def get_aligned_face(self, img, force=False):
bounding_boxes, points = detect_face.detect_face(
img, self.det_minsize, self.pnet, self.rnet, self.onet,
self.det_threshold, self.det_factor)
if bounding_boxes.shape[0] == 0 and force:
bounding_boxes, points = detect_face.detect_face_force(
img, None, self.pnet, self.rnet, self.onet)
if bounding_boxes.shape[0] == 0:
return None
bindex = 0
nrof_faces = bounding_boxes.shape[0]
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] -
det[:, 1])
img_center = img_size / 2
offsets = np.vstack([(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
bindex = np.argmax(bounding_box_size - offset_dist_squared *
2.0) # some extra weight on the centering
det = bounding_boxes[:, 0:4]
det = det[bindex, :]
points = points[:, bindex]
landmark = points.reshape((2, 5)).T
#points need to be transpose, points = points.reshape( (5,2) ).transpose()
det = np.squeeze(det)
bb = det
points = list(points.flatten())
assert (len(points) == 10)
str_image_size = "%d,%d" % (self.image_size[0], self.image_size[1])
warped = face_preprocess.preprocess(img,
bbox=bb,
landmark=landmark,
image_size=str_image_size)
warped = np.transpose(warped, (2, 0, 1))
print(warped.shape)
return warped
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
datamap = {}
pp = 0
datasize = 0
verr = 0
for line in open(args.input_dir + "_clean_list.txt", 'r'):
pp += 1
if pp % 10000 == 0:
print('loading list', pp)
line = line.strip()[2:]
if not line.startswith('m.'):
continue
vec = line.split('/')
assert len(vec) == 2
# print(line)
person = vec[0]
img = vec[1]
try:
img_id = int(img.split('.')[0])
except ValueError:
# print('value error', line)
verr += 1
continue
if not person in datamap:
labelid = len(datamap)
datamap[person] = [labelid, {img_id: 1}]
else:
datamap[person][1][img_id] = 1
datasize += 1
print('dataset size', args.name, datasize)
print('dataset err', verr)
print('Creating networks and loading parameters')
with tf.Graph().as_default():
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 100 # minimum size of face
# threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
threshold = [0.6, 0.6, 0.3] # three steps's threshold
factor = 0.709 # scale factor
print(minsize)
print(threshold)
print(factor)
# Add a random key to the filename to allow alignment using multiple processes
# random_key = np.random.randint(0, high=99999)
# bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
output_filename = os.path.join(output_dir,
'faceinsight_align_%s.lst' % args.name)
with open(output_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
nrof_changed = 0
nrof_iou3 = 0
nrof_force = 0
for line in open(args.input_dir, 'r'):
vec = line.strip().split()
person = vec[0]
img_id = int(vec[1])
v = datamap.get(person, None)
if v is None:
continue
# TODO
# if not img_id in v[1]:
# continue
labelid = v[0]
img_str = base64.b64decode(vec[-1])
nparr = np.fromstring(img_str, np.uint8)
img = cv2.imdecode(nparr, cv2.CV_LOAD_IMAGE_COLOR)
img = img[..., ::-1] # to rgb
if nrof_images_total % 100 == 0:
print("Processing %d, (%d)" %
(nrof_images_total, nrof_successfully_aligned))
nrof_images_total += 1
target_dir = os.path.join(output_dir, person)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
target_path = os.path.join(target_dir, "%d.jpg" % img_id)
_minsize = minsize
fimage = edict()
fimage.bbox = None
fimage.image_path = target_path
fimage.classname = str(labelid)
if fimage.bbox is not None:
_bb = fimage.bbox
_minsize = min([
_bb[2] - _bb[0], _bb[3] - _bb[1], img.shape[0] // 2,
img.shape[1] // 2
])
else:
_minsize = min(img.shape[0] // 5, img.shape[1] // 5)
bounding_boxes, points = detect_face.detect_face(
img, _minsize, pnet, rnet, onet, threshold, factor)
bindex = -1
nrof_faces = bounding_boxes.shape[0]
if fimage.bbox is None and nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] -
det[:, 1])
img_center = img_size / 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]
])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
bindex = np.argmax(
bounding_box_size - offset_dist_squared *
2.0) # some extra weight on the centering
if fimage.bbox is not None:
if nrof_faces > 0:
assert (bounding_boxes.shape[0] == points.shape[1])
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
index2 = [0.0, 0]
for i in xrange(det.shape[0]):
_det = det[i]
iou = IOU(fimage.bbox, _det)
if iou > index2[0]:
index2[0] = iou
index2[1] = i
if index2[0] > -0.3:
bindex = index2[1]
nrof_iou3 += 1
if bindex < 0:
bounding_boxes, points = detect_face.detect_face_force(
img, fimage.bbox, pnet, rnet, onet)
bindex = 0
nrof_force += 1
if bindex >= 0:
det = bounding_boxes[:, 0:4]
det = det[bindex, :]
points = points[:, bindex]
landmark = points.reshape((2, 5)).T
# points need to be transpose, points = points.reshape( (5,2) ).transpose()
det = np.squeeze(det)
bb = det
points = list(points.flatten())
assert (len(points) == 10)
warped = face_preprocess.preprocess(img,
bbox=bb,
landmark=landmark,
image_size=args.image_size)
misc.imsave(target_path, warped)
nrof_successfully_aligned += 1
oline = '%d\t%s\t%d' % (1, fimage.image_path,
int(fimage.classname))
# oline = '%d\t%s\t%d\t%d\t%d\t%d\t%d\t' % (0,fimage.image_path, int(fimage.classname), bb[0], bb[1], bb[2], bb[3])
# oline += '\t'.join([str(x) for x in points])
text_file.write("%s\n" % oline)
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' %
nrof_successfully_aligned)
print('Number of changed: %d' % nrof_changed)
print('Number of iou3: %d' % nrof_iou3)
print('Number of force: %d' % nrof_force)
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
#facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
image_dir = os.path.join(args.input_dir, 'facescrub')
dataset = face_image.get_dataset('facescrub', image_dir)
print('dataset size', len(dataset))
bbox = {}
for label_file in ['facescrub_actors.txt', 'facescrub_actresses.txt']:
label_file = os.path.join(args.input_dir, label_file)
pp = 0
for line in open(label_file, 'r'):
pp += 1
if pp == 1:
continue
vec = line.split("\t")
key = (vec[0], int(vec[2]))
value = [int(x) for x in vec[4].split(',')]
bbox[key] = value
print('bbox size', len(bbox))
valid_key = {}
json_data = open(
os.path.join(args.input_dir,
'facescrub_uncropped_features_list.json')).read()
json_data = json.loads(json_data)['path']
for _data in json_data:
key = _data.split('/')[-1]
pos = key.rfind('.')
