diff --git a/Font/platech.ttf b/Font/platech.ttf
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diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -1,2 +1,24 @@
-# hpyerlpr
 # 车牌识别
+
+### Pipeline
+
+step1. 使用opencv 的 HAAR cascade 检测车牌大致位置 
+
+step2. Extend 检测到的大致位置的矩形区域
+
+step3. 使用类似于mser的方式的多级二值化+ransac拟合车牌的上下边界
+
+step4. 使用CNN regression回归车牌左右边界
+
+step5. 使用CNN滑动窗切割字符
+
+step6. 使用CNN识别字符
+
+### 简单测试方式
+
+```python
+from hyperlpr import  pipline as  pp
+import cv2
+image = cv2.imread("filename")
+image,res  = pp.SimpleRecognizePlate(image)
+```
\ No newline at end of file
diff --git a/batch.py b/batch.py
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+++ b/batch.py
@@ -0,0 +1,17 @@
+#coding=utf-8
+import os
+from hyperlpr import  pipline as  pp
+
+import cv2
+
+parent= "/Users/yujinke/车牌图片/收费站_完成标注"
+
+for filename in os.listdir(parent):
+    path = os.path.join(parent,filename)
+    print path
+    if path.endswith(".jpg") or path.endswith(".png"):
+        image =  cv2.imread(path)
+
+        image,res  = pp.SimpleRecognizePlate(image)
+        cv2.imshow("image",image)
+        cv2.waitKey(0)
\ No newline at end of file
diff --git a/benchmark.py b/benchmark.py
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+++ b/benchmark.py
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+#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= "/Users/yujinke/车牌图片/云南车牌"
+parent= "/Users/yujinke/车牌图片/收费站_完成标注"
+
+# parent= "./cache/bad2"
+
+
+
+
+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.encode("utf-8")
+            B = name.encode("utf-8")
+            print one.encode("utf-8"),"<->",name.encode("utf-8"),"编辑距离:",comparestring(A,B)
+            if  comparestring(A,B)<2:
+                count_lev+=1
+            if one.encode("utf-8") == name.encode("utf-8"):
+                count_p+=1
+                break
+            else:
+                print "error",one.encode("utf-8"), name.encode("utf-8")
+                count_d+=1
+                # cv2.imshow("image",image)
+                # cv2.waitKey(0)
+                cv2.imwrite("./cache/bad2/"+name+".png",image)
+                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)
+
+
+        #
+        # cv2.imshow("image",image)
+        # cv2.waitKey(0)
+
+# print count_p/float(count)
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diff --git a/hyperlpr/__init__.py b/hyperlpr/__init__.py
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diff --git a/hyperlpr/__init__.pyc b/hyperlpr/__init__.pyc
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diff --git a/hyperlpr/cache.py b/hyperlpr/cache.py
new file mode 100644
index 0000000..378d0b2
--- /dev/null
+++ b/hyperlpr/cache.py
@@ -0,0 +1,11 @@
+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)
+
+
diff --git a/hyperlpr/cache.pyc b/hyperlpr/cache.pyc
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diff --git a/hyperlpr/deskew.py b/hyperlpr/deskew.py
new file mode 100644
index 0000000..b0a011c
--- /dev/null
+++ b/hyperlpr/deskew.py
@@ -0,0 +1,111 @@
+#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;
+
+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 = v_rot(image,int((90-skew_v)*1.5),image.shape,60)
+    return deskew
+
+
+
+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()
diff --git a/hyperlpr/deskew.pyc b/hyperlpr/deskew.pyc
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index 0000000..16641bf
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diff --git a/hyperlpr/detect.py b/hyperlpr/detect.py
new file mode 100644
index 0000000..90865e7
--- /dev/null
+++ b/hyperlpr/detect.py
@@ -0,0 +1,76 @@
+
+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
diff --git a/hyperlpr/detect.pyc b/hyperlpr/detect.pyc
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diff --git a/hyperlpr/finemapping.py b/hyperlpr/finemapping.py
new file mode 100644
index 0000000..f9c1772
--- /dev/null
+++ b/hyperlpr/finemapping.py
@@ -0,0 +1,130 @@
+#coding=utf-8
+import cv2
+import numpy as np
+
+from skimage.filters import (threshold_otsu, threshold_niblack,
+                             threshold_sauvola)
+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 = 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 = deskew.fastDeskew(image)
+
+    return image
\ No newline at end of file
diff --git a/hyperlpr/finemapping.pyc b/hyperlpr/finemapping.pyc
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index 0000000..c7ffcd5
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diff --git a/hyperlpr/finemapping_vertical.py b/hyperlpr/finemapping_vertical.py
new file mode 100644
index 0000000..11870f3
--- /dev/null
+++ b/hyperlpr/finemapping_vertical.py
@@ -0,0 +1,39 @@
+#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=[12, 50, 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 = PReLU(shared_axes=[1, 2], name='prelu1')(x)
+    x = MaxPool2D(pool_size=2)(x)
+    x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x)
+    x = PReLU(shared_axes=[1, 2], name='prelu2')(x)
