def predict(self, image):
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_with_pad(image)
image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE))
elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3):
image = resize_with_pad(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
image = image.astype('float32')
image /= 255
result = self.model.predict_proba(image)
print(result)
result = self.model.predict_classes(image)
return result[0]
def predict(self, image):
if image.shape != (1, IMAGE_SIZE, IMAGE_SIZE, 3):
image = resize_with_pad(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
image = image.astype('float32')
image /= 255
result = self.model.predict_proba(image)
return result[0]
def predict(self, predict_image):
image = resize_with_pad(predict_image)
image = image.astype('float32')
image /= 255
result = self.model.predict(np.array([image]))[0]
whois = 0 if (result[0] > result[1]) else 1
return whois
def predict(self, image, img_channels=3):
if K.image_dim_ordering() == 'th' and image.shape != (
1, img_channels, IMAGE_SIZE, IMAGE_SIZE):
image = resize_with_pad(image)
image = image.reshape((1, img_channels, IMAGE_SIZE, IMAGE_SIZE))
elif K.image_dim_ordering() == 'tf' and image.shape != (
1, IMAGE_SIZE, IMAGE_SIZE, img_channels):
image = resize_with_pad(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, img_channels))
image = image.astype('float32')
image /= 255
if DEBUG_MUTE:
result = self.model.predict_proba(image, verbose=0)
result = self.model.predict_classes(image, verbose=0)
else:
result = self.model.predict_proba(image)
print(result)
result = self.model.predict_classes(image)
print(result)
return result[0]