def recognise_face(imagepath, database, model):
encoding = img_to_encoding(imagepath, model)
identity = None
min_dist = 100
for (name, db_enc) in database.items():
dist = np.linalg.norm(db_enc - encoding)
print('distance for %s is %s' % (name, dist))
if dist < min_dist:
min_dist = dist
identity = name
if min_dist > 0.7:
return "\n----Can't recognize face----"
else:
return "\nWelcome Mr. {}".format(str(identity))
### encoding test_face
#test_face_img = cv2.resize(test_face_img, (96,96), interpolation=cv2.INTER_CUBIC)
#normalized_face_img, normalized_face_lndmarks = normalize_faces_landmarks(out_img_size=(96,96),
# in_img=test_face_img,
# landmarks=test_face_lndmarks)
normalized_face_img, normalized_face_lndmarks = align_face(out_img_size=(96,96),
in_img=test_img,
landmarks=test_face_lndmarks)
aligned_faces_list.append(normalized_face_img)
#test_face_encodings = img_to_encoding(test_face_img, faceNet)
test_face_encodings = img_to_encoding(normalized_face_img, faceNet)
dist, identity, matched_idx=compute_dist_label(reg_faces_encodings, index_dic, test_face_encodings, THRESHOLD=0.76)
#cv2.imwrite(identity+".jpg", test_face_img)
######## drawing faces and labels or person names
if(identity == "unknown"):
DRAW_COLOR = (255, 255, 0)
unimg=np.zeros((96,96,3), dtype= np.uint)
matched_face_img_list.append(unimg)
else:
matched_face_img_list.append(reg_aligned_faces[matched_idx])
def save_encoded_faces_pickle(images_path, out_pic_file_path, faceNet):
'''
load face images from the images_path, detect faces, encode them and save the results pickle file
'''
threshold = [0.3, 0.6, 0.7]
faceDetector_mtcnn = mtcnn()
# prepare our registered people data
# assuming images folder contain individual images in subfolders
# each person images are put in corresponding subfolders, and each image of a person contains only face
person_subfolders = []
for p in os.listdir(images_path):
ppath = os.path.join(images_path, p)
if os.path.isdir(ppath):
person_subfolders.append(ppath)
#initial person name label Map
name_label_map = {}
# integer values of each person label
labels = []
person_image_paths = []
for i, person_subfolder in enumerate(person_subfolders):
for p in os.listdir(person_subfolder):
ppath = os.path.join(person_subfolder, p)
if p.endswith('jpg'):
person_image_paths.append(ppath)
labels.append(i)
name_label_map[p] = person_subfolder
# now process each person images one by one and encode them using the faceNet model
# we will stored each encodings in face_encodings and their corresponding labels in index dictionary
index_dic = {}
i = 0
faces_encoding = None
for image_path in person_image_paths:
debug("encoding face detected in : {}".format(image_path))
image_cv = cv2.imread(image_path, 1)
img_draw, face_imgs, face_bboxes, faces_landmarks = extract_draw_faces(
faceDetector_mtcnn, image_cv, threshold)
cv2.imshow("face image", img_draw)
if len(face_imgs) == 0:
continue
face_lndmrk_draw = face_imgs[0].copy()
lndmrks = np.array(faces_landmarks[0])
cv2.putText(face_lndmrk_draw, '1', (lndmrks[0][0], lndmrks[0][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[0][0], lndmrks[0][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '2', (lndmrks[1][0], lndmrks[1][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[1][0], lndmrks[1][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '3', (lndmrks[2][0], lndmrks[2][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[2][0], lndmrks[2][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '4', (lndmrks[3][0], lndmrks[3][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[3][0], lndmrks[3][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '5', (lndmrks[4][0], lndmrks[4][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[4][0], lndmrks[4][1]), 2,
(0, 255, 0))
cv2.imshow("face", face_lndmrk_draw)
#face_cv = cv2.resize(face_imgs[0], (96,96), interpolation=cv2.INTER_CUBIC)
#cv2.imshow("face 96x96", face_cv)
#aligned_face_cv, aligned_face_lndmarks = normalize_faces_landmarks(out_img_size=(96,96), in_img=face_imgs[0], landmarks=lndmrks)
