setScene(scene)

    def resetRectText(self, res_set):
        max_no = len(res_set)

        if max_no > 5:
            max_no = 5

        for i in range(0, 5):
            if i < max_no:
                curr_rect = res_set[i][2]
                self.rect_item_array[i].setRect(int(curr_rect[0]), int(
                    curr_rect[1]), int(curr_rect[2]), int(curr_rect[3]))
                self.rect_item_array[i].setVisible(True)

                self.text_item_array[i].setText(
                    res_set[i][4] + " " + res_set[i][3])
                self.text_item_array[i].setPos(
                    int(curr_rect[0]), int(curr_rect[1]) - 48)
                self.text_item_array[i].setVisible(True)
            else:
                self.text_item_array[i].setVisible(False)
                self.rect_item_array[i].setVisible(False)

    def wheelEvent(self, event):
        factor = event.angleDelta().y() / 120.0
        if event.angleDelta().y() / 120.0 > 0:
            factor = 1.08
        else:
            factor = 0.92

        if self.curr_factor > 0.1 and self.curr_factor < 10:
            self.curr_factor = self.curr_factor * factor
            self.scale(factor, factor)

    def resetPixmap(self, image):

        self.frame_item.setPixmap(QPixmap.fromImage(image))


 class HyperLprWindow(QMainWindow):

    start_init_signal = pyqtSignal()

    def __init__(self):

        super().__init__()

        self.initUI()

    def initUI(self):

        self.statusBar().showMessage('Ready')

        self.left_action = QAction('上一个', self)
        self.left_action.setShortcut(QKeySequence.MoveToPreviousChar)
        self.left_action.triggered.connect(self.analyze_last_one_image)

        self.right_action = QAction('下一个', self)
        self.right_action.setShortcut(QKeySequence.MoveToNextChar)
        self.right_action.triggered.connect(self.analyze_next_one_image)

        self.rename_image_action = QAction('保存e2e文件名', self)
        self.rename_image_action.setShortcut(QKeySequence.MoveToPreviousLine)
        self.rename_image_action.triggered.connect(self.rename_current_image_with_info)

        self.statusBar()

        menubar = self.menuBar()
        fileMenu = menubar.addMenu('&Function')
        fileMenu.addAction(self.left_action)
        fileMenu.addAction(self.right_action)
        fileMenu.addAction(self.rename_image_action)

        self.image_window_view = HyperLprImageView()

        table_widget_header_labels = [
            "文件名",
            "分割识别",
            "置信度",
            "颜色",
            "E2E识别",
            "E2E置信度"]

        self.hyperlpr_tableview = QTableWidget(
            0, len(table_widget_header_labels))
        self.hyperlpr_tableview.setHorizontalHeaderLabels(
            table_widget_header_labels)

        self.hyperlpr_tableview.setSelectionBehavior(
            QAbstractItemView.SelectItems)
        self.hyperlpr_tableview.setSelectionMode(
            QAbstractItemView.SingleSelection)
        self.hyperlpr_tableview.setEditTriggers(
            QAbstractItemView.NoEditTriggers)
        self.hyperlpr_tableview.horizontalHeader().setSectionResizeMode(
            QHeaderView.ResizeToContents)
        self.hyperlpr_tableview.setEditTriggers(
            QAbstractItemView.NoEditTriggers)

        self.hyperlpr_tableview.cellClicked.connect(
            self.recognize_one_license_plate)

        self.left_button = QPushButton("<")
        self.left_button.setFixedWidth(60)
        self.right_button = QPushButton(">")
        self.right_button.setFixedWidth(60)
        self.left_button.setEnabled(False)
        self.right_button.setEnabled(False)
        self.left_button.clicked.connect(self.analyze_last_one_image)
        self.right_button.clicked.connect(self.analyze_next_one_image)
        left_right_layout = QHBoxLayout()
        left_right_layout.addStretch()
        left_right_layout.addWidget(self.left_button)
        left_right_layout.addStretch()
        left_right_layout.addWidget(self.right_button)
        left_right_layout.addStretch()

        self.location_label = QLabel("车牌目录", self)
        self.location_text = QLineEdit(self)
        self.location_text.setEnabled(False)
        #self.location_text.setFixedWidth(300)
        self.location_button = QPushButton("...")
        self.location_button.clicked.connect(self.select_new_dir)

        self.location_layout = QHBoxLayout()
        self.location_layout.addWidget(self.location_label)
        self.location_layout.addWidget(self.location_text)
        self.location_layout.addWidget(self.location_button)
        self.location_layout.addStretch()

        self.check_box = QCheckBox("与文件名比较车牌")
        self.check_box.setChecked(True)

        self.update_file_path_button = QPushButton('批量识别')
        self.update_file_path_button.clicked.connect(
            self.batch_recognize_all_images)

        self.update_file_path_layout = QHBoxLayout()
        self.update_file_path_layout.addWidget(self.check_box)
        self.update_file_path_layout.addWidget(self.update_file_path_button)
        self.update_file_path_layout.addStretch()

        self.save_as_e2e_filename_button = QPushButton("保存e2e文件名")
        self.save_as_e2e_filename_button.setEnabled(False)
        self.save_as_e2e_filename_button.clicked.connect(self.rename_current_image_with_info)
        self.save_layout = QHBoxLayout()
        self.save_layout.addWidget(self.save_as_e2e_filename_button)
        self.save_layout.addStretch()

        self.top_layout = QVBoxLayout()
        self.top_layout.addLayout(left_right_layout)
        self.top_layout.addLayout(self.location_layout)
        self.top_layout.addLayout(self.update_file_path_layout)
        self.top_layout.addLayout(self.save_layout)

        function_groupbox = QGroupBox("功能区")
        function_groupbox.setLayout(self.top_layout)

        license_plate_image_label = QLabel("车牌图")
        self.license_plate_widget = QLabel("")

        block_image_label = QLabel("分割图")
        self.block_plate_widget = QLabel("")

        filename_label = QLabel("文件名：")
        self.filename_edit = QLineEdit()

        segmentation_recognition_label = QLabel("分割识别：")
        self.segmentation_recognition_edit = QLineEdit()
        self.segmentation_recognition_edit.setFont(QFont("黑体", 24, QFont.Bold))
        # self.segmentation_recognition_edit.setStyleSheet("color:red")

        confidence_label = QLabel("分割识别\n置信度")
        self.confidence_edit = QLineEdit()
        #self.confidence_edit.setFont(QFont("黑体", 24, QFont.Bold))
        # self.confidence_edit.setStyleSheet("color:red")

        plate_color_label = QLabel("车牌颜色")
        self.plate_color_edit = QLineEdit()
        self.plate_color_edit.setFont(QFont("黑体", 24, QFont.Bold))
        # self.plate_color_edit.setStyleSheet("color:red")

        e2e_recognization_label = QLabel("e2e识别：")
        self.e2e_recognization_edit = QLineEdit()
        self.e2e_recognization_edit.setFont(QFont("黑体", 24, QFont.Bold))
        # self.e2e_recognization_edit.setStyleSheet("color:red")

        e2e_confidence_label = QLabel("e2e置信度")
        self.e2e_confidence_edit = QLineEdit()
        #self.e2e_confidence_edit.setFont(QFont("黑体", 24, QFont.Bold))
        # self.e2e_confidence_edit.setStyleSheet("color:red")

        info_gridlayout = QGridLayout()
        line_index = 0
        info_gridlayout.addWidget(filename_label, line_index, 0)
        info_gridlayout.addWidget(self.filename_edit, line_index, 1)
        line_index += 1
        info_gridlayout.addWidget(license_plate_image_label, line_index, 0)
        info_gridlayout.addWidget(self.license_plate_widget, line_index, 1)
        line_index += 1
        info_gridlayout.addWidget(e2e_recognization_label, line_index, 0)
        info_gridlayout.addWidget(self.e2e_recognization_edit, line_index, 1)
        line_index += 1
        info_gridlayout.addWidget(
            segmentation_recognition_label, line_index, 0)
        info_gridlayout.addWidget(
            self.segmentation_recognition_edit, line_index, 1)
        line_index += 1
        info_gridlayout.addWidget(plate_color_label, line_index, 0)
        info_gridlayout.addWidget(self.plate_color_edit, line_index, 1)
        line_index += 1
        info_gridlayout.addWidget(block_image_label, line_index, 0)
        info_gridlayout.addWidget(self.block_plate_widget, line_index, 1)
        line_index += 1
        info_gridlayout.addWidget(confidence_label, line_index, 0)
        info_gridlayout.addWidget(self.confidence_edit, line_index, 1)
        line_index += 1
        info_gridlayout.addWidget(e2e_confidence_label, line_index, 0)
        info_gridlayout.addWidget(self.e2e_confidence_edit, line_index, 1)

        info_widget = QGroupBox("分割识别&e2e")

        info_widget.setLayout(info_gridlayout)

        right_splitter = QSplitter(Qt.Vertical)
        right_splitter.addWidget(self.hyperlpr_tableview)
        right_splitter.addWidget(function_groupbox)
        right_splitter.addWidget(info_widget)
        right_splitter.setStretchFactor(0, 2)
        right_splitter.setStretchFactor(2, 1)

        main_splitter = QSplitter(Qt.Horizontal)
        main_splitter.addWidget(self.image_window_view)
        main_splitter.addWidget(right_splitter)
        main_splitter.setStretchFactor(0, 1)

        self.image_filename_list = []
        self.hyperlpr_dir_path = ""
        self.segmentation_recognition_correct_number = 0
        self.color_correct_number = 0
        self.e2e_recognization_correct_number = 0
        self.current_row = 0

        self.batch_recognization_thread = LicenseRecognizationThread()
        self.batch_recognization_thread.recognization_done_signal.connect(
            self.recognization_done_slot)
        self.batch_recognization_thread.start()

        self.start_init_signal.connect(self.read_path_and_show_one_image)

        self.setCentralWidget(main_splitter)

        self.setWindowTitle("HyperLPR车牌识别软件v1.0")

        self.start_init_signal.emit()

    def read_path_and_show_one_image(self):

        hyperlpr_dir_info_filepath = QDir.homePath() + "/hyperlpr_dir_file"
        if os.path.exists(hyperlpr_dir_info_filepath):
            with open(hyperlpr_dir_info_filepath, 'r') as f:
                self.hyperlpr_dir_path = f.read()

        if len(self.hyperlpr_dir_path) > 0:
            self.reset_info_gui()

        if len(self.image_filename_list) > 0:
            self.recognize_and_show_one_image(self.image_filename_list[0], 0)

    def select_new_dir(self):

        self.hyperlpr_dir_path = QFileDialog.getExistingDirectory(
            self, "读取文件夹", QDir.currentPath())

        if len(self.hyperlpr_dir_path) > 0:
            hyperlpr_dir_info_filepath = QDir.homePath() + "/hyperlpr_dir_file"
            with open(hyperlpr_dir_info_filepath, 'w') as f:
                f.write(self.hyperlpr_dir_path)
            self.reset_info_gui()

    def rename_current_image_with_info(self):
        if len(self.hyperlpr_dir_path) > 0:
            target_dir_path = self.hyperlpr_dir_path + "/result"
            if not os.path.exists(target_dir_path):
                os.makedirs(target_dir_path)
            if len(self.plate_color_edit.text())>0 and len(self.e2e_recognization_edit.text())>0:
                orign_path = os.path.join(self.hyperlpr_dir_path, self.filename_edit.text())
                target_path = os.path.join(target_dir_path,self.plate_color_edit.text()+"-"+self.e2e_recognization_edit.text()+".jpg")
                shutil.copyfile(orign_path, target_path)

    def reset_info_gui(self):

        self.location_text.setText(self.hyperlpr_dir_path)
        self.scan_files_with_new_dir(self.hyperlpr_dir_path)
        self.fill_table_with_new_info()

    def scan_files_with_new_dir(self, path):

        name_list = os.listdir(path)  # 列出文件夹下所有的目录与文件
        self.image_filename_list.clear()
        for i in range(0, len(name_list)):
            if name_list[i].endswith(
                    ".jpg") or name_list[i].endswith(".png"):
                self.image_filename_list.append(name_list[i])

    def fill_table_with_new_info(self):
        self.hyperlpr_tableview.clearContents()
        row_count = self.hyperlpr_tableview.rowCount()
        for i in range(row_count, -1, -1):
            self.hyperlpr_tableview.removeRow(i)

        for i in range(0, len(self.image_filename_list)):
            row = self.hyperlpr_tableview.rowCount()
            self.hyperlpr_tableview.insertRow(row)

            item0 = QTableWidgetItem()
            item0.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(row, 0, item0)
            self.hyperlpr_tableview.item(
                row, 0).setText(
                self.image_filename_list[i])

            item1 = QTableWidgetItem()
            item1.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(row, 1, item1)

            item2 = QTableWidgetItem()
            item2.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(row, 2, item2)

            item3 = QTableWidgetItem()
            item3.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(row, 3, item3)

            item4 = QTableWidgetItem()
            item4.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(row, 4, item4)

            item5 = QTableWidgetItem()
            item5.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(row, 5, item5)

        if len(self.image_filename_list) > 0:
            self.left_button.setEnabled(True)
            self.right_button.setEnabled(True)
            self.save_as_e2e_filename_button.setEnabled(True)

    def analyze_last_one_image(self):
        if self.current_row > 0:
            self.recognize_one_license_plate(self.current_row-1, 0)

    def analyze_next_one_image(self):
        if self.current_row < (len(self.image_filename_list)-1):
            self.recognize_one_license_plate(self.current_row + 1, 0)

    def recognize_one_license_plate(self, row, col):
        if col == 0 and row < len(self.image_filename_list):
            self.current_row = row
            self.recognize_and_show_one_image(
                self.image_filename_list[row], row)

    def recognize_and_show_one_image(self, image_filename_text, row):

        if image_filename_text.endswith(".jpg"):

            print(image_filename_text)
            path = os.path.join(self.hyperlpr_dir_path, image_filename_text)
            image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), -1)
            image, res_set = SimpleRecognizePlateWithGui(image)
            img = QImage(
                image.data,
                image.shape[1],
                image.shape[0],
                image.shape[1] * image.shape[2],
                QImage.Format_RGB888)
            self.image_window_view.resetPixmap(img.rgbSwapped())
            self.image_window_view.resetRectText(res_set)

            if len(res_set) > 0:
                curr_rect = res_set[0][2]
                image_crop = image[int(curr_rect[1]):int(
                    curr_rect[1] + curr_rect[3]), int(curr_rect[0]):int(curr_rect[0] + curr_rect[2])]
                curr_plate = cv2.resize(image_crop, (204, 108))
                plate_img = QImage(
                    curr_plate.data,
                    curr_plate.shape[1],
                    curr_plate.shape[0],
                    curr_plate.shape[1] *
                    curr_plate.shape[2],
                    QImage.Format_RGB888)
                self.license_plate_widget.setPixmap(
                    QPixmap.fromImage(plate_img.rgbSwapped()))

