}
pylab.rcParams['figure.figsize'] = (8.0, 10.0)
show_maskobb = False
anno_file_name = ['small', 'dota-v1.5']
img_dir = './data/{}/{}/images'.format(anno_file_name[0],
anno_file_name[1])
anno_dir = './data/{}/{}/labels'.format(anno_file_name[0],
anno_file_name[1])
for anno_name in os.listdir(anno_dir):
file_name = os.path.splitext(os.path.basename(anno_name))[0]
anno_file = os.path.join(anno_dir, anno_name)
objects = wwtool.simpletxt_parse(anno_file)
image_name = os.path.join(img_dir, file_name + '.png')
im = cv2.imread(image_name)
bboxes = []
labels = []
for obj in objects:
bbox = obj['bbox']
label = obj['label']
bboxes.append(bbox)
labels.append({label: coco_class[label]})
wwtool.imshow_bboxes(im,
bboxes,
labels=labels,
import os
import wwtool
label_fold = './data/stanford_campus/v1/trainval_test/labels'
image_fold = './data/stanford_campus/v1/trainval_test/images'
for label_name in sorted(os.listdir(label_fold)):
print(label_name)
label_file = os.path.join(label_fold, label_name)
image_file = os.path.join(image_fold, label_name.split('.')[0] + '.png')
objects = wwtool.simpletxt_parse(label_file)
bbox, label = [single_object['bbox'] for single_object in objects
], [single_object['label'] for single_object in objects]
wwtool.imshow_bboxes(image_file, bbox)
if __name__ == "__main__":
image_format = '.png'
origin_image_path = './data/visdrone/v1/trainval/images'
origin_label_path = './data/visdrone/v1/trainval/labels'
filtered_image_path = './data/visdrone/v1/trainval_filtered/images'
filtered_label_path = './data/visdrone/v1/trainval_filtered/labels'
wwtool.mkdir_or_exist(filtered_image_path)
wwtool.mkdir_or_exist(filtered_label_path)
filter_count = 1
progress_bar = mmcv.ProgressBar(len(os.listdir(origin_label_path)))
for label_name in os.listdir(origin_label_path):
image_objects = wwtool.simpletxt_parse(
os.path.join(origin_label_path, label_name))
filtered_objects = []
for image_object in image_objects:
if convert_classes[origin_class[image_object['label']]] == None:
filter_count += 1
continue
else:
image_object['label'] = convert_classes[origin_class[
image_object['label']]]
filtered_objects.append(image_object)
if len(filtered_objects) > 0:
img = cv2.imread(
os.path.join(origin_image_path,
os.path.splitext(label_name)[0] + image_format))
save_image_file = os.path.join(