if pos < 0:
print(_data)
else:
key = key[0:pos]
keys = key.split('_')
#print(key)
if len(keys) != 2:
print('err', key, _data)
continue
#assert len(keys)==2
key = (keys[0], int(keys[1]))
valid_key[key] = 1
#print(key)
print('valid keys', len(valid_key))
print('Creating networks and loading parameters')
with tf.Graph().as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 100 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
image_size = [112, 96]
image_size = [112, 112]
src = np.array([[30.2946, 51.6963], [65.5318, 51.5014], [48.0252, 71.7366],
[33.5493, 92.3655], [62.7299, 92.2041]],
dtype=np.float32)
if image_size[1] == 112:
src[:, 0] += 8.0
# Add a random key to the filename to allow alignment using multiple processes
#random_key = np.random.randint(0, high=99999)
#bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
#output_filename = os.path.join(output_dir, 'faceinsight_align_%s.lst' % args.name)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
output_filename = os.path.join(args.output_dir, 'lst')
with open(output_filename, "w") as text_file:
nrof_images_total = 0
nrof = np.zeros((5, ), dtype=np.int32)
for fimage in dataset:
if nrof_images_total % 100 == 0:
print("Processing %d, (%s)" % (nrof_images_total, nrof))
nrof_images_total += 1
#if nrof_images_total<950000:
# continue
image_path = fimage.image_path
if not os.path.exists(image_path):
print('image not found (%s)' % image_path)
continue
#print(image_path)
filename = os.path.splitext(os.path.split(image_path)[1])[0]
_paths = fimage.image_path.split('/')
print(fimage.image_path)
a, b = _paths[-2], _paths[-1]
pb = b.rfind('.')
bname = b[0:pb]
pb = bname.rfind('_')
body = bname[(pb + 1):]
img_id = int(body)
key = (a, img_id)
if not key in valid_key:
continue
#print(b, img_id)
assert key in bbox
fimage.bbox = bbox[key]
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim < 2:
print('Unable to align "%s", img dim error' % image_path)
#text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = to_rgb(img)
img = img[:, :, 0:3]
tb = bname.replace(' ', '_') + ".png"
ta = a.replace(' ', '_')
target_dir = os.path.join(args.output_dir, ta)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
target_file = os.path.join(target_dir, tb)
warped = None
if fimage.landmark is not None:
dst = fimage.landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst,
src[0:3, :] * 1.5 + image_size[0] * 0.25)
M = tform.params[0:2, :]
warped0 = cv2.warpAffine(
img,
M, (image_size[1] * 2, image_size[0] * 2),
borderValue=0.0)
_minsize = image_size[0]
bounding_boxes, points = detect_face.detect_face(
warped0, _minsize, pnet, rnet, onet, threshold, factor)
if bounding_boxes.shape[0] > 0:
bindex = 0
det = bounding_boxes[bindex, 0:4]
#points need to be transpose, points = points.reshape( (5,2) ).transpose()
dst = points[:, bindex].reshape((2, 5)).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
warped = cv2.warpAffine(warped0,
M,
(image_size[1], image_size[0]),
borderValue=0.0)
nrof[0] += 1
#assert fimage.bbox is not None
if warped is None and fimage.bbox is not None:
_minsize = img.shape[0] // 4
bounding_boxes, points = detect_face.detect_face(
img, _minsize, pnet, rnet, onet, threshold, factor)
if bounding_boxes.shape[0] > 0:
det = bounding_boxes[:, 0:4]
bindex = -1
index2 = [0.0, 0]
for i in range(det.shape[0]):
_det = det[i]
iou = IOU(fimage.bbox, _det)
if iou > index2[0]:
index2[0] = iou
index2[1] = i
if index2[0] > 0.3:
bindex = index2[1]
if bindex >= 0:
dst = points[:, bindex].reshape((2, 5)).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
warped = cv2.warpAffine(
img,
M, (image_size[1], image_size[0]),
borderValue=0.0)
nrof[1] += 1
#print('1',target_file,index2[0])
if warped is None and fimage.bbox is not None:
bb = fimage.bbox