                # print(res_set[0][6])
                block_crop = image[0:24, 0:(24 * int(res_set[0][6]))]
                curr_block = cv2.resize(
                    block_crop, (24 * int(res_set[0][6]), 24))
                block_image = QImage(
                    curr_block.data,
                    curr_block.shape[1],
                    curr_block.shape[0],
                    curr_block.shape[1] *
                    curr_block.shape[2],
                    QImage.Format_RGB888)
                self.block_plate_widget.setPixmap(
                    QPixmap.fromImage(block_image.rgbSwapped()))

                self.segmentation_recognition_edit.setText(res_set[0][0])
                if res_set[0][0] in image_filename_text:
                    self.segmentation_recognition_edit.setStyleSheet("color:black")
                else:
                    self.segmentation_recognition_edit.setStyleSheet("color:red")


                self.filename_edit.setText(image_filename_text)
                self.confidence_edit.setText("%.3f" % (float(res_set[0][1])))

                self.plate_color_edit.setText(res_set[0][3])
                if res_set[0][3] in image_filename_text:
                    self.plate_color_edit.setStyleSheet("color:black")
                else:
                    self.plate_color_edit.setStyleSheet("color:red")

                self.e2e_recognization_edit.setText(res_set[0][4])
                if res_set[0][4] in image_filename_text:
                    self.e2e_recognization_edit.setStyleSheet("color:black")
                else:
                    self.e2e_recognization_edit.setStyleSheet("color:red")

                self.e2e_confidence_edit.setText(
                    "%.3f" % (float(res_set[0][5])))
            else:
                self.license_plate_widget.clear()
                self.block_plate_widget.clear()
                self.segmentation_recognition_edit.setText("")
                self.filename_edit.setText(image_filename_text)
                self.confidence_edit.setText("")
                self.plate_color_edit.setText("")
                self.e2e_recognization_edit.setText("")
                self.e2e_confidence_edit.setText("")

            self.fill_table_widget_with_res_info(res_set, row)

    def batch_recognize_all_images(self):
        self.segmentation_recognition_correct_number = 0
        self.color_correct_number = 0
        self.e2e_recognization_correct_number = 0
        self.batch_recognization_thread.set_parameter(
            self.image_filename_list, self.hyperlpr_dir_path)

    def recognization_done_slot(self, result_list):
        row = result_list[0]
        res_set = result_list[1]
        self.fill_table_widget_with_res_info(res_set, row)

        if row == len(self.image_filename_list) - 1:
            total_number = len(self.image_filename_list)

            row_count = self.hyperlpr_tableview.rowCount()
            if row_count > total_number:
                self.hyperlpr_tableview.removeRow(total_number)

            self.hyperlpr_tableview.insertRow(total_number)

            item0 = QTableWidgetItem()
            item0.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(total_number, 0, item0)
            self.hyperlpr_tableview.item(
                total_number, 0).setText(
                "统计结果")

            item1 = QTableWidgetItem()
            item1.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(total_number, 1, item1)
            self.hyperlpr_tableview.item(
                total_number,
                1).setText(
                "{0} / {1} = {2: .3f}".format(
                    self.segmentation_recognition_correct_number,
                    total_number,
                    self.segmentation_recognition_correct_number /
                    total_number))

            item2 = QTableWidgetItem()
            item2.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(total_number, 2, item2)

            item3 = QTableWidgetItem()
            item3.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(total_number, 3, item3)
            self.hyperlpr_tableview.item(
                total_number, 3).setText(
                "{0} / {1} = {2: .3f}".format(self.e2e_recognization_correct_number, total_number,
                                              self.e2e_recognization_correct_number / total_number))

            item4 = QTableWidgetItem()
            item4.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(total_number, 4, item4)
            self.hyperlpr_tableview.item(
                total_number, 4).setText(
                "{0} / {1} = {2: .3f}".format(self.color_correct_number, total_number,
                                              self.color_correct_number / total_number))

            item5 = QTableWidgetItem()
            item5.setTextAlignment(Qt.AlignCenter)
            self.hyperlpr_tableview.setItem(total_number, 5, item5)

    def fill_table_widget_with_res_info(self, res_set, row):
        image_filename_text = self.image_filename_list[row]
        if len(res_set) > 0:

            self.hyperlpr_tableview.item(row, 1).setText(res_set[0][0])
            if res_set[0][0] in image_filename_text:
                self.hyperlpr_tableview.item(
                    row, 1).setForeground(
                    QBrush(
                        QColor(
                            0, 0, 255)))
                self.segmentation_recognition_correct_number += 1
            else:
                self.hyperlpr_tableview.item(
                    row, 1).setForeground(
                    QBrush(
                        QColor(
                            255, 0, 0)))

            self.hyperlpr_tableview.item(
                row, 2).setText(
                "%.3f" %
                (float(
                    res_set[0][1])))

            self.hyperlpr_tableview.item(row, 3).setText(res_set[0][3])
            if res_set[0][3] in image_filename_text:
                self.hyperlpr_tableview.item(
                    row, 3).setForeground(
                    QBrush(
                        QColor(
                            0, 0, 255)))
                self.color_correct_number += 1
            else:
                self.hyperlpr_tableview.item(
                    row, 3).setForeground(
                    QBrush(
                        QColor(
                            255, 0, 0)))

            self.hyperlpr_tableview.item(row, 4).setText(res_set[0][4])
            if res_set[0][4] in image_filename_text:
                self.hyperlpr_tableview.item(
                    row, 4).setForeground(
                    QBrush(
                        QColor(
                            0, 0, 255)))
                self.e2e_recognization_correct_number += 1
            else:
                self.hyperlpr_tableview.item(
                    row, 4).setForeground(
                    QBrush(
                        QColor(
                            255, 0, 0)))

            self.hyperlpr_tableview.item(
                row, 5).setText(
                "%.3f" %
                (float(
                    res_set[0][5])))


 if __name__ == '__main__':

    app = QApplication(sys.argv)

    hyper_lpr_widow = HyperLprWindow()

    hyper_lpr_widow.showMaximized()

    sys.exit(app.exec_())
--- a/Prj-Linux/CMakeLists.txt
+++ b/Prj-Linux/CMakeLists.txt
@@ -1,3 +1,3 @@
 cmake_minimum_required(VERSION 3.6)
 project(SwiftPR)
 add_subdirectory(lpr)
 project(HyperLPR)
 add_subdirectory(hyperlpr)
--- a/Prj-Linux/lpr/.idea/dictionaries/yujinke.xml
+++ b/Prj-Linux/lpr/.idea/dictionaries/yujinke.xml
@@ -0,0 +1,3 @@
 <component name="ProjectDictionaryState">
  <dictionary name="yujinke" />
 </component>
--- a/Prj-Linux/lpr/.idea/encodings.xml
+++ b/Prj-Linux/lpr/.idea/encodings.xml
@@ -0,0 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
  <component name="Encoding">
    <file url="PROJECT" charset="UTF-8" />
  </component>
 </project>
--- a/Prj-Linux/lpr/.idea/lpr.iml
+++ b/Prj-Linux/lpr/.idea/lpr.iml
@@ -0,0 +1,408 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="CPP_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$">
      <sourceFolder url="file://$MODULE_DIR$/src/util.h" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/Recognizer.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/PlateSegmentation.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/CNNRecognizer.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/FastDeskew.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/FineMapping.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/Pipeline.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/PlateDetection.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/src/SegmentationFreeRecognizer.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/include/PlateDetection.h" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/CMakeLists.txt" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/tests/test_segmentationFree.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/tests/test_recognization.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/tests/test_finemapping.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/tests/test_fastdeskew.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/tests/test_segmentation.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/tests/test_detection.cpp" isTestSource="false" />
      <sourceFolder url="file://$MODULE_DIR$/tests/test_pipeline.cpp" isTestSource="false" />
    </content>
    <orderEntry type="sourceFolder" forTests="false" />
    <orderEntry type="module-library">
      <library name="Header Search Paths">
        <CLASSES>
          <root url="file:///Library/Developer/CommandLineTools/SDKs/MacOSX10.14.sdk/System/Library/Frameworks/Intents.framework/Headers" />
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--- a/Prj-Linux/lpr/.idea/misc.xml
+++ b/Prj-Linux/lpr/.idea/misc.xml
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--- a/Prj-Linux/lpr/.idea/modules.xml
+++ b/Prj-Linux/lpr/.idea/modules.xml
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--- a/Prj-Linux/lpr/.idea/workspace.xml
+++ b/Prj-Linux/lpr/.idea/workspace.xml
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--- a/Prj-Linux/lpr/CMakeLists.txt
+++ b/Prj-Linux/lpr/CMakeLists.txt
@@ -8,17 +8,11 @@ include_directories( ${OpenCV_INCLUDE_DIRS})
 include_directories(include)

 set(SRC_DETECTION src/PlateDetection.cpp  src/util.h include/PlateDetection.h)

 set(SRC_FINEMAPPING src/FineMapping.cpp )

 set(SRC_FASTDESKEW src/FastDeskew.cpp )

 set(SRC_SEGMENTATION src/PlateSegmentation.cpp )

 set(SRC_RECOGNIZE src/Recognizer.cpp src/CNNRecognizer.cpp)

 set(SRC_PIPLINE src/Pipeline.cpp)

 set(SRC_SEGMENTATIONFREE src/SegmentationFreeRecognizer.cpp )

 #set(SOURCE_FILES main.cpp)
--- a/Prj-Linux/lpr/include/PlateDetection.h
+++ b/Prj-Linux/lpr/include/PlateDetection.h
@@ -15,19 +15,11 @@ namespace pr{
        PlateDetection();
        void LoadModel(std::string filename_cascade);
        void plateDetectionRough(cv::Mat InputImage,std::vector<pr::PlateInfo>  &plateInfos,int min_w=36,int max_w=800);
 //        std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat InputImage,int min_w= 60,int max_h = 400);


 //        std::vector<pr::PlateInfo> plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);



    private:
        cv::CascadeClassifier cascade;


    };

 }// namespace pr

 #endif //SWIFTPR_PLATEDETECTION_H

--- a/Prj-Linux/lpr/include/PlateInfo.h
+++ b/Prj-Linux/lpr/include/PlateInfo.h
@@ -6,13 +6,9 @@
 #define SWIFTPR_PLATEINFO_H
 #include <opencv2/opencv.hpp>
 namespace pr {

    typedef std::vector<cv::Mat> Character;

    enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK,UNKNOWN};
    enum CharType {CHINESE,LETTER,LETTER_NUMS,INVALID};


    class PlateInfo {
        public:
            std::vector<std::pair<CharType,cv::Mat>> plateChars;
@@ -72,10 +68,6 @@ namespace pr {
                plateCoding.push_back(charProb);
            }

    //        cv::Mat getPlateChars(int id) {
    //            if(id<PlateChars.size())
    //                return PlateChars[id];
    //        }
            std::string decodePlateNormal(std::vector<std::string> mappingTable) {
                std::string decode;
                for(auto plate:plateCoding) {
@@ -84,10 +76,6 @@ namespace pr {

                        decode += mappingTable[std::max_element(prob,prob+31) - prob];
                        confidence+=*std::max_element(prob,prob+31);


 //                        std::cout<<*std::max_element(prob,prob+31)<<std::endl;

                    }

                    else if(plate.first == LETTER) {
@@ -98,22 +86,16 @@ namespace pr {
                    else if(plate.first == LETTER_NUMS) {
                        decode += mappingTable[std::max_element(prob+31,prob+65)- prob];
                        confidence+=*std::max_element(prob+31,prob+65);
 //                        std::cout<<*std::max_element(prob+31,prob+65)<<std::endl;

                    }
                    else if(plate.first == INVALID)
                    {
                        decode+='*';
                    }

                }
                name = decode;

                confidence/=7;

                return decode;
            }

    private:
        cv::Mat licensePlate;
        cv::Rect ROI;
--- a/Prj-Linux/lpr/include/niBlackThreshold.h
+++ b/Prj-Linux/lpr/include/niBlackThreshold.h
@@ -68,10 +68,6 @@ void niBlackThreshold( InputArray _src, OutputArray _dst, double maxValue,
        thresh.convertTo(thresh, src.depth());

        thresh.convertTo(test, src.depth());
 //
 //        cv::imshow("imagex",test);
 //        cv::waitKey(0);

    }
    // Prepare output image
    _dst.create(src.size(), src.type());
--- a/Prj-Linux/lpr/src/FineMapping.cpp
+++ b/Prj-Linux/lpr/src/FineMapping.cpp
@@ -1,6 +1,5 @@
 #include "FineMapping.h"
 namespace pr{

    const int FINEMAPPING_H = 60 ;
    const int FINEMAPPING_W = 140;
    const int PADDING_UP_DOWN = 30;
@@ -14,17 +13,12 @@ namespace pr{

    FineMapping::FineMapping(std::string prototxt,std::string caffemodel) {
         net = cv::dnn::readNetFromCaffe(prototxt, caffemodel);

    }

    cv::Mat FineMapping::FineMappingHorizon(cv::Mat FinedVertical,int leftPadding,int rightPadding)
    {

 //        if(FinedVertical.channels()==1)
 //            cv::cvtColor(FinedVertical,FinedVertical,cv::COLOR_GRAY2BGR);
        cv::Mat inputBlob = cv::dnn::blobFromImage(FinedVertical, 1/255.0, cv::Size(66,16),
                                      cv::Scalar(0,0,0),false);

        net.setInput(inputBlob,"data");
        cv::Mat prob = net.forward();
        int front = static_cast<int>(prob.at<float>(0,0)*FinedVertical.cols);
@@ -35,9 +29,8 @@ namespace pr{
        if(back>FinedVertical.cols-1) back=FinedVertical.cols - 1;
        cv::Mat cropped  = FinedVertical.colRange(front,back).clone();
        return  cropped;


    }

    std::pair<int,int> FitLineRansac(std::vector<cv::Point> pts,int zeroadd = 0 )
    {
        std::pair<int,int> res;
--- a/Prj-Linux/lpr/src/Pipeline.cpp
+++ b/Prj-Linux/lpr/src/Pipeline.cpp
@@ -7,8 +7,6 @@

 namespace pr {



    const int HorizontalPadding = 4;
    PipelinePR::PipelinePR(std::string detector_filename,
                           std::string finemapping_prototxt, std::string finemapping_caffemodel,
@@ -20,35 +18,26 @@ namespace pr {
        plateSegmentation = new PlateSegmentation(segmentation_prototxt, segmentation_caffemodel);
        generalRecognizer = new CNNRecognizer(charRecognization_proto, charRecognization_caffemodel);
        segmentationFreeRecognizer =  new SegmentationFreeRecognizer(segmentationfree_proto,segmentationfree_caffemodel);

