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@@ -8,30 +8,85 @@ import numpy as np |
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import cv2 |
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import cv2 |
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def getModel(): |
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def getModel(): |
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input = Input(shape=[12, 50, 3]) # change this shape to [None,None,3] to enable arbitraty shape input |
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input = Input(shape=[16, 66, 3]) # change this shape to [None,None,3] to enable arbitraty shape input |
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x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input) |
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x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input) |
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x = PReLU(shared_axes=[1, 2], name='prelu1')(x) |
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x = Activation("relu", name='relu1')(x) |
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x = MaxPool2D(pool_size=2)(x) |
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x = MaxPool2D(pool_size=2)(x) |
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x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x) |
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x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x) |
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x = PReLU(shared_axes=[1, 2], name='prelu2')(x) |
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x = Activation("relu", name='relu2')(x) |
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x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x) |
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x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x) |
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x = PReLU(shared_axes=[1, 2], name='prelu3')(x) |
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x = Activation("relu", name='relu3')(x) |
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x = Flatten()(x) |
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x = Flatten()(x) |
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output = Dense(2)(x) |
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output = PReLU(name='prelu4')(output) |
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output = Dense(2,name = "dense")(x) |
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output = Activation("relu", name='relu4')(output) |
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model = Model([input], [output]) |
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model = Model([input], [output]) |
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return model |
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return model |
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model = getModel() |
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model = getModel() |
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model.load_weights("./model/model12.h5") |
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model.load_weights("./model/model12.h5") |
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def getmodel(): |
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return model |
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def gettest_model(): |
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input = Input(shape=[16, 66, 3]) # change this shape to [None,None,3] to enable arbitraty shape input |
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A = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input) |
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B = Activation("relu", name='relu1')(A) |
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C = MaxPool2D(pool_size=2)(B) |
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x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(C) |
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x = Activation("relu", name='relu2')(x) |
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x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x) |
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K = Activation("relu", name='relu3')(x) |
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x = Flatten()(K) |
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dense = Dense(2,name = "dense")(x) |
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output = Activation("relu", name='relu4')(dense) |
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x = Model([input], [output]) |
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x.load_weights("./model/model12.h5") |
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ok = Model([input], [dense]) |
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for layer in ok.layers: |
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print layer |
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return ok |
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def finemappingVertical(image): |
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def finemappingVertical(image): |
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resized = cv2.resize(image,(50,12)) |
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resized = cv2.resize(image,(66,16)) |
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resized = resized.astype(np.float)/255 |
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resized = resized.astype(np.float)/255 |
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res= model.predict(np.array([resized]))[0] |
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res= model.predict(np.array([resized]))[0] |
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print "keras_predict",res |
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res =res*image.shape[1] |
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res =res*image.shape[1] |
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res = res.astype(np.int) |
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res = res.astype(np.int) |
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image = image[0:35,res[0]+4:res[1]] |
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H,T = res |
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H-=3 |
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#3 79.86 |
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#4 79.3 |
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#5 79.5 |
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#6 78.3 |
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#T |
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#T+1 80.9 |
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#T+2 81.75 |
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#T+3 81.75 |
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if H<0: |
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H=0 |
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T+=2; |
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if T>= image.shape[1]-1: |
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T= image.shape[1]-1 |
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image = image[0:35,H:T+2] |
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image = cv2.resize(image, (int(136), int(36))) |
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image = cv2.resize(image, (int(136), int(36))) |
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return image |
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return image |