genericDataSetLoader.py 文件源码

def standardizeImages(self):
        print "Standardizing Images..."
        self.trainingDataXStandardized = []
        self.testingDataXStandardized = []
        with tf.Session() as sess:
            for i in range(self.trainingDataX.shape[0]):
                print str(i)+"/"+str(self.trainingDataX.shape[0])
                self.trainingDataXStandardized.append(tf.image.per_image_standardization(self.trainingDataX[i]).eval())

            for i in range(self.testingDataX.shape[0]):
                print str(i)+"/"+str(self.testingDataX.shape[0])
                self.testingDataXStandardized.append(tf.image.per_image_standardization(self.testingDataX[i]).eval())
        #self.trainingDataX = tf.map_fn(lambda img:tf.image.per_image_standardization(img), self.trainingDataX, dtype=tf.float32)
        #self.testingDataX = tf.map_fn(lambda img:tf.image.per_image_standardization(img), self.testingDataX, dtype=tf.float32)
        #print self.trainingDataXStandardized[0]
        self.trainingDataX = np.array(self.trainingDataXStandardized)
        self.testingDataX = np.array(self.testingDataXStandardized)
        print self.testingDataX.shape
        print self.trainingDataX.shape
        #with tf.Session() as sess:
        #    self.trainingDataX = self.trainingDataX.eval()
        #    self.testingDataX = self.testingDataX.eval()
        print "Images standardized...Saving them..."
        self.__save("preparedDataStandardized.pkl")