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):
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 apply_24_net(image,iname,nn,bb):
X24 = []
X12 = []
for c,e in enumerate(bb):
x1,y1,x2,y2 = e
sub = image[y1:y2,x1:x2]
sub12 = fr.resize_frame(sub,(12,12))
sub24 = fr.resize_frame(sub,(24,24))
X12.append(fr.frame_to_vect(sub12))
X24.append(fr.frame_to_vect(sub24))
X12 = sp.array(X12)
X24 = sp.array(X24)
pred = nn.predict(X=X24,X12=X12)
return bb[pred==1]
def apply_24_net(image, iname, nn, bb):
X24 = []
X12 = []
for c, e in enumerate(bb):
x1, y1, x2, y2 = e
sub = image[y1:y2, x1:x2]
sub12 = fr.resize_frame(sub, (12, 12))
sub24 = fr.resize_frame(sub, (24, 24))
X12.append(fr.frame_to_vect(sub12))
X24.append(fr.frame_to_vect(sub24))
X12 = sp.array(X12)
X24 = sp.array(X24)
pred = nn.predict(X=X24, X12=X12)
return bb[pred == 1]
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 frames2batch(k = 12,batch_size = 1024, is_calib = False):
pos = util.get_files(rootdir = 'F:\\train_data\\pos\\')
neg = util.get_files(rootdir = 'F:\\train_data\\neg\\')
pos = shuffle(pos)
neg = shuffle(neg)
total = pos + neg
total = shuffle(total)
batch = []
c = 0
bpath = 'F:\\train_data\\batch\\'
for item_path in total:
frame = fr.get_frame(item_path)
frame_r = fr.resize_frame(frame,(k,k))
if frame_r == None:
continue
vec = fr.frame_to_vect(frame_r)
label = 1 if item_path.split('\\')[-1].find('pos') > 0 else 0
print(item_path,label)
batch.append((vec,label))
if len(batch) > 0 and len(batch) % batch_size == 0:
batch = sp.array(batch)
sp.savez(bpath + str(c) + '_' + str(k) + ('_' if not is_calib else '_calib-') + 'net',batch)
batch = []
c += 1
if len(batch) > 0 and len(batch) % batch_size == 0:
batch = sp.array(batch)
sp.savez(bpath + str(c) + '_' + str(k) + ('_' if not is_calib else '_calib') + '-net',batch)
batch = []
c += 1
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 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 frames2batch(k=12, batch_size=1024, is_calib=False):
pos = util.get_files(rootdir='F:\\train_data\\pos\\')
neg = util.get_files(rootdir='F:\\train_data\\neg\\')
pos = shuffle(pos)
neg = shuffle(neg)
total = pos + neg
total = shuffle(total)
batch = []
c = 0
bpath = 'F:\\train_data\\batch\\'
for item_path in total:
frame = fr.get_frame(item_path)
frame_r = fr.resize_frame(frame, (k, k))
if frame_r == None:
continue
vec = fr.frame_to_vect(frame_r)
label = 1 if item_path.split('\\')[-1].find('pos') > 0 else 0
print(item_path, label)
batch.append((vec, label))
if len(batch) > 0 and len(batch) % batch_size == 0:
batch = sp.array(batch)
sp.savez(
bpath + str(c) + '_' + str(k) +
('_' if not is_calib else '_calib-') + 'net', batch)
batch = []
c += 1
if len(batch) > 0 and len(batch) % batch_size == 0:
batch = sp.array(batch)
sp.savez(
bpath + str(c) + '_' + str(k) +
('_' if not is_calib else '_calib') + '-net', batch)
batch = []
c += 1
def apply_calib_net(image, iname, nn, bb):
X_calib = []
for c, e in enumerate(bb):
x1, y1, x2, y2 = e
sub = image[y1:y2, x1:x2]
v = int(nn.nn_name[:2])
subr = fr.resize_frame(sub, (v, v))
X_calib.append(fr.frame_to_vect(subr))
#fr.write_frame('F:\\1\\'+str(c),sub)
X_calib = sp.array(X_calib)
pred_proba = nn.predict_proba(X_calib)
nbb = []
for e, lb in zip(bb, pred_proba):
t = 0.3
x1, y1, x2, y2 = e
sn, xn, yn = mean_pattern(lb, t)
h = abs(y1 - y2)
w = abs(x1 - x2)
#ii,jj,hh,ww = [int(e) for e in calib(x1,y1,h,w,lb)]
#if sp.any(sp.array([ii,jj,hh,ww]) < 0):
# continue
ii, jj, hh, ww = [
