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3D_new_run_test.py
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3D_new_run_test.py
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###################################################
#
# Script to
# - Calculate prediction of the test dataset
# - Calculate the parameters to evaluate the prediction
#
##################################################
#Python
import time
from PIL import Image
import numpy as np
np.set_printoptions(threshold=np.inf)
import scipy.io as sio
import configparser
from matplotlib import pyplot as plt
#Keras
from keras.models import model_from_json
from keras.models import Model
#scikit learn
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import f1_score
import sys
sys.path.insert(0, './lib/')
import sys
sys.setrecursionlimit(4000)
import os
os.environ["CUDA_VISIBLE_DEVICES"]='0'
# help_functions.py
#from help_functions import *
# extract_patches.py
#from extract_patches import recompone
#from extract_patches import recompone_overlap
#from extract_patches import paint_border
#from extract_patches import kill_border
#from extract_patches import pred_only_FOV
#from extract_patches import get_data_testing
#from extract_patches import get_data_testing_overlap
# pre_processing.py
#from pre_processing import my_PreProc
import h5py
#group a set of images row per columns
def group_images(data,per_row):
assert data.shape[0]%per_row==0
assert (data.shape[1]==1 or data.shape[1]==3)
data = np.transpose(data,(0,2,3,1)) #corect format for imshow
all_stripe = []
for i in range(int(data.shape[0]/per_row)):
stripe = data[i*per_row]
for k in range(i*per_row+1, i*per_row+per_row):
stripe = np.concatenate((stripe,data[k]),axis=1)
all_stripe.append(stripe)
totimg = all_stripe[0]
for i in range(1,len(all_stripe)):
totimg = np.concatenate((totimg,all_stripe[i]),axis=0)
return totimg
#visualize image (as PIL image, NOT as matplotlib!)
def visualize(data,filename):
assert (len(data.shape)==3) #height*width*channels
img = None
if data.shape[2]==1: #in case it is black and white
data = np.reshape(data,(data.shape[0],data.shape[1]))
if np.max(data)>1:
img = Image.fromarray(data.astype(np.uint8)) #the image is already 0-255
else:
img = Image.fromarray((data*255).astype(np.uint8)) #the image is between 0-1
img.save(filename + '.png')
return img
#========= CONFIG FILE TO READ FROM =======
config = configparser.RawConfigParser()
config.read('./configuration.txt')
#===========================================
#working directory
path_data = config.get('data paths', 'path_local')
#loading test images
test_imgs_original = path_data + config.get('data paths', 'test_imgs_original')
test_data_ori = h5py.File(test_imgs_original,'r')
test_imgs_orig=np.array(test_data_ori['image'])
print("***************")
print(np.max(test_imgs_orig))
test_imgs_orig/=np.max(test_imgs_orig)
test_img_orig=test_imgs_orig[10,0,2,:,:]
print ("ori_images size :")
print (test_img_orig.shape)
print('max ori:')
print(np.max(test_img_orig))
print('min ori:')
print(np.min(test_img_orig))
full_img_height = test_imgs_orig.shape[3]
full_img_width = test_imgs_orig.shape[4]
#model name directory
name_experiment = config.get('experiment name', 'name')
path_experiment = './model/'
N_visual = int(config.get('testing settings', 'N_group_visual'))
#================ Run the prediction of the images ==================================
best_last = config.get('testing settings', 'best_last')
#Load the saved model
model = model_from_json(open(path_experiment+name_experiment +'_architecture.json').read())
model.load_weights(path_experiment+name_experiment + '_'+best_last+'_weights.h5')
#Calculate the predictions
start_time=time.time()
predictions = model.predict(test_imgs_orig, batch_size=1, verbose=2)
run_time=time.time()-start_time
print('run time:')
print(run_time)
print ("predicted images size :")
print (predictions[0].shape)
print('max pred:')
print(np.max(predictions[0]))
print('min pred:')
print(np.min(predictions[0]))
#orig_imgs = test_imgs_orig[:,:,0:full_img_height,0:full_img_width]
orig_imgs = test_imgs_orig[:,0,2,0:full_img_height,0:full_img_width]
n_data=orig_imgs.shape[0]
orig_imgs=np.reshape(orig_imgs,(n_data,1,full_img_height,full_img_width))
pred_imgs = predictions[:,:,0:full_img_height,0:full_img_width]
save_path='./exp_test_result/'
if not os.path.exists(save_path):
os.mkdir(save_path)
print ('preds_shape:' +str(pred_imgs.shape))
pred_save=np.array(pred_imgs)
sio.savemat(save_path+'preds.mat',{'preds':pred_save})
print ("Orig imgs shape: " +str(orig_imgs.shape))
print ("pred imgs shape: " +str(pred_imgs.shape))
# visualize(group_images(orig_imgs,N_visual),save_path+"all_originals")#.show()
# visualize(group_images(pred_imgs,N_visual),save_path+"all_predictions")#.show()
# ##visualize results comparing mask and prediction:
# assert (orig_imgs.shape[0]==pred_imgs.shape[0])
# N_predicted = orig_imgs.shape[0]
# group = N_visual
# assert (N_predicted%group==0)
# for i in range(int(N_predicted/group)):
# orig_stripe = group_images(orig_imgs[i*group:(i*group)+group,:,:,:],group)
# #masks_stripe = group_images(gtruth_masks[i*group:(i*group)+group,:,:,:],group)
# pred_stripe = group_images(pred_imgs[i*group:(i*group)+group,:,:,:],group)
# total_img = np.concatenate((orig_stripe,pred_stripe),axis=0)
# visualize(total_img,save_path+name_experiment +"_Original_Prediction"+str(i))#.show()