本文整理汇总了Python中dlib.net方法的典型用法代码示例。如果您正苦于以下问题：Python dlib.net方法的具体用法？Python dlib.net怎么用？Python dlib.net使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类dlib的用法示例。
在下文中一共展示了dlib.net方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: prepare_data
# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import net [as 别名]
def prepare_data(video_dir, output_dir, max_video_limit=1, screen_display=False):
"""
Args:
1. video_dir: Directory storing all videos to be processed.
2. output_dir: Directory where all mouth region images are to be stored.
3. max_video_limit: Puts a limit on number of videos to be used for processing.
4. screen_display: Decides whether to use screen (to display video being processed).
"""
video_file_paths = sorted(glob.glob(video_dir + "*.mp4"))[:max_video_limit]
load_trained_models()
if not FACE_DETECTOR_MODEL:
print "[ERROR]: Please ensure that you have dlib's landmarks predictor file " + \
"at data/dlib_data/. You can download it here: " + \
"http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2"
return False
for path in video_file_paths:
extract_mouth_regions(path, output_dir, screen_display)
return True 示例2: ensure_dlib_model
# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import net [as 别名]
def ensure_dlib_model():
if not os.path.isfile(predictor_path):
import urllib.request
urllib.request.urlretrieve("http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2",
filename="models/shape_predictor_5_face_landmarks.dat.bz2") 示例3: compute_template
# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import net [as 别名]
def compute_template(globspec='images/lfw_aegan/*/*.png',image_dims=[400,400],predictor_path='models/shape_predictor_68_face_landmarks.dat',center_crop=None,subsample=1):
# Credit: http://dlib.net/face_landmark_detection.py.html
detector=dlib.get_frontal_face_detector()
predictor=dlib.shape_predictor(predictor_path)
template=numpy.zeros((68,2),dtype=numpy.float64)
count=0
if not center_crop is None:
center_crop=numpy.asarray(center_crop)
cy,cx=(numpy.asarray(image_dims)-center_crop)//2
# compute mean landmark locations
S=sorted(glob.glob(globspec))
S=S[::subsample]
for ipath in S:
print("Processing file: {}".format(ipath))
img=(skimage.transform.resize(skimage.io.imread(ipath)/255.0,tuple(image_dims)+(3,),order=2,mode='nearest')*255).clip(0,255).astype(numpy.ubyte)
if not center_crop is None:
img=img[cy:cy+center_crop[0],cx:cx+center_crop[0]]
upsample=0
dets=detector(img,upsample)
if len(dets)!=1: continue
for k,d in enumerate(dets):
shape=predictor(img, d)
for i in range(68):
template[i]+=(shape.part(i).y,shape.part(i).x)
count+=1
template/=float(count)
return template
# lfw_aegan 400x400 template map
# [[ 251.58852868 201.50275826] # 33 where nose meets upper-lip
# [ 172.69409809 168.66523086] # 39 inner-corner of left eye
# [ 171.72236076 232.09718129]] # 42 inner-corner or right eye 示例4: _get_dlib_data_file
# 需要导入模块: import dlib [as 别名]
# 或者: from dlib import net [as 别名]
def _get_dlib_data_file(dat_name):
dat_dir = os.path.relpath('%s/../3rdparty' % os.path.basename(__file__))
dat_path = '%s/%s' % (dat_dir, dat_name)
if not os.path.isdir(dat_dir):
os.mkdir(dat_dir)
# Download trained shape detector
if not os.path.isfile(dat_path):
with urlopen('http://dlib.net/files/%s.bz2' % dat_name) as response:
with bz2.BZ2File(response) as bzf, open(dat_path, 'wb') as f:
shutil.copyfileobj(bzf, f)
return dat_path