max(0, int(e)) for e in calib_apply(x1, y1, h, w, sn, xn, yn)
]
#print(ii,jj,hh,ww)
nbb.append([ii, jj, ii + ww, jj + hh])
for c, e in enumerate(nbb):
x1, y1, x2, y2 = e
sub = image[y1:y2, x1:x2]
#fr.write_frame('F:\\1\\'+str(c) + 'calib',sub)
return sp.array(nbb)
def apply_calib_net(image,iname,nn,bb):
X_calib = []
for c,e in enumerate(bb):
x1,y1,x2,y2 = e
sub = image[y1:y2,x1:x2]
v = int(nn.nn_name[:2])
subr = fr.resize_frame(sub,(v,v))
X_calib.append(fr.frame_to_vect(subr))
#fr.write_frame('F:\\1\\'+str(c),sub)
X_calib = sp.array(X_calib)
pred_proba = nn.predict_proba(X_calib)
nbb = []
for e,lb in zip(bb,pred_proba):
t = 0.3
x1,y1,x2,y2 = e
sn,xn,yn = mean_pattern(lb,t)
h = abs(y1-y2)
w = abs(x1-x2)
#ii,jj,hh,ww = [int(e) for e in calib(x1,y1,h,w,lb)]
#if sp.any(sp.array([ii,jj,hh,ww]) < 0):
# continue
ii,jj,hh,ww = [max(0,int(e)) for e in calib_apply(x1,y1,h,w,sn,xn,yn)]
#print(ii,jj,hh,ww)
nbb.append([ii,jj,ii+ww,jj+hh])
for c,e in enumerate(nbb):
x1,y1,x2,y2 = e
sub = image[y1:y2,x1:x2]
#fr.write_frame('F:\\1\\'+str(c) + 'calib',sub)
return sp.array(nbb)
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_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 apply_12_net(image, iname, nn, p_level=16, k=1.18, debug=1):
py = fr.get_frame_pyramid(image, k=k, t=p_level)
suff = nn.nn_name[:2]
patch_size = int(suff)
rows, cols, d = image.shape
s = time()
rbb = []
## check last k levels
k = 1
t_level = p_level - k
for m, frame in enumerate(py):
if m < t_level:
continue
H, W, dim = frame.shape
X_test = []
pnt = []
for u in range(0, H - patch_size + 1, 4):
for v in range(0, W - patch_size + 1, 4):
subframe = fr.get_patch(frame, u, v, (patch_size, patch_size))
#subframe = frame[y:y+h,x:x+w]
X_test.append(fr.frame_to_vect(subframe))
pnt.append((v, u))
pnt = sp.array(pnt)
X_test = sp.array(X_test)
pred = nn.predict(X_test)
pnt = pnt[pred == 1]
if len(pred[pred == 1]) == 0:
print('no detection on', str(m))
continue
else:
print('detection on', str(m))
bb = []
for p in pnt:
i = p[0]
j = p[1]
if debug:
cv2.rectangle(frame, (i, j), (i + patch_size, j + patch_size),
(255, 0, 255), 1)
bb.append([i, j, i + patch_size, j + patch_size])
for e in bb:
x1, y1, x2, y2 = e
ox1 = math.floor(image.shape[1] * (x1 / frame.shape[1]))
oy1 = math.floor(image.shape[0] * (y1 / frame.shape[0]))
ox2 = math.floor(image.shape[1] * (x2 / frame.shape[1]))
oy2 = math.floor(image.shape[0] * (y2 / frame.shape[0]))
if m >= p_level - k:
rbb.append([ox1, oy1, ox2, oy2])
if debug:
cv2.imwrite(
iname[:-4] + '_pipeline_' + 'lev' + str(m) + '_' + nn.nn_name +
'_.jpg', frame)
f = time()
print('time is:', f - s)
return sp.array(rbb)
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_wider_calib_data(n=None, k=12):
'''
for calibration net
return X - features
y - labels
cnt - count of examples
'''
X, y = [], []
sn = (0.83, 0.91, 1.0, 1.10, 1.21)
xn = (-0.17, 0.0, 0.17)
yn = (-0.17, 0.0, 0.17)
prod = [e for e in itertools.product(sn, xn, yn)]
inv_calib = lambda i, j, h, w, n: [
round(i - (-prod[n][1] * w / (prod[n][0]**-1))),
round(j - (-prod[n][2] * h / (prod[n][0]**-1))),
round(h / prod[n][0]**-1),
round(w / prod[n][0]**-1)
]
suff = str(k)
X_name = 'train_data_icalib_' + suff + '.npy'
y_name = 'labels_icalib_' + suff + '.npy'
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 iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
H, W = img.shape[:2]
bbox_list = bboxs[iname[:-4]]
for bbox in bbox_list:
label = sp.random.randint(0, 45)
i, j, h, w = [