    }

    PipelinePR::~PipelinePR() {

        delete plateDetection;
        delete fineMapping;
        delete plateSegmentation;
        delete generalRecognizer;
        delete segmentationFreeRecognizer;


    }

    std::vector<PlateInfo> PipelinePR:: RunPiplineAsImage(cv::Mat plateImage,int method) {
        std::vector<PlateInfo> results;
        std::vector<pr::PlateInfo> plates;
        plateDetection->plateDetectionRough(plateImage,plates,36,700);

        for (pr::PlateInfo plateinfo:plates) {

            cv::Mat image_finemapping = plateinfo.getPlateImage();
            image_finemapping = fineMapping->FineMappingVertical(image_finemapping);
            image_finemapping = pr::fastdeskew(image_finemapping, 5);



            //Segmentation-based

            if(method==SEGMENTATION_BASED_METHOD)
            {
                image_finemapping = fineMapping->FineMappingHorizon(image_finemapping, 2, HorizontalPadding);
--- a/Prj-Linux/lpr/tests/test_detection.cpp
+++ b/Prj-Linux/lpr/tests/test_detection.cpp
@@ -29,6 +29,4 @@ int main()
    cv::imshow("image",image);
    cv::waitKey(0);
    return 0 ;


 }
--- a/Prj-Linux/lpr/tests/test_pipeline.cpp
+++ b/Prj-Linux/lpr/tests/test_pipeline.cpp
@@ -136,51 +136,31 @@ void TEST_PIPELINE(){

 void TEST_CAM()
 {

    cv::VideoCapture capture("test1.mp4");
    cv::Mat frame;

    pr::PipelinePR prc("model/cascade.xml",
                       "model/HorizonalFinemapping.prototxt","model/HorizonalFinemapping.caffemodel",
                       "model/Segmentation.prototxt","model/Segmentation.caffemodel",
                       "model/CharacterRecognization.prototxt","model/CharacterRecognization.caffemodel",
                       "model/SegmentationFree.prototxt","model/SegmentationFree.caffemodel"
    );





    while(1) {
        //读取下一帧
        if (!capture.read(frame)) {
            std::cout << "读取视频失败" << std::endl;
            exit(1);
        }
 //
 //        cv::transpose(frame,frame);
 //        cv::flip(frame,frame,2);

 //        cv::resize(frame,frame,cv::Size(frame.cols/2,frame.rows/2));



        std::vector<pr::PlateInfo> res = prc.RunPiplineAsImage(frame,pr::SEGMENTATION_FREE_METHOD);

        for(auto st:res) {
            if(st.confidence>0.75) {
                std::cout << st.getPlateName() << " " << st.confidence << std::endl;
                cv::Rect region = st.getPlateRect();

                cv::rectangle(frame,cv::Point(region.x,region.y),cv::Point(region.x+region.width,region.y+region.height),cv::Scalar(255,255,0),2);
            }
        }

        cv::imshow("image",frame);
        cv::waitKey(1);



    }

 }
--- a/README.md
+++ b/README.md
@@ -15,14 +15,14 @@
 #### 快速上手

 ```python
 #导入包
 #导入hyperlpr
 from hyperlpr import *
 #导入OpenCV库
 import cv2
 #读入图片
 image = cv2.imread("demo.jpg")
 #识别结果
 print(HyperLPR_PlateRecogntion(image))
 print(HyperLPR_plate_recognition(image))
 ```

 #### Q&A
@@ -194,7 +194,7 @@ int main(){

 #### 获取帮助

 - HyperLPR讨论QQ群1: 673071218, 群2: 746123554(已满) ,加前请备注HyperLPR交流。
 - HyperLPR讨论QQ群1: 673071218, 群2: 746123554(已满) ,加前请备注HyperLPR交流,高精度版本商务合作可在群内联系。

 ### 作者和贡献者信息：

--- a/WebAPI.py
+++ b/WebAPI.py
@@ -17,15 +17,11 @@ app = Flask(__name__)

 def recognize(filename):
    image = cv2.imread(filename)
    #通过文件名读入一张图片 放到 image中
    return pipline.RecognizePlateJson(image)
    #识别一张图片并返回json结果

 #识别函数
    return HyperLPR_plate_recognition(image)
    #return pipline.RecognizePlateJson(image)

 import base64


 def recognizeBase64(base64_code):
    file_bytes = np.asarray(bytearray(base64.b64decode(base64_code)),dtype=np.uint8)
    image_data_ndarray = cv2.imdecode(file_bytes,1)
@@ -46,7 +42,6 @@ def upload_file():
        print "识别时间",time.time() - t0
        return res
        #返回识别结果

        # return 'file uploaded successfully'
    return render_template('upload.html')

--- a/benchmark.py
+++ b/benchmark.py
@@ -1,75 +0,0 @@
 #coding=utf-8
 import os
 import numpy as np
 import cv2
 import json
 from hyperlpr import  pipline as  pp
 import sys
 from Levenshtein import StringMatcher as sm

 reload(sys)
 sys.setdefaultencoding("utf-8")
 parent= "you folder"

 def comparestring(a,b):
    g = 0
    if len(a) == len(b):
        for x,y in zip(a,b):
            if x!=y:
                g+=1
    return g
 count = 0 ;
 count_p = 0
 count_d = 0
 count_lev = 0
 count_undetected = 0
 roi = [470,400,650,580]
 for filename in os.listdir(parent):

    path = os.path.join(parent,filename)
    print path

    if path.endswith(".jpg") or path.endswith(".png"):
        ics,name = os.path.split(path)


        name,ext = name.split(".")

        image =  cv2.imread(path)
        image = image[roi[1]:roi[1]+roi[3],roi[0]:roi[0]+roi[2]]
        # cv2.imshow("test",image)
        # cv2.waitKey(0)
        info,dataset = pp.SimpleRecognizePlate(image)
        ext = ext.strip()
        name = name.strip()

        if len(dataset)==0:
            count_undetected +=1

            # cv2.imwrite("./cache/bad2/" + name + ".png", image)

        for one in dataset:

            # p = sm.StringMatcher(seq1=one.encode("utf-8"),seq2=name.encode("utf-8"))
            A = one.decode("utf-8")
            B = name.decode("utf-8")
            print one.decode("utf-8"),"<->",name.decode("utf-8"),"编辑距离:",comparestring(A,B)
            if  comparestring(A,B)<3:
                count_lev+=1
            else:
                cv2.imwrite("./cache/bad2/"+B+"->"+A+".png",image)

            if one.decode("utf-8") == name.decode("utf-8"):
                count_p+=1
                break
            else:
                print "error",one.decode("utf-8"), name.decode("utf-8")
                count_d+=1
                # cv2.imshow("image",image)
                # cv2.waitKey(0)
                # break
        count+=1
        print count_p / float(count),"编辑距离[1]:",count_lev/float(count),u"识出",count_p,u"总数",count,u"未识出",count_d,u"未检测出",count_undetected
        if count_p+count_d+count_undetected!=count:
            print dataset,len(dataset)
            # exit(0)
--- a/config.json
+++ b/config.json
@@ -1,18 +0,0 @@
 {
  "global":{
    "debug":true
  },
  "log":{
  },
  "detect":{
  },
  "recognize":{
  },
  "detectTest":{
    "detectPath":"/Users/universe/Documents/work/data/Plate/boundingbox",
    "outputPath":"/Users/universe/ProgramUniverse/python/Forked/HyperLPR/out/detect_test"
  },
  "colorTest":{
    "colorPath":"/Users/universe/Documents/work/data/Plate/colour/u"
  }
 }
--- a/demo.py
+++ b/demo.py
@@ -1,59 +0,0 @@
 import sys
 reload(sys)
 sys.setdefaultencoding("utf-8")




 import time

 def SpeedTest(image_path):
    grr = cv2.imread(image_path)
    model = pr.LPR("model/cascade.xml", "model/model12.h5", "model/ocr_plate_all_gru.h5")
    model.SimpleRecognizePlateByE2E(grr)
    t0 = time.time()
    for x in range(20):
        model.SimpleRecognizePlateByE2E(grr)
    t = (time.time() - t0)/20.0
    print "Image size :" + str(grr.shape[1])+"x"+str(grr.shape[0]) +  " need " + str(round(t*1000,2))+"ms"

    

 from PIL import ImageFont
 from PIL import Image
 from PIL import ImageDraw
 fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0)

 def drawRectBox(image,rect,addText):
    cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2,cv2.LINE_AA)
    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1,
                  cv2.LINE_AA)
    img = Image.fromarray(image)
    draw = ImageDraw.Draw(img)
    draw.text((int(rect[0]+1), int(rect[1]-16)), addText.decode("utf-8"), (255, 255, 255), font=fontC)
    imagex = np.array(img)
    return imagex





 import HyperLPRLite as pr
 import cv2
 import numpy as np
 grr = cv2.imread("images_rec/2_.jpg")
 model = pr.LPR("model/cascade.xml","model/model12.h5","model/ocr_plate_all_gru.h5")
 for pstr,confidence,rect in model.SimpleRecognizePlateByE2E(grr):
        if confidence>0.7:
            image = drawRectBox(grr, rect, pstr+" "+str(round(confidence,3)))
            print "plate_str:"
            print pstr
            print "plate_confidence"
            print confidence
            
 cv2.imshow("image",image)
 cv2.waitKey(0)



 SpeedTest("images_rec/2_.jpg")
--- a/hyperlpr/init.py
+++ b/hyperlpr/init.py
--- a/hyperlpr/cache.py
+++ b/hyperlpr/cache.py
@@ -1,11 +0,0 @@
 import cv2
 import os
 import hashlib

 def verticalMappingToFolder(image):
    name = hashlib.md5(image.data).hexdigest()[:8]
    print name

    cv2.imwrite("./cache/finemapping/"+name+".png",image)


--- a/hyperlpr/colourDetection.py
+++ b/hyperlpr/colourDetection.py
@@ -1,103 +0,0 @@
 # -- coding: UTF-8
 import cv2
 import matplotlib.pyplot as plt
 from sklearn.cluster import KMeans
 import os

 boundaries = [
    ([100,80,0],[240,220,110]), # yellow
    ([0,40,50],[110,180,250]), # blue
    ([0,60,0],[60,160,70]), # green
 ]
 color_attr = ["黄牌","蓝牌",'绿牌','白牌','黑牌']

 threhold_green = 13
 threhold_blue = 13
 threhold_yellow1 = 50
 threhold_yellow2 = 70

 # plt.figure()
 # plt.axis("off")
 # plt.imshow(image)
 # plt.show()

 import numpy as np
 def centroid_histogram(clt):
    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
    (hist, _) = np.histogram(clt.labels_, bins=numLabels)

    # normalize the histogram, such that it sums to one
    hist = hist.astype("float")
    hist /= hist.sum()

    # return the histogram
    return hist


 def plot_colors(hist, centroids):
    bar = np.zeros((50, 300, 3), dtype="uint8")
    startX = 0

    for (percent, color) in zip(hist, centroids):

        endX = startX + (percent * 300)
        cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
                      color.astype("uint8").tolist(), -1)
        startX = endX

    # return the bar chart
    return bar

 def search_boundaries(color):
    for i,color_bound in enumerate(boundaries):
        if np.all(color >= color_bound[0]) and np.all(color <= color_bound[1]):
            return i
    return -1

 def judge_color(color):
    r = color[0]
    g = color[1]
    b = color[2]
    if g - r >= threhold_green and g - b >= threhold_green:
        return 2
    if b - r >= threhold_blue and b - g >= threhold_blue:
        return 1
    if r- b > threhold_yellow2 and g - b > threhold_yellow2:
        return 0
    if r > 200 and b > 200 and g > 200:
        return 3
    if r < 50 and b < 50 and g < 50:
        return 4
    return -1

 def judge_plate_color(img):
    image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    image = image.reshape((image.shape[0] * image.shape[1], 3))
    clt = KMeans(n_clusters=2)
    clt.fit(image)

    hist = centroid_histogram(clt)
    index = np.argmax(hist)
    #print clt.cluster_centers_[index]
    #color_index = search_boundaries(clt.cluster_centers_[index])
    color_index = judge_color(clt.cluster_centers_[index])
    if color_index == -1:
        if index == 0:
            secound_index = 1
        else:
            secound_index = 0
        color_index = judge_color(clt.cluster_centers_[secound_index])

    if color_index == -1:
        print clt.cluster_centers_
        bar = plot_colors(hist, clt.cluster_centers_)
        # show our color bart
        plt.figure()
        plt.axis("off")
        plt.imshow(bar)
        plt.show()

    if color_index != -1:
        return color_attr[color_index],clt.cluster_centers_[index]
    else:
        return None,clt.cluster_centers_[index]
--- a/hyperlpr/config.py
+++ b/hyperlpr/config.py
@@ -1,6 +0,0 @@
 import json



 with open("/Users/universe/ProgramUniverse/zeusees/HyperLPR/config.json") as f:
    configuration = json.load(f)
--- a/hyperlpr/deskew.py
+++ b/hyperlpr/deskew.py
@@ -1,111 +0,0 @@
 #coding=utf-8
 import numpy as np
 import cv2
 import time;
 from matplotlib import pyplot as plt
 import math

 from scipy.ndimage import filters
 #
 # def strokeFiter():
 #     pass;

 def angle(x,y):
    return int(math.atan2(float(y),float(x))*180.0/3.1415);

 def h_rot(src, angle, scale=1.):
    w = src.shape[1]
    h = src.shape[0]
    rangle = np.deg2rad(angle)
    nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
    nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale

    rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)

    rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))

    rot_mat[0,2] += rot_move[0]
    rot_mat[1,2] += rot_move[1]
    return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
    pass


 def v_rot(img,angel,shape,max_angel):

    size_o = [shape[1],shape[0]]

    size = (shape[1]+ int(shape[0]*np.cos((float(max_angel )/180) * 3.14)),shape[0])


    interval = abs( int( np.sin((float(angel) /180) * 3.14)* shape[0]));

    pts1 = np.float32([[0,0]         ,[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
    if(angel>0):

        pts2 = np.float32([[interval,0],[0,size[1]  ],[size[0],0  ],[size[0]-interval,size_o[1]]])
    else:
        pts2 = np.float32([[0,0],[interval,size[1]  ],[size[0]-interval,0  ],[size[0],size_o[1]]])