#croped = img[bb[1]:bb[3],bb[0]:bb[2],:]
bounding_boxes, points = detect_face.detect_face_force(
img, bb, pnet, rnet, onet)
assert bounding_boxes.shape[0] == 1
_box = bounding_boxes[0]
if _box[4] >= 0.3:
dst = points[:, 0].reshape((2, 5)).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
warped = cv2.warpAffine(img,
M,
(image_size[1], image_size[0]),
borderValue=0.0)
nrof[2] += 1
#print('2',target_file)
if warped is None:
roi = np.zeros((4, ), dtype=np.int32)
roi[0] = int(img.shape[1] * 0.06)
roi[1] = int(img.shape[0] * 0.06)
roi[2] = img.shape[1] - roi[0]
roi[3] = img.shape[0] - roi[1]
if fimage.bbox is not None:
bb = fimage.bbox
h = bb[3] - bb[1]
w = bb[2] - bb[0]
x = bb[0]
y = bb[1]
#roi = np.copy(bb)
_w = int((float(h) / image_size[0]) * image_size[1])
x += (w - _w) // 2
#x = min( max(0,x), img.shape[1] )
x = max(0, x)
xw = x + _w
xw = min(xw, img.shape[1])
roi = np.array((x, y, xw, y + h), dtype=np.int32)
nrof[3] += 1
else:
nrof[4] += 1
#print('3',bb,roi,img.shape)
#print('3',target_file)
warped = img[roi[1]:roi[3], roi[0]:roi[2], :]
#print(warped.shape)
warped = cv2.resize(warped, (image_size[1], image_size[0]))
bgr = warped[..., ::-1]
cv2.imwrite(target_file, bgr)
oline = '%d\t%s\t%d\n' % (1, target_file, int(
fimage.classname))
text_file.write(oline)
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
#facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
image_dir = os.path.join(args.input_dir, 'facescrub')
dataset = face_image.get_dataset('facescrub', image_dir)
print('dataset size', len(dataset))
bbox = {}
for label_file in ['facescrub_actors.txt', 'facescrub_actresses.txt']:
label_file = os.path.join(args.input_dir, label_file)
pp = 0
for line in open(label_file, 'r'):
pp+=1
if pp==1:
continue
vec = line.split("\t")
key = (vec[0], int(vec[2]))
value = [int(x) for x in vec[4].split(',')]
bbox[key] = value
print('bbox size', len(bbox))
valid_key = {}
json_data = open(os.path.join(args.input_dir, 'facescrub_uncropped_features_list.json')).read()
json_data = json.loads(json_data)['path']
for _data in json_data:
key = _data.split('/')[-1]
pos = key.rfind('.')
if pos<0:
print(_data)
else:
key = key[0:pos]
keys = key.split('_')
#print(key)
if len(keys)!=2:
print('err', key, _data)
continue
#assert len(keys)==2
key = (keys[0], int(keys[1]))
valid_key[key] = 1
#print(key)
print('valid keys', len(valid_key))
print('Creating networks and loading parameters')
with tf.Graph().as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 100 # minimum size of face
threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
factor = 0.709 # scale factor
image_size = [112,96]
image_size = [112,112]
src = np.array([
[30.2946, 51.6963],
[65.5318, 51.5014],
[48.0252, 71.7366],
[33.5493, 92.3655],
[62.7299, 92.2041] ], dtype=np.float32 )
if image_size[1]==112:
src[:,0] += 8.0
# Add a random key to the filename to allow alignment using multiple processes
#random_key = np.random.randint(0, high=99999)
#bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
#output_filename = os.path.join(output_dir, 'faceinsight_align_%s.lst' % args.name)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
output_filename = os.path.join(args.output_dir, 'lst')
with open(output_filename, "w") as text_file:
nrof_images_total = 0
nrof = np.zeros( (5,), dtype=np.int32)
for fimage in dataset:
if nrof_images_total%100==0:
print("Processing %d, (%s)" % (nrof_images_total, nrof))
nrof_images_total += 1
#if nrof_images_total<950000:
# continue
image_path = fimage.image_path
if not os.path.exists(image_path):
print('image not found (%s)'%image_path)
continue
#print(image_path)
filename = os.path.splitext(os.path.split(image_path)[1])[0]
_paths = fimage.image_path.split('/')
print(fimage.image_path)
a,b = _paths[-2], _paths[-1]
pb = b.rfind('.')