+    x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
+    x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
+    x = Flatten()(x)
+    output = Dense(2)(x)
+    output = PReLU(name='prelu4')(output)
+    model = Model([input], [output])
+    return model
+
+model = getModel()
+model.load_weights("./model/model12.h5")
+
+
+def finemappingVertical(image):
+    resized = cv2.resize(image,(50,12))
+    resized = resized.astype(np.float)/255
+    res= model.predict(np.array([resized]))[0]
+    res  =res*image.shape[1]
+    res = res.astype(np.int)
+    image = image[0:35,res[0]+4:res[1]]
+    image = cv2.resize(image, (int(136), int(36)))
+    return image
\ No newline at end of file
diff --git a/hyperlpr/finemapping_vertical.pyc b/hyperlpr/finemapping_vertical.pyc
new file mode 100644
index 0000000..47b81ad
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diff --git a/hyperlpr/niblack_thresholding.py b/hyperlpr/niblack_thresholding.py
new file mode 100644
index 0000000..d157d8b
--- /dev/null
+++ b/hyperlpr/niblack_thresholding.py
@@ -0,0 +1,20 @@
+import cv2
+import numpy as np
+
+from skimage.filters import (threshold_otsu, threshold_niblack,
+                             threshold_sauvola)
+
+
+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()
\ No newline at end of file
diff --git a/hyperlpr/niblack_thresholding.pyc b/hyperlpr/niblack_thresholding.pyc
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index 0000000..5bdd93c
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diff --git a/hyperlpr/pipline.py b/hyperlpr/pipline.py
new file mode 100644
index 0000000..c621191
--- /dev/null
+++ b/hyperlpr/pipline.py
@@ -0,0 +1,247 @@
+#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);
+
+
+#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] + 80), 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
+
+
+        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<5:
+            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"
+
+        image_gray = cv2.cvtColor(image_rgb,cv2.COLOR_BGR2GRAY)
+
+
+        cv2.imwrite("./"+str(j)+".jpg",image_gray)
+        # image_gray = horizontalSegmentation(image_gray)
+
+
+        t2 = time.time()
+        val = segmentation.slidingWindowsEval(image_gray)
+        if len(val)==3:
+            blocks, res, confidence = val
+            if confidence/7>0.0:
+                image =  drawRectBox(image,rect,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
+
+            plate_name =  res
+            res_json = {}
+            if confidence/7>0.0:
+                res_json["Name"] = plate_name.decode()
+                res_json["Type"] = td.plateType[ptype]
+                res_json["Confidence"] = confidence/7;
+                res_json["x"] = int(rect[0])
+                res_json["y"] = int(rect[1])
+                res_json["w"] = int(rect[2])
+                res_json["h"] = int(rect[3])
+                # print "车牌:",res,"置信度:",confidence/7
+                jsons.append(res_json)
+
+
+            else:
+                pass
+                # print "不确定的车牌:", res, "置信度:", confidence
+    print jsons
+    print json.dumps(jsons,ensure_ascii=False,encoding="gb2312")
+
+    return json.dumps(jsons,ensure_ascii=False,encoding="gb2312")
+
+
+
+
+
+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)
+        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
+
+
+
+
diff --git a/hyperlpr/pipline.pyc b/hyperlpr/pipline.pyc
new file mode 100644
index 0000000..49fff22
Binary files /dev/null and b/hyperlpr/pipline.pyc differ
diff --git a/hyperlpr/precise.py b/hyperlpr/precise.py
new file mode 100644
index 0000000..e69de29
diff --git a/hyperlpr/recognizer.py b/hyperlpr/recognizer.py
new file mode 100644
index 0000000..f1ca050
--- /dev/null
+++ b/hyperlpr/recognizer.py
@@ -0,0 +1,156 @@
+#coding=utf-8
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Convolution2D, MaxPooling2D
+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(Convolution2D(32, 5, 5,
+                            border_mode='valid',
+                            input_shape=(img_rows, img_cols,1)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
+    model.add(Dropout(0.25))
+    model.add(Convolution2D(32, 3, 3))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
+    model.add(Dropout(0.25))
+    model.add(Convolution2D(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(Convolution2D(32, 5, 5,
+                            border_mode='valid',
+                            input_shape=(img_rows, img_cols,1)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
+    model.add(Dropout(0.25))
+    model.add(Convolution2D(32, 3, 3))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
+    model.add(Dropout(0.25))
+    model.add(Convolution2D(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.load_weights("./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
+
diff --git a/hyperlpr/recognizer.pyc b/hyperlpr/recognizer.pyc
new file mode 100644
index 0000000..a1295de