int(e) for e in inv_calib(bbox[1], bbox[0],
bbox[2], bbox[3], label)
]
face = fr.get_patch(img, i, j, (h, w))
face_r, good_example = Datasets.sample_resize(face, k)
if good_example:
#print('orig:',bbox[1],bbox[0],bbox[2],bbox[3])
#print('inv_calib:',i,j,h,w)
vec_icalib = fr.frame_to_vect(face_r)
X.append(vec_icalib)
y.append(label)
print('face calib:', label, ipath)
y = sp.array(y)
sp.save(y_name, y)
X = sp.array(X)
sp.save(X_name, X)
return X, y
def apply_12_net(image,iname,nn,p_level = 16,k = 1.18,debug = 1):
py = fr.get_frame_pyramid(image,k=k,t=p_level)
suff = nn.nn_name[:2]
patch_size = int(suff)
rows,cols,d = image.shape
s = time()
rbb = []
## check last k levels
k = 1
t_level = p_level-k
for m,frame in enumerate(py):
if m < t_level:
continue
H,W,dim = frame.shape
X_test = []
pnt = []
for u in range(0,H - patch_size + 1,4):
for v in range(0,W - patch_size + 1,4):
subframe = fr.get_patch(frame,u,v,(patch_size,patch_size))
#subframe = frame[y:y+h,x:x+w]
X_test.append(fr.frame_to_vect(subframe))
pnt.append((v,u))
pnt = sp.array(pnt)
X_test = sp.array(X_test)
pred = nn.predict(X_test)
pnt = pnt[pred==1]
if len(pred[pred==1]) == 0:
print('no detection on',str(m))
continue
else:
print('detection on',str(m))
bb = []
for p in pnt:
i = p[0]
j = p[1]
if debug:
cv2.rectangle(frame, (i, j), (i+patch_size, j+patch_size), (255,0,255), 1)
bb.append([i,j,i+patch_size,j+patch_size])
for e in bb:
x1,y1,x2,y2 = e
ox1 = math.floor(image.shape[1] * (x1/frame.shape[1]))
oy1 = math.floor(image.shape[0] * (y1/frame.shape[0]))
ox2 = math.floor(image.shape[1] * (x2/frame.shape[1]))
oy2 = math.floor(image.shape[0] * (y2/frame.shape[0]))
if m >= p_level - k:
rbb.append([ox1,oy1,ox2,oy2])
if debug:
cv2.imwrite(iname[:-4]+'_pipeline_' + 'lev'+str(m) + '_' + nn.nn_name + '_.jpg',frame)
f = time()
print('time is:',f-s)
return sp.array(rbb)
def apply_12_net(image,frame,iname,m,nn,debug = 0):
suff = nn.nn_name[:2]
patch_size = int(suff)
if debug:
s = time()
rbb = []
H,W,dim = frame.shape
X_test = []
pnt = []
for u in range(0,H - patch_size + 1,4):
for v in range(0,W - patch_size + 1,4):
subframe = fr.get_patch(frame,u,v,(patch_size,patch_size))
X_test.append(fr.frame_to_vect(subframe))
pnt.append((v,u))
pnt = sp.array(pnt)
X_test = sp.array(X_test)
proba = nn.predict_proba(X_test)
pred = nn.predict(X_test)
pnt = pnt[pred==1]
pos_proba = proba[pred==1][:,1]
if debug:
if len(pnt) == 0:
print('no detection on',str(m))
else:
print('detection on',str(m),frame.shape)
bb = []
for p in pnt:
i = p[0]
j = p[1]
bb.append([i,j,i+patch_size,j+patch_size])
bb = sp.array(bb)
for e in bb:
x1,y1,x2,y2 = e
ratio_x = image.shape[1]/frame.shape[1]
ratio_y = image.shape[0]/frame.shape[0]
ox1 = int(round(x1 * ratio_x))
oy1 = int(round(y1 * ratio_y))
ox2 = int(round(x2 * ratio_x))
oy2 = int(round(y2 * ratio_y))
rbb.append([ox1,oy1,ox2,oy2])
if debug:
f = time()
print('Finish... time is:',f-s)
return (sp.array(rbb),pos_proba)
def get_train_calib_data(k = 12):
'''
for calibration net
return X - features
y - labels
cnt - count of examples
'''
sp.random.seed(42)
X_data,y_data = [],[]
suff = str(k)
c = 0
X_name = 'train_data_icalib_'+ suff + '.npz'
y_name = 'labels_icalib_'+ suff + '.npz'
label = -1
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)
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))
#fr.write_frame('F:\\1\\' + str(c) + 'orig',face)
c += 1
for ((new_y,new_x,new_w,new_h),label) in [(util.calib(y,x,w,h,k),k) for k in sp.random.randint(0,45,5)]:
