HyperLPR/hyperlpr/finemapping_vertical.py

#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