Binary files /dev/null and b/hyperlpr/recognizer.pyc differ
diff --git a/hyperlpr/segmentation.py b/hyperlpr/segmentation.py
new file mode 100644
index 0000000..e7664f6
--- /dev/null
+++ b/hyperlpr/segmentation.py
@@ -0,0 +1,323 @@
+#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 Convolution2D, MaxPooling2D
+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(Convolution2D(nb_filters, nb_conv, nb_conv,
+                            border_mode='valid',
+                            input_shape=(img_rows, img_cols,1)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
+    model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(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(Convolution2D(nb_filters, nb_conv, nb_conv,
+                            border_mode='valid',
+                            input_shape=(img_rows, img_cols, 1)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
+    model.add(Convolution2D(nb_filters, nb_conv * 2, nb_conv * 2))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(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")
+model2.load_weights("./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
+from skimage.filters import (threshold_otsu, threshold_niblack,
+                             threshold_sauvola)
+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 = model.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
diff --git a/hyperlpr/segmentation.pyc b/hyperlpr/segmentation.pyc
new file mode 100644
index 0000000..64887d4
Binary files /dev/null and b/hyperlpr/segmentation.pyc differ
diff --git a/hyperlpr/typeDistinguish.py b/hyperlpr/typeDistinguish.py
new file mode 100644
index 0000000..35d0924
--- /dev/null
+++ b/hyperlpr/typeDistinguish.py
@@ -0,0 +1,57 @@
+#coding=utf-8
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Convolution2D, MaxPooling2D
+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(Convolution2D(16, 5, 5,
+                            border_mode='valid',
+                            input_shape=(img_rows, img_cols,3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(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")
+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()
+
+
diff --git a/hyperlpr/typeDistinguish.pyc b/hyperlpr/typeDistinguish.pyc
new file mode 100644
index 0000000..a9d227a
Binary files /dev/null and b/hyperlpr/typeDistinguish.pyc differ
diff --git a/model/cascade.xml b/model/cascade.xml
new file mode 100644
index 0000000..277775b
--- /dev/null
+++ b/model/cascade.xml
@@ -0,0 +1,12117 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>13</height>
+  <width>51</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9900001287460327e-001</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-001</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>20</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>8</maxWeakCount>
+      <stageThreshold>-1.9158077239990234e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 344 -8.1478752195835114e-002</internalNodes>
+          <leafValues>
+            6.2639594078063965e-001 -8.1564724445343018e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            7.7114331722259521e-001 -4.9504086375236511e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            8.1202191114425659e-001 -2.8070560097694397e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -2.5367051362991333e-001 7.3795551061630249e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            7.1150565147399902e-001 -3.0462509393692017e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -2.8375166654586792e-001 7.3174893856048584e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            7.5333666801452637e-001 -1.9880458712577820e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            7.5299704074859619e-001 -2.3570337891578674e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>12</maxWeakCount>
+      <stageThreshold>-2.2599112987518311e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 577 -1.4449171721935272e-002</internalNodes>
+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            6.1277741193771362e-001 -3.2827928662300110e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -3.3253005146980286e-001 7.2171914577484131e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -3.2760250568389893e-001 4.9508789181709290e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
+          <internalNodes>
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+          <leafValues>
+            7.1757602691650391e-001 -2.0989039540290833e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
+            -1.8845251202583313e-001 6.7252415418624878e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 689 -6.1739999800920486e-003</internalNodes>
+          <leafValues>
+            6.3675141334533691e-001 -2.4877758324146271e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 230 -9.2421043664216995e-003</internalNodes>
+          <leafValues>
+            6.5140277147293091e-001 -2.1352872252464294e-001</leafValues></_>
