def load_annotations_mat(self):
"""
Load ILSVRC2012 annotations (.mat file).
"""
# load annotation file
filename = self.get_file_path('meta.mat')
return load_matlab(filename)
def load_annotations(self):
"""
Load annotations from file and split them to train and test sets.
"""
annot_filepath, images_dir = self.fetch_annotations_images_paths()
# load annotations file
annotations = load_matlab(annot_filepath)
data = {
"train": [],
"test": []
}
image_filenames = os.listdir(images_dir)
image_filenames.sort()
for i in range(0, 2000):
if i >= 1000:
set_name = 'test'
else:
set_name = 'train'
filename = os.path.join(self.data_path, 'images', image_filenames[i])
joints = []
for j in range(0, 14):
joints.append([annotations['joints'][0][j][i], # x
annotations['joints'][1][j][i], # y
annotations['joints'][2][j][i]]) # is_visible (0 - visible,
# 1 - hidden)
data[set_name].append({"filename": filename, "joints": joints})
return data
def load_annotations(self):
"""
Load annotations from file and split them to train and test sets.
"""
annot_filepath = os.path.join(self.data_path, 'FLIC', 'examples.mat')
# load annotations file
annotations = load_matlab(annot_filepath)
data = {
"train": [],
"test": []
}
for i, annot in enumerate(annotations['examples'][0]):
if annot[-1][0][0] == 0:
set_name = 'train'
else:
set_name = 'test'
width, height, _ = annot[4][0].tolist()
moviename = annot[1][0]
filename = os.path.join(self.data_path, 'FLIC', 'images', annot[3][0])
torso_box = annot[6][0].squeeze().tolist(), # [x1,y1,x2,y2]
parts = [
# [x, y, is_visible]
[annot[2][0][0], annot[2][1][0], 1], # -- 1, Left_Shoulder
[annot[2][0][1], annot[2][1][1], 1], # -- 2, Left_Elbow
[annot[2][0][2], annot[2][1][2], 1], # -- 3, Left_Wrist
[annot[2][0][3], annot[2][1][3], 1], # -- 4, Right_Shoulder
[annot[2][0][4], annot[2][1][4], 1], # -- 5, Right_Elbow
[annot[2][0][5], annot[2][1][5], 1], # -- 6, Right_Wrist
[annot[2][0][6], annot[2][1][6], 1], # -- 7, Left_Hip
[annot[2][0][9], annot[2][1][9], 1], # -- 8, Right_Hip
[annot[2][0][12], annot[2][1][12], 1], # -- 9, Left_Eye
[annot[2][0][13], annot[2][1][13], 1], # -- 10, Right_Eye
[annot[2][0][16], annot[2][1][16], 1] # -- 11, Nose
]
data[set_name].append({
"filename": filename,
"width": width,
"height": height,
"moviename": moviename,
"torso_box": torso_box,
"parts": parts
})
return data
def load_annotations(self):
"""
Load annotations from file and split them to train and test sets.
"""
annot_filepath_lsp = os.path.join(self.data_path, 'lsp_dataset', 'joints.mat')
annot_filepath_lspe = os.path.join(self.data_path, 'joints.mat')
# load annotations file
annotations_lsp = load_matlab(annot_filepath_lsp)
annotations_lspe = load_matlab(annot_filepath_lspe)
data = {
"train": [],
"test": []
}
# ---------------------------
# add lsp train + test data
# ---------------------------
image_filenames_lsp = os.listdir(os.path.join(self.data_path, 'lsp_dataset', 'images'))
image_filenames_lsp.sort()
for i in range(0, 2000):
if i >= 1000:
set_name = 'test'
else:
set_name = 'train'
filename = os.path.join(self.data_path, 'lsp_dataset',
'images', image_filenames_lsp[i])
joints = []
for j in range(0, 14):
# is_visible => (0 - visible, 1 - hidden)
joints.append([annotations_lsp['joints'][0][j][i], # x
annotations_lsp['joints'][1][j][i], # y
annotations_lsp['joints'][2][j][i]]) # is_visible
data[set_name].append({"filename": filename, "joints": joints})
# -----------------------
# add lspe train data
# -----------------------
image_filenames_lspe = os.listdir(os.path.join(self.data_path, 'images'))
image_filenames_lspe.sort()
set_name = 'train'
for i in range(0, 10000):
filename = os.path.join(self.data_path, 'images', image_filenames_lspe[i])
joints = []
for j in range(0, 14):
# is_visible => (0 - visible, 1 - hidden)
joints.append([annotations_lspe['joints'][j][0][i], # x
annotations_lspe['joints'][j][1][i], # y
annotations_lspe['joints'][j][2][i]]) # is_visible
data[set_name].append({"filename": filename, "joints": joints})
return data
def load_annotations(self):
"""
Load annotations from file and split them to train and test sets.
