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 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 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)
oy = round(result[0][0],4)
print('&&&&&&&&&&&&&',round(result[0][0],4), '@@@',round(result[0][1],4))
result = self.model.predict_classes(image)
print('*******************************',result[0])
return result[0], oy
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
# 樣本比率 proba
# result = self.model.predict_proba(image)
# print(str(result[0][0])+'\n',str(result[0][1])+'\n',str(result[0][0]+result[0][1])+'\n')
# 分類條件
result = self.model.predict_classes(image)
# print(result.size)
return result[0]
def predict(self, image):
if image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_with_pad(image)
image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE))
image = image.astype('float32')
image /= 255
result = self.model.predict_proba(image)
print(result)
result = self.model.predict_classes(image)
return result[0]
self.model.save(file_path)
def load(self, file_path=FILE_PATH):
print('Model Loaded.')
self.model = load_model(file_path)
def predict(self, image):
<<<<<<< HEAD
#print('image.shape:', image.shape)
'''
if image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_with_pad(image)
image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE))
'''
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 evaluate(self, dataset):
score = self.model.evaluate(dataset.X_test, dataset.Y_test, verbose=0)
print("%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100))