+        <_>
+          <internalNodes>
+            0 -1 48 -1.0526084899902344e-001</internalNodes>
+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 281 1.2764739990234375e-001</internalNodes>
+          <leafValues>
+            1.6771897673606873e-001 -7.7668786048889160e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>14</maxWeakCount>
+      <stageThreshold>-2.6526770591735840e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+          <leafValues>
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+          <leafValues>
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+          <leafValues>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
+          <internalNodes>
+            0 -1 373 -1.4500595629215240e-002</internalNodes>
+          <leafValues>
+            5.2634650468826294e-001 -2.5339555740356445e-001</leafValues></_>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>16</maxWeakCount>
+      <stageThreshold>-2.2411971092224121e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>20</maxWeakCount>
+      <stageThreshold>-2.3278577327728271e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
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diff --git a/model/cascadelbp.xml b/model/cascadelbp.xml
new file mode 100644
index 0000000..1117b89
--- /dev/null
+++ b/model/cascadelbp.xml
@@ -0,0 +1,842 @@
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+    <!-- stage 4 -->
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+    <!-- stage 9 -->
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diff --git a/model/char_chi_sim.h5 b/model/char_chi_sim.h5
new file mode 100644
index 0000000..478f1b4
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diff --git a/model/char_judgement.h5 b/model/char_judgement.h5
new file mode 100644
index 0000000..436aee4
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diff --git a/model/char_judgement1.h5 b/model/char_judgement1.h5
new file mode 100644
index 0000000..efaa094
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diff --git a/model/char_rec.h5 b/model/char_rec.h5
new file mode 100644
index 0000000..bfc4948
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diff --git a/model/model12.h5 b/model/model12.h5
new file mode 100644
index 0000000..141df7b
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diff --git a/model/plate_type.h5 b/model/plate_type.h5
new file mode 100644
index 0000000..fadedf0
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diff --git a/templates/upload.html b/templates/upload.html
new file mode 100644
index 0000000..a04fcf3
--- /dev/null
+++ b/templates/upload.html
@@ -0,0 +1,12 @@
+<html>
+   <body>
+   <h1>车牌识别</h1>
+
+      <form action = "uploader" method = "POST"
+         enctype = "multipart/form-data">
+         <input type = "file" name = "file" />
+         <input type = "submit"/>
+      </form>
+
+   </body>
+</html>
\ No newline at end of file
diff --git a/upload.py b/upload.py
new file mode 100644
index 0000000..db4a34c
--- /dev/null
+++ b/upload.py
@@ -0,0 +1,61 @@
+#coding=utf-8
+from flask import Flask, render_template, request
+#导入flaks 框架 渲染模板 和请求框架
+from werkzeug import secure_filename
+
+import cv2
+import numpy as np
+
+#导入opencv
+
+from hyperlpr import pipline
+#导入车牌识别库
+
+
+app = Flask(__name__)
+#设置App 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
+
+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)
+    return pipline.RecognizePlateJson(image_data_ndarray)
+
+
+import time
+
+@app.route('/uploader', methods=['GET', 'POST'])#设置请求路由
+def upload_file():
+    if request.method == 'POST':
+        #如果请求方法是POST
+        f = request.files['file']
+        f.save("./images_rec/"+secure_filename(f.filename))
+        #保存请求上来的文件
+        t0 = time.time()
+        res = recognize("./images_rec/"+secure_filename(f.filename))
+        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')
+
+
+
+if __name__ == '__main__':
+    #入口函数
+
+    app.run("0.0.0.0",port=8000)
+    #运行app 指定IP 指定端口
+
diff --git a/wxpy_uploader.py b/wxpy_uploader.py
new file mode 100644
index 0000000..f7475e2
--- /dev/null
+++ b/wxpy_uploader.py
@@ -0,0 +1,45 @@
+#coding=utf-8
+
+from wxpy import *
+import numpy
+import cv2
+import time
+import os
+from hyperlpr import pipline
+
+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
+
+# 人脸检测的功能，这里用到了OpenCV里面的人脸检测代码
+# 由于接收和处理图片都需要一点点时间 这里偷懒直接用sleep
+# 建议更改成多进程模式，否则容易造成进程混乱
+
+bot = Bot(console_qr=True, cache_path=True)
+
+# friend = bot.friends().search(u'庾金科')[0]
+# friend.send(u'你好，车牌识别功能已开启测试')
+
+@bot.register(Friend,PICTURE)
+def face_msg(msg):
+    image_name = msg.file_name
+    friend = msg.chat
+    print msg.chat
+    print '接收图片'
+    # face(image_name)
+    msg.get_file('' + msg.file_name)
+    # time.sleep(1)
+    json_text = recognize(image_name)
+    # face(image_name)
+    # time.sleep(0.5)
+    msg.reply(json_text)
+    msg.reply_image("0.jpg")
+
+
+    # msg.reply(json_text)
+
+
+    os.remove(image_name)
+embed()
\ No newline at end of file