face = fr.get_patch(frame,new_y,new_x,(new_w,new_h))
no_neg_calib = sp.all(sp.array([new_x,new_y,new_h,new_w]) > 0)
face_r,good_example = Datasets.sample_resize(face,k,k)
if good_example and no_neg_calib:
#fr.write_frame('F:\\1\\' + str(c) + 'calib_'+str(label) ,face)
print('face:',fpath,label)
vec = fr.frame_to_vect(face_r)
X_data.append(vec)
y_data.append(label)
y_data = sp.array(y_data)
sp.savez(y_name,y_data)
X_data = sp.array(X_data)
sp.savez(X_name,X_data)
return X_data,y_data
def get_train_wider_calib_data(n = None,k = 12):
'''
for calibration net
return X - features
y - labels
cnt - count of examples
'''
X,y = [],[]
sn = (0.83, 0.91, 1.0, 1.10, 1.21)
xn = (-0.17, 0.0, 0.17)
yn = (-0.17, 0.0, 0.17)
prod = [e for e in itertools.product(sn,xn,yn)]
inv_calib = lambda i,j,h,w,n: [ round(i-(-prod[n][1]*w/(prod[n][0]**-1))),round(j-(-prod[n][2]*h/(prod[n][0]**-1))),round(h/prod[n][0]**-1),round(w/prod[n][0]**-1) ]
suff = str(k)
X_name = 'train_data_icalib_'+ suff + '.npy'
y_name = 'labels_icalib_'+ suff + '.npy'
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 iname in files:
if fnmatch(iname, pattern):
ipath = os.path.join(path, iname)
img = fr.get_frame(ipath)
H,W = img.shape[:2]
bbox_list = bboxs[iname[:-4]]
for bbox in bbox_list:
label = sp.random.randint(0,45)
i,j,h,w = [int(e) for e in inv_calib(bbox[1],bbox[0],bbox[2],bbox[3],label)]
face = fr.get_patch(img,i,j,(h,w))
face_r,good_example = Datasets.sample_resize(face,k)
if good_example:
#print('orig:',bbox[1],bbox[0],bbox[2],bbox[3])
#print('inv_calib:',i,j,h,w)
vec_icalib = fr.frame_to_vect(face_r)
X.append(vec_icalib)
y.append(label)
print('face calib:',label,ipath)
y = sp.array(y)
sp.save(y_name,y)
X = sp.array(X)
sp.save(X_name,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 apply_calib_net(image,iname,nn,bb,T=0.3):
X_calib = []
for c,e in enumerate(bb):
x1,y1,x2,y2 = e
sub = image[y1:y2,x1:x2]
v = int(nn.nn_name[:2])
subr = fr.resize_frame(sub,(v,v))
X_calib.append(fr.frame_to_vect(subr))
#fr.write_frame('F:\\1\\'+str(c),sub)
X_calib = sp.array(X_calib)
pred_proba = nn.predict_proba(X_calib)
nbb = []
for e,lb in zip(bb,pred_proba):
t = 1/45
x1,y1,x2,y2 = e
ds, dx, dy = mean_pattern(lb,t)
h = abs(y1-y2)
w = abs(x1-x2)
ii,jj,hh,ww = [int(max(0,e)) for e in util.calib_apply(x1,y1,h,w,ds,dx,dy)]
nbb.append([ii,jj,ii+ww,jj+hh])
return sp.array(nbb)
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_calib_data(k=12):
'''
for calibration net
return X - features
y - labels
cnt - count of examples
'''
sp.random.seed(42)
X_data, y_data = [], []
suff = str(k)
c = 0
X_name = 'train_data_icalib_' + suff + '.npz'
y_name = 'labels_icalib_' + suff + '.npz'
label = -1
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)
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))
#fr.write_frame('F:\\1\\' + str(c) + 'orig',face)
c += 1
for ((new_y, new_x, new_w, new_h),
label) in [(util.calib(y, x, w, h, k), k)
for k in sp.random.randint(0, 45, 5)]:
face = fr.get_patch(frame, new_y, new_x, (new_w, new_h))
no_neg_calib = sp.all(
sp.array([new_x, new_y, new_h, new_w]) > 0)
face_r, good_example = Datasets.sample_resize(face, k, k)
if good_example and no_neg_calib:
#fr.write_frame('F:\\1\\' + str(c) + 'calib_'+str(label) ,face)
print('face:', fpath, label)
vec = fr.frame_to_vect(face_r)
X_data.append(vec)
y_data.append(label)
y_data = sp.array(y_data)
sp.savez(y_name, y_data)
X_data = sp.array(X_data)
sp.savez(X_name, X_data)
return X_data, y_data
