def detect_face(data):
from retinaface.detector import detector
from utils import align_face
src_path = data['src_path']
dst_path = data['dst_path']
boxB = np.array(data['boxB'])
img = cv.imread(src_path)
if img is not None:
img, ratio = resize(img)
boxB = boxB * ratio
try:
bboxes, landmarks = detector.detect_faces(img)
if len(bboxes) > 0:
i = select_face(bboxes, boxB)
bbox, landms = bboxes[i], landmarks[i]
img = align_face(img, [landms])
dirname = os.path.dirname(dst_path)
os.makedirs(dirname, exist_ok=True)
cv.imwrite(dst_path, img)
except ValueError as err:
print(err)
except cv.error as err:
print(err)
return True
def detect_face(data):
from retinaface.detector import detector
from utils import align_face
src_path = data['src_path']
dst_path = data['dst_path']
img_raw = cv.imread(src_path)
if img_raw is not None:
img = resize(img_raw)
try:
bboxes, landmarks = detector.detect_faces(img,
confidence_threshold=0.9)
if len(bboxes) > 0:
bbox, landms = bboxes[0], landmarks[0]
img = align_face(img, [landms])
dirname = os.path.dirname(dst_path)
os.makedirs(dirname, exist_ok=True)
cv.imwrite(dst_path, img)
return True
except ValueError as err:
print(err)
img = cv.resize(img, (im_size, im_size))
cv.imwrite(dst_path, img)
return False
return False
def get_image(full_path, landmarks, transformer):
img = align_face(full_path, landmarks) # BGR
img = img[..., ::-1] # RGB
img = Image.fromarray(img, 'RGB') # RGB
img = transformer(img)
img = img.to(device)
return img
def get_image(filename):
_, _, landmarks = get_face_all_attributes(filename)
img = align_face(filename, landmarks)
img = transforms.ToPILImage()(img)
img = transformer(img)
img = img.to(device)
return img
def read_image(filename):
img = cv2.imread(filename)
rectangles = utils.detect_face(img)
faces = []
for rect in rectangles:
landmarks = utils.align_face(img, rect)
faces.append(((rect.left(), rect.top(), rect.right(),
rect.bottom()), landmarks))
return img, faces
def save_aligned(old_fn, new_fn):
old_fn = os.path.join(extracted_folder, old_fn)
_, _, landmarks = get_central_face_attributes(old_fn)
unaligned_img = read_image_from_file(old_fn)
img = align_face(unaligned_img, landmarks) # BGR
# img = align_face(old_fn, landmarks)
new_fn = os.path.join('images', new_fn)
cv.imwrite(new_fn, img)
def crop_one_image(filepath, oldkey, newkey):
new_fn = filepath.replace(oldkey, newkey)
tardir = os.path.dirname(new_fn)
if not os.path.isdir(tardir):
os.makedirs(tardir)
if not os.path.exists(new_fn):
is_valid, bounding_boxes, landmarks = get_central_face_attributes(filepath)
if is_valid:
img = align_face(filepath, landmarks)
cv.imwrite(new_fn, img)
def detect_face(data):
from utils import get_central_face_attributes, align_face
src_path = data['src_path']
dst_path = data['dst_path']
with torch.no_grad():
has_face, bboxes, landmarks = get_central_face_attributes(src_path)
if has_face:
img = align_face(src_path, landmarks)
cv.imwrite(dst_path, img)
return True
def get_image(samples, transformer, file):
filtered = [sample for sample in samples if file in sample['full_path'].replace('\\', '/')]
assert (len(filtered) == 1), 'len(filtered): {} file:{}'.format(len(filtered), file)
sample = filtered[0]
full_path = sample['full_path']
landmarks = sample['landmarks']
img = align_face(full_path, landmarks)
img = transforms.ToPILImage()(img)
img = transformer(img)
img = img.to(device)
return img
def load_image(self, filename):
img = cv2.imread(filename)
rectangles = utils.detect_face(img)
if not rectangles:
return False
rect = rectangles[0]
landmarks = utils.align_face(img, rect)
self.img_rect = (rect.left(), rect.top(), rect.width(), rect.height())
self.clear_sequence()
self.add_to_sequence(img, landmarks)
return True
def get_image(samples, file):
filtered = [
sample for sample in samples
if file in sample['full_path'].replace('\\', '/')
]
assert (len(filtered) == 1), 'len(filtered): {} file:{}'.format(
