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):
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 face_predict(self, image):
#Check the order of parameters
if K.image_dim_ordering() == 'th' and image.shape != (1, 3, IMAGE_SIZE, IMAGE_SIZE):
image = resize_image(image) #change it size to the same as train_set
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
#ouput predict value
result = self.model.predict_proba(image)
print('result:', result)
result = self.model.predict_classes(image)
return result[0]
def predict(base64_file):
if global_model is None:
return 'error'
image = ld.resize_image(base64_file, size, size)
# print(image, file=sys.stdout)
result = {
'predictions': []
}
predictions = global_model.predict([image])
for i, label in enumerate(label_array):
result['predictions'].append({
'label': label,
'proba': predictions[0][i].item()
})
return json.dumps(result)
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation
import numpy as np
import loadData as ld
import reseau as re
path = sys.argv[1]
fileName = path.split('/')[len(path.split('/'))-1]
folderPath = path[:len(path)-len(fileName)-1]
size = settings.size
ld.resize_image(folderPath, fileName, size, size)
extension = fileName.split('.')[len(fileName.split('.'))-1]
img = cv2.imread('./data/'+fileName[:len(fileName)-len(extension)]+'jpg').astype(np.float32, casting='unsafe')
model = re.getReseau()
model.load("dataviz-classifier.tfl")
prediction = model.predict([img])
print(prediction)