def E(level=1):
if level == 0:
from common import level1 as P
P = partial(P, FOnly=True) # high order function, here we only test LEVEL-1 F CNN
elif level == 1:
from level import level1 as P
elif level == 2:
from level import level2 as P
else:
from level import level3 as P
data = getDataFromTxt(TXT)
error = np.zeros((len(data), 5))
for i in range(len(data)):
imgPath, bbox, landmarkGt = data[i]
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert(img is not None)
logger("process %s" % imgPath)
landmarkP = P(img, bbox)
plot_point[i] = landmarkP
# real landmark
landmarkP = bbox.reprojectLandmark(landmarkP)
landmarkGt = bbox.reprojectLandmark(landmarkGt)
error[i] = evaluateError(landmarkGt, landmarkP, bbox)
return error
def E():
data = getDataFromTxt(TXT)
error = np.zeros((len(data), 3))
for i in range(len(data)):
imgPath, bbox, landmarkGt = data[i]
landmarkGt = landmarkGt[:3, :]
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert (img is not None)
logger("process %s" % imgPath)
landmarkP = EN(img, bbox)
# real landmark
landmarkP = bbox.reprojectLandmark(landmarkP)
landmarkGt = bbox.reprojectLandmark(landmarkGt)
error[i] = evaluateError(landmarkGt, landmarkP, bbox)
return error
def E():
data = getDataFromTxt(TXT)
error = np.zeros((len(data), 3))
for i in range(len(data)):
imgPath, bbox, landmarkGt = data[i]
landmarkGt = landmarkGt[:3, :]
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert(img is not None)
logger("process %s" % imgPath)
landmarkP = EN(img, bbox)
# real landmark
landmarkP = bbox.reprojectLandmark(landmarkP)
landmarkGt = bbox.reprojectLandmark(landmarkGt)
error[i] = evaluateError(landmarkGt, landmarkP, bbox)
return error
def generate(ftxt, mode, argument=False):
"""
Generate Training Data for LEVEL-2
mode = train or test
"""
data = getDataFromTxt(ftxt)
trainData = defaultdict(lambda: dict(patches=[], landmarks=[]))
for (imgPath, bbox, landmarkGt) in data:
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert (img is not None)
logger("process %s" % imgPath)
landmarkPs = randomShiftWithArgument(landmarkGt, 0.05)
if not argument:
landmarkPs = [landmarkPs[0]]
for landmarkP in landmarkPs:
for idx, name, padding in types:
patch, patch_bbox = getPatch(img, bbox, landmarkP[idx],
padding)
patch = cv2.resize(patch, (15, 15))
patch = patch.reshape((1, 15, 15))
trainData[name]['patches'].append(patch)
_ = patch_bbox.project(bbox.reproject(landmarkGt[idx]))
trainData[name]['landmarks'].append(_)
for idx, name, padding in types:
logger('writing training data of %s' % name)
patches = np.asarray(trainData[name]['patches'])
landmarks = np.asarray(trainData[name]['landmarks'])
patches = processImage(patches)
shuffle_in_unison_scary(patches, landmarks)
with h5py.File('train/2_%s/%s.h5' % (name, mode), 'w') as h5:
h5['data'] = patches.astype(np.float32)
h5['landmark'] = landmarks.astype(np.float32)
with open('train/2_%s/%s.txt' % (name, mode), 'w') as fd:
fd.write('train/2_%s/%s.h5' % (name, mode))
def generate(ftxt, mode, argument=False):
"""
Generate Training Data for LEVEL-2
mode = train or test
"""
data = getDataFromTxt(ftxt)
trainData = defaultdict(lambda: dict(patches=[], landmarks=[]))
for (imgPath, bbox, landmarkGt) in data:
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert(img is not None)
logger("process %s" % imgPath)
landmarkPs = randomShiftWithArgument(landmarkGt, 0.05)
if not argument:
landmarkPs = [landmarkPs[0]]
for landmarkP in landmarkPs:
for idx, name, padding in types:
patch, patch_bbox = getPatch(img, bbox, landmarkP[idx], padding)
patch = cv2.resize(patch, (15, 15))