len(filtered), file)
sample = filtered[0]
full_path = sample['full_path']
landmarks = sample['landmarks']
img = align_face(full_path, landmarks) # BGR
return img
def crop(self, folder):
if os.path.isdir(folder):
files = os.listdir(folder)
if not os.path.exists(folder + '_crop'):
os.makedirs(folder + '_crop')
else:
raise ValueError("Folder is not exist")
for file in files:
filepath = os.path.join(folder, file)
new_fn = os.path.join(folder + '_crop', file)
bounding_boxes, landmarks = get_central_face_attributes(filepath)
img = align_face(filepath, landmarks)
cv2.imwrite(new_fn, img)
def detect_face(data):
from utils import align_face
src_path = data['src_path']
dst_path = data['dst_path']
with torch.no_grad():
has_face, bboxes, landmarks = get_central_face_attributes(src_path)
if has_face:
img = align_face(src_path, landmarks)
dirname = os.path.dirname(dst_path)
os.makedirs(dirname, exist_ok=True)
cv.imwrite(dst_path, img)
return True
def get_model_compatible_input(gray_frame, face):
img_arr = utils.align_face(gray_frame, face, desiredLeftEye)
img_arr = utils.preprocess_img(img_arr, resize=False)
landmarks = shape_predictor(
gray_frame,
face,
)
roi1, roi2 = utils.extract_roi1_roi2(gray_frame, landmarks)
roi1 = np.expand_dims(roi1, 0)
roi2 = np.expand_dims(roi2, 0)
roi1 = roi1 / 255.
roi2 = roi2 / 255.
return [img_arr, roi1, roi2]
def get_image(samples, file):
filtered = [
sample for sample in samples
if file in sample['full_path'].replace('\\', '/')
]
assert (len(filtered) == 1), 'len(filtered): {} file:{}'.format(
len(filtered), file)
sample = filtered[0]
full_path = sample['full_path']
landmarks = sample['landmarks']
img = align_face(full_path, landmarks) # BGR
name_path = os.path.dirname(full_path)
image_name = "aligned_" + os.path.basename(full_path)
cv.imwrite(os.path.join(name_path, image_name), img)
return img
def get_image(samples, transformer, file):
filtered = [sample for sample in samples if file in sample['full_path'].replace('\\', '/')]
assert (len(filtered) == 1), 'len(filtered): {} file:{}'.format(len(filtered), file)
sample = filtered[0]
full_path = sample['full_path']
landmarks = sample['landmarks']
unaligned_img = read_image_from_file(full_path)
img = align_face(unaligned_img, landmarks) # BGR
# img = blur_and_grayscale(img)
# img = img[..., ::-1] # RGB
img = Image.fromarray(img, 'RGB') # RGB
img = transformer(img)
img = img.to(device)
return img
def show_align():
with open(pickle_file, 'rb') as file:
data = pickle.load(file)
samples = random.sample(data['samples'], 10)
for i, sample in enumerate(samples):
full_path = sample['full_path']
landmarks = sample['landmarks']
raw = cv.imread(full_path)
raw = cv.resize(raw, (224, 224))
img = align_face(full_path, landmarks)
filename = 'images/{}_raw.jpg'.format(i)
cv.imwrite(filename, raw)
filename = 'images/{}_img.jpg'.format(i)
cv.imwrite(filename, img)
def __getitem__(self, i):
sample = self.samples[i]
full_path = sample['full_path']
landmarks = sample['landmarks']
try:
img = align_face(full_path, landmarks)
except Exception:
print('full_path: ' + full_path)
raise
img = transforms.ToPILImage()(img)
img = self.transformer(img)
class_id = sample['class_id']
return img, class_id
def haar_detector(frame):
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_frame = np.zeros(gray_frame.shape, dtype="uint8")
offset = 15
x_pos, y_pos = 10, 40
faces = cascade_detector.detectMultiScale(gray_frame, 1.32, 5)
for idx, face in enumerate(faces):
if hist_eq:
gray_frame = cv2.equalizeHist(gray_frame)
img_arr = utils.align_face(gray_frame, utils.bb_to_rect(face),
desiredLeftEye)
face_frame = cv2.resize(img_arr, (48, 48),
interpolation=cv2.INTER_CUBIC)
img_arr = utils.preprocess_img(img_arr, resize=False)
predicted_proba = model.predict(img_arr)
predicted_label = np.argmax(predicted_proba[0])
x, y, w, h = face
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
