def face_predict(self, image):
# 依然是根据后端系统确定维度顺序
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE,
IMAGE_SIZE):
image = resize_image(
image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE
image = image.reshape(
(1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测
elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE,
IMAGE_SIZE, 3):
image = resize_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
# 数据浮点并归一化
image = image.astype('float32')
image /= 255
# 给出输入属于各个类别的概率
result_probability = self.model.predict_proba(image)
print('result:', result_probability, max(result_probability[0]))
# 给出类别预测：0-n
result = self.model.predict_classes(image)
# 返回类别预测结果
return max(result_probability[0]), result[0]
def face_predict(self, image):
# 依然是根据后端系统确定维度顺序
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE,
IMAGE_SIZE):
image = resize_image(
image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE
image = image.reshape(
(1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测
elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE,
IMAGE_SIZE, 3):
image = resize_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
# 浮点并归一化
image = image.astype('float32')
image /= 255
# 给出输入属于各个类别的概率，我们是二值类别，则该函数会给出输入图像属于0和1的概率各为多少
result = self.model.predict_proba(image)
print('result:', result)
# 给出类别预测：0或者1
result = self.model.predict_classes(image)
# 返回类别预测结果
return result[0]
def face_predict(self, image):
#依然是根據後端系統確定維度順序
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE,
IMAGE_SIZE):
image = resize_image(image) #尺寸必須與訓練集一致都應該是IMAGE_SIZE x IMAGE_SIZE
image = image.reshape(
(1, 3, IMAGE_SIZE, IMAGE_SIZE)) #與模型訓練不同，這次只是針對1張圖片進行預測
elif K.image_dim_ordering() == 'tf' and image.shape != (1, IMAGE_SIZE,
IMAGE_SIZE, 3):
image = resize_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
#浮點並歸一化
image = image.astype('float32')
image /= 255
#給出輸入屬於各個類別的概率，我們是二值類別，則該函式會給出輸入影象屬於0和1的概率各為多少
result = self.model.predict_proba(image)
print('result:', result)
#給出類別預測：0或者1
result = self.model.predict_classes(image)
#返回類別預測結果
return result[0]
def face_predict(self, image):
# 依然是根据后端系统确定维度顺序
if K.image_data_format() == 'channels_first' and image.shape != (
1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_image(
image) # 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE
image = image.reshape(
(1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 与模型训练不同，这次只是针对1张图片进行预测
elif K.image_data_format() == 'channels_last' and image.shape != (
1, IMAGE_SIZE, IMAGE_SIZE, 3):
image = resize_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
# 浮点并归一化
image = image.astype('float32')
image /= 255
# 给出输入属于各个类别的概率
result_probability = self.model.predict(image)
# print('result:', result_probability)
# 给出类别预测(改）
if max(result_probability[0]) >= 0.9:
result = self.model.predict_classes(image)
print('result:', result)
# 返回类别预测结果
return result[0]
else:
return -1
def face_predict(self, image):
# 判断后端系统：TensorFlow/Theano，确定维度顺序
if K.image_data_format() == 'channels_first' and image.shape != (
1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_image(image) # 尺寸与训练集一致：IMAGE_SIZE x IMAGE_SIZE
image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) # 针对1张图片进行预测
elif K.image_data_format() == 'channels_last' and image.shape != (
1, IMAGE_SIZE, IMAGE_SIZE, 3):
image = resize_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
# 改为浮点数并归一化
image = image.astype('float32')
image /= 255
# 计算输入属于各个类别的概率，输出概率最大的类别
result_probability = self.model.predict_proba(image)
print('result:', result_probability, max(result_probability[0]))
# 给出类别预测
result = self.model.predict_classes(image)
# 返回类别预测结果
return max(result_probability[0]), result[0]
