def main():
feature, tar = get_feature()
print(u.get_time(), ' train starts!')
# clf = get_model_rf(feature, tar)
clf = get_model_xgb(feature, tar)
print(u.get_time(), ' train done!')
u.build_dir(output_dir)
model_save_dir = os.path.join(output_dir, use_model + '_model.m')
joblib.dump(clf, model_save_dir)
test = get_test()
print(u.get_time(), ' test starts!')
result = clf.predict_proba(test)
print(u.get_time(), ' test done!')
np.savetxt(os.path.join(output_dir, 'result.csv'), result, fmt='%.6f', delimiter=',')
np.save(os.path.join(output_dir, 'result.npy'), result)
print('all finished')
def main():
try:
for folder_seq in range(3):
image_sir = os.path.join(src_dir, folder[folder_seq])
for pig in range(1 + 10 * folder_seq, 11 + 10 * folder_seq):
num = 1
pig_folder = os.path.join(src_dir, str(pig))
u.build_dir(pig_folder)
for file in os.listdir(image_sir):
if file.startswith(str(pig) + '_'):
image_name = str(pig) + '_' + str(num) + SUFFIX
u.copyFiles(os.path.join(image_sir, file),
os.path.join(pig_folder, image_name))
print(file + ' finished transferring')
num += 1
print(str(pig) + ' finished!')
except Exception as e:
# os.system("shutdown -t 5")
raise e
def main():
u.build_dir(output_dir)
for image_name in os.listdir(src_dir):
img = cv2.imread(os.path.join(src_dir, image_name))
height, width = img.shape[0], img.shape[1]
if height > width:
expend_scale = (height - width) / 2
# expend_image = cv2.copyMakeBorder(img, 0,0,expend_scale,expend_scale,cv2.BORDER_CONSTANT, value=BLACK)
expend_image = cv2.copyMakeBorder(img, 0, 0, expend_scale,
expend_scale, cv2.BORDER_REFLECT)
elif height < width:
expend_scale = (width - height) / 2
# expend_image = cv2.copyMakeBorder(img, expend_scale, expend_scale, 0, 0, cv2.BORDER_CONSTANT, value=BLACK)
expend_image = cv2.copyMakeBorder(img, expend_scale, expend_scale,
0, 0, cv2.BORDER_REFLECT)
else:
expend_image = img
cv2.imwrite(os.path.join(output_dir, image_name), expend_image)
print(expend_image.shape)
print(image_name + ' has been handled!')
print('all finished')
##--- choose one picture in the dataset with a interval ---###
import os
import cv2
# import cv
import utils
# tqdm for progress visualization
from tqdm import tqdm
source_dir = "/media/zq610/2C9BDE0469DC4DFC/ubuntu/dl_dataset/turtlebot/raw_frames"
output_dir = "/media/zq610/2C9BDE0469DC4DFC/ubuntu/dl_dataset/turtlebot/VOC_type/JPEGImages"
interval = 20
if __name__ == '__main__':
utils.build_dir(output_dir)
index = 0
folders = os.listdir(source_dir)
# folders = ['two_small_1', 'two_small_2', 'two_small_3', 'two_small_4', ]
for folder in folders:
pic_source = os.path.join(source_dir, folder)
pics = os.listdir(pic_source)
# sorted不改变原list,只是返回一个list
# key可以使用lambda表达式,这个x是形参,代表每一个pics中的元素
pics.sort(key=lambda x: int(x.split('.')[0].split('_')[2]))
# pics = sorted(pics, key=lambda pics:int(pics.split('.')[0].split('_')[2]))
count = 0
for tmp_index in tqdm(range(0, len(pics), interval)):
# videos = ['turtlebot2_7.mp4', 'turtlebot3_7.mp4']
videos = [
'two_small_1.mp4', 'two_small_2.mp4', 'two_small_3.mp4',
'two_small_4.mp4'
]
for each_video in videos:
print('start process {}'.format(each_video))
each_video_name, _ = each_video.split('.')
# get the dir of source files and target saving folder
each_video_file = os.path.join(source_dir, each_video)
each_video_frame_folder_dir = os.path.join(output_dir, each_video_name)
if not os.path.exists(each_video_frame_folder_dir):
utils.build_dir(each_video_frame_folder_dir)
else:
continue
cap = cv2.VideoCapture(each_video_file)
frame_count = 0
success = True
while (success):
success, frame = cap.read()
params = [1, 100
] # 1 means save jpeg, 100 means the qulity of picture
cv2.imwrite(
os.path.join(each_video_frame_folder_dir,
each_video_name + "_{}.jpg".format(frame_count)),
frame, params)
frame_count += 1