text = f"Person {idx+1}: {label2text[predicted_label]}"
utils.draw_text_with_backgroud(frame, text, x + 5, y, font_scale=0.4)
text = f"Person {idx+1} : "
y_pos = y_pos + 2 * offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
for k, v in label2text.items():
text = f"{v}: {round(predicted_proba[0][k]*100, 3)}%"
y_pos = y_pos + offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
return frame, face_frame
def dlib_detector(frame_orig):
frame = frame_orig.copy()
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
offset = 15
x_pos, y_pos = 10, 40
faces = hog_detector(gray_frame)
for idx, face in enumerate(faces):
if hist_eq:
gray_frame = cv2.equalizeHist(gray_frame)
img_arr = utils.align_face(gray_frame, face, desiredLeftEye)
img_arr = utils.preprocess_img(img_arr, resize=False)
predicted_proba = model.predict(img_arr)
predicted_label = np.argmax(predicted_proba[0])
x, y, w, h = rect_to_bb(face)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
text = f"Person {idx+1}: {label2text[predicted_label]}"
utils.draw_text_with_backgroud(frame, text, x + 5, y, font_scale=0.4)
text = f"Person {idx+1} : "
y_pos = y_pos + 2 * offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
for k, v in label2text.items():
text = f"{v}: {round(predicted_proba[0][k]*100, 3)}%"
y_pos = y_pos + offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
return frame
def detect_face(data):
from retinaface.detector import detect_faces
from utils import select_significant_face, align_face
src_path = data['src_path']
dst_path = data['dst_path']
img_raw = cv.imread(src_path)
if img_raw is not None:
img = resize(img_raw)
bboxes, landmarks = detect_faces(img, top_k=5, keep_top_k=5)
if len(bboxes) > 0:
i = select_significant_face(bboxes)
bbox, landms = bboxes[i], landmarks[i]
img = align_face(img, [landms])
dirname = os.path.dirname(dst_path)
os.makedirs(dirname, exist_ok=True)
cv.imwrite(dst_path, img)
return True
def extract_oneface(image, marigin=16):
# detecting faces
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
h, w, c = image.shape
total_boxes, points = detect_face(image, 20, pnet, rnet, onet,
[0.6, 0.7, 0.7], 0.709)
for idx, (bounding_box, keypoints) in enumerate(zip(total_boxes,
points.T)):
bounding_boxes = {
'box': [
int(bounding_box[0]),
int(bounding_box[1]),
int(bounding_box[2] - bounding_box[0]),
int(bounding_box[3] - bounding_box[1])
],
'confidence':
bounding_box[-1],
'keypoints': {
'left_eye': (int(keypoints[0]), int(keypoints[5])),
'right_eye': (int(keypoints[1]), int(keypoints[6])),
'nose': (int(keypoints[2]), int(keypoints[7])),
'mouth_left': (int(keypoints[3]), int(keypoints[8])),
'mouth_right': (int(keypoints[4]), int(keypoints[9])),
}
}
bounding_box = bounding_boxes['box']
keypoints = bounding_boxes['keypoints']
# align face and extract it out
align_image = align_face(image, keypoints)
align_image = cv2.cvtColor(align_image, cv2.COLOR_RGB2BGR)
xmin = max(bounding_box[0] - marigin, 0)
ymin = max(bounding_box[1] - marigin, 0)
xmax = min(bounding_box[0] + bounding_box[2] + marigin, w)
ymax = min(bounding_box[1] + bounding_box[3] + marigin, h)
crop_image = align_image[ymin:ymax, xmin:xmax, :]
# "just need only one face"
return crop_image
def download(tokens, idx, num):
url = tokens[0].replace('"', '').strip()
left = float(tokens[1].strip())
right = float(tokens[2].strip())
top = float(tokens[3].strip())
bottom = float(tokens[4].strip())
filename = url[url.rfind("/") + 1:].strip()
fullname = os.path.join(download_folder, filename)
# if not os.path.isfile(fullname):
# process = Popen(["wget", '-N', url, "-P", download_folder], stdout=PIPE)
# (output, err) = process.communicate()
# exit_code = process.wait()
filename = '{}_{}.jpg'.format(idx, num)
filename = os.path.join(image_folder, filename)
# print(filename)
if os.path.isfile(filename) and os.path.getsize(filename) > 1000:
img = cv.imread(filename)
if img is not None:
h, w = img.shape[:2]