"""
annot_filepath = os.path.join(self.data_path,
'mpii_human_pose_v1_u12_2',
'mpii_human_pose_v1_u12_1.mat')
if self.verbose:
print('\n> Loading annotations file: {}'.format(annot_filepath))
# load annotations file
annotations = load_matlab(annot_filepath)
# total number of files
nfiles = len(annotations["RELEASE"][0][0][3])
# progressbar
if self.verbose:
print('\n> Parsing data from the annotations...')
prgbar = progressbar.ProgressBar(max_value=nfiles)
data = {"train": [], "test": []}
# cycle all files
for ifile in range(nfiles):
if annotations['RELEASE'][0][0][1][0][ifile] == 0:
set_name = 'test'
else:
set_name = 'train'
# single person
single_person = [0]
if any(annotations['RELEASE'][0][0][3][ifile][0]):
for i in range(len(annotations['RELEASE'][0][0][3][ifile][0])):
single_person.append(
int(annotations['RELEASE'][0][0][3][ifile][0][i][0]))
# activity/action id
act = {"cat_name": '', "act_name": '', "act_id": -1}
if any(annotations['RELEASE'][0][0][4][ifile][0][0]):
act = {
"cat_name":
str(annotations['RELEASE'][0][0][4][ifile][0][0][0]),
"act_name":
str(annotations['RELEASE'][0][0][4][ifile][0][1][0]),
"act_id":
int(annotations['RELEASE'][0][0][4][ifile][0][2][0][0])
}
# image annots
image_filename = os.path.join(
self.data_path, 'images',
str(annotations['RELEASE'][0][0][0][0][ifile][0][0][0][0][0]))
if any(annotations['RELEASE'][0][0][0][0][ifile][3][0]):
frame_sec = int(
annotations['RELEASE'][0][0][0][0][ifile][2][0][0])
video_idx = int(
annotations['RELEASE'][0][0][0][0][ifile][3][0][0])
else:
