code stringlengths 3 6.57k |
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print(image_path) |
os.path.isfile(image_path) |
print(f'Cannot find image {file_name} according to annotations.') |
root_node.iter('object') |
obj.find('name') |
print(f'Label "{cls_name}" not in "{config.classes}"') |
obj.find('bndbox') |
int(bnd_box.find('xmin') |
int(bnd_box.find('ymin') |
int(bnd_box.find('xmax') |
int(bnd_box.find('ymax') |
labels.append([y_min, x_min, y_max, x_max, cls_map[cls_name]]) |
abs(x_max - x_min) |
abs(y_max - y_min) |
append(ann) |
images.append(img_id) |
np.array(labels) |
root_node.find("size") |
int(size.find('width') |
int(size.find('height') |
append(image) |
cls_map.items() |
append(cat) |
open(json_file, 'w') |
json.dumps(json_dict) |
json_fp.write(json_str) |
json_fp.close() |
create_coco_label(is_training) |
enumerate(train_cls) |
os.path.join(coco_root, config.instances_set.format(data_type) |
COCO(anno_json) |
coco.loadCats(coco.getCatIds() |
coco.getImgIds() |
coco.loadImgs(img_id) |
coco.getAnnIds(imgIds=img_id, iscrowd=None) |
coco.loadAnns(anno_ids) |
os.path.join(coco_root, data_type, file_name) |
annos.append(list(map(round, [y_min, x_min, y_max, x_max]) |
len(annos) |
images.append(img_id) |
np.array(annos) |
anno_parser(annos_str) |
list(map(int, anno_str.strip() |
split(',') |
annos.append(anno) |
filter_valid_data(image_dir, anno_path) |
os.path.isdir(image_dir) |
RuntimeError("Path given is not valid.") |
os.path.isfile(anno_path) |
RuntimeError("Annotation file is not valid.") |
open(anno_path, "rb") |
f.readlines() |
enumerate(lines) |
line.decode("utf-8") |
strip() |
str(line_str) |
split(' ') |
os.path.join(image_dir, file_name) |
os.path.isfile(image_path) |
images.append(img_id) |
anno_parser(line_split[1:]) |
voc_data_to_mindrecord(mindrecord_dir, is_training, prefix="ssd.mindrecord", file_num=8) |
os.path.join(mindrecord_dir, prefix) |
FileWriter(mindrecord_path, file_num) |
create_voc_label(is_training) |
writer.add_schema(ssd_json, "ssd_json") |
open(image_path, 'rb') |
f.read() |
np.array(image_anno_dict[img_id], dtype=np.int32) |
np.array([img_id], dtype=np.int32) |
writer.write_raw_data([row]) |
writer.commit() |
data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="ssd.mindrecord", file_num=8) |
os.path.join(mindrecord_dir, prefix) |
FileWriter(mindrecord_path, file_num) |
create_coco_label(is_training) |
filter_valid_data(config.image_dir, config.anno_path) |
writer.add_schema(ssd_json, "ssd_json") |
open(image_path, 'rb') |
f.read() |
np.array(image_anno_dict[img_id], dtype=np.int32) |
np.array([img_id], dtype=np.int32) |
writer.write_raw_data([row]) |
writer.commit() |
C.Decode() |
ds.map(operations=decode, input_columns=["image"]) |
C.HWC2CHW() |
C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4) |
preprocess_fn(img_id, image, annotation, is_training) |
ds.batch(batch_size, drop_remainder=True) |
ds.repeat(repeat_num) |
create_mindrecord(dataset="coco", prefix="ssd.mindrecord", is_training=True) |
print("Start create dataset!") |
os.path.join(mindrecord_dir, prefix + "0") |
os.path.exists(mindrecord_file) |
os.path.isdir(mindrecord_dir) |
os.makedirs(mindrecord_dir) |
os.path.isdir(config.coco_root) |
print("Create Mindrecord.") |
data_to_mindrecord_byte_image("coco", is_training, prefix) |
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