patch = patch.reshape((1, 15, 15))
trainData[name]['patches'].append(patch)
_ = patch_bbox.project(bbox.reproject(landmarkGt[idx]))
trainData[name]['landmarks'].append(_)
for idx, name, padding in types:
logger('writing training data of %s'%name)
patches = np.asarray(trainData[name]['patches'])
landmarks = np.asarray(trainData[name]['landmarks'])
patches = processImage(patches)
shuffle_in_unison_scary(patches, landmarks)
with h5py.File('train/2_%s/%s.h5'%(name, mode), 'w') as h5:
h5['data'] = patches.astype(np.float32)
h5['landmark'] = landmarks.astype(np.float32)
with open('train/2_%s/%s.txt'%(name, mode), 'w') as fd:
fd.write('train/2_%s/%s.h5'%(name, mode))
def generate_hdf5(ftxt, output, fname, argument=False):
data = getDataFromTxt(ftxt)
F_imgs = []
F_landmarks = []
for (imgPath, landmarkGt, bbox) in data:
img = cv2.imread(imgPath, cv2.IMREAD_GRAYSCALE)
assert (img is not None)
logger("process %s" % imgPath)
# plt.imshow(img)
# plt.show()
f_face = img[int(bbox[0]):int(bbox[2]), int(bbox[1]):int(bbox[3])]
plt.imshow(f_face)
plt.show()
f_face = cv2.resize(f_face, (39, 39))
f_face = f_face.reshape((1, 39, 39))
f_landmark = landmarkGt.reshape((10))
F_imgs.append(f_face)
F_landmarks.append(f_landmark)
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
F_imgs = processImage(F_imgs)
shuffle_in_unison_scary(F_imgs, F_landmarks)
# full face
base = join(OUTPUT, '1_F')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = F_imgs.astype(np.float32)
h5['landmark'] = F_landmarks.astype(np.float32)
padding,
mode='constant',
constant_values=(padval, padval))
# tile the filters into an image
data = data.reshape((n, n) + data.shape[1:]).transpose(
(0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
data = data.reshape((n * data.shape[1], n * data.shape[3]) +
data.shape[4:])
plt.figure() #新的绘图区
plt.imshow(data)
plt.savefig('/home/yhb-pc/CNN_Face_keypoint/log/%s.png' % name)
if __name__ == '__main__':
data = getDataFromTxt(TXT, with_landmark=False)
imgPath, bbox = data[18]
img = cv2.imread(imgPath)
assert (img is not None)
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
logger("process %s" % imgPath)
net = level1_Forward(imgGray, bbox).cnn
for i in range(4):
feat = net.blobs['conv%s' % str(i + 1)].data
vis_square(feat[0], 'conv%s' % str(i + 1), padval=1)
feat = net.blobs['pool2'].data
vis_square(feat[0], 'pool2', padval=1)
for i in range(4):
filters = net.params['conv%s' % str(i + 1)][0].data
vis_square(filters.reshape(
# force the number of filters to be square
n = int(np.ceil(np.sqrt(data.shape[0])))
padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)
data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))
# tile the filters into an image
data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
plt.figure() #新的绘图区
plt.imshow(data)
plt.savefig('/home/yhb-pc/CNN_Face_keypoint/log/%s.png'%name)
if __name__ == '__main__':
data = getDataFromTxt(TXT, with_landmark=False)
imgPath, bbox = data[18]
img = cv2.imread(imgPath)
assert(img is not None)
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
logger("process %s" % imgPath)
net = level1_Forward(imgGray, bbox).cnn
for i in range(4):
feat = net.blobs['conv%s'%str(i+1)].data
vis_square(feat[0], 'conv%s'%str(i+1), padval=1)
feat = net.blobs['pool2'].data
vis_square(feat[0], 'pool2', padval=1)
for i in range(4):
filters = net.params['conv%s'%str(i+1)][0].data
vis_square(filters.reshape(len(filters)*len(filters[0]),len(filters[0,0])
def generateData(ftxt,data_path,net,augmentation=True):
if net == "PNet":
size = 12