frame_sec, video_idx = -1, -1
# properties field names ('scale', 'objpos', ...)
try:
pnames = annotations['RELEASE'][0][0][0][0][ifile][1][
0].dtype.names
except IndexError:
data[set_name].append({
"image_filename": image_filename,
"frame_sec": frame_sec,
"video_idx": video_idx,
"poses_annotations": [],
"activity": act,
"single_person": single_person
})
continue # skip rest
# parse keypoints
poses_annots = []
if any(annotations['RELEASE'][0][0][0][0][ifile][3][0]):
for i in range(
len(annotations['RELEASE'][0][0][0][0][ifile][1][0])):
try:
keypoints = [[0, 0, 0]] * 16 # [x, y, is_visible]
annot = annotations['RELEASE'][0][0][0][0][ifile][1][
0][i][4][0][0][0][0]
vnames = annot.dtype.names
for j in range(len(annot)):
x = float(annot[j][vnames.index('x')][0][0])
y = float(annot[j][vnames.index('y')][0][0])
idx = int(annot[j][vnames.index('id')][0][0])
try:
is_visible = int(
annot[j][vnames.index('is_visible')][0])
except (ValueError, IndexError):
is_visible = -1
try:
keypoints[idx] = [x, y, is_visible]
except IndexError as k:
if set_name == 'test' or self.is_full:
print('Error: ', str(k))
keypoints[idx] = [0, 0, 0]
else:
continue # skip this annotation
except (AttributeError, IndexError):
keypoints = [[0, 0, 0]] * 16 # [x, y, is_visible]
try:
x1 = float(annotations['RELEASE'][0][0][0][0][ifile][1]
[0][i][pnames.index('x1')][0][0])
y1 = float(annotations['RELEASE'][0][0][0][0][ifile][1]
[0][i][pnames.index('y1')][0][0])
x2 = float(annotations['RELEASE'][0][0][0][0][ifile][1]
[0][i][pnames.index('x2')][0][0])
y2 = float(annotations['RELEASE'][0][0][0][0][ifile][1]
[0][i][pnames.index('y2')][0][0])
except ValueError:
if set_name == 'test' or self.is_full:
x1, y1, x2, y2 = -1, -1, -1, -1
else:
continue # skip this annotation
try:
annot_ptr = annotations['RELEASE'][0][0][0][0][ifile][
1][0]
objnames = annot_ptr[i][pnames.index(
'objpos')][0].dtype.names
# objnames = annotations['RELEASE'][0][0][0][0][ifile][1][0][i]
# [pnames.index('objpos')][0].dtype.names
scale = float(annotations['RELEASE'][0][0][0][0][ifile]
[1][0][i][pnames.index('scale')][0][0])
objpos = {
"x":
float(annotations['RELEASE'][0][0][0][0][ifile][1]
[0][i][pnames.index('objpos')][0][0][
objnames.index('x')][0][0]),
"y":
float(annotations['RELEASE'][0][0][0][0][ifile][1]
[0][i][pnames.index('objpos')][0][0][
objnames.index('y')][0][0])
}
except (ValueError, IndexError):
if set_name == 'test' or self.is_full:
scale = -1
objpos = {"x": -1, "y": -1}
else:
continue # skip this annotation
poses_annots.append({
"x1": x1,
"y1": y1,
"x2": x2,
"y2": y2,
"keypoints": keypoints,
"scale": scale,
"objpos": objpos
})
else:
if set_name == 'test' or self.is_full:
for i in range(
len(annotations['RELEASE'][0][0][0][0][ifile][1]
[0])):
try:
annot_ptr = annotations['RELEASE'][0][0][0][0][
ifile][1][0]
objnames = annot_ptr[i][pnames.index(
'objpos')][0].dtype.names
# objnames = annotations['RELEASE'][0][0][0][0][ifile][1][0][i]
# [pnames.index('objpos')][0].dtype.names
scale = float(
annotations['RELEASE'][0][0][0][0][ifile][1][0]
[i][pnames.index('scale')][0][0])
objpos = {
"x":
float(annotations['RELEASE'][0][0][0][0][ifile]
[1][0][i][pnames.index('objpos')][0][0][
objnames.index('x')][0][0]),
"y":
float(annotations['RELEASE'][0][0][0][0][ifile]
[1][0][i][pnames.index('objpos')][0][0][
objnames.index('y')][0][0])
}
except (IndexError, ValueError, AttributeError):
scale = -1
objpos = {"x": -1, "y": -1}
poses_annots.append({"scale": scale, "objpos": objpos})
else:
continue # skip this annotation
# add fields to data
data[set_name].append({
"image_filename": image_filename,
"frame_sec": frame_sec,
"video_idx": video_idx,
"poses_annotations": poses_annots,
"activity": act,
"single_person": single_person
})
# update progressbar
if self.verbose:
prgbar.update(ifile)
# update progressbar
if self.verbose:
prgbar.finish()
# fetch video ids
videonames = []
for ivideo in range(len(annotations['RELEASE'][0][0][5][0])):
videonames.append(
str(annotations['RELEASE'][0][0][5][0][ivideo][0]))
return data, videonames
def load_file(self, filename):
"""Loads the data of the annotation file."""
return load_matlab(filename)