path=sys.argv[1]

with open(path) as f:

config=json.load(f)

batch_size=int(config['batch_size'])

nb_classes=int(config['nb_classes'])

weight_path=config['weights']

#####################First level of Classification ################################

##### load model

model=None

model=new.load_model(nb_classes,weight_path)

####### specify the loss function

sgd = SGD(lr=0.00005, decay = 1e-5, momentum=0.99, nesterov=True)

#sgd = SGD(lr=0.00005, decay = 1e-6, momentum=0.9, nesterov=True)

model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

#model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd, metrics=[metrics.mae, metrics.sparse_categorical_accuracy])

######## load data

test={}

with open(config['data_path']+'/'+config['dataset_name']+'.test','rb') as f:

test=pickle.load(f)

x_test,y_test,imgname=test['data'],test['labels'],test['imgname']

x_ts = x_test.reshape((-1,227,227,1))

print(x_ts.shape, 'test samples')

print(y_test.shape, 'test sample labels')

##### evalution and prediction and confusion matrix formation

scores=model.evaluate(x_ts,y_test,batch_size=batch_size,verbose=0)

print("model %s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

prediction= model.predict_classes(x_ts,verbose=1)

#print(prediction)

np.save('prediction.npy', prediction)

pre=np.array(prediction)

pre=MultiLabelBinarizer().fit_transform(pre.reshape(-1, 1))

orig=y_test

print('')

print('')

print('score for first level classification: ',scores)

'''

count = 0

for i in range(0,len(pre)):

if not np.array_equal(orig[i],pre[i]):

print(imgname[i],"_",orig[i],"_",pre[i],"_False")

count = count + 1

print (count)

'''

aa=[0,1]

aa = np.array(aa)

print('')

print('')

print(MultiLabelBinarizer().fit_transform(aa.reshape(-1, 1)))

print("0-Nontumor 1-Tumor")

a=[0,1]

a=np.array(a)

b=[1,0]

b=np.array(b)

y_true = []

y_pred = []

print(range(len(prediction)))

for i in range(len(prediction)):

if np.array_equal(orig[i],a):

y_true.append(1)

elif np.array_equal(orig[i],b):

y_true.append(0)

for i in range(len(prediction)):

if np.array_equal(pre[i],a):

y_pred.append(1)

elif np.array_equal(pre[i],b):

y_pred.append(0)

cm = ConfusionMatrix(y_true, y_pred)

print('')

print('')

print('*****************************Confusion Matrix for first level Classification****************************')

print(cm)

print('')

print('')

############################ Second level Classification ###############################

'''

path=sys.argv[2]

with open(path) as f:

config2=json.load(f)

batch_size2=int(config2['batch_size'])

nb_classes2=int(config2['nb_classes'])

weight_path2=config2['weights']

##### load model2

model2=None

model2=new.load_model(nb_classes2,weight_path2)

####### specify the loss function

sgd2 = SGD(lr=0.00005, decay = 1e-6, momentum=0.9, nesterov=True)

model2.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

#model.compile(loss='sparse_categorical_crossentropy', optimizer=sgd, metrics=[metrics.mae, metrics.sparse_categorical_accuracy])

######## load data

test={}

with open(config2['data_path']+'/'+config2['dataset_name']+'.further','rb') as f:

test2=pickle.load(f)

x_test2,y_test2,imgname2=test2['data'],test2['labels'],test2['imgname']

x_ts2 = x_test2.reshape((-1,227,227,1))

count=0

tumorname = []

for i in range(0,len(pre)):

if np.array_equal(pre[i],a):

tumorname.append(imgname[i])

count+=1

print(count)

print(len(tumorname))

print(len(imgname2))

tumor = []

tumorlabels = []

count=0

count1=0

for i in range(len(tumorname)):

for j in range(len(imgname2)):

if tumorname[i] == imgname2[j]:

tumor.append(x_ts2[j])

tumorlabels.append(y_test2[j])

count+=1

print(count)

tumor = np.array(tumor)

tumorlabels = np.array(tumorlabels)

overview(0,207, tumor)

print(tumor.shape, ' predicted tumor samples')

print(tumorlabels.shape, 'predicted tumor sample labels')

print('')

##### evalution and prediction and confusion matrix formation

scores2=model2.evaluate(tumor,tumorlabels,batch_size=batch_size2,verbose=0)

print("model2 %s: %.2f%%" % (model2.metrics_names[1], scores2[1]*100))

prediction2= model2.predict_classes(tumor,verbose=1)

#print(prediction)

np.save('prediction2.npy', prediction)

pre2=np.array(prediction2)

pre2=MultiLabelBinarizer().fit_transform(pre2.reshape(-1, 1))

orig2=tumorlabels

print('')

print('')

print('score for second level classification',scores2)

count2 = 0

aa=[0,1,2,3]

aa = np.array(aa)

print('')

print(aa,MultiLabelBinarizer().fit_transform(aa.reshape(-1, 1)))

print("0-astrocytoma 1-gbm 2- mixed 3- oligodendroglioma")

print('')

a=[1,0,0,0]

b=[0,1,0,0]

c=[0,0,1,0]

d=[0,0,0,1]

a=np.array(a)

b=np.array(b)

c=np.array(c)

d=np.array(d)

y_true2 = []

y_pred2 = []

for i in range(len(prediction2)):

if np.array_equal(orig2[i],a):

y_true2.append(0)

elif np.array_equal(orig2[i],b):

y_true2.append(1)

elif np.array_equal(orig2[i],c):

y_true2.append(2)

elif np.array_equal(orig2[i],d):

y_true2.append(3)

for i in range(len(prediction2)):

if np.array_equal(pre2[i],a):

y_pred2.append(0)

elif np.array_equal(pre2[i],b):

y_pred2.append(1)

elif np.array_equal(pre2[i],c):

y_pred2.append(2)

elif np.array_equal(pre2[i],d):

y_pred2.append(3)

cm2 = ConfusionMatrix(y_true2, y_pred2)

print('')

print('*****************************Confusion Matrix for SECOND level Classification****************************')

print(cm2)

# cm2.print_stats()

print('')

counter=0

for i in range(len(pre2)):

if np.array_equal(pre2[i],a):

#print(tumorname[i],'__',a,'__astrocytoma')

counter+=1

print('')

print(counter,'__astrocytoma__images')

counter=0

for i in range(len(pre2)):

if np.array_equal(pre2[i],b):

#print(tumorname[i],'__',b,'__gbm')

counter+=1

print('')

print(counter,'__gbm__images')

counter=0

for i in range(len(pre2)):

if np.array_equal(pre2[i],c):

#print(tumorname[i],'__',c,'__mixed')

counter+=1

print('')

print(counter,'__mixed__images')

counter=0

for i in range(len(pre2)):

if np.array_equal(pre2[i],d):

#print(tumorname[i],'__',d,'__oligodendroglioma')

counter+=1

print('')

print(counter,'__oligodendroglioma__images')

# layer_name = 'predictions'

# layer_idx = [idx for idx, layer in enumerate(model.layers) if layer.name == layer_name][0]

# Images corresponding to tiger, penguin, dumbbell, speedboat, spider

# heatmaps = []

# for img in tumor:

# # Predict the corresponding class for use in `visualize_saliency`.

# pred_class = np.argmax(model.predict(np.array([img_to_array(img)])))

# # Here we are asking it to show attention such that prob of `pred_class` is maximized.

# heatmap = visualize_saliency(model, layer_idx, [pred_class], seed_img, text=tumorlabels)

# heatmaps.append(heatmap)

# cv2.imwrite('predictions.png',stitch_images(heatmaps))