#aligned_face_cv, aligned_face_lndmarks = align_face(out_img_size=(96,96), in_img=face_imgs[0], landmarks=lndmrks)
aligned_face_cv, aligned_face_lndmarks = align_face(out_img_size=(96,
96),
in_img=image_cv,
landmarks=lndmrks)
aligned_face_draw = aligned_face_cv.copy()
cv2.putText(aligned_face_draw, '1',
(aligned_face_lndmarks[0][0], aligned_face_lndmarks[0][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(aligned_face_draw,
(aligned_face_lndmarks[0][0], aligned_face_lndmarks[0][1]),
2, (0, 255, 0))
cv2.putText(aligned_face_draw, '2',
(aligned_face_lndmarks[1][0], aligned_face_lndmarks[1][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(aligned_face_draw,
(aligned_face_lndmarks[1][0], aligned_face_lndmarks[1][1]),
2, (0, 255, 0))
cv2.imshow("aligned", aligned_face_draw)
cv2.waitKey(0)
# we have assumed that a single image will have only the face of the person
# compute the encodings
#f_encoding = img_to_encoding(face_cv, faceNet)
f_encoding = img_to_encoding(aligned_face_cv, faceNet)
if faces_encoding is None:
faces_encoding = f_encoding
else:
faces_encoding = np.concatenate((faces_encoding, f_encoding),
axis=0)
# save the label for this face in index_dic
# later, this will be used for verification or identification of a person
index_dic[i] = image_path
i = i + 1
# save the face encodings and label index dict
np.save(os.path.join(out_pic_file_path, 'faces_encoding.npy'),
faces_encoding)
with open(os.path.join(out_pic_file_path, 'index_dic.pkl'), 'wb') as f:
cPickle.dump(index_dic, f)
def create_hdf5_facedataset(in_images_folder_path,
out_hdf5_dataset_fullPath_name, faceNet):
'''
Create hdf5 face dataset by registering each person face.
The dataset contains the face images in opencv format, each image encodings obtained
using faceNet, and labels/person names
Input:
in_images_folder_path: path to a folder where each person images are stored subfolders
we assume that the subfolders, name represent face identity, on this path
contain images of a specific invidividual only, image of a person contains only face
out_hdf5_dataset_fullName: full path of the dataset to be saved
faceNet deep faceNet keras model
'''
person_subfolders = []
for p in os.listdir(in_images_folder_path):
ppath = os.path.join(in_images_folder_path, p)
if os.path.isdir(ppath):
person_subfolders.append(ppath)
face_image_paths = []
for i, person_subfolder in enumerate(person_subfolders):
for filename in os.listdir(person_subfolder):
img_path = os.path.join(person_subfolder, filename)
if filename.endswith('jpg'):
face_image_paths.append(img_path)
#person_name = os.path.splitext(os.path.dirname(img_path).split('/')[-1] )[0]
#print(person_name)
print("Total face images", len(face_image_paths))
image_shape = (len(face_image_paths), 96, 96, 3)
encod_shape = (len(face_image_paths), 128)
# open hdf5 file and create dataset
hdf5_file = h5py.File(out_hdf5_dataset_fullPath_name, mode='w')
#hdf5_file.create_dataset("faces_encoding", shape=encod_shape, dtype=np.float32)
# create resizable dataset as expected there may be any face in the training face images
hdf5_file.create_dataset("faces_encoding", (3000, 128),
maxshape=(None, 128),
dtype=np.float32)
dt = h5py.special_dtype(vlen=str)
#hdf5_file.create_dataset("index_dic", shape=(len(face_image_paths),), dtype=dt)
hdf5_file.create_dataset("index_dic", (3000, ),
maxshape=(None, ),
dtype=dt)
#hdf5_file.create_dataset("face_imgs_cv", shape=image_shape,dtype=np.uint8)
hdf5_file.create_dataset("face_imgs_cv", (3000, 96, 96, 3),
maxshape=(None, 96, 96, 3),
dtype=np.uint8)
# define throshod for facedetection and load mtcnn detector
threshold = [0.3, 0.6, 0.7]
faceDetector_mtcnn = mtcnn()
# now process each person images one by one and encode them using the faceNet model
# we will stored each encodings in face_encodings and their corresponding labels in index dictionary
img_cnt = -1
for image_path in face_image_paths:
print("encoding face detected in : {}".format(image_path))
image_cv = cv2.imread(image_path, 1)