bname = b[0:pb]
pb = bname.rfind('_')
body = bname[(pb+1):]
img_id = int(body)
key = (a, img_id)
if not key in valid_key:
continue
#print(b, img_id)
assert key in bbox
fimage.bbox = bbox[key]
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim<2:
print('Unable to align "%s", img dim error' % image_path)
#text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = to_rgb(img)
img = img[:,:,0:3]
tb = bname.replace(' ','_')+".png"
ta = a.replace(' ','_')
target_dir = os.path.join(args.output_dir, ta)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
target_file = os.path.join(target_dir, tb)
warped = None
if fimage.landmark is not None:
dst = fimage.landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst, src[0:3,:]*1.5+image_size[0]*0.25)
M = tform.params[0:2,:]
warped0 = cv2.warpAffine(img,M,(image_size[1]*2,image_size[0]*2), borderValue = 0.0)
_minsize = image_size[0]
bounding_boxes, points = detect_face.detect_face(warped0, _minsize, pnet, rnet, onet, threshold, factor)
if bounding_boxes.shape[0]>0:
bindex = 0
det = bounding_boxes[bindex,0:4]
#points need to be transpose, points = points.reshape( (5,2) ).transpose()
dst = points[:, bindex].reshape( (2,5) ).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
warped = cv2.warpAffine(warped0,M,(image_size[1],image_size[0]), borderValue = 0.0)
nrof[0]+=1
#assert fimage.bbox is not None
if warped is None and fimage.bbox is not None:
_minsize = img.shape[0]//4
bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, threshold, factor)
if bounding_boxes.shape[0]>0:
det = bounding_boxes[:,0:4]
bindex = -1
index2 = [0.0, 0]
for i in xrange(det.shape[0]):
_det = det[i]
iou = IOU(fimage.bbox, _det)
if iou>index2[0]:
index2[0] = iou
index2[1] = i
if index2[0]>0.3:
bindex = index2[1]
if bindex>=0:
dst = points[:, bindex].reshape( (2,5) ).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
nrof[1]+=1
#print('1',target_file,index2[0])
if warped is None and fimage.bbox is not None:
bb = fimage.bbox
#croped = img[bb[1]:bb[3],bb[0]:bb[2],:]
bounding_boxes, points = detect_face.detect_face_force(img, bb, pnet, rnet, onet)
assert bounding_boxes.shape[0]==1
_box = bounding_boxes[0]
if _box[4]>=0.3:
dst = points[:, 0].reshape( (2,5) ).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
nrof[2]+=1
#print('2',target_file)
if warped is None:
roi = np.zeros( (4,), dtype=np.int32)
roi[0] = int(img.shape[1]*0.06)
roi[1] = int(img.shape[0]*0.06)
roi[2] = img.shape[1]-roi[0]
roi[3] = img.shape[0]-roi[1]
if fimage.bbox is not None:
bb = fimage.bbox
h = bb[3]-bb[1]
w = bb[2]-bb[0]
x = bb[0]
y = bb[1]
#roi = np.copy(bb)
_w = int( (float(h)/image_size[0])*image_size[1] )
x += (w-_w)//2
#x = min( max(0,x), img.shape[1] )
x = max(0,x)
xw = x+_w
xw = min(xw, img.shape[1])
roi = np.array( (x, y, xw, y+h), dtype=np.int32)
nrof[3]+=1
else:
nrof[4]+=1
#print('3',bb,roi,img.shape)
#print('3',target_file)
warped = img[roi[1]:roi[3],roi[0]:roi[2],:]
#print(warped.shape)
warped = cv2.resize(warped, (image_size[1], image_size[0]))
bgr = warped[...,::-1]
cv2.imwrite(target_file, bgr)
oline = '%d\t%s\t%d\n' % (1,target_file, int(fimage.classname))
text_file.write(oline)
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
#facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
dataset = face_image.get_dataset(args.name, args.input_dir)
print('dataset size', args.name, len(dataset))
print('Creating networks and loading parameters')
with tf.Graph().as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 100 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
if args.name == 'lfw' or args.name == 'webface' or args.name == 'vgg':
minsize = 20
threshold = [0.6, 0.7, 0.9]
factor = 0.85
if args.name == 'ytf':
minsize = 20
threshold = [0.6, 0.7, 0.4]
factor = 0.85
print(minsize)
print(threshold)
print(factor)
# Add a random key to the filename to allow alignment using multiple processes
#random_key = np.random.randint(0, high=99999)
#bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
output_filename = os.path.join(output_dir,
'faceinsight_align_%s.lst' % args.name)
with open(output_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
nrof_changed = 0
nrof_iou3 = 0
nrof_force = 0
for fimage in dataset:
if nrof_images_total % 100 == 0:
print("Processing %d, (%d)" %
(nrof_images_total, nrof_successfully_aligned))
nrof_images_total += 1
image_path = fimage.image_path
if not os.path.exists(image_path):
print('image not found (%s)' % image_path)
continue
filename = os.path.splitext(os.path.split(image_path)[1])[0]
#print(image_path)
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim < 2:
print('Unable to align "%s", img dim error' % image_path)
#text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = to_rgb(img)
img = img[:, :, 0:3]
_minsize = minsize
if fimage.bbox is not None:
_bb = fimage.bbox
_minsize = min([
_bb[2] - _bb[0], _bb[3] - _bb[1], img.shape[0] // 2,
img.shape[1] // 2
])
bounding_boxes, points = detect_face.detect_face(
