def generate(demo_dir, list_dir, savename, check):
imagelist, num_image = load_list_from_folders(demo_dir, ext_filter=['png'], depth=1)
assert num_image == check, 'The number of images is not right vs. {:}'.format(num_image)
if not osp.isdir(list_dir): os.makedirs(list_dir)
gap, x1, y1, x2, y2 = 5, 5, 5, 450, 680
imagelist.sort()
#DemoData = OrderedDict()
DemoData = []
for idx, image in enumerate(imagelist):
if idx < 2 or idx + 2 >= len(imagelist): continue
data = {'points' : None,
'box' : [gap, gap, x2-x1-gap, y2-y1-gap],
'box-DET': [gap, gap, x2-x1-gap, y2-y1-gap],
'box-default': [gap, gap, x2-x1-gap, y2-y1-gap],
'normalizeL-default': math.sqrt( (x2-x1-2*gap) * (y2-y1-2*gap) ),
'name' : 'demo'}
data['previous_frame'] = imagelist[idx-1]
data['current_frame'] = imagelist[idx]
data['next_frame'] = imagelist[idx+1]
DemoData.append( data )
savepath = osp.join(list_dir, savename)
torch.save(DemoData, savepath)
print('there are {:} images for the demo video sequence, and save them into {:}.'.format(num_image, savepath))
def generate_300w_list(root, save_dir, box_data, SUFFIX):
assert osp.isdir(root), '{} is not dir'.format(root)
#assert osp.isdir(save_dir), '{} is not dir'.format(save_dir)
if not osp.isdir(save_dir): os.makedirs(save_dir)
train_length, common_length, challenge_length = 3148, 554, 135
subsets = ['afw', 'helen', 'ibug', 'lfpw']
dir_lists = [osp.join(root, subset) for subset in subsets]
imagelist, num_image = load_list_from_folders(dir_lists, ext_filter=['png', 'jpg', 'jpeg'], depth=3)
train_set, common_set, challenge_set = [], [], []
for image_path in imagelist:
name, ext = osp.splitext(image_path)
anno_path = name + '.pts'
assert osp.isfile(anno_path), 'annotation for : {} does not exist'.format(image_path)
if name.find('ibug') > 0:
challenge_set.append( (image_path, anno_path) )
elif name.find('afw') > 0:
train_set.append( (image_path, anno_path) )
elif name.find('helen') > 0 or name.find('lfpw') > 0:
if name.find('trainset') > 0:
train_set.append( (image_path, anno_path) )
elif name.find('testset') > 0:
common_set.append( (image_path, anno_path) )
else:
raise Exception('Unknow name : {}'.format(name))
else:
raise Exception('Unknow name : {}'.format(name))
assert len(train_set) == train_length, 'The length is not right for train : {} vs {}'.format(len(train_set), train_length)
assert len(common_set) == common_length, 'The length is not right for common : {} vs {}'.format(len(common_set), common_length)
assert len(challenge_set) == challenge_length, 'The length is not right for challeng : {} vs {}'.format(len(common_set), common_length)
with open(osp.join(save_dir, '300w.train.' + SUFFIX), 'w') as txtfile:
for cpair in train_set:
#box_str = datasets.dataset_utils.for_generate_box_str(cpair[1], 68, EXPAND_RATIO)
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
with open(osp.join(save_dir, '300w.test.common.' + SUFFIX), 'w') as txtfile:
for cpair in common_set:
#box_str = datasets.dataset_utils.for_generate_box_str(cpair[1], 68, EXPAND_RATIO)
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
with open(osp.join(save_dir, '300w.test.challenge.' + SUFFIX), 'w') as txtfile:
for cpair in challenge_set:
#box_str = datasets.dataset_utils.for_generate_box_str(cpair[1], 68, EXPAND_RATIO)
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
with open(osp.join(save_dir, '300w.test.full.' + SUFFIX), 'w') as txtfile:
fullset = common_set + challenge_set
for cpair in fullset:
#box_str = datasets.dataset_utils.for_generate_box_str(cpair[1], 68, EXPAND_RATIO)
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
def make_300W_train_list(root, box_data, output_file_name):
subsets = ['afw', 'helen', 'ibug', 'lfpw']
dir_lists = [osp.join(root, subset) for subset in subsets]
imagelist, _ = load_list_from_folders(dir_lists, ext_filter=['png', 'jpg', 'jpeg'], depth=3)
with open(output_file_name, 'w') as txtfile:
for image_path in imagelist:
basename, _ = osp.splitext(image_path)
anno_path = basename + '.pts'
box_str = return_box(image_path, anno_path, box_data)
txtfile.write('{} {} {}\n'.format(image_path, anno_path, box_str))
def generate(demo_dir, list_dir, savename, check):
