def get_face(img, pnet, rnet, onet, image_size):
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
margin = 44
input_image_size = image_size
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = detect_face.detect_face(img=img,
minsize=minsize,
pnet=pnet,
rnet=rnet,
onet=onet,
threshold=threshold,
factor=factor)
all_faces = []
all_bb = []
if not len(bounding_boxes) == 0:
for face in bounding_boxes:
det = np.squeeze(face[0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
face_img = imresize(arr=cropped,
size=(input_image_size, input_image_size),
mode='RGB')
all_faces.append(face_img)
all_bb.append(bb)
return all_faces, all_bb
else:
return all_faces, all_bb
def get_face(img, pnet, rnet, onet, image_size):
""" to get the face from the image
Args:
img: image file
Returns:
numpy arrays of faces and corresponding faces
"""
logger.info(msg="get_face called")
minsize = 20
threshold = [0.6, 0.7, 0.7]
factor = 0.709
margin = 44
input_image_size = image_size
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = detect_face.detect_face(img=img,
minsize=minsize,
pnet=pnet,
rnet=rnet,
onet=onet,
threshold=threshold,
factor=factor)
all_faces = []
all_bb = []
if not len(bounding_boxes) == 0:
for face in bounding_boxes:
det = np.squeeze(face[0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
# face_img = imresize(arr=cropped, size=(
# input_image_size, input_image_size), mode='RGB')
face_img = skimage.transform.resize(
cropped, (input_image_size, input_image_size))
all_faces.append(face_img)
all_bb.append(bb)
return all_faces, all_bb