img, _minsize, pnet, rnet, onet, threshold, factor)
bindex = -1
nrof_faces = bounding_boxes.shape[0]
if fimage.bbox is None and nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (
det[:, 3] - det[:, 1])
img_center = img_size / 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 - img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]
])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
bindex = np.argmax(
bounding_box_size - offset_dist_squared *
2.0) # some extra weight on the centering
if fimage.bbox is not None:
if nrof_faces > 0:
assert (bounding_boxes.shape[0] == points.shape[1])
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
index2 = [0.0, 0]
for i in xrange(det.shape[0]):
_det = det[i]
iou = IOU(fimage.bbox, _det)
if iou > index2[0]:
index2[0] = iou
index2[1] = i
if index2[0] > -0.3:
bindex = index2[1]
nrof_iou3 += 1
if bindex < 0:
bounding_boxes, points = detect_face.detect_face_force(
img, fimage.bbox, pnet, rnet, onet)
bindex = 0
nrof_force += 1
#if bindex<0:
# _img = img[fimage.bbox[1]:fimage.bbox[3], fimage.bbox[0]:fimage.bbox[2],:]
# woffset = fimage.bbox[0]
# hoffset = fimage.bbox[1]
# _minsize = min( [_img.shape[0]//3, _img.shape[1]//3] )
# bounding_boxes, points = detect_face.detect_face(_img, _minsize, pnet, rnet, onet, [0.6,0.7,0.01], factor)
# nrof_faces = bounding_boxes.shape[0]
# print(nrof_faces)
# if nrof_faces>0:
# #print(points.shape)
# #assert(nrof_faces>0)
# bounding_boxes[:,0]+=woffset
# bounding_boxes[:,2]+=woffset
# bounding_boxes[:,1]+=hoffset
# bounding_boxes[:,3]+=hoffset
# points[0:5,:] += woffset
# points[5:10,:] += hoffset
# bindex = 0
# score = bounding_boxes[bindex,4]
# print(score)
# if score<=0.0:
# bindex = -1
# else:
# nrof_force+=1
#if bindex<0:
# _bb = fimage.bbox
# _minsize = min( [_bb[2]-_bb[0], _bb[3]-_bb[1], img.shape[0]//2, img.shape[1]//2] )
# bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, [0.6,0.7,0.1], factor)
# nrof_faces = bounding_boxes.shape[0]
# print(nrof_faces)
# if nrof_faces>0:
# bindex = 0
#if fimage.bbox is not None and bounding_boxes.shape[0]==0:
# bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, [0.6,0.7,0.3], factor)
#print(bounding_boxes.shape, points.shape)
#print(nrof_faces, points.shape)
if bindex >= 0:
det = bounding_boxes[:, 0:4]
det = det[bindex, :]
points = points[:, bindex]
#points need to be transpose, points = points.reshape( (5,2) ).transpose()
det = np.squeeze(det)
#bb = np.zeros(4, dtype=np.int32)
#bb[0] = np.maximum(det[0]-args.margin/2, 0)
#bb[1] = np.maximum(det[1]-args.margin/2, 0)
#bb[2] = np.minimum(det[2]+args.margin/2, img_size[1])
#bb[3] = np.minimum(det[3]+args.margin/2, img_size[0])
bb = det
#print(points.shape)
points = list(points.flatten())
assert (len(points) == 10)
#cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
#scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
#misc.imsave(output_filename, scaled)
nrof_successfully_aligned += 1
oline = '%d\t%s\t%d\t%d\t%d\t%d\t%d\t' % (
0, fimage.image_path, int(
fimage.classname), bb[0], bb[1], bb[2], bb[3])
oline += '\t'.join([str(x) for x in points])
text_file.write("%s\n" % oline)
else:
print('Unable to align "%s", no face detected' %
image_path)
if args.force > 0:
if fimage.bbox is None:
oline = '%d\t%s\t%d\n' % (0, fimage.image_path,
int(fimage.classname))
else:
bb = fimage.bbox
oline = '%d\t%s\t%d\t%d\t%d\t%d\t%d\n' % (
0, fimage.image_path, int(fimage.classname),
bb[0], bb[1], bb[2], bb[3])
text_file.write(oline)
#text_file.write('%s\n' % (output_filename))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' %
nrof_successfully_aligned)
print('Number of changed: %d' % nrof_changed)
print('Number of iou3: %d' % nrof_iou3)
print('Number of force: %d' % nrof_force)
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
#facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
dataset = face_image.get_dataset(args.name, args.input_dir)
print('dataset size', args.name, len(dataset))
print('Creating networks and loading parameters')
with tf.Graph().as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 100 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
#image_size = [112,96]
image_size = [112, 112]
src = np.array([[30.2946, 51.6963], [65.5318, 51.5014], [48.0252, 71.7366],
[33.5493, 92.3655], [62.7299, 92.2041]],
dtype=np.float32)
if image_size[1] == 112:
src[:, 0] += 8.0
# Add a random key to the filename to allow alignment using multiple processes
#random_key = np.random.randint(0, high=99999)
#bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
#output_filename = os.path.join(output_dir, 'faceinsight_align_%s.lst' % args.name)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
output_filename = os.path.join(args.output_dir, 'lst')
with open(output_filename, "w") as text_file:
nrof_images_total = 0
nrof = np.zeros((5, ), dtype=np.int32)
for fimage in dataset:
if nrof_images_total % 100 == 0:
print("Processing %d, (%s)" % (nrof_images_total, nrof))
nrof_images_total += 1
#if nrof_images_total<950000:
# continue
image_path = fimage.image_path
if not os.path.exists(image_path):
print('image not found (%s)' % image_path)
continue
filename = os.path.splitext(os.path.split(image_path)[1])[0]
#print(image_path)
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim < 2:
print('Unable to align "%s", img dim error' % image_path)
#text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = to_rgb(img)
img = img[:, :, 0:3]
_paths = fimage.image_path.split('/')
a, b, c = _paths[-3], _paths[-2], _paths[-1]
target_dir = os.path.join(args.output_dir, a, b)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
target_file = os.path.join(target_dir, c)
warped = None
if fimage.landmark is not None:
dst = fimage.landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst,
src[0:3, :] * 1.5 + image_size[0] * 0.25)
M = tform.params[0:2, :]
warped0 = cv2.warpAffine(
img,
M, (image_size[1] * 2, image_size[0] * 2),
borderValue=0.0)
_minsize = image_size[0]
bounding_boxes, points = detect_face.detect_face(
warped0, _minsize, pnet, rnet, onet, threshold, factor)
if bounding_boxes.shape[0] > 0:
bindex = 0
det = bounding_boxes[bindex, 0:4]
#points need to be transpose, points = points.reshape( (5,2) ).transpose()
dst = points[:, bindex].reshape((2, 5)).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
warped = cv2.warpAffine(warped0,
M,
(image_size[1], image_size[0]),
borderValue=0.0)
nrof[0] += 1 # ! tight bbox
#assert fimage.bbox is not None
if warped is None and fimage.bbox is not None:
_minsize = img.shape[0] // 4
bounding_boxes, points = detect_face.detect_face(
img, _minsize, pnet, rnet, onet, threshold, factor)
if bounding_boxes.shape[0] > 0:
det = bounding_boxes[:, 0:4]
bindex = -1
index2 = [0.0, 0]
for i in xrange(det.shape[0]):
_det = det[i]
iou = IOU(fimage.bbox, _det)
if iou > index2[0]:
index2[0] = iou
index2[1] = i
if index2[0] > 0.3:
bindex = index2[1]
if bindex >= 0:
dst = points[:, bindex].reshape((2, 5)).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
warped = cv2.warpAffine(
img,
M, (image_size[1], image_size[0]),
borderValue=0.0)
nrof[1] += 1 # ! small loose bbox
#print('1',target_file,index2[0])
if warped is None and fimage.bbox is not None:
bb = fimage.bbox
#croped = img[bb[1]:bb[3],bb[0]:bb[2],:]
bounding_boxes, points = detect_face.detect_face_force(
img, bb, pnet, rnet, onet)
assert bounding_boxes.shape[0] == 1
_box = bounding_boxes[0]
if _box[4] >= 0.3:
dst = points[:, 0].reshape((2, 5)).T
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2, :]
warped = cv2.warpAffine(img,
M,
(image_size[1], image_size[0]),
borderValue=0.0)
nrof[2] += 1 # adjust label bbox
#print('2',target_file)
if warped is None:
roi = np.zeros((4, ), dtype=np.int32)
roi[0] = int(img.shape[1] * 0.06)
roi[1] = int(img.shape[0] * 0.06)
roi[2] = img.shape[1] - roi[0]
roi[3] = img.shape[0] - roi[1]
if fimage.bbox is not None:
bb = fimage.bbox
h = bb[3] - bb[1]
w = bb[2] - bb[0]
x = bb[0]
y = bb[1]
#roi = np.copy(bb)
_w = int((float(h) / image_size[0]) * image_size[1])
x += (w - _w) // 2
#x = min( max(0,x), img.shape[1] )
x = max(0, x)
xw = x + _w
xw = min(xw, img.shape[1])
roi = np.array((x, y, xw, y + h), dtype=np.int32)
nrof[3] += 1 # label bbox
else:
nrof[4] += 1 # tight inside bbox
#print('3',bb,roi,img.shape)
#print('3',target_file)
warped = img[roi[1]:roi[3], roi[0]:roi[2], :]
#print(warped.shape)
warped = cv2.resize(warped, (image_size[1], image_size[0]))
bgr = warped[..., ::-1]
cv2.imwrite(target_file, bgr)
oline = '%d\t%s\t%d\n' % (1, target_file, int(
fimage.classname))
text_file.write(oline)
landmark = []
for line in open(txt_path, 'r'):
vec = line.strip().split(',')
x = np.array([float(x) for x in vec], dtype=np.float32)
landmark.append(x)
landmark = np.array(landmark, dtype=np.float32)
# print(landmark.shape)
img = misc.imread(img_path)
_bbox = None
_landmark = None
bounding_boxes, points = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
nrof[0] += 1
else:
bounding_boxes, points = detect_face.detect_face_force(img, minsize, pnet, rnet, onet)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
nrof[1] += 1
else:
nrof[2] += 1
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (det[:, 3] - det[:, 1])
img_center = img_size / 2
offsets = np.vstack(
[(det[:, 0] + det[:, 2]) / 2 - img_center[1], (det[:, 1] + det[:, 3]) / 2 - img_center[0]])
offset_dist_squared = np.sum(np.power(offsets, 2.0), 0)
landmark = []
for line in open(txt_path, 'r'):
vec = line.strip().split(',')
x = np.array( [float(x) for x in vec], dtype=np.float32)
landmark.append(x)
landmark = np.array(landmark, dtype=np.float32)
#print(landmark.shape)
img = misc.imread(img_path)
_bbox = None
_landmark = None
bounding_boxes, points = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces>0:
nrof[0]+=1
else:
bounding_boxes, points = detect_face.detect_face_force(img, minsize, pnet, rnet, onet)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces>0:
nrof[1]+=1
else:
nrof[2]+=1
if nrof_faces>0:
det = bounding_boxes[:,0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces>1:
bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
img_center = img_size / 2
offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
offset_dist_squared = np.sum(np.power(offsets,2.0),0)
bindex = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
src_path,_ = os.path.split(os.path.realpath(__file__))
#facenet.store_revision_info(src_path, output_dir, ' '.join(sys.argv))
dataset = face_image.get_dataset(args.name, args.input_dir)
print('dataset size', args.name, len(dataset))
print('Creating networks and loading parameters')
with tf.Graph().as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 100 # minimum size of face
threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
factor = 0.709 # scale factor
if args.name=='lfw' or args.name=='webface' or args.name=='vgg':
minsize = 20
threshold = [0.6,0.7,0.9]
factor = 0.85
print(minsize)
print(threshold)
print(factor)
# Add a random key to the filename to allow alignment using multiple processes
#random_key = np.random.randint(0, high=99999)
#bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
output_filename = os.path.join(output_dir, 'faceinsight_align_%s.lst' % args.name)
with open(output_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
nrof_changed = 0
nrof_iou3 = 0
nrof_force = 0
for fimage in dataset:
if nrof_images_total%100==0:
print("Processing %d, (%d)" % (nrof_images_total, nrof_successfully_aligned))
nrof_images_total += 1
image_path = fimage.image_path
if not os.path.exists(image_path):
print('image not found (%s)'%image_path)
continue
filename = os.path.splitext(os.path.split(image_path)[1])[0]
#print(image_path)
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim<2:
print('Unable to align "%s", img dim error' % image_path)
#text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = to_rgb(img)
img = img[:,:,0:3]
_minsize = minsize
if fimage.bbox is not None:
_bb = fimage.bbox
_minsize = min( [_bb[2]-_bb[0], _bb[3]-_bb[1], img.shape[0]//2, img.shape[1]//2] )
bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, threshold, factor)
bindex = -1
nrof_faces = bounding_boxes.shape[0]
if fimage.bbox is None and nrof_faces>0:
det = bounding_boxes[:,0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces>1:
bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
img_center = img_size / 2
offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
offset_dist_squared = np.sum(np.power(offsets,2.0),0)
bindex = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
if fimage.bbox is not None:
if nrof_faces>0:
assert(bounding_boxes.shape[0]==points.shape[1])
det = bounding_boxes[:,0:4]
img_size = np.asarray(img.shape)[0:2]
index2 = [0.0, 0]
for i in xrange(det.shape[0]):
_det = det[i]
iou = IOU(fimage.bbox, _det)
if iou>index2[0]:
index2[0] = iou
index2[1] = i
if index2[0]>-0.3:
bindex = index2[1]
nrof_iou3+=1
if bindex<0:
bounding_boxes, points = detect_face.detect_face_force(img, fimage.bbox, pnet, rnet, onet)
bindex = 0
nrof_force+=1
#if bindex<0:
# _img = img[fimage.bbox[1]:fimage.bbox[3], fimage.bbox[0]:fimage.bbox[2],:]
# woffset = fimage.bbox[0]
# hoffset = fimage.bbox[1]
# _minsize = min( [_img.shape[0]//3, _img.shape[1]//3] )
# bounding_boxes, points = detect_face.detect_face(_img, _minsize, pnet, rnet, onet, [0.6,0.7,0.01], factor)
# nrof_faces = bounding_boxes.shape[0]
# print(nrof_faces)
# if nrof_faces>0:
# #print(points.shape)
# #assert(nrof_faces>0)
# bounding_boxes[:,0]+=woffset
# bounding_boxes[:,2]+=woffset
# bounding_boxes[:,1]+=hoffset
# bounding_boxes[:,3]+=hoffset
# points[0:5,:] += woffset
# points[5:10,:] += hoffset
# bindex = 0
# score = bounding_boxes[bindex,4]
# print(score)
# if score<=0.0:
# bindex = -1
# else:
# nrof_force+=1
#if bindex<0:
# _bb = fimage.bbox
# _minsize = min( [_bb[2]-_bb[0], _bb[3]-_bb[1], img.shape[0]//2, img.shape[1]//2] )
# bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, [0.6,0.7,0.1], factor)
# nrof_faces = bounding_boxes.shape[0]
# print(nrof_faces)
# if nrof_faces>0:
# bindex = 0
#if fimage.bbox is not None and bounding_boxes.shape[0]==0:
# bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, [0.6,0.7,0.3], factor)
#print(bounding_boxes.shape, points.shape)
#print(nrof_faces, points.shape)
if bindex>=0:
det = bounding_boxes[:,0:4]
det = det[bindex,:]
points = points[:, bindex]
#points need to be transpose, points = points.reshape( (5,2) ).transpose()
det = np.squeeze(det)
#bb = np.zeros(4, dtype=np.int32)
#bb[0] = np.maximum(det[0]-args.margin/2, 0)
#bb[1] = np.maximum(det[1]-args.margin/2, 0)
#bb[2] = np.minimum(det[2]+args.margin/2, img_size[1])
#bb[3] = np.minimum(det[3]+args.margin/2, img_size[0])
bb = det
#print(points.shape)
points = list(points.flatten())
assert(len(points)==10)
#cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
#scaled = misc.imresize(cropped, (args.image_size, args.image_size), interp='bilinear')
#misc.imsave(output_filename, scaled)
nrof_successfully_aligned += 1
