def get_thatch_template(self):
#get some thatch roof
roof = None
for img_name in listdir(self.in_path):
if img_name.endswith('.jpg'):
roofs = DataLoader().get_roofs(
self.in_path + img_name[:-3] + 'xml', '')
for r in roofs:
if r.roof_type == 'thatch':
roof = r
break
if roof is not None:
break
#extract patch
img = cv2.imread(self.in_path + img_name)
template = img[roof.ymin:roof.ymin + roof.height,
roof.xmin:roof.xmin + roof.width]
img = cv2.imwrite('thatch_template.jpg', template)
import numpy as np
import pandas as pd
from get_data import DataLoader
from LR import LR
if __name__ == "__main__":
file_name = 'data/train.csv'
test_file = 'data/test.csv'
data_loader = DataLoader(file_name)
raw_data = data_loader.get_data_as_df()
data_dict = data_loader.get_data_by_month(raw_data)
train_data, labels, mean_x, std_x = data_loader.get_final_data(data_dict)
#x_train_set,label_train_set,\
#x_validation,label_validation = data_loader.split_train_and_valid(train_data,labels)
test_data = pd.read_csv(test_file, header=None, encoding='big5')
test_data = test_data.iloc[:, 2:]
test_data[test_data == 'NR'] = 0
test_data = test_data.to_numpy()
test_x = np.empty([240, 18 * 9], dtype=float)
for i in range(240):
test_x[i] = test_data[18 * i:18 * (i + 1), :].reshape(1, -1)
for i in range(len(test_x)):
for j in range(len(test_x[0])):
if std_x[j] != 0:
test_x[i][j] = (test_x[i][j] - mean_x[j]) / std_x[j]
test_x = np.concatenate((np.ones([240, 1]), test_x), axis=1).astype(float)
linear_model = LR(train_data, labels)
linear_model.train()
linear_model.get_predict_csv(test_x)