elif net == "RNet":
size = 24
elif net == "ONet":
size = 48
else:
print('Net type error')
return
image_id = 0
f=open(os.path.join(output,"landmark_%s_aug.txt"%(size)),'w')
# get image path , bounding box, and landmarks from file 'ftxt'
data=getDataFromTxt(ftxt,data_path=data_path)
idx=0
for (imagePath,bbox,landmarkGt) in data:
images=[]
landmarks=[]
img=cv2.imread(imagePath)
img_h,img_w,img_c=img.shape
#原图所在的坐标
if bbox.right<bbox.left or bbox.bottom<bbox.top:
continue
gt_box = np.array([bbox.left,bbox.top,bbox.right,bbox.bottom])
f_face = img[bbox.top:bbox.bottom+1,bbox.left:bbox.right+1]
#cv2.imshow("face",f_face)
#cv2.waitKey(0)
f_face=cv2.resize(f_face,(size,size))
landmark=np.zeros((5,2))
#normalize land mark by dividing the width and height of the ground truth bounding box
# landmakrGt is a list of tuples
for index,one in enumerate(landmarkGt):
# (( x - bbox.left)/ width of bounding box, (y - bbox.top)/ height of bounding box
rv = ((one[0]-gt_box[0])/(gt_box[2]-gt_box[0]), (one[1]-gt_box[1])/(gt_box[3]-gt_box[1]))
landmark[index]=rv
images.append(f_face)
landmarks.append(landmark.reshape(10))
landmark=np.zeros((5,2))
if augmentation:
idx+=1
if idx%100==0:
print(idx,"images done")
x1,y1,x2,y2=gt_box
gt_w=x2-x1+1
gt_h=y2-y1+1
#长宽太小得人脸就不做变换了
if max(gt_w,gt_h)<40 or x1<0 or y1<0:
continue
#random shift
for i in range(10):
bbox_size=random.randint(int(min(gt_w, gt_h) * 0.8), np.ceil(1.25 * max(gt_w, gt_h)))
delta_x = random.randint(int(-gt_w * 0.2), int(gt_w * 0.2))
delta_y = random.randint(int(-gt_h * 0.2), int(gt_h * 0.2))
nx1 = int(max(x1+gt_w/2-bbox_size/2+delta_x,0))
ny1 = int(max(y1+gt_h/2-bbox_size/2+delta_y,0))
nx2 = nx1 + bbox_size
ny2 = ny1 + bbox_size
if nx2 > img_w or ny2 > img_h:
continue
crop_box = np.array([nx1,ny1,nx2,ny2])
cropped_im = img[ny1:ny2+1,nx1:nx2+1,:]
resized_im = cv2.resize(cropped_im, (size, size))
#cal iou
iou = IoU(crop_box, np.expand_dims(gt_box,0))
if iou>0.65:
images.append(resized_im)
#normalize
for index, one in enumerate(landmarkGt):
rv = ((one[0]-nx1)/bbox_size, (one[1]-ny1)/bbox_size)
landmark[index] = rv
landmarks.append(landmark.reshape(10))
landmark=np.zeros((5,2))
_landmark=landmarks[-1].reshape(-1,2)
bbox=BBox([nx1,ny1,nx2,ny2])
#mirror
if random.choice([0,1])>0:
face_flipped,landmark_flipped=flip(resized_im,_landmark)
face_flipped=cv2.resize(face_flipped,(size,size))
images.append(face_flipped)
landmarks.append(landmark_flipped.reshape(10))
#rotate逆时针旋转
if random.choice([0,1])>0:
#reprojectLandmark将归一化的landmark恢复至原始坐标
face_rotated,landmark_rotated=rotate(img,bbox,bbox.reprojectLandmark(_landmark),5)
#重新归一化旋转后的landmark
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated=cv2.resize(face_rotated,(size,size))
images.append(face_rotated)
landmarks.append(landmark_rotated.reshape(10))
#flip
face_flipped, landmark_flipped = flip(face_rotated, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
images.append(face_flipped)
landmarks.append(landmark_flipped.reshape(10))
#顺时针rotate
if random.choice([0,1])>0:
#reprojectLandmark将归一化的landmark恢复至原始坐标
face_rotated,landmark_rotated=rotate(img,bbox,bbox.reprojectLandmark(_landmark),-5)
#重新归一化旋转后的landmark
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated=cv2.resize(face_rotated,(size,size))
images.append(face_rotated)
landmarks.append(landmark_rotated.reshape(10))
#flip
face_flipped, landmark_flipped = flip(face_rotated, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (size, size))
images.append(face_flipped)
landmarks.append(landmark_flipped.reshape(10))
images,landmarks=np.asarray(images),np.asarray(landmarks)
