def main():
#特征处理
image_array, label = image_feature.read_train_data()
feature = []
for num, image in enumerate(image_array):
feature_vec = image_feature.feature_transfer(image)
feature.append(feature_vec)
print(np.array(feature).shape)
print(np.array(label).shape)
#训练模型
image_model.trainModel(feature, label)
def featrue_generate(image_array):
feature = []
for num, image in enumerate(image_array):
feature_each_image = []
for im_meta in image:
fea_vector = image_feature.feature_transfer(im_meta)
feature_each_image.append(fea_vector)
if len(feature_each_image) == 0:
feature_each_image = [[0] * (image_width + image_height)
] * int(image_character_num)
feature.append(feature_each_image)
print("预测数据的长度:", len(feature))
print("预测数据特征示例:", feature[0])
return feature
def main():
# image_process.main() #处理原始验证码，并存到文件
# feature, label = image_feature.main() #特征处理
#特征处理
image_array, label = image_feature.read_train_data()
feature = []
for num, image in enumerate(image_array):
feature_vec = image_feature.feature_transfer(image)
# print('label: ',image_label[num])
# print(feature)
feature.append(feature_vec)
print(np.array(feature).shape)
print(np.array(label).shape)
#训练模型
result = image_model.trainModel(feature, label)