    M  = cv2.getPerspectiveTransform(pts1,pts2);
    dst = cv2.warpPerspective(img,M,size);
    return dst,M;

 def skew_detection(image_gray):
    h, w = image_gray.shape[:2]
    eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5)
    angle_sur = np.zeros(180,np.uint);
    eigen = eigen.reshape(h, w, 3, 2)
    flow = eigen[:,:,2]
    vis = image_gray.copy()
    vis[:] = (192 + np.uint32(vis)) / 2
    d = 12
    points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
    for x, y in points:
       vx, vy = np.int32(flow[y, x]*d)
       # cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA)
       ang = angle(vx,vy);
       angle_sur[(ang+180)%180] +=1;
    # torr_bin = 30
    angle_sur = angle_sur.astype(np.float)
    angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min())
    angle_sur = filters.gaussian_filter1d(angle_sur,5)
    skew_v_val =  angle_sur[20:180-20].max();
    skew_v = angle_sur[30:180-30].argmax() + 30;
    skew_h_A = angle_sur[0:30].max()
    skew_h_B = angle_sur[150:180].max()
    skew_h = 0;
    if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3):
        if skew_h_A>=skew_h_B:
            skew_h = angle_sur[0:20].argmax()
        else:
            skew_h = - angle_sur[160:180].argmax()
    return skew_h,skew_v




 def fastDeskew(image):
    image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    skew_h,skew_v = skew_detection(image_gray)

    print "校正角度 h ",skew_h,"v",skew_v

    deskew,M = v_rot(image,int((90-skew_v)*1.5),image.shape,60)
    return deskew,M



 if __name__ == '__main__':
    fn = 'test_data/test4.png'

    img = cv2.imread(fn)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    skew_h,skew_v = skew_detection(img,gray)
    img = v_rot(img,(90-skew_v ),img.shape,60)
    # img = h_rot(img,skew_h)
    # if img.shape[0]>img.shape[1]:
    #     img = h_rot(img, -90)

    plt.show()

    cv2.waitKey()
--- a/hyperlpr/detect.py
+++ b/hyperlpr/detect.py
@@ -1,76 +0,0 @@

 import cv2
 import numpy as np



 watch_cascade = cv2.CascadeClassifier('./model/cascade.xml')


 def computeSafeRegion(shape,bounding_rect):
    top = bounding_rect[1] # y
    bottom  = bounding_rect[1] + bounding_rect[3] # y +  h
    left = bounding_rect[0] # x
    right =   bounding_rect[0] + bounding_rect[2] # x +  w

    min_top = 0
    max_bottom = shape[0]
    min_left = 0
    max_right = shape[1]

    # print "computeSateRegion input shape",shape
    if top < min_top:
        top = min_top
        # print "tap top 0"
    if left < min_left:
        left = min_left
        # print "tap left 0"

    if bottom > max_bottom:
        bottom = max_bottom
        #print "tap max_bottom max"
    if right > max_right:
        right = max_right
        #print "tap max_right max"

    # print "corr",left,top,right,bottom
    return [left,top,right-left,bottom-top]


 def cropped_from_image(image,rect):
    x, y, w, h = computeSafeRegion(image.shape,rect)
    return image[y:y+h,x:x+w]


 def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
    print image_gray.shape

    if top_bottom_padding_rate>0.2:
        print "error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate
        exit(1)

    height = image_gray.shape[0]
    padding =    int(height*top_bottom_padding_rate)
    scale = image_gray.shape[1]/float(image_gray.shape[0])

    image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))

    image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]

    image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)

    watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))

    cropped_images = []
    for (x, y, w, h) in watches:
        cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
        x -= w * 0.14
        w += w * 0.28
        y -= h * 0.6
        h += h * 1.1;

        cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))


        cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin])
    return cropped_images
--- a/hyperlpr/e2e.py
+++ b/hyperlpr/e2e.py
@@ -1,63 +0,0 @@
 #coding=utf-8
 from keras import backend as K
 from keras.models import load_model
 from keras.layers import *
 import numpy as np
 import random
 import string

 import cv2
 import e2emodel as model
 chars = [u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",
             u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",
             u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L", u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",
             u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南",u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"
             ];
 pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",)
 import time



 def fastdecode(y_pred):
    results = ""
    confidence = 0.0
    table_pred = y_pred.reshape(-1, len(chars)+1)

    res = table_pred.argmax(axis=1)

    for i,one in enumerate(res):
        if one<len(chars) and (i==0 or (one!=res[i-1])):
            results+= chars[one]
            confidence+=table_pred[i][one]
    confidence/= len(results)
    return results,confidence

 def recognizeOne(src):
    # x_tempx= cv2.imread(src)
    x_tempx = src
    # x_tempx = cv2.bitwise_not(x_tempx)
    x_temp = cv2.resize(x_tempx,( 160,40))
    x_temp = x_temp.transpose(1, 0, 2)
    t0 = time.time()
    y_pred = pred_model.predict(np.array([x_temp]))
    y_pred = y_pred[:,2:,:]
    # plt.imshow(y_pred.reshape(16,66))
    # plt.show()

    #
    # cv2.imshow("x_temp",x_tempx)
    # cv2.waitKey(0)
    return fastdecode(y_pred)
 #
 #
 # import os
 #
 # path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping"
 # for filename in os.listdir(path):
 #     if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"):
 #         x = os.path.join(path,filename)
 #         recognizeOne(x)
 #         # print time.time() - t0
 #
 #         # cv2.imshow("x",x)
 #         # cv2.waitKey()
--- a/hyperlpr/e2emodel.py
+++ b/hyperlpr/e2emodel.py
@@ -1,34 +0,0 @@

 from keras import backend as K
 from keras.models import *
 from keras.layers import *
 import e2e


 def ctc_lambda_func(args):
    y_pred, labels, input_length, label_length = args
    y_pred = y_pred[:, 2:, :]
    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)


 def construct_model(model_path):
    input_tensor = Input((None, 40, 3))
    x = input_tensor
    base_conv = 32

    for i in range(3):
        x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = MaxPooling2D(pool_size=(2, 2))(x)
    x = Conv2D(256, (5, 5))(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(1024, (1, 1))(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(len(e2e.chars)+1, (1, 1))(x)
    x = Activation('softmax')(x)
    base_model = Model(inputs=input_tensor, outputs=x)
    base_model.load_weights(model_path)
    return base_model
--- a/hyperlpr/finemapping.py
+++ b/hyperlpr/finemapping.py
@@ -1,130 +0,0 @@
 #coding=utf-8
 import cv2
 import numpy as np


 import niblack_thresholding as nt

 import deskew

 def fitLine_ransac(pts,zero_add = 0 ):
    if len(pts)>=2:
        [vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
        lefty = int((-x * vy / vx) + y)
        righty = int(((136- x) * vy / vx) + y)
        return lefty+30+zero_add,righty+30+zero_add
    return 0,0



 #精定位算法
 def findContoursAndDrawBoundingBox(image_rgb):


    line_upper  = [];
    line_lower = [];

    line_experiment = []
    grouped_rects = []
    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)

    # for k in np.linspace(-1.5, -0.2,10):
    for k in np.linspace(-50, 0, 15):

        # thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
        # binary_niblack = gray_image > thresh_niblack
        # binary_niblack = binary_niblack.astype(np.uint8) * 255

        binary_niblack = cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,17,k)
        # cv2.imshow("image1",binary_niblack)
        # cv2.waitKey(0)
        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
        for contour in contours:
            bdbox = cv2.boundingRect(contour)
            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1200) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
                line_upper.append([bdbox[0],bdbox[1]])
                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])

                line_experiment.append([bdbox[0],bdbox[1]])
                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
                # grouped_rects.append(bdbox)

    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
    leftyA, rightyA = fitLine_ransac(np.array(line_lower),3)
    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)

    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-3)

    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
    image,M = deskew.fastDeskew(image)

    return image



 #多级
 def findContoursAndDrawBoundingBox2(image_rgb):


    line_upper  = [];
    line_lower = [];

    line_experiment = []

    grouped_rects = []

    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)

    for k in np.linspace(-1.6, -0.2,10):
    # for k in np.linspace(-15, 0, 15):
    # #
    #     thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
    #     binary_niblack = gray_image > thresh_niblack
    #     binary_niblack = binary_niblack.astype(np.uint8) * 255

        binary_niblack = nt.niBlackThreshold(gray_image,19,k)
        # cv2.imshow("binary_niblack_opencv",binary_niblack_)
        # cv2.imshow("binary_niblack_skimage", binary_niblack)

        # cv2.waitKey(0)
        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours:
            bdbox = cv2.boundingRect(contour)
            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1000) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
                line_upper.append([bdbox[0],bdbox[1]])
                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])

                line_experiment.append([bdbox[0],bdbox[1]])
                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
                # grouped_rects.append(bdbox)

    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
    leftyA, rightyA = fitLine_ransac(np.array(line_lower),2)
    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)

    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-4)

    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
    image,M= deskew.fastDeskew(image)


    return image
--- a/hyperlpr/finemapping_vertical.py
+++ b/hyperlpr/finemapping_vertical.py
@@ -1,92 +0,0 @@
 #coding=utf-8
 from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense
 from keras.models import Model, Sequential
 from keras.layers.advanced_activations import PReLU
 from keras.optimizers import adam
 import numpy as np

 import cv2

 def getModel():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = Activation("relu", name='relu1')(x)
    x = MaxPool2D(pool_size=2)(x)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = Activation("relu", name='relu3')(x)
    x = Flatten()(x)
    output = Dense(2,name = "dense")(x)
    output = Activation("relu", name='relu4')(output)
    model = Model([input], [output])
    return model



 model = getModel()
 model.load_weights("./model/model12.h5")


 def getmodel():
    return model

 def gettest_model():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    B = Activation("relu", name='relu1')(A)
    C = MaxPool2D(pool_size=2)(B)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    K = Activation("relu", name='relu3')(x)


    x = Flatten()(K)
    dense = Dense(2,name = "dense")(x)
    output = Activation("relu", name='relu4')(dense)
    x = Model([input], [output])
    x.load_weights("./model/model12.h5")
    ok = Model([input], [dense])

    for layer in ok.layers:
        print layer

    return ok




 def finemappingVertical(image):
    resized = cv2.resize(image,(66,16))
    resized = resized.astype(np.float)/255
    res= model.predict(np.array([resized]))[0]
    print "keras_predict",res
    res  =res*image.shape[1]
    res = res.astype(np.int)
    H,T = res
    H-=3
    #3 79.86
    #4 79.3
    #5 79.5
    #6 78.3


    #T
    #T+1 80.9
    #T+2 81.75
    #T+3 81.75



    if H<0:
        H=0
    T+=2;

    if T>= image.shape[1]-1:
        T= image.shape[1]-1

    image = image[0:35,H:T+2]

    image = cv2.resize(image, (int(136), int(36)))
    return image
--- a/hyperlpr/niblack_thresholding.py
+++ b/hyperlpr/niblack_thresholding.py
@@ -1,18 +0,0 @@
 import cv2
 import numpy as np



 def niBlackThreshold(  src,  blockSize,  k,  binarizationMethod= 0 ):
    mean = cv2.boxFilter(src,cv2.CV_32F,(blockSize, blockSize),borderType=cv2.BORDER_REPLICATE)
    sqmean = cv2.sqrBoxFilter(src, cv2.CV_32F, (blockSize, blockSize), borderType = cv2.BORDER_REPLICATE)
    variance = sqmean - (mean*mean)
    stddev  = np.sqrt(variance)
    thresh = mean + stddev * float(-k)
    thresh = thresh.astype(src.dtype)
    k = (src>thresh)*255
    k = k.astype(np.uint8)
    return k


 # cv2.imshow()
--- a/hyperlpr/pipline.py
+++ b/hyperlpr/pipline.py
@@ -1,264 +0,0 @@
 #coding=utf-8
 import detect
 import  finemapping  as  fm

 import segmentation
 import cv2

 import time
 import numpy as np

 from PIL import ImageFont
 from PIL import Image
 from PIL import ImageDraw
 import json

 import sys
 import typeDistinguish as td


 reload(sys)
 sys.setdefaultencoding("utf-8")

 fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0);

 import e2e
 #寻找车牌左右边界

 def find_edge(image):
    sum_i = image.sum(axis=0)
    sum_i =  sum_i.astype(np.float)
    sum_i/=image.shape[0]*255
    # print sum_i

    start= 0 ;
    end = image.shape[1]-1

    for i,one in enumerate(sum_i):
        if one>0.4:
            start = i;
            if start-3<0:
                start = 0
            else:
                start -=3

            break;
    for i,one in enumerate(sum_i[::-1]):

        if one>0.4:
            end = end - i;
            if end+4>image.shape[1]-1:
                end = image.shape[1]-1
            else:
                end+=4
            break
    return start,end


 #垂直边缘检测

 def verticalEdgeDetection(image):
    image_sobel = cv2.Sobel(image.copy(),cv2.CV_8U,1,0)
    # image = auto_canny(image_sobel)

    # img_sobel, CV_8U, 1, 0, 3, 1, 0, BORDER_DEFAULT
    # canny_image  = auto_canny(image)
    flag,thres = cv2.threshold(image_sobel,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY)
    print flag
    flag,thres = cv2.threshold(image_sobel,int(flag*0.7),255,cv2.THRESH_BINARY)
    # thres = simpleThres(image_sobel)
    kernal = np.ones(shape=(3,15))
    thres = cv2.morphologyEx(thres,cv2.MORPH_CLOSE,kernal)
    return thres

 #确定粗略的左右边界
 def horizontalSegmentation(image):

    thres = verticalEdgeDetection(image)
    # thres = thres*image
    head,tail = find_edge(thres)
    # print head,tail
    # cv2.imshow("edge",thres)
    tail = tail+5
    if tail>135:
        tail = 135
    image = image[0:35,head:tail]
    image = cv2.resize(image, (int(136), int(36)))
    return image



 #打上boundingbox和标签
 def drawRectBox(image,rect,addText):
    cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2,cv2.LINE_AA)
    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1,
                  cv2.LINE_AA)

    img = Image.fromarray(image)
    draw = ImageDraw.Draw(img)
    draw.text((int(rect[0]+1), int(rect[1]-16)), addText.decode("utf-8"), (255, 255, 255), font=fontC)
    imagex = np.array(img)

    return imagex




 import cache
 import finemapping_vertical as fv


 def RecognizePlateJson(image):

