cleaning directory

Edmond Yoo

2018-09-16 dd461cb457045c779d331459cad880a7f8490dc6

cleaning directory

 __init__.py

 data/test1_yolo_out_py.jpg

 data/test23.jpg

 data/test24.jpg

 data/test25.jpg

 data/test26.jpg

 data/test27.jpg

 setup_train.py

@@ -7,7 +7,7 @@
def main():
    random.seed()
    data_list = []
    for subdir in glob('%s/train/*' % transform_data.data_dir):
    for subdir in glob('%s/train/*_10' % transform_data.data_dir):
        for data in glob(subdir + "/*.jpg"):
            data_list.append(os.path.abspath(data))
    random.shuffle(data_list)
@@ -15,10 +15,10 @@
    test_ratio = 0.1
    test_list = data_list[:int(test_ratio * len(data_list))]
    train_list = data_list[int(test_ratio * len(data_list)):]
    with open('%s/train.txt' % transform_data.darknet_dir, 'w') as train_txt:
    with open('%s/train_10.txt' % transform_data.darknet_dir, 'w') as train_txt:
        for data in train_list:
            train_txt.write(data + '\n')
    with open('%s/test.txt' % transform_data.darknet_dir, 'w') as test_txt:
    with open('%s/test_10.txt' % transform_data.darknet_dir, 'w') as test_txt:
        for data in test_list:
            test_txt.write(data + '\n')
    return

 transform_data.py

@@ -545,15 +545,15 @@

            if i % 3 == 0:
                generator.generate_non_obstructive()
                generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_custom/%s_%d'
                generator.export_training_data(visibility=0.0, out_name='%s/train/non_obstructive_10/%s%d'
                                                                        % (data_dir, out_name, j), aug=seq)
            elif i % 3 == 1:
                generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))
                generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_custom/%s_%d'
                generator.export_training_data(visibility=0.0, out_name='%s/train/horizontal_span_10/%s%d'
                                                                        % (data_dir, out_name, j), aug=seq)
            else:
                generator.generate_vertical_span(theta=random.uniform(-math.pi, math.pi))
                generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_custom/%s_%d'
                generator.export_training_data(visibility=0.0, out_name='%s/train/vertical_span_10/%s%d'
                                                                        % (data_dir, out_name, j), aug=seq)

            #generator.generate_horizontal_span(theta=random.uniform(-math.pi, math.pi))