print(images)
print(np.shape(landmarks))
for i in range(len(images)):
if np.sum(np.where(landmarks[i] <= 0, 1, 0)) > 0:
continue
if np.sum(np.where(landmarks[i] >= 1, 1, 0)) > 0:
continue
#保存图片
cv2.imwrite(os.path.join(dstdir,'%d.jpg'%(image_id)),images[i])
landmark=map(str,list(landmarks[i]))
f.write(os.path.join(dstdir,'%d.jpg'%(image_id))+" -2 "+" ".join(landmark)+"\n")
image_id+=1
f.close()
return images,landmarks
def generate_hdf5_data(filelist, output, fname, argument=False):
data = getDataFromTxt(filelist)
F_imgs = []
F_landmarks = []
EN_imgs = []
EN_landmarks = []
NM_imgs = []
NM_landmarks = []
for (imgPath, bbox, landmarkGt) in data:
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert (img is not None)
logger("process %s" % imgPath)
# Paper Table2 jitter F-layer
f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
f_face = img[f_bbox.top:f_bbox.bottom + 1,
f_bbox.left:f_bbox.right + 1]
## data argument
if argument and np.random.rand() > -1:
### flip
face_flipped, landmark_flipped = flip(f_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation +5 degrees
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), 5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha,
(39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha,
landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation -5 degrees
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), -5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha,
(39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha,
landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
f_face = cv2.resize(f_face, (39, 39))
en_face = f_face[:31, :]
nm_face = f_face[8:, :]
f_face = f_face.reshape((1, 39, 39))
f_landmark = landmarkGt.reshape((10))
F_imgs.append(f_face)
F_landmarks.append(f_landmark)
## data argument for EN
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(en_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
(1, 31, 39))
landmark_flipped = landmark_flipped[:3, :].reshape((6))
EN_imgs.append(face_flipped)
EN_landmarks.append(landmark_flipped)
en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
en_landmark = landmarkGt[:3, :].reshape((6))
EN_imgs.append(en_face)
EN_landmarks.append(en_landmark)
## data argument for NM
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape(
(1, 31, 39))
landmark_flipped = landmark_flipped[2:, :].reshape((6))
NM_imgs.append(face_flipped)
NM_landmarks.append(landmark_flipped)
nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
nm_landmark = landmarkGt[2:, :].reshape((6))
NM_imgs.append(nm_face)
NM_landmarks.append(nm_landmark)
#Convert the list to array
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
NM_imgs, NM_landmarks = np.asarray(NM_imgs), np.asarray(NM_landmarks)
### normalize the data and shu
F_imgs = processImage(F_imgs)
shuffle_in_unison_scary(F_imgs, F_landmarks)
EN_imgs = processImage(EN_imgs)
shuffle_in_unison_scary(EN_imgs, EN_landmarks)
NM_imgs = processImage(NM_imgs)
shuffle_in_unison_scary(NM_imgs, NM_landmarks)
# full face
base = join(OUTPUT, '1_F')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = F_imgs.astype(np.float32)
h5['landmark'] = F_landmarks.astype(np.float32)
# eye and nose
base = join(OUTPUT, '1_EN')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = EN_imgs.astype(np.float32)
h5['landmark'] = EN_landmarks.astype(np.float32)
# nose and mouth
base = join(OUTPUT, '1_NM')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = NM_imgs.astype(np.float32)
h5['landmark'] = NM_landmarks.astype(np.float32)
