def get_aflw_face_data(k = 12):
dbpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW'
dbpath = join(dbpath,'aflw.sqlite')
rfpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW\\img'
conn = sqlite3.connect(dbpath)
X = []
for file_id,x,y,h,w in conn.execute('SELECT file_id,x,y,h,w FROM Faces NATURAL JOIN FaceRect'):
fpath = join(rfpath,file_id)
frame = fr.get_frame(fpath)
no_neg = sp.all(sp.array([x,y,h,w]) > 0) ## ignore a bad data in sql table
if frame != None and no_neg:
face = fr.get_patch(frame,y,x,(h,w))
face_r,good_example = Datasets.sample_resize(face,k)
if good_example:
print('face:',fpath)
vec = fr.frame_to_vect(face_r)
X.append(vec)
face_flip = fr.flip_frame(face)
face_flip_r = fr.resize_frame(face_flip,(k,k))
vec = fr.frame_to_vect(face_flip_r)
X.append(vec)
#fr.write_frame('F:\\1\\'+'flip'+str(file_id),face_flip)
#fr.write_frame('F:\\1\\'+str(file_id),face)
X = sp.array(X)
y = sp.ones(len(X))
return X,y
def get_aflw_face_data(k=12):
dbpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW'
dbpath = join(dbpath, 'aflw.sqlite')
rfpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW\\img'
conn = sqlite3.connect(dbpath)
X = []
for file_id, x, y, h, w in conn.execute(
'SELECT file_id,x,y,h,w FROM Faces NATURAL JOIN FaceRect'):
fpath = join(rfpath, file_id)
frame = fr.get_frame(fpath)
no_neg = sp.all(
sp.array([x, y, h, w]) > 0) ## ignore a bad data in sql table
if frame != None and no_neg:
face = fr.get_patch(frame, y, x, (h, w))
face_r, good_example = Datasets.sample_resize(face, k)
if good_example:
print('face:', fpath)
vec = fr.frame_to_vect(face_r)
X.append(vec)
face_flip = fr.flip_frame(face)
face_flip_r = fr.resize_frame(face_flip, (k, k))
vec = fr.frame_to_vect(face_flip_r)
X.append(vec)
#fr.write_frame('F:\\1\\'+'flip'+str(file_id),face_flip)
#fr.write_frame('F:\\1\\'+str(file_id),face)
X = sp.array(X)
y = sp.ones(len(X))
return X, y
def get_aflw_face_data(k = 12, on_drive = False):
dbpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW'
dbpath = join(dbpath,'aflw.sqlite')
rfpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW\\img'
conn = sqlite3.connect(dbpath)
X = []
c = 0
for file_id,x,y,ra,rb,theta in conn.execute('SELECT file_id,x,y,ra,rb,theta FROM Faces NATURAL JOIN FaceEllipse'):
fpath = join(rfpath,file_id)
frame = fr.get_frame(fpath)
x1,y1,x2,y2 = util.ellipse2bbox(a = ra, b = rb, angle = theta, cx = x, cy = y)
x = x1
y = y1
h = abs(y2-y1)
w = abs(x2-x1)
no_neg = sp.all(sp.array([x,y,h,w]) > 0) ## ignore a bad data in sql table
if frame != None and no_neg:
y,x,w,h = [int(e) for e in (y,x,w,h)]
face = fr.get_patch(frame,y,x,(w,h))
face_r,good_example = Datasets.sample_resize(face,k,k)
if good_example:
print('face:',fpath)
vec = fr.frame_to_vect(face_r)
if not on_drive:
X.append(vec)
face_flip_r = fr.flip_frame(face_r)
vec = fr.frame_to_vect(face_flip_r)
X.append(vec)
else:
for item in Datasets.data_augmentation(frame,y,x,w,h):
fr.write_frame('F:\\train_data\\pos\\' + str(c) + '_' + str(file_id)[:-4] + '_' + 'pos',item)
c +=1
X = sp.array(X)
y = sp.ones(len(X))
return X,y
def get_aflw_face_data(k=12, on_drive=False):
dbpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW'
dbpath = join(dbpath, 'aflw.sqlite')
rfpath = 'F:\\datasets\\image_data_sets\\faces\\AFLW\\img'
conn = sqlite3.connect(dbpath)
X = []
c = 0
for file_id, x, y, ra, rb, theta in conn.execute(
'SELECT file_id,x,y,ra,rb,theta FROM Faces NATURAL JOIN FaceEllipse'
):
fpath = join(rfpath, file_id)
frame = fr.get_frame(fpath)
x1, y1, x2, y2 = util.ellipse2bbox(a=ra,
b=rb,
angle=theta,
cx=x,
cy=y)
x = x1
y = y1
h = abs(y2 - y1)
w = abs(x2 - x1)
no_neg = sp.all(
sp.array([x, y, h, w]) > 0) ## ignore a bad data in sql table
if frame != None and no_neg:
y, x, w, h = [int(e) for e in (y, x, w, h)]
face = fr.get_patch(frame, y, x, (w, h))
face_r, good_example = Datasets.sample_resize(face, k, k)
if good_example:
print('face:', fpath)