imagelist, num_image = load_list_from_folders(demo_dir,
ext_filter=['png'],
depth=1)
assert num_image == check, 'The number of images is not right vs. {:}'.format(
num_image)
if not osp.isdir(list_dir): os.makedirs(list_dir)
gap, x1, y1, x2, y2 = 5, 5, 5, 450, 680
imagelist.sort()
txtfile = open(osp.join(list_dir, savename), 'w')
for idx, image in enumerate(imagelist):
if idx < 2 or idx + 2 >= len(imagelist): continue
box_str = '{:.1f} {:.1f} {:.1f} {:.1f}'.format(gap, gap, x2 - x1 - gap,
y2 - y1 - gap)
txtfile.write('{:} {:} {:}\n'.format(image, 'none', box_str))
txtfile.flush()
txtfile.close()
print('there are {:} images for the demo video sequence'.format(num_image))
def generate_300w_list(root, save_dir, box_data, SUFFIXS):
assert osp.isdir(root), '{} is not dir'.format(root)
#assert osp.isdir(save_dir), '{} is not dir'.format(save_dir)
if not osp.isdir(save_dir): os.makedirs(save_dir)
train_length, common_length, challenge_length = 3148, 554, 135
subsets = ['afw', 'helen', 'ibug', 'lfpw']
dir_lists = [osp.join(root, subset) for subset in subsets]
imagelist, num_image = load_list_from_folders(dir_lists, ext_filter=['png', 'jpg', 'jpeg'], depth=3)
train_set, common_set, challenge_set = [], [], []
for image_path in imagelist:
name, ext = osp.splitext(image_path)
anno_path = name + '.pts'
assert osp.isfile(anno_path), 'annotation for : {} does not exist'.format(image_path)
if name.find('ibug') > 0:
challenge_set.append( (image_path, anno_path) )
elif name.find('afw') > 0:
train_set.append( (image_path, anno_path) )
elif name.find('helen') > 0 or name.find('lfpw') > 0:
if name.find('trainset') > 0:
train_set.append( (image_path, anno_path) )
elif name.find('testset') > 0:
common_set.append( (image_path, anno_path) )
else:
raise Exception('Unknow name : {}'.format(name))
else:
raise Exception('Unknow name : {}'.format(name))
assert len(train_set) == train_length, 'The length is not right for train : {} vs {}'.format(len(train_set), train_length)
assert len(common_set) == common_length, 'The length is not right for common : {} vs {}'.format(len(common_set), common_length)
assert len(challenge_set) == challenge_length, 'The length is not right for challeng : {} vs {}'.format(len(common_set), common_length)
mean_landmark = {SUFFIX : [[]for i in range(68)] for SUFFIX in SUFFIXS}
trainData = []#OrderedDict()
for cpair in train_set:
landmarks = datasets.dataset_utils.anno_parser(cpair[1], 68)
data = {'points': landmarks[0],
'name' : get_name(cpair[0])}
for SUFFIX in SUFFIXS:
box = return_box(cpair[0], cpair[1], box_data, SUFFIX)
data['box-{:}'.format(SUFFIX)] = box
for idx in range(68):
if int(landmarks[0][2, idx] + 0.5) == 0: continue
x, y = float(landmarks[0][0,idx]-box[0]), float(landmarks[0][1,idx]-box[1])
x, y = normalize_L(x, box[2]-box[0]), normalize_L(y, box[3]-box[1])
#x, y = x / (box[2]-box[0]), y / (box[3]-box[1])
mean_landmark[SUFFIX][idx].append( (x,y) )
data['box-default'] = data['box-GTB']
data['previous_frame'] = None
data['current_frame'] = cpair[0]
data['next_frame'] = None
trainData.append( data )
torch.save(trainData, osp.join(save_dir, '300w.train.pth'))
for SUFFIX in SUFFIXS:
allp = mean_landmark[SUFFIX]
allp = np.array(allp)
mean_landmark[SUFFIX] = np.mean(allp, axis=1)
mean_landmark[SUFFIX] = mean_landmark[SUFFIX] * 0.9
image = draw_points(mean_landmark[SUFFIX], 600, 500, True)
image.save(osp.join(save_dir, '300w.train-{:}.png'.format(SUFFIX)))
mean_landmark['default'] = mean_landmark['DET']
torch.save(mean_landmark, osp.join(save_dir, '300w.train-mean.pth'))
print ('Training Set : {:5d} facial images.'.format(len(trainData)))
commonData = []#OrderedDict()
for cpair in common_set:
landmarks = datasets.dataset_utils.anno_parser(cpair[1], 68)
data = {'points': landmarks[0]}
for SUFFIX in SUFFIXS:
box = return_box(cpair[0], cpair[1], box_data, SUFFIX)
data['box-{:}'.format(SUFFIX)] = box
data['box-default'] = data['box-GTB']
data['previous_frame'] = None
data['current_frame'] = cpair[0]
data['next_frame'] = None
commonData.append( data )
#commonData[cpair[0]] = data
torch.save(commonData, osp.join(save_dir, '300w.test-common.pth'))
