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 apply_24_48_net(image,iname,nn,bb,debug=0):
X = []
k = int(nn.nn_name[:2])
for c,e in enumerate(bb):
x1,y1,x2,y2 = e
sub = image[y1:y2,x1:x2]
subk = fr.resize_frame(sub,(k,k))
X.append(fr.frame_to_vect(subk))
X = sp.array(X)
pred = nn.predict(X=X)
proba = nn.predict_proba(X=X)
pos_proba = proba[pred==1][:,1]
bb = bb[pred==1]
pos_pred = pred[pred==1]
if nn.nn_name[:2] == '48':
bb = bb[pos_proba > 0.999998]
pred = pred[pos_proba > 0.999998]
pos_proba = pos_proba[pos_proba > 0.999998]
if debug:
j = 0
for e,lb,p in zip(bb,pos_pred,pos_proba):
x1,y1,x2,y2 = e
sub = image[y1:y2,x1:x2]
fr.write_frame('F:\\2\\'+str(j)+ '-'+str(lb) + '-' +str(p) + '-',sub)
j += 1
return (bb,pos_proba)
def get_fddb_face_data(k = 12, on_drive = False):
root = 'F:\\datasets\\image_data_sets\\faces\\FDDB\\'
iroot = os.path.join(root,'originalPics')
eroot = os.path.join(root,'FDDB-folds')
pattern = '-ellipseList.txt'
c = 0
X,y = [],[]
for path, subdirs, files in os.walk(eroot):
for fname in files:
if fname.find(pattern) > 0:
fpath = os.path.join(path,fname)
print(fpath)
with open(fpath) as f:
lines = sp.array(f.readlines())
paths_indx = sp.where([line.find('/') > 0 for line in lines])[0]
counts_indx = paths_indx + 1
paths = sp.array([e.strip() for e in lines[paths_indx]])
ellipces = []
for i in counts_indx:
cnt = int(lines[i])
ellipces.append(lines[i+1:i+cnt+1])
ellipces = [ [ [float(num) for num in line.split()[:-1]] for line in e] for e in ellipces]
ellipces = sp.array(ellipces)
for iname,ells in zip(paths[:],ellipces[:]):
ppath = os.path.join(iroot,iname.replace('/','\\')) + '.jpg'
file_id = iname.split('/')[-1]
frame = fr.get_frame(ppath)
for item in ells:
ra,rb,theta,x,y = item
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)
print(file_id,(y,x,h,w))
non_neg = x > 0 and y > 0
if not non_neg:
continue
if on_drive:
for item in Datasets.data_augmentation(frame,y,x,w,h):
fr.write_frame('F:\\train_data\\pos\\' + str(c) + '_' + str(file_id) + '_pos',item)
c +=1
else:
pass
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_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
def get_fddb_face_data(k=12, on_drive=False):
root = 'F:\\datasets\\image_data_sets\\faces\\FDDB\\'
iroot = os.path.join(root, 'originalPics')
eroot = os.path.join(root, 'FDDB-folds')
pattern = '-ellipseList.txt'
c = 0
X, y = [], []
for path, subdirs, files in os.walk(eroot):
for fname in files:
if fname.find(pattern) > 0:
fpath = os.path.join(path, fname)
print(fpath)
with open(fpath) as f:
lines = sp.array(f.readlines())
paths_indx = sp.where(
[line.find('/') > 0 for line in lines])[0]
counts_indx = paths_indx + 1
paths = sp.array(
[e.strip() for e in lines[paths_indx]])
ellipces = []
for i in counts_indx:
cnt = int(lines[i])
ellipces.append(lines[i + 1:i + cnt + 1])
ellipces = [[[float(num) for num in line.split()[:-1]]
for line in e] for e in ellipces]
ellipces = sp.array(ellipces)
for iname, ells in zip(paths[:], ellipces[:]):
ppath = os.path.join(iroot, iname.replace(
'/', '\\')) + '.jpg'
file_id = iname.split('/')[-1]
frame = fr.get_frame(ppath)
for item in ells:
ra, rb, theta, x, y = item
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)
print(file_id, (y, x, h, w))
non_neg = x > 0 and y > 0
if not non_neg:
continue
if on_drive:
for item in Datasets.data_augmentation(
frame, y, x, w, h):
fr.write_frame(
'F:\\train_data\\pos\\' + str(c) +
'_' + str(file_id) + '_pos', item)
c += 1
else:
pass
X = sp.array(X)
y = sp.ones(len(X))
return X, y