vec = fr.frame_to_vect(face_r)
if not on_drive:
X.append(vec)
face_flip_r = fr.flip_frame(face_r)
vec = fr.frame_to_vect(face_flip_r)
X.append(vec)
else:
for item in Datasets.data_augmentation(
frame, y, x, w, h):
fr.write_frame(
'F:\\train_data\\pos\\' + str(c) + '_' +
str(file_id)[:-4] + '_' + 'pos', item)
c += 1
X = sp.array(X)
y = sp.ones(len(X))
return X, y
def data_augmentation(frame,y,x,w,h):
face = fr.get_patch(frame,y,x,(w,h))
face_flip = fr.flip_frame(face)
t1 = sp.random.randint(0,3)
t2 = sp.random.randint(0,3)
face_narrow = fr.get_patch(frame,y,x,(w-t2,h-t2))
face_wide = fr.get_patch(frame,y,x,(w+t2,h+t2))
t1 = sp.random.randint(0,3)
t2 = sp.random.randint(0,3)
face_shift1 = fr.get_patch(frame,y+t1,x+t1,(w+t2,h+t2))
face_shift2 = fr.get_patch(frame,y-t1,x-t1,(w-t2,h-t2))
th = float((1 if sp.random.randint(0,2) % 2 == 0 else -1) * sp.random.randint(45,90))
face_rot = fr.frame_rotate(face,theta = th)
faces_list = filter(lambda x: x != None,[face,face_flip,face_narrow,face_wide,face_shift1,face_shift2,face_rot])
return faces_list
def get_train_face_wider_data(k=12, write_to_disk=False):
'''
cut faces (positive examples) by bboxes from all images in dataset
return X - features
y - labels
cnt - count of examples
'''
X, y = [], []
root = 'F:\\Datasets\\image_data_sets\\faces\\WIDERFace\\'
pattern = "*.jpg"
bboxs = Datasets.load_wider_face(
os.path.join(root, 'wider_face_split', 'wider_face_train_v7.mat'))
for path, subdirs, files in os.walk(root, 'WIDER_train'):
for indx, iname in enumerate(files):
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
print('face:', ipath)
img = fr.get_frame(ipath)
H, W, dim = img.shape
bbox_list = bboxs[iname[:-4]]
for bbox in bbox_list:
face = fr.get_patch(img, bbox[1], bbox[0],
(bbox[2], bbox[3]))
#fr.write_frame('F:\\1\\' + str(c),face)
face_r, good_example = Datasets.sample_resize(
face, k, k)
if good_example:
vec = fr.frame_to_vect(face_r)
X.append(vec)
y.append(1)
face_r_flip = fr.flip_frame(face_r)
vec = fr.frame_to_vect(face_r_flip)
X.append(vec)
y.append(1)
X = sp.array(X)
y = sp.array(y)
#y = sp.ones(len(X))
return X, y
def get_train_face_wider_data(k = 12,write_to_disk = False):
'''
cut faces (positive examples) by bboxes from all images in dataset
return X - features
y - labels
cnt - count of examples
'''
X,y = [],[]
root = 'F:\\Datasets\\image_data_sets\\faces\\WIDERFace\\'
pattern = "*.jpg"
bboxs = Datasets.load_wider_face(os.path.join(root,'wider_face_split','wider_face_train_v7.mat'))
for path, subdirs, files in os.walk(root,'WIDER_train'):
for indx,iname in enumerate(files):
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
print('face:',ipath)
img = fr.get_frame(ipath)
H,W,dim = img.shape
bbox_list = bboxs[iname[:-4]]
for bbox in bbox_list:
face = fr.get_patch(img,bbox[1],bbox[0],(bbox[2],bbox[3]))
#fr.write_frame('F:\\1\\' + str(c),face)
face_r,good_example = Datasets.sample_resize(face,k,k)
if good_example:
vec = fr.frame_to_vect(face_r)
X.append(vec)
y.append(1)
face_r_flip = fr.flip_frame(face_r)
vec = fr.frame_to_vect(face_r_flip)
X.append(vec)
y.append(1)
X = sp.array(X)
y = sp.array(y)
#y = sp.ones(len(X))
return X,y
def data_augmentation(frame, y, x, w, h):
face = fr.get_patch(frame, y, x, (w, h))
face_flip = fr.flip_frame(face)
t1 = sp.random.randint(0, 3)
t2 = sp.random.randint(0, 3)
face_narrow = fr.get_patch(frame, y, x, (w - t2, h - t2))
face_wide = fr.get_patch(frame, y, x, (w + t2, h + t2))
t1 = sp.random.randint(0, 3)
t2 = sp.random.randint(0, 3)
face_shift1 = fr.get_patch(frame, y + t1, x + t1, (w + t2, h + t2))
face_shift2 = fr.get_patch(frame, y - t1, x - t1, (w - t2, h - t2))
th = float((1 if sp.random.randint(0, 2) % 2 == 0 else -1) *
sp.random.randint(45, 90))
face_rot = fr.frame_rotate(face, theta=th)
faces_list = filter(lambda x: x != None, [
face, face_flip, face_narrow, face_wide, face_shift1, face_shift2,
face_rot
])
return faces_list
