def get_train_non_face_data(k=12, write_to_disk=False):
'''
cut non-faces (negative examples) by pick random patch (if in not overlaped with any
face bbox from all images in dataset
return X - features
y - labels
cnt - count of examples
'''
X = []
root = 'F:\\Datasets\\image_data_sets\\non-faces'
pattern = "*.jpg"
for path, subdirs, files in os.walk(root):
for iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
print('non_face:', ipath)
if img == None:
continue
H, W = img.shape[:2]
non_face = shuffle(fr.split_frame(img, wshape=(k, k)),
random_state=42)[:25]
for e in non_face:
X.append(fr.frame_to_vect(e))
X = sp.array(X)
y = sp.zeros(len(X))
return X, y
def get_train_non_face_data(k = 12,write_to_disk = False):
'''
cut non-faces (negative examples) by pick random patch (if in not overlaped with any
face bbox from all images in dataset
return X - features
y - labels
cnt - count of examples
'''
X = []
root = 'F:\\Datasets\\image_data_sets\\non-faces'
pattern = "*.jpg"
for path, subdirs, files in os.walk(root):
for iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
print('non_face:',ipath)
if img == None:
continue
H,W = img.shape[:2]
non_face = shuffle(fr.split_frame(img,wshape=(k,k)),random_state=42)[:25]
for e in non_face:
X.append(fr.frame_to_vect(e))
X = sp.array(X)
y = sp.zeros(len(X))
return X,y
def get_train_non_face_data(k=12, patch_per_img=25, on_drive=False):
'''
cut non-faces (negative examples) by pick random patch (if in not overlaped with any
face bbox from all images in dataset
return X - features
y - labels
cnt - count of examples
'''
X = []
def yield_gen(data):
data = shuffle(data)[:patch_per_img]
for e in data:
yield e
root = 'F:\\Datasets\\image_data_sets\\non-faces'
pattern = "*.jpg"
c = 0
for path, subdirs, files in os.walk(root):
for iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
print('non_face:', ipath)
if img == None:
continue
H, W = img.shape[:2]
if not on_drive:
for e in yield_gen(fr.split_frame(img, wshape=(k, k))):
X.append(fr.frame_to_vect(e))
else:
for e in yield_gen(
fr.split_frame(img,
wshape=(util.k_max,
util.k_max))):
fr.write_frame(
'F:\\train_data\\neg\\' + str(c) + '_'
'nonface' + '_neg', e)
c += 1
X = sp.array(X)
y = sp.zeros(len(X))
return X, y
def get_train_non_face_data(k = 12,patch_per_img = 25,on_drive = False):
'''
cut non-faces (negative examples) by pick random patch (if in not overlaped with any
face bbox from all images in dataset
return X - features
y - labels
cnt - count of examples
'''
X = []
def yield_gen(data):
data = shuffle(data)[:patch_per_img]
for e in data:
yield e
root = 'F:\\Datasets\\image_data_sets\\non-faces'
pattern = "*.jpg"
c = 0
for path, subdirs, files in os.walk(root):
for iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
print('non_face:',ipath)
if img == None:
continue
H,W = img.shape[:2]
if not on_drive:
for e in yield_gen(fr.split_frame(img,wshape=(k,k))):
X.append(fr.frame_to_vect(e))
else:
for e in yield_gen(fr.split_frame(img,wshape=(util.k_max,util.k_max))):
fr.write_frame('F:\\train_data\\neg\\' + str(c) + '_' 'nonface'+'_neg',e)
c += 1
X = sp.array(X)
y = sp.zeros(len(X))
return X,y