print ('Common-Test : {:5d} facial images.'.format(len(commonData)))
challengeData = [] #OrderedDict()
for cpair in challenge_set:
landmarks = datasets.dataset_utils.anno_parser(cpair[1], 68)
data = {'points': landmarks[0]}
for SUFFIX in SUFFIXS:
box = return_box(cpair[0], cpair[1], box_data, SUFFIX)
data['box-{:}'.format(SUFFIX)] = box
data['box-default'] = data['box-GTB']
data['previous_frame'] = None
data['current_frame'] = cpair[0]
data['next_frame'] = None
challengeData.append( data )
torch.save(challengeData, osp.join(save_dir, '300w.test-challenge.pth'))
print ('Challenge-Test : {:5d} facial images.'.format(len(challengeData)))
fullset = copy.deepcopy(commonData) + copy.deepcopy(challengeData)
#fullset.update( challengeData )
torch.save(fullset, osp.join(save_dir, '300w.test-full.pth'))
print ('Full-Test : {:5d} facial images.'.format(len(fullset)))
print ('Save all dataset files into {:}'.format(save_dir))
"""
def generage_300w_list(root, save_dir, box_data, SUFFIX):
assert osp.isdir(root), '{} is not dir'.format(root)
if not osp.isdir(save_dir): os.makedirs(save_dir)
train_length, common_length, challeng_length = 3148, 554, 135
subsets = ['afw', 'helen', 'ibug', 'lfpw']
dir_lists = [osp.join(root, subset) for subset in subsets]
imagelist, num_image = load_list_from_folders(
dir_lists, ext_filter=['png', 'jpg', 'jpeg'], depth=3)
indoor, indoor_num = load_list_from_folders(
[osp.join(root, '300W', '01_Indoor')],
ext_filter=['png', 'jpg', 'jpeg'],
depth=3)
otdoor, otdoor_num = load_list_from_folders(
[osp.join(root, '300W', '02_Outdoor')],
ext_filter=['png', 'jpg', 'jpeg'],
depth=3)
assert indoor_num == 300 and otdoor_num == 300, 'The number of images are not right for 300-W-IO: {} & {}'.format(
indoor_num, otdoor_num)
train_set, common_set, challeng_set = [], [], []
for image_path in imagelist:
name, ext = osp.splitext(image_path)
anno_path = name + '.pts'
assert osp.isfile(
anno_path), 'annotation {} for : {} does not exist'.format(
image_path, anno_path)
if name.find('ibug') > 0:
challeng_set.append((image_path, anno_path))
elif name.find('afw') > 0:
train_set.append((image_path, anno_path))
elif name.find('helen') > 0 or name.find('lfpw') > 0:
if name.find('trainset') > 0:
train_set.append((image_path, anno_path))
elif name.find('testset') > 0:
common_set.append((image_path, anno_path))
else:
raise Exception('Unknow name : {}'.format(name))
else:
raise Exception('Unknow name : {}'.format(name))
assert len(
train_set
) == train_length, 'The length is not right for train : {} vs {}'.format(
len(train_set), train_length)
assert len(
common_set
) == common_length, 'The length is not right for common : {} vs {}'.format(
len(common_set), common_length)
assert len(
challeng_set
) == challeng_length, 'The length is not right for challeng : {} vs {}'.format(
len(common_set), common_length)
all_lines = []
with open(osp.join(save_dir, '300w.train.' + SUFFIX), 'w') as txtfile:
for cpair in train_set:
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
all_lines.append('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
with open(osp.join(save_dir, '300w.test.common.' + SUFFIX),
'w') as txtfile:
for cpair in common_set:
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
all_lines.append('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
with open(osp.join(save_dir, '300w.test.challenge.' + SUFFIX),
'w') as txtfile:
for cpair in challeng_set:
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
all_lines.append('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
with open(osp.join(save_dir, '300w.test.full.' + SUFFIX), 'w') as txtfile:
for cpair in common_set:
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
all_lines.append('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
for cpair in challeng_set:
box_str = return_box(cpair[0], cpair[1], box_data, SUFFIX)
txtfile.write('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
all_lines.append('{} {} {}\n'.format(cpair[0], cpair[1], box_str))
txtfile.close()
with open(osp.join(save_dir, '300w.all.' + SUFFIX), 'w') as txtfile:
for line in all_lines:
txtfile.write('{}'.format(line))
txtfile.close()
