def read_dataset(dataset):
'''data format:
data = <
dataset: 'otb'/'vot2013'/'vot2014'/'vot2015'
data: {'seq': <gts: np.array, frames: [string]>}
>
'''
# create the container
data = Data()
data.dataset = dataset
# find the current path
cur_dir = os.path.dirname(os.path.realpath(__file__))
if dataset[:3] == 'vot':
# find sequences
dir_path = os.path.join(cur_dir, 'data', 'vot', dataset)
seqs = [path for path in os.listdir(dir_path) \
if os.path.isdir(os.path.join(dir_path,path))]
# load sequences
data.data = {}
for seq in seqs:
print('loading...',dataset,seq)
gt_path = os.path.join(dir_path,seq,'groundtruth.txt')
gt = pd.read_csv(gt_path, header=None).as_matrix()
if gt.shape[1] >= 6:
x = np.amin(gt[:, ::2], axis=1)
y = np.amin(gt[:, 1::2], axis=1)
width = np.amax(gt[:, ::2], axis=1) - x
height = np.amax(gt[:, 1::2], axis=1) - y
gt = np.c_[x, y, width, height]
data.data[seq] = Data()
data.data[seq].gts = gt
data.data[seq].frames = [os.path.join(dir_path, seq, '%08d.jpg'%(i+1)) for i in range(gt.shape[0])]
elif dataset[:3] == 'otb':
# find sequences
dir_path = os.path.join(cur_dir, 'data', 'otb')
seqs = [path for path in os.listdir(dir_path) \
if os.path.isdir(os.path.join(dir_path,path)) \
and path != 'Jogging' and path != 'Skating2']
seqs = seqs + ['Jogging-1', 'Jogging-2', 'Skating2-1', 'Skating2-2']
# load sequences
data.data = {}
for seq in seqs:
print('loading...',dataset,seq)
if seq == 'Jogging-1' or seq == 'Jogging-2' or seq == 'Skating2-1' or seq == 'Skating2-2':
gt_path = os.path.join(dir_path,seq[:-2],'groundtruth_rect.'+seq[-1]+'.txt')
img_dir_path = os.path.join(dir_path,seq[:-2],'img')
elif seq == 'Human4':
gt_path = os.path.join(dir_path,seq,'groundtruth_rect.2.txt')
img_dir_path = os.path.join(dir_path,seq,'img')
else:
gt_path = os.path.join(dir_path,seq,'groundtruth_rect.txt')
img_dir_path = os.path.join(dir_path,seq,'img')
gt = pd.read_csv(gt_path, header=None, sep='[\s*\,*]+').as_matrix()
data.data[seq] = Data()
data.data[seq].gts = gt
if seq == 'Board':
data.data[seq].frames = [os.path.join(img_dir_path, '%05d.jpg'%(i+1)) for i in range(gt.shape[0])]
elif seq == 'BlurCar1':
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+247)) for i in range(gt.shape[0])]
elif seq == 'BlurCar3':
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+3)) for i in range(gt.shape[0])]
elif seq == 'BlurCar4':
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+18)) for i in range(gt.shape[0])]
else:
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+1)) for i in range(gt.shape[0])]
for i in range(len(data.data[seq].frames)):
if gt[i][2] < 1 or gt[i][3] < 1:
print(str(i))
exit(0)
# test for the existence of the first frame
print('testing...',dataset,seq)
io.imread(data.data[seq].frames[0])
return data
def read_seq(dataset, seq):
# create the container
data = Data()
data.dataset = dataset
# find the current path
cur_dir = os.path.dirname(os.path.realpath(__file__))
print('loading...',dataset,seq)
if dataset[:3] == 'vot':
dir_path = os.path.join(cur_dir, 'data', 'vot', dataset)
data.data = {}
gt_path = os.path.join(dir_path,seq,'groundtruth.txt')
gt = pd.read_csv(gt_path, header=None).as_matrix()
if gt.shape[1] >= 6:
x = np.amin(gt[:, ::2], axis=1)
y = np.amin(gt[:, 1::2], axis=1)
width = np.amax(gt[:, ::2], axis=1) - x
height = np.amax(gt[:, 1::2], axis=1) - y
gt = np.c_[x, y, width, height]
data.data[seq] = Data()
data.data[seq].gts = gt
data.data[seq].frames = [os.path.join(dir_path, seq, '%08d.jpg'%(i+1)) for i in range(gt.shape[0])]
elif dataset[:3] == 'otb':
dir_path = os.path.join(cur_dir, 'data', 'otb')
data.data = {}
if seq == 'Jogging-1' or seq == 'Jogging-2' or seq == 'Skating2-1' or seq == 'Skating2-2':
gt_path = os.path.join(dir_path,seq[:-2],'groundtruth_rect.'+seq[-1]+'.txt')
img_dir_path = os.path.join(dir_path,seq[:-2],'img')
elif seq == 'Human4':
gt_path = os.path.join(dir_path,seq,'groundtruth_rect.2.txt')
img_dir_path = os.path.join(dir_path,seq,'img')
else:
gt_path = os.path.join(dir_path,seq,'groundtruth_rect.txt')
img_dir_path = os.path.join(dir_path,seq,'img')