    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)

    jsons = []

    for j,plate in enumerate(images):


        plate,rect,origin_plate =plate
        res, confidence = e2e.recognizeOne(origin_plate)
        print "res",res

        cv2.imwrite("./"+str(j)+"_rough.jpg",plate)

        # print "车牌类型:",ptype
        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
        plate  =cv2.resize(plate,(136,int(36*2.5)))
        t1 = time.time()


        ptype = td.SimplePredict(plate)
        if ptype>0 and ptype<4:
            plate = cv2.bitwise_not(plate)
        # demo = verticalEdgeDetection(plate)

        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
        image_rgb = fv.finemappingVertical(image_rgb)
        cache.verticalMappingToFolder(image_rgb)
        # print time.time() - t1,"校正"
        print "e2e:",e2e.recognizeOne(image_rgb)[0]
        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)


        cv2.imwrite("./"+str(j)+".jpg",image_gray)
        # image_gray = horizontalSegmentation(image_gray)


        t2 = time.time()
        res, confidence = e2e.recognizeOne(image_rgb)
        res_json = {}
        if confidence  > 0.6:
            res_json["Name"] = res
            res_json["Type"] = td.plateType[ptype]
            res_json["Confidence"] = confidence;
            res_json["x"] = int(rect[0])
            res_json["y"] = int(rect[1])
            res_json["w"] = int(rect[2])
            res_json["h"] = int(rect[3])
            jsons.append(res_json)
    print json.dumps(jsons,ensure_ascii=False,encoding="gb2312")

    return json.dumps(jsons,ensure_ascii=False,encoding="gb2312")




 def SimpleRecognizePlateByE2E(image):
    t0 = time.time()
    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
    res_set = []
    for j,plate in enumerate(images):
        plate, rect, origin_plate  =plate
        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
        plate  =cv2.resize(plate,(136,36*2))
        res,confidence = e2e.recognizeOne(origin_plate)
        print "res",res

        t1 = time.time()
        ptype = td.SimplePredict(plate)
        if ptype>0 and ptype<5:
            # pass
            plate = cv2.bitwise_not(plate)
        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
        image_rgb = fv.finemappingVertical(image_rgb)
        image_rgb = fv.finemappingVertical(image_rgb)
        cache.verticalMappingToFolder(image_rgb)
        cv2.imwrite("./"+str(j)+".jpg",image_rgb)
        res,confidence = e2e.recognizeOne(image_rgb)
        print res,confidence
        res_set.append([[],res,confidence])

        if confidence>0.7:
            image = drawRectBox(image, rect, res+" "+str(round(confidence,3)))
    return image,res_set







 def SimpleRecognizePlate(image):
    t0 = time.time()
    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
    res_set = []
    for j,plate in enumerate(images):
        plate, rect, origin_plate  =plate
        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
        plate  =cv2.resize(plate,(136,36*2))
        t1 = time.time()

        ptype = td.SimplePredict(plate)
        if ptype>0 and ptype<5:
            plate = cv2.bitwise_not(plate)

        image_rgb = fm.findContoursAndDrawBoundingBox(plate)

        image_rgb = fv.finemappingVertical(image_rgb)
        cache.verticalMappingToFolder(image_rgb)
        print "e2e:", e2e.recognizeOne(image_rgb)
        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY)

        # image_gray = horizontalSegmentation(image_gray)
        cv2.imshow("image_gray",image_gray)
        # cv2.waitKey()

        cv2.imwrite("./"+str(j)+".jpg",image_gray)
        # cv2.imshow("image",image_gray)
        # cv2.waitKey(0)
        print "校正",time.time() - t1,"s"
        # cv2.imshow("image,",image_gray)
        # cv2.waitKey(0)
        t2 = time.time()
        val = segmentation.slidingWindowsEval(image_gray)
        # print val
        print "分割和识别",time.time() - t2,"s"
        if len(val)==3:
            blocks, res, confidence = val
            if confidence/7>0.7:
                image =  drawRectBox(image,rect,res)
                res_set.append(res)
                for i,block in enumerate(blocks):

                    block_ = cv2.resize(block,(25,25))
                    block_ = cv2.cvtColor(block_,cv2.COLOR_GRAY2BGR)
                    image[j * 25:(j * 25) + 25, i * 25:(i * 25) + 25] = block_
                    if image[j*25:(j*25)+25,i*25:(i*25)+25].shape == block_.shape:
                        pass


            if confidence>0:
                print "车牌:",res,"置信度:",confidence/7
            else:
                pass

                # print "不确定的车牌:", res, "置信度:", confidence

    print time.time() - t0,"s"
    return image,res_set




--- a/hyperlpr/plateStructure.py
+++ b/hyperlpr/plateStructure.py
--- a/hyperlpr/precise.py
+++ b/hyperlpr/precise.py
--- a/hyperlpr/recognizer.py
+++ b/hyperlpr/recognizer.py
@@ -1,154 +0,0 @@
 #coding=utf-8
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D,MaxPool2D
 from keras.optimizers import SGD
 from keras import backend as K

 K.set_image_dim_ordering('tf')


 import cv2
 import numpy as np



 index = {u"京": 0, u"沪": 1, u"津": 2, u"渝": 3, u"冀": 4, u"晋": 5, u"蒙": 6, u"辽": 7, u"吉": 8, u"黑": 9, u"苏": 10, u"浙": 11, u"皖": 12,
         u"闽": 13, u"赣": 14, u"鲁": 15, u"豫": 16, u"鄂": 17, u"湘": 18, u"粤": 19, u"桂": 20, u"琼": 21, u"川": 22, u"贵": 23, u"云": 24,
         u"藏": 25, u"陕": 26, u"甘": 27, u"青": 28, u"宁": 29, u"新": 30, u"0": 31, u"1": 32, u"2": 33, u"3": 34, u"4": 35, u"5": 36,
         u"6": 37, u"7": 38, u"8": 39, u"9": 40, u"A": 41, u"B": 42, u"C": 43, u"D": 44, u"E": 45, u"F": 46, u"G": 47, u"H": 48,
         u"J": 49, u"K": 50, u"L": 51, u"M": 52, u"N": 53, u"P": 54, u"Q": 55, u"R": 56, u"S": 57, u"T": 58, u"U": 59, u"V": 60,
         u"W": 61, u"X": 62, u"Y": 63, u"Z": 64,u"港":65,u"学":66 ,u"O":67 ,u"使":68,u"警":69,u"澳":70,u"挂":71};

 chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
             "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
             "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P",
         "Q", "R", "S", "T", "U", "V", "W", "X",
             "Y", "Z","港","学","O","使","警","澳","挂" ];



 def Getmodel_tensorflow(nb_classes):
    # nb_classes = len(charset)

    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 32
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3

    # x = np.load('x.npy')
    
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(512, (3, 3)))
    # model.add(Activation('relu'))
    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
    # model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model




 def Getmodel_ch(nb_classes):
    # nb_classes = len(charset)

    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 32
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3

    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(512, (3, 3)))
    # model.add(Activation('relu'))
    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
    # model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(756))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model



 model  = Getmodel_tensorflow(65)
 #构建网络

 model_ch = Getmodel_ch(31)

 model_ch.load_weights("./model/char_chi_sim.h5")
 # model_ch.save_weights("./model/char_chi_sim.h5")
 model.load_weights("./model/char_rec.h5")
 # model.save("./model/char_rec.h5")


 def SimplePredict(image,pos):
    image = cv2.resize(image, (23, 23))
    image = cv2.equalizeHist(image)
    image = image.astype(np.float) / 255
    image -= image.mean()
    image = np.expand_dims(image, 3)
    if pos!=0:
        res = np.array(model.predict(np.array([image]))[0])
    else:
        res = np.array(model_ch.predict(np.array([image]))[0])

    zero_add = 0 ;

    if pos==0:
        res = res[:31]
    elif pos==1:
        res = res[31+10:65]
        zero_add = 31+10
    else:
        res = res[31:]
        zero_add = 31

    max_id = res.argmax()


    return res.max(),chars[max_id+zero_add],max_id+zero_add

--- a/hyperlpr/segmentation.py
+++ b/hyperlpr/segmentation.py
@@ -1,320 +0,0 @@
 #coding=utf-8
 import cv2
 import numpy as np

 # from matplotlib import pyplot as plt
 import scipy.ndimage.filters as f
 import scipy

 import time
 import scipy.signal as l





 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D, MaxPool2D
 from keras.optimizers import SGD
 from keras import backend as K

 K.set_image_dim_ordering('tf')


 def Getmodel_tensorflow(nb_classes):
    # nb_classes = len(charset)
    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 16
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3
    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols,1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Flatten())
    model.add(Dense(256))
    model.add(Dropout(0.5))

    model.add(Activation('relu'))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='sgd',
                  metrics=['accuracy'])
    return model



 def Getmodel_tensorflow_light(nb_classes):
    # nb_classes = len(charset)
    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 8
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3
    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols, 1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Conv2D(nb_filters, (nb_conv * 2, nb_conv * 2)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Flatten())
    model.add(Dense(32))
    # model.add(Dropout(0.25))

    model.add(Activation('relu'))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model




 model  = Getmodel_tensorflow_light(3)
 model2  = Getmodel_tensorflow(3)

 import os
 model.load_weights("./model/char_judgement1.h5")
 # model.save("./model/char_judgement1.h5")
 model2.load_weights("./model/char_judgement.h5")
 # model2.save("./model/char_judgement.h5")


 model = model2
 def get_median(data):
   data = sorted(data)
   size = len(data)
   # print size

   if size % 2 == 0: # 判断列表长度为偶数
    median = (data[size//2]+data[size//2-1])/2
    data[0] = median
   if size % 2 == 1: # 判断列表长度为奇数
    median = data[(size-1)//2]
    data[0] = median
   return data[0]
 import time

 def searchOptimalCuttingPoint(rgb,res_map,start,width_boundingbox,interval_range):
    t0  = time.time()
    #
    # for x in xrange(10):
    #     res_map = np.vstack((res_map,res_map[-1]))
    length = res_map.shape[0]
    refine_s = -2;

    if width_boundingbox>20:
        refine_s = -9
    score_list = []
    interval_big = int(width_boundingbox * 0.3)  #
    p = 0
    for zero_add in xrange(start,start+50,3):
        # for interval_small in xrange(-0,width_boundingbox/2):
            for i in xrange(-8,int(width_boundingbox/1)-8):
                for refine in xrange(refine_s,width_boundingbox/2+3):
                    p1 = zero_add# this point is province
                    p2 = p1 + width_boundingbox +refine #
                    p3 = p2 + width_boundingbox + interval_big+i+1
                    p4 = p3 + width_boundingbox +refine
                    p5 = p4 + width_boundingbox +refine
                    p6 = p5 + width_boundingbox +refine
                    p7 = p6 + width_boundingbox +refine
                    if p7>=length:
                        continue
                    score = res_map[p1][2]*3 -(res_map[p3][1]+res_map[p4][1]+res_map[p5][1]+res_map[p6][1]+res_map[p7][1])+7
                    # print score
                    score_list.append([score,[p1,p2,p3,p4,p5,p6,p7]])
                    p+=1
    print p

    score_list = sorted(score_list , key=lambda x:x[0])
    # for one in score_list[-1][1]:
    #     cv2.line(debug,(one,0),(one,36),(255,0,0),1)
    # #
    # cv2.imshow("one",debug)
    # cv2.waitKey(0)
    #
    print "寻找最佳点",time.time()-t0
    return score_list[-1]


 import sys

 sys.path.append('../')
 import recognizer as cRP
 import niblack_thresholding as nt

 def refineCrop(sections,width=16):
    new_sections = []
    for section in sections:
        # cv2.imshow("section¡",section)

        # cv2.blur(section,(3,3),3)

        sec_center = np.array([section.shape[1]/2,section.shape[0]/2])
        binary_niblack = nt.niBlackThreshold(section,17,-0.255)
        imagex, contours, hierarchy  = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
        boxs = []
        for contour in contours:
            x,y,w,h = cv2.boundingRect(contour)

            ratio = w/float(h)
            if ratio<1 and h>36*0.4 and y<16\
                    :
                box = [x,y,w,h]

                boxs.append([box,np.array([x+w/2,y+h/2])])
                # cv2.rectangle(section,(x,y),(x+w,y+h),255,1)




        # print boxs

        dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs])
        if len(dis_)==0:
            kernal = [0, 0, section.shape[1], section.shape[0]]
        else:
            kernal = boxs[dis_.argmin()][0]

        center_c  = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2)
        w_2 = int(width/2)
        h_2 = kernal[3]/2

        if center_c[0] - w_2< 0:
            w_2 = center_c[0]
        new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]]
        # print new_box[2]/float(new_box[3])
        if new_box[2]/float(new_box[3])>0.5:
            # print "异常"
            h = int((new_box[2]/0.35 )/2)
            if h>35:
                h = 35
            new_box[1] = center_c[1]- h
            if new_box[1]<0:
                new_box[1] = 1

            new_box[3] = h*2








        section  = section[new_box[1]:new_box[1]+new_box[3],new_box[0]:new_box[0]+new_box[2]]
        # cv2.imshow("section",section)
        # cv2.waitKey(0)
        new_sections.append(section)
        # print new_box


    return new_sections





 def slidingWindowsEval(image):
    windows_size = 16;
    stride = 1
    height= image.shape[0]
    t0 = time.time()
    data_sets = []

    for i in range(0,image.shape[1]-windows_size+1,stride):
        data = image[0:height,i:i+windows_size]
        data = cv2.resize(data,(23,23))
        # cv2.imshow("image",data)
        data = cv2.equalizeHist(data)
        data = data.astype(np.float)/255
        data=  np.expand_dims(data,3)
        data_sets.append(data)

    res = model2.predict(np.array(data_sets))
    print "分割",time.time() - t0

    pin = res
    p = 1 -  (res.T)[1]
    p = f.gaussian_filter1d(np.array(p,dtype=np.float),3)
    lmin = l.argrelmax(np.array(p),order = 3)[0]
    interval = []
    for i in xrange(len(lmin)-1):
        interval.append(lmin[i+1]-lmin[i])

    if(len(interval)>3):
        mid  = get_median(interval)
    else:
        return []
    pin = np.array(pin)
    res =  searchOptimalCuttingPoint(image,pin,0,mid,3)

    cutting_pts = res[1]
    last =  cutting_pts[-1] + mid
    if last < image.shape[1]:
        cutting_pts.append(last)
    else:
        cutting_pts.append(image.shape[1]-1)
    name = ""
    confidence =0.00
    seg_block = []
    for x in xrange(1,len(cutting_pts)):
        if x != len(cutting_pts)-1 and x!=1:
            section = image[0:36,cutting_pts[x-1]-2:cutting_pts[x]+2]
        elif  x==1:
            c_head = cutting_pts[x - 1]- 2
            if c_head<0:
                c_head=0
            c_tail = cutting_pts[x] + 2
            section = image[0:36, c_head:c_tail]
        elif x==len(cutting_pts)-1:
            end = cutting_pts[x]
            diff = image.shape[1]-end
            c_head = cutting_pts[x - 1]
            c_tail = cutting_pts[x]
            if diff<7 :
                section = image[0:36, c_head-5:c_tail+5]
            else:
                diff-=1
                section = image[0:36, c_head - diff:c_tail + diff]
        elif  x==2:
            section = image[0:36, cutting_pts[x - 1] - 3:cutting_pts[x-1]+ mid]
        else:
            section = image[0:36,cutting_pts[x-1]:cutting_pts[x]]
        seg_block.append(section)
    refined = refineCrop(seg_block,mid-1)

    t0 = time.time()
    for i,one in enumerate(refined):
        res_pre = cRP.SimplePredict(one, i )
        # cv2.imshow(str(i),one)
        # cv2.waitKey(0)
        confidence+=res_pre[0]
        name+= res_pre[1]
    print "字符识别",time.time() - t0

    return refined,name,confidence
--- a/hyperlpr/typeDistinguish.py
+++ b/hyperlpr/typeDistinguish.py
@@ -1,56 +0,0 @@
 #coding=utf-8
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D, MaxPool2D
 from keras.optimizers import SGD
 from keras import backend as K

