def face_predict(self, image):
#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。
image = image.reshape((1, 3, IMAGE_SIZE, IMAGE_SIZE)) #只针对1张图片来预测
elif K.image_data_format() == 'channels_last' and image.shape != (
1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
#图片归一化，先变为浮点数
image = image.astype('float32')
image /= 255
#给出输入属于各个类别的概率，当前是二值类别，则该函数会给出输入图像
#属于0和1的概率各是多少。
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):
# 依然是根据后端系统确定维度顺序
# 尺寸必须与训练集一致都应该是IMAGE_SIZE x IMAGE_SIZE
if K.image_data_format() == 'channels_first' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
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))
#image = img.reshape((1, 1, IMAGE_SIZE, IMAGE_SIZE))
# 浮点并归一化
image = image.astype('float32')
image /= 255.0
# 则该函数会给出输入图像属于各个目标的概率
result = self.model.predict(image)
#print('result:', result )
#找出概率最高的
max_index = np.argmax(result)
#第一个参数为概率最高的label的index,第二个参数为对应概率
return max_index,result[0][max_index]
def face_predict(self, image):
# 維度
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE,
IMAGE_SIZE):
image = resize_image(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_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
# 數據標準化
image = image.astype('float32')
image /= 255
# 每個圖片的機率
result = self.model.predict_proba(image)
print('result:', result)
# 給出答案
good = False
for percent in result[0]:
if percent >= 0.55:
good = True
result = self.model.predict_classes(image)
# 輸出預測結果
if good == True:
outputresult = result[0]
else:
outputresult = -1
print(outputresult)
return outputresult
def face_predict(self, image):
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE,
IMAGE_SIZE):
image = resize_image(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_image(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)
result = self.model.predict_classes(image)
return result[0]
def predict(self, image):
image = resize_image(image)
image_embedding = img_to_encoding(np.array([image]), facenet)
label = self.model.predict(
image_embedding
) # predict方法返回值是array of shape [n_samples]，因此下面要用label[0]从array中取得数值，https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html#sklearn.neighbors.KNeighborsClassifier.predict
return label[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)
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_image(image)
image = image.reshape((1, IMAGE_SIZE, IMAGE_SIZE, 3))
# 浮点并归一化
image = image.astype('float32')
image /= 255.0
# 给出输入属于各个类别的概率，这里是二值类别，输出属于0和1的概率各为多少
# 给出类别预测：0或者1
result = self.model.predict_classes(image)
print("result----" + str(result))
# 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))
#浮点并归一化
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，則該函數會給出輸入圖像屬於0和1的概率各為多少
result = self.model.predict_proba(image)
print('result:', result)
#给出類別預測：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):
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(image)
#print('result:', result)
gender_class = np.argmax(result)
print(gender_class)
# 返回类别预测结果
return gender_class
def face_predict(self, image):
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)
print(result)
# 返回类别预测结果
return result[0]
def predict(self, image):
image = resize_image(image)
image_embedding = img_to_encoding(np.array([image]), facenet)
label = self.model.predict(image_embedding)
return label[0]
