improve the acccuracy to 81.75%(0-error) 94.1%(1-error)

hyperlpr/finemapping_vertical.py

@@ -8,30 +8,85 @@ 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
+    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 = PReLU(shared_axes=[1, 2], name='prelu1')(x)
+    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 = PReLU(shared_axes=[1, 2], name='prelu2')(x)
+    x = Activation("relu", name='relu2')(x)
     x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x)
-    x = PReLU(shared_axes=[1, 2], name='prelu3')(x)
+    x = Activation("relu", name='relu3')(x)
     x = Flatten()(x)
-    output = Dense(2)(x)
-    output = PReLU(name='prelu4')(output)
+    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,(50,12))
+    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)
-    image = image[0:35,res[0]+4:res[1]]
+    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