 K.set_image_dim_ordering('tf')


 import cv2
 import numpy as np


 plateType  = [u"蓝牌",u"单层黄牌",u"新能源车牌",u"白色",u"黑色-港澳"]
 def Getmodel_tensorflow(nb_classes):
    # nb_classes = len(charset)

    img_rows, img_cols = 9, 34
    # number of convolutional filters to use
    nb_filters = 32
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3

    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(16, (5, 5),input_shape=(img_rows, img_cols,3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Flatten())
    model.add(Dense(64))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model

 model = Getmodel_tensorflow(5)
 model.load_weights("./model/plate_type.h5")
 model.save("./model/plate_type.h5")
 def SimplePredict(image):
    image = cv2.resize(image, (34, 9))
    image = image.astype(np.float) / 255
    res = np.array(model.predict(np.array([image]))[0])
    return res.argmax()


--- a/hyperlpr_py3/init.py
+++ b/hyperlpr_py3/init.py
--- a/hyperlpr_py3/cache.py
+++ b/hyperlpr_py3/cache.py
@@ -1,11 +0,0 @@
 import cv2
 import os
 import hashlib

 def verticalMappingToFolder(image):
    name = hashlib.md5(image.data).hexdigest()[:8]
    print(name)

    cv2.imwrite("./cache/finemapping/"+name+".png",image)


--- a/hyperlpr_py3/colourDetection.py
+++ b/hyperlpr_py3/colourDetection.py
@@ -1,103 +0,0 @@
 # -- coding: UTF-8
 import cv2
 import matplotlib.pyplot as plt
 from sklearn.cluster import KMeans
 import os

 boundaries = [
    ([100,80,0],[240,220,110]), # yellow
    ([0,40,50],[110,180,250]), # blue
    ([0,60,0],[60,160,70]), # green
 ]
 color_attr = ["黄牌","蓝牌",'绿牌','白牌','黑牌']

 threhold_green = 13
 threhold_blue = 13
 threhold_yellow1 = 50
 threhold_yellow2 = 70

 # plt.figure()
 # plt.axis("off")
 # plt.imshow(image)
 # plt.show()

 import numpy as np
 def centroid_histogram(clt):
    numLabels = np.arange(0, len(np.unique(clt.labels_)) + 1)
    (hist, _) = np.histogram(clt.labels_, bins=numLabels)

    # normalize the histogram, such that it sums to one
    hist = hist.astype("float")
    hist /= hist.sum()

    # return the histogram
    return hist


 def plot_colors(hist, centroids):
    bar = np.zeros((50, 300, 3), dtype="uint8")
    startX = 0

    for (percent, color) in zip(hist, centroids):

        endX = startX + (percent * 300)
        cv2.rectangle(bar, (int(startX), 0), (int(endX), 50),
                      color.astype("uint8").tolist(), -1)
        startX = endX

    # return the bar chart
    return bar

 def search_boundaries(color):
    for i,color_bound in enumerate(boundaries):
        if np.all(color >= color_bound[0]) and np.all(color <= color_bound[1]):
            return i
    return -1

 def judge_color(color):
    r = color[0]
    g = color[1]
    b = color[2]
    if g - r >= threhold_green and g - b >= threhold_green:
        return 2
    if b - r >= threhold_blue and b - g >= threhold_blue:
        return 1
    if r- b > threhold_yellow2 and g - b > threhold_yellow2:
        return 0
    if r > 200 and b > 200 and g > 200:
        return 3
    if r < 50 and b < 50 and g < 50:
        return 4
    return -1

 def judge_plate_color(img):
    image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    image = image.reshape((image.shape[0] * image.shape[1], 3))
    clt = KMeans(n_clusters=2)
    clt.fit(image)

    hist = centroid_histogram(clt)
    index = np.argmax(hist)
    #print clt.cluster_centers_[index]
    #color_index = search_boundaries(clt.cluster_centers_[index])
    color_index = judge_color(clt.cluster_centers_[index])
    if color_index == -1:
        if index == 0:
            secound_index = 1
        else:
            secound_index = 0
        color_index = judge_color(clt.cluster_centers_[secound_index])

    if color_index == -1:
        print(clt.cluster_centers_)
        bar = plot_colors(hist, clt.cluster_centers_)
        # show our color bart
        plt.figure()
        plt.axis("off")
        plt.imshow(bar)
        plt.show()

    if color_index != -1:
        return color_attr[color_index],clt.cluster_centers_[index]
    else:
        return None,clt.cluster_centers_[index]
--- a/hyperlpr_py3/config.py
+++ b/hyperlpr_py3/config.py
@@ -1,6 +0,0 @@
 import json



 with open("/Users/universe/ProgramUniverse/zeusees/HyperLPR/config.json") as f:
    configuration = json.load(f)
--- a/hyperlpr_py3/deskew.py
+++ b/hyperlpr_py3/deskew.py
@@ -1,100 +0,0 @@
 #coding=utf-8
 import numpy as np
 import cv2
 import time
 from matplotlib import pyplot as plt
 import math

 from scipy.ndimage import filters
 #
 # def strokeFiter():
 #     pass;

 def angle(x,y):
    return int(math.atan2(float(y),float(x))*180.0/3.1415)


 def h_rot(src, angle, scale=1.0):
    w = src.shape[1]
    h = src.shape[0]
    rangle = np.deg2rad(angle)
    nw = (abs(np.sin(rangle)*h) + abs(np.cos(rangle)*w))*scale
    nh = (abs(np.cos(rangle)*h) + abs(np.sin(rangle)*w))*scale
    rot_mat = cv2.getRotationMatrix2D((nw*0.5, nh*0.5), angle, scale)
    rot_move = np.dot(rot_mat, np.array([(nw-w)*0.5, (nh-h)*0.5,0]))
    rot_mat[0,2] += rot_move[0]
    rot_mat[1,2] += rot_move[1]
    return cv2.warpAffine(src, rot_mat, (int(math.ceil(nw)), int(math.ceil(nh))), flags=cv2.INTER_LANCZOS4)
    pass


 def v_rot(img, angel, shape, max_angel):
    size_o = [shape[1],shape[0]]
    size = (shape[1]+ int(shape[0]*np.cos((float(max_angel )/180) * 3.14)),shape[0])
    interval = abs( int( np.sin((float(angel) /180) * 3.14)* shape[0]))
    pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
    if(angel>0):
        pts2 = np.float32([[interval,0],[0,size[1]  ],[size[0],0  ],[size[0]-interval,size_o[1]]])
    else:
        pts2 = np.float32([[0,0],[interval,size[1]  ],[size[0]-interval,0  ],[size[0],size_o[1]]])

    M  = cv2.getPerspectiveTransform(pts1,pts2)
    dst = cv2.warpPerspective(img,M,size)
    return dst,M


 def skew_detection(image_gray):
    h, w = image_gray.shape[:2]
    eigen = cv2.cornerEigenValsAndVecs(image_gray,12, 5)
    angle_sur = np.zeros(180,np.uint)
    eigen = eigen.reshape(h, w, 3, 2)
    flow = eigen[:,:,2]
    vis = image_gray.copy()
    vis[:] = (192 + np.uint32(vis)) / 2
    d = 12
    points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
    for x, y in points:
        vx, vy = np.int32(flow[int(y), int(x)]*d)
        # cv2.line(rgb, (x-vx, y-vy), (x+vx, y+vy), (0, 355, 0), 1, cv2.LINE_AA)
        ang = angle(vx,vy)
        angle_sur[(ang+180)%180] +=1

    # torr_bin = 30
    angle_sur = angle_sur.astype(np.float)
    angle_sur = (angle_sur-angle_sur.min())/(angle_sur.max()-angle_sur.min())
    angle_sur = filters.gaussian_filter1d(angle_sur,5)
    skew_v_val =  angle_sur[20:180-20].max()
    skew_v = angle_sur[30:180-30].argmax() + 30
    skew_h_A = angle_sur[0:30].max()
    skew_h_B = angle_sur[150:180].max()
    skew_h = 0
    if (skew_h_A > skew_v_val*0.3 or skew_h_B > skew_v_val*0.3):
        if skew_h_A>=skew_h_B:
            skew_h = angle_sur[0:20].argmax()
        else:
            skew_h = - angle_sur[160:180].argmax()
    return skew_h,skew_v


 def fastDeskew(image):
    image_gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    skew_h,skew_v = skew_detection(image_gray)
    print("校正角度 h ",skew_h,"v",skew_v)
    deskew,M = v_rot(image,int((90-skew_v)*1.5),image.shape,60)
    return deskew,M



 if __name__ == '__main__':
    fn = './dataset/0.jpg'

    img = cv2.imread(fn)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    skew_h,skew_v = skew_detection(gray)
    img = v_rot(img,(90-skew_v ),img.shape,60)
    # img = h_rot(img,skew_h)
    # if img.shape[0]>img.shape[1]:
    #     img = h_rot(img, -90)

    plt.show()
    cv2.waitKey()
--- a/hyperlpr_py3/detect.py
+++ b/hyperlpr_py3/detect.py
@@ -1,76 +0,0 @@

 import cv2
 import numpy as np



 watch_cascade = cv2.CascadeClassifier('./model/cascade.xml')


 def computeSafeRegion(shape,bounding_rect):
    top = bounding_rect[1] # y
    bottom  = bounding_rect[1] + bounding_rect[3] # y +  h
    left = bounding_rect[0] # x
    right =   bounding_rect[0] + bounding_rect[2] # x +  w

    min_top = 0
    max_bottom = shape[0]
    min_left = 0
    max_right = shape[1]

    # print "computeSateRegion input shape",shape
    if top < min_top:
        top = min_top
        # print "tap top 0"
    if left < min_left:
        left = min_left
        # print "tap left 0"

    if bottom > max_bottom:
        bottom = max_bottom
        #print "tap max_bottom max"
    if right > max_right:
        right = max_right
        #print "tap max_right max"

    # print "corr",left,top,right,bottom
    return [left,top,right-left,bottom-top]


 def cropped_from_image(image,rect):
    x, y, w, h = computeSafeRegion(image.shape,rect)
    return image[y:y+h,x:x+w]


 def detectPlateRough(image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05):
    print(image_gray.shape)

    if top_bottom_padding_rate>0.2:
        print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate)
        exit(1)

    height = image_gray.shape[0]
    padding =    int(height*top_bottom_padding_rate)
    scale = image_gray.shape[1]/float(image_gray.shape[0])

    image = cv2.resize(image_gray, (int(scale*resize_h), resize_h))

    image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]]

    image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY)

    watches = watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40))

    cropped_images = []
    for (x, y, w, h) in watches:
        cropped_origin = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))
        x -= w * 0.14
        w += w * 0.28
        y -= h * 0.6
        h += h * 1.1;

        cropped = cropped_from_image(image_color_cropped, (int(x), int(y), int(w), int(h)))


        cropped_images.append([cropped,[x, y+padding, w, h],cropped_origin])
    return cropped_images
--- a/hyperlpr_py3/e2e.py
+++ b/hyperlpr_py3/e2e.py
@@ -1,63 +0,0 @@
 #coding=utf-8
 from keras import backend as K
 from keras.models import load_model
 from keras.layers import *
 import numpy as np
 import random
 import string

 import cv2
 from . import e2emodel as model
 chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
             "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
             "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
             "Y", "Z","港","学","使","警","澳","挂","军","北","南","广","沈","兰","成","济","海","民","航","空"
             ];
 pred_model = model.construct_model("./model/ocr_plate_all_w_rnn_2.h5",)
 import time



 def fastdecode(y_pred):
    results = ""
    confidence = 0.0
    table_pred = y_pred.reshape(-1, len(chars)+1)

    res = table_pred.argmax(axis=1)

    for i,one in enumerate(res):
        if one<len(chars) and (i==0 or (one!=res[i-1])):
            results+= chars[one]
            confidence+=table_pred[i][one]
    confidence/= len(results)
    return results,confidence

 def recognizeOne(src):
    # x_tempx= cv2.imread(src)
    x_tempx = src
    # x_tempx = cv2.bitwise_not(x_tempx)
    x_temp = cv2.resize(x_tempx,( 160,40))
    x_temp = x_temp.transpose(1, 0, 2)
    t0 = time.time()
    y_pred = pred_model.predict(np.array([x_temp]))
    y_pred = y_pred[:,2:,:]
    # plt.imshow(y_pred.reshape(16,66))
    # plt.show()

