def __getitem__(self, idx):
image_name = os.path.join(self.root_dir,
self.key_pts_frame.iloc[idx, 0])
image = mpimg.imread(image_name)
# if image has an alpha color channel, get rid of it
if(image.shape[2] == 4):
image = image[:,:,0:3]
key_pts = self.key_pts_frame.iloc[idx, 1:].to_numpy()
key_pts = key_pts.astype('float').reshape(-1, 2)
image, key_pts = face_alignment(image, key_pts)
ground_truth = generate_trainable_gt(key_pts)
image_data = image
sample = {'image_data': image_data, 'image': image, 'keypoints': key_pts, 'ground_truth': ground_truth}
if self.transform:
sample = self.transform(sample)
return sample
def extract_face_alignment(video_path, output_dir):
detector = dlib.get_frontal_face_detector()
predictor_1 = dlib.shape_predictor(
'.\\Models\\shape_predictor_5_face_landmarks.dat')
predictor_2 = dlib.shape_predictor(
'.\\Models\\shape_predictor_68_face_landmarks.dat')
cap = cv2.VideoCapture(video_path)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
fps = cap.get(cv2.CAP_PROP_FPS)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
scale = 1280 / width
Write_frames = cv2.VideoWriter('Videos/tmp/frames.avi', fourcc, fps,
(int(width * scale), int(height * scale)))
falign = face_alignment()
for i in range(frames):
flag, frame = cap.read()
if (flag == True):
frame = cv2.resize(frame,
(int(width * scale), int(height * scale)))
dets = detector(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), 0)
if (len(dets) > 0):
big_face = 0
for j, d in enumerate(dets):
if (d.right() - d.left() > big_face):
big_face = d.right() - d.left()
face_location = dets[j]
shape_1 = predictor_1(frame, face_location)
shape_2 = predictor_2(frame, face_location)
aligned_face, norm_land = falign.face_align_by_5(
frame, shape_2, 255, 1.0)
filename = 'img' + str_idx(i) + '.jpg'
filename = os.path.join(output_dir, filename)
cv2.imwrite(filename, aligned_face)
return 1
# loop over the subset of facial landmarks, drawing the specific face part
for (x, y) in shape[i:j]:
if name == 'right eye':
eye_landmarks[0].append(x)
eye_landmarks[1].append(y)
elif name == 'left eye':
eye_landmarks[2].append(x)
eye_landmarks[3].append(y)
# cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)
#
# cv2.imshow("Image", clone)
# FACE ALIGNMENT & GET ANGLE, EYES CENTER
output, angle, eyes_center = face_alignment(
image=image, eye_landmarks=eye_landmarks)
# cv2.imshow("output", output)
# EYE PATCH EXTRACTION
gray2 = cv2.cvtColor(output, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale image
rect2 = detector(gray2, 1)
if len(rect2) == 0:
break
rect2 = rect2[0]
shape2 = predictor(gray2, rect2)
shape2 = face_utils.shape_to_np(shape2)
roi = []
for (m, (name, (i, j))) in enumerate([('right eye', (36, 42)),
('left eye', (42, 48))]):
# extract the ROI of the face region as a separate image
folder_name = f.split('.')[0]
new_dir_f = new_dir + '/' + folder_name
try:
os.mkdir(new_dir_f)
except Exception as e:
print(str(e))
vidcap = cv2.VideoCapture(video_path)
success, image = vidcap.read()
face_cascade = cv2.CascadeClassifier(
'haarcascade_frontalface_default.xml')
count = 0
while success:
# Allign face
image = face_alignment(image)
copy_image = image.copy()
# Convert the image to RGB colorspace
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Convert the image to gray
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
# Detect faces in the image using pre-Trained face dectector
faces = face_cascade.detectMultiScale(gray_image, 1.25, 6)
print('Number of faces detected:', len(faces))
# Get the bounding box for each detected face
from helper import *
from face_alignment import face_alignment
if __name__ == '__main__':
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
args = parse_args()
face_alignment(args)
def detect_micro_LBP_Webcam( ):
print(' Processing Micro-expression by webcam ')
detector = dlib.get_frontal_face_detector()
predictor_2 = dlib.shape_predictor('.//Models//shape_predictor_68_face_landmarks.dat')
cap = cv2.VideoCapture(0)
window_len = int (30 / 2)
processing_list = []
falign = face_alignment()
frames = 2000
Farr = np.zeros(frames)
micro_array = np.zeros(frames)
i = 0
face_location=None
res = 0
while True:
flag, frame = cap.read()
if (flag==True):
dets = detector(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), 0)
if (len(dets) > 0):
big_face=0
for j,d in enumerate(dets):
if (d.right()-d.left()>big_face):
big_face=d.right()-d.left()
face_location=dets[j]
shape_2 = predictor_2(frame, face_location)
#shape_1 = predictor_1(frame, face_location)
#aligned_face = falign.face_registration(frame, shape_2, 225, 1.0)
aligned_face , norm_landmark = falign.face_align_by_5(frame, shape_2, 255, 1.0)
processing_list.append(aligned_face)
count = len(processing_list)
if (count > window_len):
processing_list.pop(0)
count = len(processing_list)
L = int(window_len/2)
idx = i
i = i + 1
dist = 0.0
if (count == window_len):
st_idx = idx - window_len + 1
apex_idx = idx - int(window_len/2)
hframe = processing_list[0]
tframe = processing_list[count-1]
curr_frame = processing_list[int(window_len/2)]
dist , idx_sorted = calculate_distance_block(curr_frame,hframe,tframe , norm_landmark)
pos = idx - int(window_len/2)
Farr[pos] = dist
res = calculate_curr_thr(Farr, i+1 , window_len)
if (res == 1):
print(pos)
pre_shape = shape_2
shape_np = falign.shape_to_np(shape_2)
d=shape_np[45,0]-shape_np[36,0]
if (res == 1):
frame = cv2.rectangle(frame, (shape_np[30,0]-d,shape_np[30,1]-d) , (shape_np[30,0]+d,shape_np[30,1]+d) ,(0,255,0), 1)
cv2.imshow('Face Micro expression demo ',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
return 1
def detect_micro_LBP(video_file_path ):
print(' Processing Micro-expression on: ', video_file_path)
detector = dlib.get_frontal_face_detector()
predictor_1 = dlib.shape_predictor('.//Models//shape_predictor_5_face_landmarks.dat')
predictor_2 = dlib.shape_predictor('.//Models//shape_predictor_68_face_landmarks.dat')
cap = cv2.VideoCapture(video_file_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps= int(cap.get(cv2.CAP_PROP_FPS))
print(fps)
frames= int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
face_location=None
window_len = int (fps / 2)
processing_list = []
falign = face_alignment()
Farr = np.zeros(frames)
micro_array = np.zeros(frames)
for i in range(frames):
flag, frame = cap.read()
if (flag==True):
start_time = time.time()
dets = detector(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), 0)
if (len(dets) > 0):
big_face=0
for j,d in enumerate(dets):
if (d.right()-d.left()>big_face):
big_face=d.right()-d.left()
face_location=dets[j]
shape_2 = predictor_2(frame, face_location)
#shape_1 = predictor_1(frame, face_location)
#aligned_face = falign.face_registration(frame, shape_2, 225, 1.0)
aligned_face , norm_landmark = falign.face_align_by_5(frame, shape_2, 255, 1.0)
processing_list.append(aligned_face)
count = len(processing_list)
if (count > window_len):
processing_list.pop(0)
count = len(processing_list)
L = int(window_len/2)
idx = i
dist = 0.0
if (count == window_len):
st_idx = idx - window_len + 1
apex_idx = idx - int(window_len/2)
hframe = processing_list[0]
tframe = processing_list[count-1]
curr_frame = processing_list[int(window_len/2)]
dist , idx_sorted = calculate_distance_block(curr_frame,hframe,tframe , norm_landmark)
pos = idx - int(window_len/2)
Farr[pos] = dist
res = calculate_curr_thr(Farr, i+1 , window_len)
if (res == 1):
print(pos)
pre_shape = shape_2
return 1
import cv2
from face_alignment import face_alignment
from face_base import find_face
from face_base import license_detection
from smooth_sharpen import smooth
from smooth_sharpen import sharpen
from face_base import face_wipeoff
import pytesseract
img = cv2.imread(
'/Users/qinfeiyu/PycharmProjects/image_recognition/valuable/Obama_1.jpg')
img = face_alignment(img)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face, face_plus = find_face(img_gray)
face_plus, lincese = license_detection(img, face_plus)
lincese = smooth(lincese)
lincese = sharpen(lincese)
cv2.imshow('license', lincese)
cv2.waitKey(0)
# cv2.imshow('license',lincese)
# cv2.waitKey(0)
lincese_gray = cv2.cvtColor(lincese, cv2.COLOR_BGR2GRAY)
# face,face_plus = find_face(lincese_gray)
# lincese_gray_noface = face_wipeoff(lincese_gray,face_plus)
from helper import *
from face_alignment import face_alignment
face_alignment("frame0.jpg", "aframe0.jpg")