if h == 224 and w == 224:
return filename
if os.path.isfile(fullname) and os.path.getsize(fullname) > 1000:
img = cv.imread(fullname)
if img is not None:
height, width = img.shape[:2]
left, right = int(round(left * width)), int(round(right * width))
top, bottom = int(round(top * height)), int(round(bottom * height))
img = img[top:bottom, left:right, :]
_, landmarks = detect_faces(img, top_k=1, keep_top_k=1)
if len(landmarks) < 1:
return None
img = align_face(img, landmarks)
cv.imwrite(filename, img)
return filename
return None
def __getitem__(self, i):
sample = self.samples[i]
full_path = sample['full_path']
landmarks = sample['landmarks']
# img = cv.imread(full_path)
img = align_face(full_path, landmarks)
img = transforms.ToPILImage()(img)
# print('img.size: ' + str(img.size))
img = self.transformer(img)
# print('img.size(): ' + str(img.size()))
# loc = sample['face_location']
# x1, y1, x2, y2 = loc[0], loc[1], loc[2], loc[3]
# img = img[y1:y2, x1:x2]
# img = cv.resize(img, (image_w, image_h))
# print('img.shape: ' + str(img.shape))
# img = img.transpose(2, 0, 1)
# assert img.shape == (3, image_h, image_w)
# assert np.max(img) <= 255
# img = torch.FloatTensor(img / 255.)
age = sample['age']
gender = sample['gender']
return img, age, gender
def detect_face(data):
src_path = data['src_path']
dst_path = data['dst_path']
# print(src_path)
img_raw = cv.imread(src_path)
if img_raw is not None:
img, _ = resize(img_raw)
try:
bboxes, landmarks = detector.detect_faces(img)
if len(bboxes) > 0:
bbox, landms = bboxes[0], landmarks[0]
img = align_face(img, [landms])
dirname = os.path.dirname(dst_path)
os.makedirs(dirname, exist_ok=True)
cv.imwrite(dst_path, img)
return True
except ValueError as err:
print(err)
return False
for i in range(5, 15, 2):
#test_face_lndmarks.append([int(bbox_lndmarks[i + 0] - left), int(bbox_lndmarks[i + 1]-top)])
test_face_lndmarks.append([int(bbox_lndmarks[i + 0]), int(bbox_lndmarks[i + 1])])
test_face_lndmarks = np.array(test_face_lndmarks)
### 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"):
def save_aligned(old_fn, new_fn):
old_fn = os.path.join('data/lfw_funneled', old_fn)
_, landmarks = get_central_face_attributes(old_fn)
img = align_face(old_fn, landmarks)
new_fn = os.path.join('images', new_fn)
cv.imwrite(new_fn, img)
def dnn_detector(frame):
frame_height = frame.shape[0]
frame_width = frame.shape[1]
blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), [104, 117, 123],
False, False)
net.setInput(blob)
detections = net.forward()
bboxes = []
idx = 0
offset = 15
x_pos, y_pos = 10, 40
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_frame = np.zeros(gray_frame.shape, dtype="uint8")
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
idx += 1
x1 = int(detections[0, 0, i, 3] * frame_width)
y1 = int(detections[0, 0, i, 4] * frame_height)
x2 = int(detections[0, 0, i, 5] * frame_width)
y2 = int(detections[0, 0, i, 6] * frame_height)
bboxes.append([x1, y1, x2, y2])
face = [x1, y1, x2 - x1, y2 - y1]
if hist_eq:
gray_frame = cv2.equalizeHist(gray_frame)
img_arr = utils.align_face(gray_frame, utils.bb_to_rect(face),
desiredLeftEye)
face_frame = cv2.resize(img_arr, (48, 48),
interpolation=cv2.INTER_CUBIC)
img_arr = utils.preprocess_img(img_arr, resize=False)
predicted_proba = model.predict(img_arr)
predicted_label = np.argmax(predicted_proba[0])
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
text = f"Person {idx}: {label2text[predicted_label]}"
utils.draw_text_with_backgroud(frame,
text,
x1 + 5,
y1,
font_scale=0.4)
text = f"Person {idx} : "
y_pos = y_pos + 2 * offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
for k, v in label2text.items():
text = f"{v}: {round(predicted_proba[0][k]*100, 3)}%"
y_pos = y_pos + offset
utils.draw_text_with_backgroud(frame,
text,
x_pos,
y_pos,
font_scale=0.3,
box_coords_2=(2, -2))
return frame, face_frame
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()