    #
    # cv2.imshow("x_temp",x_tempx)
    # cv2.waitKey(0)
    return fastdecode(y_pred)
 #
 #
 # import os
 #
 # path = "/Users/yujinke/PycharmProjects/HyperLPR_Python_web/cache/finemapping"
 # for filename in os.listdir(path):
 #     if filename.endswith(".png") or filename.endswith(".jpg") or filename.endswith(".bmp"):
 #         x = os.path.join(path,filename)
 #         recognizeOne(x)
 #         # print time.time() - t0
 #
 #         # cv2.imshow("x",x)
 #         # cv2.waitKey()
--- a/hyperlpr_py3/e2emodel.py
+++ b/hyperlpr_py3/e2emodel.py
@@ -1,34 +0,0 @@

 from keras import backend as K
 from keras.models import *
 from keras.layers import *
 from . import e2e


 def ctc_lambda_func(args):
    y_pred, labels, input_length, label_length = args
    y_pred = y_pred[:, 2:, :]
    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)


 def construct_model(model_path):
    input_tensor = Input((None, 40, 3))
    x = input_tensor
    base_conv = 32

    for i in range(3):
        x = Conv2D(base_conv * (2 ** (i)), (3, 3),padding="same")(x)
        x = BatchNormalization()(x)
        x = Activation('relu')(x)
        x = MaxPooling2D(pool_size=(2, 2))(x)
    x = Conv2D(256, (5, 5))(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(1024, (1, 1))(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    x = Conv2D(len(e2e.chars)+1, (1, 1))(x)
    x = Activation('softmax')(x)
    base_model = Model(inputs=input_tensor, outputs=x)
    base_model.load_weights(model_path)
    return base_model
--- a/hyperlpr_py3/finemapping.py
+++ b/hyperlpr_py3/finemapping.py
@@ -1,130 +0,0 @@
 #coding=utf-8
 import cv2
 import numpy as np


 from . import niblack_thresholding as nt

 from . import deskew

 def fitLine_ransac(pts,zero_add = 0 ):
    if len(pts)>=2:
        [vx, vy, x, y] = cv2.fitLine(pts, cv2.DIST_HUBER, 0, 0.01, 0.01)
        lefty = int((-x * vy / vx) + y)
        righty = int(((136- x) * vy / vx) + y)
        return lefty+30+zero_add,righty+30+zero_add
    return 0,0



 #精定位算法
 def findContoursAndDrawBoundingBox(image_rgb):


    line_upper  = [];
    line_lower = [];

    line_experiment = []
    grouped_rects = []
    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)

    # for k in np.linspace(-1.5, -0.2,10):
    for k in np.linspace(-50, 0, 15):

        # thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
        # binary_niblack = gray_image > thresh_niblack
        # binary_niblack = binary_niblack.astype(np.uint8) * 255

        binary_niblack = cv2.adaptiveThreshold(gray_image,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,17,k)
        # cv2.imshow("image1",binary_niblack)
        # cv2.waitKey(0)
        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
        for contour in contours:
            bdbox = cv2.boundingRect(contour)
            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1200) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
                line_upper.append([bdbox[0],bdbox[1]])
                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])

                line_experiment.append([bdbox[0],bdbox[1]])
                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
                # grouped_rects.append(bdbox)

    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
    leftyA, rightyA = fitLine_ransac(np.array(line_lower),3)
    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)

    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-3)

    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
    image,M = deskew.fastDeskew(image)

    return image



 #多级
 def findContoursAndDrawBoundingBox2(image_rgb):


    line_upper  = [];
    line_lower = [];

    line_experiment = []

    grouped_rects = []

    gray_image = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)

    for k in np.linspace(-1.6, -0.2,10):
    # for k in np.linspace(-15, 0, 15):
    # #
    #     thresh_niblack = threshold_niblack(gray_image, window_size=21, k=k)
    #     binary_niblack = gray_image > thresh_niblack
    #     binary_niblack = binary_niblack.astype(np.uint8) * 255

        binary_niblack = nt.niBlackThreshold(gray_image,19,k)
        # cv2.imshow("binary_niblack_opencv",binary_niblack_)
        # cv2.imshow("binary_niblack_skimage", binary_niblack)

        # cv2.waitKey(0)
        imagex, contours, hierarchy = cv2.findContours(binary_niblack.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours:
            bdbox = cv2.boundingRect(contour)
            if (bdbox[3]/float(bdbox[2])>0.7 and bdbox[3]*bdbox[2]>100 and bdbox[3]*bdbox[2]<1000) or (bdbox[3]/float(bdbox[2])>3 and bdbox[3]*bdbox[2]<100):
                # cv2.rectangle(rgb,(bdbox[0],bdbox[1]),(bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]),(255,0,0),1)
                line_upper.append([bdbox[0],bdbox[1]])
                line_lower.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])

                line_experiment.append([bdbox[0],bdbox[1]])
                line_experiment.append([bdbox[0]+bdbox[2],bdbox[1]+bdbox[3]])
                # grouped_rects.append(bdbox)

    rgb = cv2.copyMakeBorder(image_rgb,30,30,0,0,cv2.BORDER_REPLICATE)
    leftyA, rightyA = fitLine_ransac(np.array(line_lower),2)
    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyA), (0, leftyA), (0, 0, 255), 1,cv2.LINE_AA)

    leftyB, rightyB = fitLine_ransac(np.array(line_upper),-4)

    rows,cols = rgb.shape[:2]

    # rgb = cv2.line(rgb, (cols - 1, rightyB), (0, leftyB), (0,255, 0), 1,cv2.LINE_AA)
    pts_map1  = np.float32([[cols - 1, rightyA], [0, leftyA],[cols - 1, rightyB], [0, leftyB]])
    pts_map2 = np.float32([[136,36],[0,36],[136,0],[0,0]])
    mat = cv2.getPerspectiveTransform(pts_map1,pts_map2)
    image = cv2.warpPerspective(rgb,mat,(136,36),flags=cv2.INTER_CUBIC)
    image,M= deskew.fastDeskew(image)


    return image
--- a/hyperlpr_py3/finemapping_vertical.py
+++ b/hyperlpr_py3/finemapping_vertical.py
@@ -1,92 +0,0 @@
 #coding=utf-8
 from keras.layers import Conv2D, Input,MaxPool2D, Reshape,Activation,Flatten, Dense
 from keras.models import Model, Sequential
 from keras.layers.advanced_activations import PReLU
 from keras.optimizers import adam
 import numpy as np

 import cv2

 def getModel():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    x = Activation("relu", name='relu1')(x)
    x = MaxPool2D(pool_size=2)(x)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    x = Activation("relu", name='relu3')(x)
    x = Flatten()(x)
    output = Dense(2,name = "dense")(x)
    output = Activation("relu", name='relu4')(output)
    model = Model([input], [output])
    return model



 model = getModel()
 model.load_weights("./model/model12.h5")


 def getmodel():
    return model

 def gettest_model():
    input = Input(shape=[16, 66, 3])  # change this shape to [None,None,3] to enable arbitraty shape input
    A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input)
    B = Activation("relu", name='relu1')(A)
    C = MaxPool2D(pool_size=2)(B)
    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C)
    x = Activation("relu", name='relu2')(x)
    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
    K = Activation("relu", name='relu3')(x)


    x = Flatten()(K)
    dense = Dense(2,name = "dense")(x)
    output = Activation("relu", name='relu4')(dense)
    x = Model([input], [output])
    x.load_weights("./model/model12.h5")
    ok = Model([input], [dense])

    for layer in ok.layers:
        print(layer)

    return ok




 def finemappingVertical(image):
    resized = cv2.resize(image,(66,16))
    resized = resized.astype(np.float)/255
    res= model.predict(np.array([resized]))[0]
    print("keras_predict",res)
    res  =res*image.shape[1]
    res = res.astype(np.int)
    H,T = res
    H-=3
    #3 79.86
    #4 79.3
    #5 79.5
    #6 78.3


    #T
    #T+1 80.9
    #T+2 81.75
    #T+3 81.75



    if H<0:
        H=0
    T+=2;

    if T>= image.shape[1]-1:
        T= image.shape[1]-1

    image = image[0:35,H:T+2]

    image = cv2.resize(image, (int(136), int(36)))
    return image
--- a/hyperlpr_py3/niblack_thresholding.py
+++ b/hyperlpr_py3/niblack_thresholding.py
@@ -1,18 +0,0 @@
 import cv2
 import numpy as np



 def niBlackThreshold(  src,  blockSize,  k,  binarizationMethod= 0 ):
    mean = cv2.boxFilter(src,cv2.CV_32F,(blockSize, blockSize),borderType=cv2.BORDER_REPLICATE)
    sqmean = cv2.sqrBoxFilter(src, cv2.CV_32F, (blockSize, blockSize), borderType = cv2.BORDER_REPLICATE)
    variance = sqmean - (mean*mean)
    stddev  = np.sqrt(variance)
    thresh = mean + stddev * float(-k)
    thresh = thresh.astype(src.dtype)
    k = (src>thresh)*255
    k = k.astype(np.uint8)
    return k


 # cv2.imshow()
--- a/hyperlpr_py3/pipline.py
+++ b/hyperlpr_py3/pipline.py
@@ -1,246 +0,0 @@
 #coding=utf-8
 from . import detect
 from . import  finemapping  as  fm

 from . import segmentation
 import cv2

 import time
 import numpy as np

 from PIL import ImageFont
 from PIL import Image
 from PIL import ImageDraw
 import json

 import sys
 from . import typeDistinguish as td
 import imp


 imp.reload(sys)
 fontC = ImageFont.truetype("./Font/platech.ttf", 14, 0);

 from . import e2e
 #寻找车牌左右边界

 def find_edge(image):
    sum_i = image.sum(axis=0)
    sum_i =  sum_i.astype(np.float)
    sum_i/=image.shape[0]*255
    # print sum_i

    start= 0 ;
    end = image.shape[1]-1

    for i,one in enumerate(sum_i):
        if one>0.4:
            start = i;
            if start-3<0:
                start = 0
            else:
                start -=3

            break;
    for i,one in enumerate(sum_i[::-1]):

        if one>0.4:
            end = end - i;
            if end+4>image.shape[1]-1:
                end = image.shape[1]-1
            else:
                end+=4
            break
    return start,end


 #垂直边缘检测
 def verticalEdgeDetection(image):
    image_sobel = cv2.Sobel(image.copy(),cv2.CV_8U,1,0)
    # image = auto_canny(image_sobel)

    # img_sobel, CV_8U, 1, 0, 3, 1, 0, BORDER_DEFAULT
    # canny_image  = auto_canny(image)
    flag,thres = cv2.threshold(image_sobel,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY)
    print(flag)
    flag,thres = cv2.threshold(image_sobel,int(flag*0.7),255,cv2.THRESH_BINARY)
    # thres = simpleThres(image_sobel)
    kernal = np.ones(shape=(3,15))
    thres = cv2.morphologyEx(thres,cv2.MORPH_CLOSE,kernal)
    return thres


 #确定粗略的左右边界
 def horizontalSegmentation(image):

    thres = verticalEdgeDetection(image)
    # thres = thres*image
    head,tail = find_edge(thres)
    # print head,tail
    # cv2.imshow("edge",thres)
    tail = tail+5
    if tail>135:
        tail = 135
    image = image[0:35,head:tail]
    image = cv2.resize(image, (int(136), int(36)))
    return image


 #打上boundingbox和标签
 def drawRectBox(image,rect,addText):
    cv2.rectangle(image, (int(rect[0]), int(rect[1])), (int(rect[0] + rect[2]), int(rect[1] + rect[3])), (0,0, 255), 2, cv2.LINE_AA)
    cv2.rectangle(image, (int(rect[0]-1), int(rect[1])-16), (int(rect[0] + 115), int(rect[1])), (0, 0, 255), -1, cv2.LINE_AA)

    img = Image.fromarray(image)
    draw = ImageDraw.Draw(img)
    #draw.text((int(rect[0]+1), int(rect[1]-16)), addText.decode("utf-8"), (255, 255, 255), font=fontC)
    draw.text((int(rect[0]+1), int(rect[1]-16)), addText, (255, 255, 255), font=fontC)
    imagex = np.array(img)

    return imagex


 from . import cache
 from . import finemapping_vertical as fv

 def RecognizePlateJson(image):
    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
    jsons = []
    for j,plate in enumerate(images):
        plate,rect,origin_plate =plate
        res, confidence = e2e.recognizeOne(origin_plate)
        print("res",res)

        cv2.imwrite("./"+str(j)+"_rough.jpg",plate)

        # print "车牌类型:",ptype
        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
        plate  =cv2.resize(plate,(136,int(36*2.5)))
        t1 = time.time()

        ptype = td.SimplePredict(plate)
        if ptype>0 and ptype<4:
            plate = cv2.bitwise_not(plate)
        # demo = verticalEdgeDetection(plate)

        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
        image_rgb = fv.finemappingVertical(image_rgb)
        cache.verticalMappingToFolder(image_rgb)
        # print time.time() - t1,"校正"
        print("e2e:",e2e.recognizeOne(image_rgb)[0])
        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)

        cv2.imwrite("./"+str(j)+".jpg",image_gray)
        # image_gray = horizontalSegmentation(image_gray)

        t2 = time.time()
        res, confidence = e2e.recognizeOne(image_rgb)
        res_json = {}
        if confidence  > 0.6:
            res_json["Name"] = res
            res_json["Type"] = td.plateType[ptype]
            res_json["Confidence"] = confidence;
            res_json["x"] = int(rect[0])
            res_json["y"] = int(rect[1])
            res_json["w"] = int(rect[2])
            res_json["h"] = int(rect[3])
            jsons.append(res_json)
    print(json.dumps(jsons,ensure_ascii=False,encoding="gb2312"))

    return json.dumps(jsons,ensure_ascii=False,encoding="gb2312")




 def SimpleRecognizePlateByE2E(image):
    t0 = time.time()
    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
    res_set = []
    for j,plate in enumerate(images):
        plate, rect, origin_plate  =plate
        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
        plate  =cv2.resize(plate,(136,36*2))
        res,confidence = e2e.recognizeOne(origin_plate)
        print("res",res)

        t1 = time.time()
        ptype = td.SimplePredict(plate)
        if ptype>0 and ptype<5:
            # pass
            plate = cv2.bitwise_not(plate)
        image_rgb = fm.findContoursAndDrawBoundingBox(plate)
        image_rgb = fv.finemappingVertical(image_rgb)
        image_rgb = fv.finemappingVertical(image_rgb)
        cache.verticalMappingToFolder(image_rgb)
        cv2.imwrite("./"+str(j)+".jpg",image_rgb)
        res,confidence = e2e.recognizeOne(image_rgb)
        print(res,confidence)
        res_set.append([[],res,confidence])

        if confidence>0.7:
            image = drawRectBox(image, rect, res+" "+str(round(confidence,3)))
    return image,res_set


 def SimpleRecognizePlate(image):
    t0 = time.time()
    images = detect.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1)
    res_set = []
    for j,plate in enumerate(images):
        plate, rect, origin_plate  =plate
        # plate = cv2.cvtColor(plate, cv2.COLOR_RGB2GRAY)
        plate  =cv2.resize(plate,(136,36*2))
        t1 = time.time()

        ptype = td.SimplePredict(plate)
        if ptype>0 and ptype<5:
            plate = cv2.bitwise_not(plate)

        image_rgb = fm.findContoursAndDrawBoundingBox(plate)

        image_rgb = fv.finemappingVertical(image_rgb)
        cache.verticalMappingToFolder(image_rgb)
        print("e2e:", e2e.recognizeOne(image_rgb))
        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_RGB2GRAY)

