from promise2012.Vnet.model_vnet3d import Vnet3dModule
from promise2012.Vnet.util import convertMetaModelToPbModel
import numpy as np
import pandas as pd
import cv2
def train():
'''
Preprocessing for dataset
'''
# Read data set (Train data from CSV file)
csvmaskdata = pd.read_csv('trainY.csv')
csvimagedata = pd.read_csv('trainX.csv')
maskdata = csvmaskdata.iloc[:, :].values
imagedata = csvimagedata.iloc[:, :].values
# shuffle imagedata and maskdata together
perm = np.arange(len(csvimagedata))
np.random.shuffle(perm)
imagedata = imagedata[perm]
maskdata = maskdata[perm]
Vnet3d = Vnet3dModule(128, 128, 64, channels=1, costname="dice coefficient")
Vnet3d.train(imagedata, maskdata, "model\\Vnet3dModule.pd", "log\\", 0.001, 0.7, 100000, 1)
def predict0():
Vnet3d = Vnet3dModule(256, 256, 64, inference=True, model_path="model\\Vnet3dModule.pd")
for filenumber in range(30):
batch_xs = np.zeros(shape=(64, 256, 256))
for index in range(64):
imgs = cv2.imread(
"D:\Data\PROMISE2012\Vnet3d_data\\test\image\\" + str(filenumber) + "\\" + str(index) + ".bmp", 0)
batch_xs[index, :, :] = imgs[128:384, 128:384]
predictvalue = Vnet3d.prediction(batch_xs)
for index in range(64):
result = np.zeros(shape=(512, 512), dtype=np.uint8)
result[128:384, 128:384] = predictvalue[index]
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel)
cv2.imwrite(
"D:\Data\PROMISE2012\Vnet3d_data\\test\image\\" + str(filenumber) + "\\" + str(index) + "mask.bmp",
result)
def meta2pd():
convertMetaModelToPbModel(meta_model="model\\Vnet3dModule.pd", pb_model="model")
train()
#predict0()
#meta2pd()