def generate_hdf5_data(filelist, output, fname, argument=False):
data = getDataFromTxt(filelist)
F_imgs = []
F_landmarks = []
EN_imgs = []
EN_landmarks = []
NM_imgs = []
NM_landmarks = []
for (imgPath, bbox, landmarkGt) in data:
img = cv2.imread(imgPath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert(img is not None)
logger("process %s" % imgPath)
# Paper Table2 jitter F-layer
f_bbox = bbox.subBBox(-0.05, 1.05, -0.05, 1.05)
f_face = img[f_bbox.top:f_bbox.bottom+1,f_bbox.left:f_bbox.right+1]
## data argument
if argument and np.random.rand() > -1:
### flip
face_flipped, landmark_flipped = flip(f_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation +5 degrees
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), 5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
### rotation -5 degrees
if np.random.rand() > 0.5:
face_rotated_by_alpha, landmark_rotated = rotate(img, f_bbox, \
bbox.reprojectLandmark(landmarkGt), -5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (39, 39))
F_imgs.append(face_rotated_by_alpha.reshape((1, 39, 39)))
F_landmarks.append(landmark_rotated.reshape(10))
### flip with rotation
face_flipped, landmark_flipped = flip(face_rotated_by_alpha, landmark_rotated)
face_flipped = cv2.resize(face_flipped, (39, 39))
F_imgs.append(face_flipped.reshape((1, 39, 39)))
F_landmarks.append(landmark_flipped.reshape(10))
f_face = cv2.resize(f_face, (39, 39))
en_face = f_face[:31, :]
nm_face = f_face[8:, :]
f_face = f_face.reshape((1, 39, 39))
f_landmark = landmarkGt.reshape((10))
F_imgs.append(f_face)
F_landmarks.append(f_landmark)
## data argument for EN
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(en_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
landmark_flipped = landmark_flipped[:3, :].reshape((6))
EN_imgs.append(face_flipped)
EN_landmarks.append(landmark_flipped)
en_face = cv2.resize(en_face, (31, 39)).reshape((1, 31, 39))
en_landmark = landmarkGt[:3, :].reshape((6))
EN_imgs.append(en_face)
EN_landmarks.append(en_landmark)
## data argument for NM
if argument and np.random.rand() > 0.5:
### flip
face_flipped, landmark_flipped = flip(nm_face, landmarkGt)
face_flipped = cv2.resize(face_flipped, (31, 39)).reshape((1, 31, 39))
landmark_flipped = landmark_flipped[2:, :].reshape((6))
NM_imgs.append(face_flipped)
NM_landmarks.append(landmark_flipped)
nm_face = cv2.resize(nm_face, (31, 39)).reshape((1, 31, 39))
nm_landmark = landmarkGt[2:, :].reshape((6))
NM_imgs.append(nm_face)
NM_landmarks.append(nm_landmark)
#Convert the list to array
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
EN_imgs, EN_landmarks = np.asarray(EN_imgs), np.asarray(EN_landmarks)
NM_imgs, NM_landmarks = np.asarray(NM_imgs),np.asarray(NM_landmarks)
### normalize the data and shu
F_imgs = processImage(F_imgs)
shuffle_in_unison_scary(F_imgs, F_landmarks)
EN_imgs = processImage(EN_imgs)
shuffle_in_unison_scary(EN_imgs, EN_landmarks)
NM_imgs = processImage(NM_imgs)
shuffle_in_unison_scary(NM_imgs, NM_landmarks)
# full face
base = join(OUTPUT, '1_F')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = F_imgs.astype(np.float32)
h5['landmark'] = F_landmarks.astype(np.float32)
# eye and nose
base = join(OUTPUT, '1_EN')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = EN_imgs.astype(np.float32)
h5['landmark'] = EN_landmarks.astype(np.float32)
# nose and mouth
base = join(OUTPUT, '1_NM')
createDir(base)
output = join(base, fname)
logger("generate %s" % output)
with h5py.File(output, 'w') as h5:
h5['data'] = NM_imgs.astype(np.float32)
h5['landmark'] = NM_landmarks.astype(np.float32)