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)