gt = pd.read_csv(gt_path, header=None, sep='[\s*\,*]+').as_matrix()
data.data[seq] = Data()
data.data[seq].gts = gt
if seq == 'Board':
data.data[seq].frames = [os.path.join(img_dir_path, '%05d.jpg'%(i+1)) for i in range(gt.shape[0])]
elif seq == 'BlurCar1':
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+247)) for i in range(gt.shape[0])]
elif seq == 'BlurCar3':
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+3)) for i in range(gt.shape[0])]
elif seq == 'BlurCar4':
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+18)) for i in range(gt.shape[0])]
else:
data.data[seq].frames = [os.path.join(img_dir_path, '%04d.jpg'%(i+1)) for i in range(gt.shape[0])]
for i in range(len(data.data[seq].frames)):
if gt[i][2] < 1 or gt[i][3] < 1:
print(str(i))
exit(0)
# test for the existence of the first frame
print('testing...',dataset,seq)
io.imread(data.data[seq].frames[0])
return data
def read_dataset(dataset):
"""
data format:
data = <
dataset: 'otb'/'vot2013'/'vot2014'/'vot2015'
data: {'seq': <gts: np.array, frames: [string]>}
>
"""
# create the container
data = Data() # 空数据
data.dataset = dataset # data的dataset成员
# find the current path
cur_dir = os.path.dirname(os.path.realpath(__file__)) # 获取当前路径
if dataset[:3] == 'vot': # vot数据集的读入方式
# find sequences
dir_path = os.path.join(cur_dir, 'data', 'vot', dataset) # 获取路径
seqs = [
path for path in os.listdir(dir_path)
if os.path.isdir(os.path.join(dir_path, path))
] # 获取目录下所有视频名字
# load sequences
data.data = {}
for seq in seqs:
print('loading...', dataset, seq)
gt_path = os.path.join(dir_path, seq, 'groundtruth.txt')
gt = pd.read_csv(gt_path,
header=None).as_matrix() # 读入groud truth的文件数据
if gt.shape[1] >= 6:
x = np.amin(gt[:, ::2], axis=1)
y = np.amin(gt[:, 1::2], axis=1)
width = np.amax(gt[:, ::2], axis=1) - x
height = np.amax(gt[:, 1::2], axis=1) - y
gt = np.c_[x, y, width, height] # 获取每帧的真值
data.data[seq] = Data()
data.data[seq].gts = gt # 保存每帧的真值
data.data[seq].frames = [
os.path.join(dir_path, seq, '%08d.jpg' % (i + 1))
for i in range(gt.shape[0])
] # 保存每帧的路径
elif dataset[:3] == 'otb': # otb数据集的读入方式
# find sequences
dir_path = os.path.join(cur_dir, 'data', 'otb') # 保存otb数据集的目录
seqs = [
path for path in os.listdir(dir_path)
if os.path.isdir(os.path.join(dir_path, path))
and path != 'Jogging' and path != 'Skating2'
] # 获取目录下所有视频名字
seqs = seqs + ['Jogging-1', 'Jogging-2', 'Skating2-1', 'Skating2-2'
] # 由于这些视频的真值保存具有特殊性，所以特殊处理
# load sequences
data.data = {}
for seq in seqs:
print('loading...', dataset, seq) # 载入指定数据集的指定视频
if seq == 'Jogging-1' or seq == 'Jogging-2' or seq == 'Skating2-1' or seq == 'Skating2-2': # 特殊视频处理
gt_path = os.path.join(dir_path, seq[:-2],
'groundtruth_rect.' + seq[-1] +
'.txt') # groundtruth单独一份
img_dir_path = os.path.join(dir_path, seq[:-2],
'img') # 图片共享一份
elif seq == 'Human4': # 特殊数据集
gt_path = os.path.join(dir_path, seq, 'groundtruth_rect.2.txt')
img_dir_path = os.path.join(dir_path, seq, 'img')
else:
gt_path = os.path.join(
dir_path, seq,
'groundtruth_rect.txt') # 普遍视频的groundtruth文件格式
img_dir_path = os.path.join(dir_path, seq, 'img') # 普通视频的图片前缀
gt = pd.read_csv(gt_path, header=None,
sep='[\s*\,*]+').as_matrix() # 读入真值
data.data[seq] = Data()
data.data[seq].gts = gt # 保存真值
if seq == 'Board':
data.data[seq].frames = [
os.path.join(img_dir_path, '%05d.jpg' % (i + 1))
for i in range(gt.shape[0])
]
elif seq == 'BlurCar1':
data.data[seq].frames = [
os.path.join(img_dir_path, '%04d.jpg' % (i + 247))
for i in range(gt.shape[0])
]
elif seq == 'BlurCar3':
data.data[seq].frames = [
os.path.join(img_dir_path, '%04d.jpg' % (i + 3))
for i in range(gt.shape[0])
]
elif seq == 'BlurCar4':
data.data[seq].frames = [
os.path.join(img_dir_path, '%04d.jpg' % (i + 18))
for i in range(gt.shape[0])
]
else:
data.data[seq].frames = [
os.path.join(img_dir_path, '%04d.jpg' % (i + 1))
for i in range(gt.shape[0])
]
for i in range(len(data.data[seq].frames)):
if gt[i][2] < 1 or gt[i][3] < 1:
print(str(i))
exit(0)
# test for the existence of the first frame
print('testing...', dataset, seq)
io.imread(data.data[seq].frames[0])
return data