img_draw, face_imgs, face_bboxes, faces_landmarks = extract_draw_faces(
faceDetector_mtcnn, image_cv, threshold)
if len(face_imgs) == 0:
continue
img_cnt += 1
lndmrks = np.array(faces_landmarks[0])
person_name = os.path.splitext(
os.path.dirname(image_path).split('/')[-1])[0]
#aligned_face_cv, aligned_face_lndmarks = normalize_faces_landmarks(out_img_size=(96,96), in_img=face_imgs[0], landmarks=lndmrks)
aligned_face_cv, aligned_face_lndmarks = align_face(out_img_size=(96,
96),
in_img=image_cv,
landmarks=lndmrks)
f_encoding = img_to_encoding(aligned_face_cv, faceNet)
print(f_encoding.shape)
#im[None].shape ==>(1, 96, 96, 3)
# writing data into dataset
print("img_cnt", img_cnt)
hdf5_file["face_imgs_cv"][img_cnt, ...] = aligned_face_cv[None]
hdf5_file["faces_encoding"][img_cnt, ...] = f_encoding
hdf5_file["index_dic"][img_cnt, ...] = person_name
## flip face vertical
img_cnt += 1
print("img_cnt", img_cnt)
#flip_aligned_face_cv = cv2.flip(aligned_face_cv,1)
#flip_lndmrks = np.flip(lndmrks, 1)
print(lndmrks)
flip_lndmrks = np.multiply(
np.subtract(lndmrks, [image_cv.shape[1], 0]), [-1, 1])
# after flip left and right position of eye changes
print(flip_lndmrks)
tmp = flip_lndmrks[0].copy()
flip_lndmrks[0] = flip_lndmrks[1].copy()
flip_lndmrks[1] = tmp.copy()
tmp = flip_lndmrks[3].copy()
flip_lndmrks[3] = flip_lndmrks[4].copy()
flip_lndmrks[3] = tmp.copy()
print(flip_lndmrks)
flip_image_cv = cv2.flip(image_cv, 1)
flip_aligned_face_cv, _ = align_face(out_img_size=(96, 96),
in_img=flip_image_cv,
landmarks=flip_lndmrks)
flip_f_encoding = img_to_encoding(flip_aligned_face_cv, faceNet)
# writing data into dataset
hdf5_file["face_imgs_cv"][img_cnt, ...] = flip_aligned_face_cv[None]
hdf5_file["faces_encoding"][img_cnt, ...] = flip_f_encoding
hdf5_file["index_dic"][img_cnt, ...] = person_name
## draw on images
face_lndmrk_draw = face_imgs[0].copy()
cv2.putText(face_lndmrk_draw, '1', (lndmrks[0][0], lndmrks[0][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[0][0], lndmrks[0][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '2', (lndmrks[1][0], lndmrks[1][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[1][0], lndmrks[1][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '3', (lndmrks[2][0], lndmrks[2][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[2][0], lndmrks[2][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '4', (lndmrks[3][0], lndmrks[3][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[3][0], lndmrks[3][1]), 2,
(0, 255, 0))
cv2.putText(face_lndmrk_draw, '5', (lndmrks[4][0], lndmrks[4][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(face_lndmrk_draw, (lndmrks[4][0], lndmrks[4][1]), 2,
(0, 255, 0))
cv2.imshow("face", face_lndmrk_draw)
aligned_face_draw = aligned_face_cv.copy()
cv2.putText(aligned_face_draw, '1',
(aligned_face_lndmarks[0][0], aligned_face_lndmarks[0][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(aligned_face_draw,
(aligned_face_lndmarks[0][0], aligned_face_lndmarks[0][1]),
2, (0, 255, 0))
cv2.putText(aligned_face_draw, '2',
(aligned_face_lndmarks[1][0], aligned_face_lndmarks[1][1]),
cv2.FONT_ITALIC, 0.4, (0, 0, 255), 1)
cv2.circle(aligned_face_draw,
(aligned_face_lndmarks[1][0], aligned_face_lndmarks[1][1]),
2, (0, 255, 0))
cv2.imshow("aligned", aligned_face_draw)
cv2.imshow("face image", img_draw)
debug("face image of {}".format(person_name))
if (cv2.waitKey(100) == ord('q')):
break
# resize datasets according to total count img_cnt
hdf5_file["faces_encoding"].resize((img_cnt, 128))
hdf5_file["index_dic"].resize((img_cnt, ))
hdf5_file["face_imgs_cv"].resize((img_cnt, 96, 96, 3))
hdf5_file.close()
def initDatabase(model):
database = {}
database[USER_LIST[0]] = utils.img_to_encoding('images/david.jpg', model)
database[USER_LIST[1]] = utils.img_to_encoding('images/neru.JPG', model)
return database
def prepare_database(model):
database = {}
for file in glob.glob("./images/*"):
identity = os.path.splitext(os.path.basename(file))[0]
database[identity] = img_to_encoding(file, model)
return database