def predictAll():
predictions = []
pictures = glob.glob(testFileDogs)
count = len(pictures)
i = 0
for each in pictures:
image = io.imread(each)
image1 = v3(image)
image2 = mobile(image)
image1 = np.expand_dims(image1, axis=0)
image2 = np.expand_dims(image2, axis=0)
print("Predicting image " + str(i) + "/" + str(count))
prediction1 = np.argmax(allv3.predict(image1))
prediction2 = np.argmax(allmobile.predict(image2))
if (prediction1 == prediction2):
predictions.append([classesAll[prediction1], each.split('/')[-1]])
else:
predictions.append([
classesAll[prediction1] + ' or ' + classesAll[prediction2],
each.split('/')[-1]
])
i += 1
printall(predictions)
def model1():
predictions = []
pictures = glob.glob(testFile)
count = len(pictures)
i = 0
for each in pictures:
image = io.imread(each)
image1 = v3(image)
image2 = mobile(image)
image1 = np.expand_dims(image1, axis=0)
image2 = np.expand_dims(image2, axis=0)
print("Predicting image " + str(i) + "/" + str(count))
prediction1 = np.argmax(Cat_Dogv3.predict(image1))
prediction2 = np.argmax(Cat_DogMobileNet.predict(image2))
if (prediction1 == prediction2):
if (prediction1 == 0):
prediction3 = np.argmax(catv3.predict(image1))
prediction4 = np.argmax(catmobile.predict(image2))
if (prediction3 == prediction4):
race = classesCats[prediction3]
else:
race = [classesCats[prediction3], classesCats[prediction4]]
else:
prediction3 = np.argmax(dogv3.predict(image1))
prediction4 = np.argmax(dogmobile.predict(image2))
if (prediction3 == prediction4):
race = classesDogs[prediction3]
else:
race = [classesDogs[prediction3], classesDogs[prediction4]]
predictions.append(
[classesCatDog[prediction1], race,
each.split('/')[-1]])
else:
predictions.append(['Unsure', each.split('/')[-1]])
i += 1
printall(predictions)
def predictDogandCat():
predictions = []
pictures = glob.glob(testFile)
count = len(pictures)
i = 0
for each in pictures:
image = io.imread(each)
image1 = v3(image)
image2 = mobile(image)
image1 = np.expand_dims(image1, axis=0)
image2 = np.expand_dims(image2, axis=0)
print("Predicting image " + str(i) + "/" + str(count))
prediction1 = np.argmax(Cat_Dogv3.predict(image1))
prediction2 = np.argmax(Cat_DogMobileNet.predict(image2))
if (prediction1 == prediction2):
predictions.append(
[classesCatDog[prediction1],
each.split('/')[-1]])
else:
predictions.append(['Unsure', each.split('/')[-1]])
i += 1
printall(predictions)