#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