        # image_gray = horizontalSegmentation(image_gray)
        cv2.imshow("image_gray",image_gray)
        # cv2.waitKey()

        cv2.imwrite("./"+str(j)+".jpg",image_gray)
        # cv2.imshow("image",image_gray)
        # cv2.waitKey(0)
        print("校正",time.time() - t1,"s")
        # cv2.imshow("image,",image_gray)
        # cv2.waitKey(0)
        t2 = time.time()
        val = segmentation.slidingWindowsEval(image_gray)
        # print val
        print("分割和识别",time.time() - t2,"s")
        if len(val)==3:
            blocks, res, confidence = val
            if confidence/7>0.7:
                image =  drawRectBox(image,rect,res)
                res_set.append(res)
                for i,block in enumerate(blocks):

                    block_ = cv2.resize(block,(25,25))
                    block_ = cv2.cvtColor(block_,cv2.COLOR_GRAY2BGR)
                    image[j * 25:(j * 25) + 25, i * 25:(i * 25) + 25] = block_
                    if image[j*25:(j*25)+25,i*25:(i*25)+25].shape == block_.shape:
                        pass


            if confidence>0:
                print("车牌:",res,"置信度:",confidence/7)
            else:
                pass

                # print "不确定的车牌:", res, "置信度:", confidence

    print(time.time() - t0,"s")
    return image,res_set




--- a/hyperlpr_py3/plateStructure.py
+++ b/hyperlpr_py3/plateStructure.py
--- a/hyperlpr_py3/precise.py
+++ b/hyperlpr_py3/precise.py
--- a/hyperlpr_py3/recognizer.py
+++ b/hyperlpr_py3/recognizer.py
@@ -1,154 +0,0 @@
 #coding=utf-8
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D,MaxPool2D
 from keras.optimizers import SGD
 from keras import backend as K

 K.set_image_dim_ordering('tf')


 import cv2
 import numpy as np



 index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12,
         "闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24,
         "藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
         "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
         "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
         "W": 61, "X": 62, "Y": 63, "Z": 64,"港":65,"学":66 ,"O":67 ,"使":68,"警":69,"澳":70,"挂":71};

 chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂",
             "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
             "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P",
         "Q", "R", "S", "T", "U", "V", "W", "X",
             "Y", "Z","港","学","O","使","警","澳","挂" ];



 def Getmodel_tensorflow(nb_classes):
    # nb_classes = len(charset)

    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 32
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3

    # x = np.load('x.npy')
    
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(512, (3, 3)))
    # model.add(Activation('relu'))
    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
    # model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model




 def Getmodel_ch(nb_classes):
    # nb_classes = len(charset)

    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 32
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3

    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(32, (5, 5),input_shape=(img_rows, img_cols,1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(32, (3, 3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Dropout(0.25))
    model.add(Conv2D(512, (3, 3)))
    # model.add(Activation('relu'))
    # model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
    # model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(756))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model



 model  = Getmodel_tensorflow(65)
 #构建网络

 model_ch = Getmodel_ch(31)

 model_ch.load_weights("./model/char_chi_sim.h5")
 # model_ch.save_weights("./model/char_chi_sim.h5")
 model.load_weights("./model/char_rec.h5")
 # model.save("./model/char_rec.h5")


 def SimplePredict(image,pos):
    image = cv2.resize(image, (23, 23))
    image = cv2.equalizeHist(image)
    image = image.astype(np.float) / 255
    image -= image.mean()
    image = np.expand_dims(image, 3)
    if pos!=0:
        res = np.array(model.predict(np.array([image]))[0])
    else:
        res = np.array(model_ch.predict(np.array([image]))[0])

    zero_add = 0 ;

    if pos==0:
        res = res[:31]
    elif pos==1:
        res = res[31+10:65]
        zero_add = 31+10
    else:
        res = res[31:]
        zero_add = 31

    max_id = res.argmax()


    return res.max(),chars[max_id+zero_add],max_id+zero_add

--- a/hyperlpr_py3/segmentation.py
+++ b/hyperlpr_py3/segmentation.py
@@ -1,307 +0,0 @@
 #coding=utf-8
 import cv2
 import numpy as np

 # from matplotlib import pyplot as plt
 import scipy.ndimage.filters as f
 import scipy

 import time
 import scipy.signal as l





 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D, MaxPool2D
 from keras.optimizers import SGD
 from keras import backend as K

 K.set_image_dim_ordering('tf')


 def Getmodel_tensorflow(nb_classes):
    # nb_classes = len(charset)
    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 16
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3
    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols,1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Flatten())
    model.add(Dense(256))
    model.add(Dropout(0.5))

    model.add(Activation('relu'))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='sgd',
                  metrics=['accuracy'])
    return model



 def Getmodel_tensorflow_light(nb_classes):
    # nb_classes = len(charset)
    img_rows, img_cols = 23, 23
    # number of convolutional filters to use
    nb_filters = 8
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3
    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(nb_filters, (nb_conv, nb_conv),input_shape=(img_rows, img_cols, 1)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Conv2D(nb_filters, (nb_conv * 2, nb_conv * 2)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Flatten())
    model.add(Dense(32))
    # model.add(Dropout(0.25))

    model.add(Activation('relu'))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model




 model  = Getmodel_tensorflow_light(3)
 model2  = Getmodel_tensorflow(3)

 import os
 model.load_weights("./model/char_judgement1.h5")
 # model.save("./model/char_judgement1.h5")
 model2.load_weights("./model/char_judgement.h5")
 # model2.save("./model/char_judgement.h5")


 model = model2
 def get_median(data):
   data = sorted(data)
   size = len(data)
   # print size

   if size % 2 == 0: # 判断列表长度为偶数
    median = (data[size//2]+data[size//2-1])//2
    data[0] = median
   if size % 2 == 1: # 判断列表长度为奇数
    median = data[(size-1)//2]
    data[0] = median
   return data[0]
 import time

 def searchOptimalCuttingPoint(rgb,res_map,start,width_boundingbox,interval_range):
    t0  = time.time()
    #
    # for x in xrange(10):
    #     res_map = np.vstack((res_map,res_map[-1]))
    length = res_map.shape[0]
    refine_s = -2;

    if width_boundingbox>20:
        refine_s = -9
    score_list = []
    interval_big = int(width_boundingbox * 0.3)  #
    p = 0
    for zero_add in range(start,start+50,3):
        # for interval_small in xrange(-0,width_boundingbox/2):
        for i in range(-8,int(width_boundingbox/1)-8):
            for refine in range(refine_s, int(width_boundingbox/2+3)):
                p1 = zero_add# this point is province
                p2 = p1 + width_boundingbox +refine #
                p3 = p2 + width_boundingbox + interval_big+i+1
                p4 = p3 + width_boundingbox +refine
                p5 = p4 + width_boundingbox +refine
                p6 = p5 + width_boundingbox +refine
                p7 = p6 + width_boundingbox +refine
                if p7>=length:
                    continue
                score = res_map[p1][2]*3 -(res_map[p3][1]+res_map[p4][1]+res_map[p5][1]+res_map[p6][1]+res_map[p7][1])+7
                # print score
                score_list.append([score,[p1,p2,p3,p4,p5,p6,p7]])
                p+=1
    print(p)

    score_list = sorted(score_list , key=lambda x:x[0])
    # for one in score_list[-1][1]:
    #     cv2.line(debug,(one,0),(one,36),(255,0,0),1)
    # #
    # cv2.imshow("one",debug)
    # cv2.waitKey(0)
    #
    print("寻找最佳点",time.time()-t0)
    return score_list[-1]


 import sys

 sys.path.append('../')
 from . import recognizer as cRP
 from . import niblack_thresholding as nt

 def refineCrop(sections,width=16):
    new_sections = []
    for section in sections:
        # cv2.imshow("section¡",section)

        # cv2.blur(section,(3,3),3)

        sec_center = np.array([section.shape[1]/2,section.shape[0]/2])
        binary_niblack = nt.niBlackThreshold(section,17,-0.255)
        imagex, contours, hierarchy  = cv2.findContours(binary_niblack,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
        boxs = []
        for contour in contours:
            x,y,w,h = cv2.boundingRect(contour)

            ratio = w/float(h)
            if ratio<1 and h>36*0.4 and y<16\
                    :
                box = [x,y,w,h]

                boxs.append([box,np.array([x+w/2,y+h/2])])
                # cv2.rectangle(section,(x,y),(x+w,y+h),255,1)




        # print boxs

        dis_ = np.array([ ((one[1]-sec_center)**2).sum() for one in boxs])
        if len(dis_)==0:
            kernal = [0, 0, section.shape[1], section.shape[0]]
        else:
            kernal = boxs[dis_.argmin()][0]

        center_c  = (kernal[0]+kernal[2]/2,kernal[1]+kernal[3]/2)
        w_2 = int(width/2)
        h_2 = kernal[3]/2

        if center_c[0] - w_2< 0:
            w_2 = center_c[0]
        new_box = [center_c[0] - w_2,kernal[1],width,kernal[3]]
        # print new_box[2]/float(new_box[3])
        if new_box[2]/float(new_box[3])>0.5:
            # print "异常"
            h = int((new_box[2]/0.35 )/2)
            if h>35:
                h = 35
            new_box[1] = center_c[1]- h
            if new_box[1]<0:
                new_box[1] = 1
            new_box[3] = h*2
        
        section  = section[int(new_box[1]):int(new_box[1]+new_box[3]), int(new_box[0]):int(new_box[0]+new_box[2])]
        # cv2.imshow("section",section)
        # cv2.waitKey(0)
        new_sections.append(section)
        # print new_box
    return new_sections


 def slidingWindowsEval(image):
    windows_size = 16;
    stride = 1
    height= image.shape[0]
    t0 = time.time()
    data_sets = []

    for i in range(0,image.shape[1]-windows_size+1,stride):
        data = image[0:height,i:i+windows_size]
        data = cv2.resize(data,(23,23))
        # cv2.imshow("image",data)
        data = cv2.equalizeHist(data)
        data = data.astype(np.float)/255
        data=  np.expand_dims(data,3)
        data_sets.append(data)

    res = model2.predict(np.array(data_sets))
    print("分割",time.time() - t0)

    pin = res
    p = 1 -  (res.T)[1]
    p = f.gaussian_filter1d(np.array(p,dtype=np.float),3)
    lmin = l.argrelmax(np.array(p),order = 3)[0]
    interval = []
    for i in range(len(lmin)-1):
        interval.append(lmin[i+1]-lmin[i])

    if(len(interval)>3):
        mid  = get_median(interval)
    else:
        return []
    pin = np.array(pin)
    res =  searchOptimalCuttingPoint(image,pin,0,mid,3)

    cutting_pts = res[1]
    last =  cutting_pts[-1] + mid
    if last < image.shape[1]:
        cutting_pts.append(last)
    else:
        cutting_pts.append(image.shape[1]-1)
    name = ""
    confidence =0.00
    seg_block = []
    for x in range(1,len(cutting_pts)):
        if x != len(cutting_pts)-1 and x!=1:
            section = image[0:36,cutting_pts[x-1]-2:cutting_pts[x]+2]
        elif  x==1:
            c_head = cutting_pts[x - 1]- 2
            if c_head<0:
                c_head=0
            c_tail = cutting_pts[x] + 2
            section = image[0:36, c_head:c_tail]
        elif x==len(cutting_pts)-1:
            end = cutting_pts[x]
            diff = image.shape[1]-end
            c_head = cutting_pts[x - 1]
            c_tail = cutting_pts[x]
            if diff<7 :
                section = image[0:36, c_head-5:c_tail+5]
            else:
                diff-=1
                section = image[0:36, c_head - diff:c_tail + diff]
        elif  x==2:
            section = image[0:36, cutting_pts[x - 1] - 3:cutting_pts[x-1]+ mid]
        else:
            section = image[0:36,cutting_pts[x-1]:cutting_pts[x]]
        seg_block.append(section)
    refined = refineCrop(seg_block,mid-1)

    t0 = time.time()
    for i,one in enumerate(refined):
        res_pre = cRP.SimplePredict(one, i )
        # cv2.imshow(str(i),one)
        # cv2.waitKey(0)
        confidence+=res_pre[0]
        name+= res_pre[1]
    print("字符识别",time.time() - t0)

    return refined,name,confidence
--- a/hyperlpr_py3/typeDistinguish.py
+++ b/hyperlpr_py3/typeDistinguish.py
@@ -1,56 +0,0 @@
 #coding=utf-8
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Conv2D, MaxPool2D
 from keras.optimizers import SGD
 from keras import backend as K

 K.set_image_dim_ordering('tf')


 import cv2
 import numpy as np


 plateType  = ["蓝牌","单层黄牌","新能源车牌","白色","黑色-港澳"]
 def Getmodel_tensorflow(nb_classes):
    # nb_classes = len(charset)

    img_rows, img_cols = 9, 34
    # number of convolutional filters to use
    nb_filters = 32
    # size of pooling area for max pooling
    nb_pool = 2
    # convolution kernel size
    nb_conv = 3

    # x = np.load('x.npy')
    # y = np_utils.to_categorical(range(3062)*45*5*2, nb_classes)
    # weight = ((type_class - np.arange(type_class)) / type_class + 1) ** 3
    # weight = dict(zip(range(3063), weight / weight.mean()))  # 调整权重，高频字优先

    model = Sequential()
    model.add(Conv2D(16, (5, 5),input_shape=(img_rows, img_cols,3)))
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(nb_pool, nb_pool)))
    model.add(Flatten())
    model.add(Dense(64))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    return model

 model = Getmodel_tensorflow(5)
 model.load_weights("./model/plate_type.h5")
 model.save("./model/plate_type.h5")
 def SimplePredict(image):
    image = cv2.resize(image, (34, 9))
    image = image.astype(np.float) / 255
    res = np.array(model.predict(np.array([image]))[0])
    return res.argmax()


--- a/wxpy_uploader.py
+++ b/wxpy_uploader.py
@@ -17,8 +17,8 @@ bot = Bot(console_qr=True, cache_path=True)
 def pr_msg(msg):
    image_name = msg.file_name
    friend = msg.chat
    print msg.chat
    print '接收图片'
    print(msg.chat)
    print('接收图片')
    # face(image_name)
    msg.get_file('' + msg.file_name)
    json_text = recognize(image_name)