oline = '%d\t%s\t%d\t%d\t%d\t%d\t%d\t' % (0,fimage.image_path, int(fimage.classname), bb[0], bb[1], bb[2], bb[3])
oline += '\t'.join([str(x) for x in points])
text_file.write("%s\n"%oline)
else:
print('Unable to align "%s", no face detected' % image_path)
if args.force>0:
if fimage.bbox is None:
oline = '%d\t%s\t%d\n' % (0,fimage.image_path, int(fimage.classname))
else:
bb = fimage.bbox
oline = '%d\t%s\t%d\t%d\t%d\t%d\t%d\n' % (0,fimage.image_path, int(fimage.classname), bb[0], bb[1], bb[2], bb[3])
text_file.write(oline)
#text_file.write('%s\n' % (output_filename))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
print('Number of changed: %d' % nrof_changed)
print('Number of iou3: %d' % nrof_iou3)
print('Number of force: %d' % nrof_force)
def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
datamap = {}
pp = 0
datasize = 0
verr = 0
for line in open(args.input_dir+"_clean_list.txt", 'r'):
pp+=1
if pp%10000==0:
print('loading list', pp)
line = line.strip()[2:]
if not line.startswith('m.'):
continue
vec = line.split('/')
assert len(vec)==2
#print(line)
person = vec[0]
img = vec[1]
try:
img_id = int(img.split('.')[0])
except ValueError:
#print('value error', line)
verr+=1
continue
if not person in datamap:
labelid = len(datamap)
datamap[person] = [labelid, {img_id : 1}]
else:
datamap[person][1][img_id] = 1
datasize+=1
print('dataset size', args.name, datasize)
print('dataset err', verr)
print('Creating networks and loading parameters')
with tf.Graph().as_default():
#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 100 # minimum size of face
threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
factor = 0.709 # scale factor
print(minsize)
print(threshold)
print(factor)
# Add a random key to the filename to allow alignment using multiple processes
#random_key = np.random.randint(0, high=99999)
#bounding_boxes_filename = os.path.join(output_dir, 'bounding_boxes_%05d.txt' % random_key)
output_filename = os.path.join(output_dir, 'faceinsight_align_%s.lst' % args.name)
with open(output_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
nrof_changed = 0
nrof_iou3 = 0
nrof_force = 0
for line in open(args.input_dir, 'r'):
vec = line.strip().split()
person = vec[0]
img_id = int(vec[1])
v = datamap.get(person, None)
if v is None:
continue
if not img_id in v[1]:
continue
labelid = v[0]
img_str = base64.b64decode(vec[-1])
nparr = np.fromstring(img_str, np.uint8)
img = cv2.imdecode(nparr, cv2.CV_LOAD_IMAGE_COLOR)
img = img[...,::-1] #to rgb
if nrof_images_total%100==0:
print("Processing %d, (%d)" % (nrof_images_total, nrof_successfully_aligned))
nrof_images_total += 1
target_dir = os.path.join(output_dir, person)
if not os.path.exists(target_dir):
os.makedirs(target_dir)
target_path = os.path.join(target_dir, "%d.jpg"%img_id)
_minsize = minsize
fimage = edict()
fimage.bbox = None
fimage.image_path = target_path
fimage.classname = str(labelid)
if fimage.bbox is not None:
_bb = fimage.bbox
_minsize = min( [_bb[2]-_bb[0], _bb[3]-_bb[1], img.shape[0]//2, img.shape[1]//2] )
bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, threshold, factor)
bindex = -1
nrof_faces = bounding_boxes.shape[0]
if fimage.bbox is None and nrof_faces>0:
det = bounding_boxes[:,0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces>1:
bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
img_center = img_size / 2
offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
offset_dist_squared = np.sum(np.power(offsets,2.0),0)
bindex = np.argmax(bounding_box_size-offset_dist_squared*2.0) # some extra weight on the centering
if fimage.bbox is not None:
if nrof_faces>0:
assert(bounding_boxes.shape[0]==points.shape[1])
det = bounding_boxes[:,0:4]
img_size = np.asarray(img.shape)[0:2]
index2 = [0.0, 0]
for i in xrange(det.shape[0]):
_det = det[i]
iou = IOU(fimage.bbox, _det)
if iou>index2[0]:
index2[0] = iou
index2[1] = i
if index2[0]>-0.3:
bindex = index2[1]
nrof_iou3+=1
if bindex<0:
bounding_boxes, points = detect_face.detect_face_force(img, fimage.bbox, pnet, rnet, onet)
bindex = 0
nrof_force+=1
if bindex>=0:
det = bounding_boxes[:,0:4]
det = det[bindex,:]
points = points[:, bindex]
landmark = points.reshape((2,5)).T
#points need to be transpose, points = points.reshape( (5,2) ).transpose()
det = np.squeeze(det)
bb = det
points = list(points.flatten())
assert(len(points)==10)
warped = face_preprocess.preprocess(img, bbox=bb, landmark = landmark, image_size=args.image_size)
misc.imsave(target_path, warped)
nrof_successfully_aligned += 1
oline = '%d\t%s\t%d' % (1,fimage.image_path, int(fimage.classname))
#oline = '%d\t%s\t%d\t%d\t%d\t%d\t%d\t' % (0,fimage.image_path, int(fimage.classname), bb[0], bb[1], bb[2], bb[3])
#oline += '\t'.join([str(x) for x in points])
text_file.write("%s\n"%oline)
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' % nrof_successfully_aligned)
print('Number of changed: %d' % nrof_changed)
print('Number of iou3: %d' % nrof_iou3)
print('Number of force: %d' % nrof_force)
