def predict():
if request.method == "POST":
flag = 0
r = request.data.decode('utf-8')
_, encoded = r.split(",", 1)
imgdata = base64.b64decode(encoded)
im_arr = np.frombuffer(imgdata, dtype=np.uint8) # im_arr is one-dim Numpy array
img = cv2.imdecode(im_arr, flags=cv2.IMREAD_COLOR)
print(img.shape)
# detector = dlib.get_frontal_face_detector()
face_model = get_face_detector()
predictor = dlib.shape_predictor('shape_68.dat')
left = [36, 37, 38, 39, 40, 41]
right = [42, 43, 44, 45, 46, 47]
kernel = np.ones((9, 9), np.uint8)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
rects = find_faces(img, face_model)
# rects = detector(gray, 1)
for rect in rects:
# print(type(rect[0]))
rec = dlib.rectangle(int(rect[0]), int(rect[1]), int(rect[2]), int(rect[3]))
shape = predictor(gray, rec)
shape = shape_to_np(shape)
mask = np.zeros(img.shape[:2], dtype=np.uint8)
mask, end_points_left = eye_on_mask(mask, left, shape)
mask, end_points_right = eye_on_mask(mask, right, shape)
mask = cv2.dilate(mask, kernel, 5)
eyes = cv2.bitwise_and(img, img, mask=mask)
mask = (eyes == [0, 0, 0]).all(axis=2)
eyes[mask] = [255, 255, 255]
mid = (shape[42][0] + shape[39][0]) // 2
eyes_gray = cv2.cvtColor(eyes, cv2.COLOR_BGR2GRAY)
# threshold = cv2.getTrackbarPos('threshold', 'image')
_, thresh = cv2.threshold(eyes_gray, 90, 255, cv2.THRESH_BINARY)
thresh = process_thresh(thresh)
eyeball_pos_left = contouring(thresh[:, 0:mid], mid, end_points_left)
eyeball_pos_right = contouring(thresh[:, mid:], mid, end_points_right, True)
flag = print_eye_pos(eyeball_pos_left, eyeball_pos_right)
op = dict()
op['flag'] = json.dumps(flag)
return jsonify(op)
"""
rear_size = 1
rear_depth = 0
front_size = img.shape[1]
front_depth = front_size * 2
val = [rear_size, rear_depth, front_size, front_depth]
point_2d = get_2d_points(img, rotation_vector, translation_vector,
camera_matrix, val)
y = (point_2d[5] + point_2d[8]) // 2
x = point_2d[2]
return (x, y)
face_model = get_face_detector()
landmark_model = get_landmark_model()
cap = cv2.VideoCapture(0)
ret, img = cap.read()
size = img.shape
font = cv2.FONT_HERSHEY_SIMPLEX
# 3D model points.
model_points = np.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye left corner
(225.0, 170.0, -135.0), # Right eye right corne
(-150.0, -150.0, -125.0), # Left Mouth corner
(150.0, -150.0, -125.0) # Right mouth corner
])
def main(debug=False):
# Load face detect model & face landmark model
face_model = face_detector.get_face_detector()
landmark_model = face_landmarks.get_landmark_model()
cv2.namedWindow('debug_window')
# Create camera instance for capture picture, speed up processing on windows platform by using cv2.CAP_DSHOW.
if platform.system().lower() == 'windows':
camera = cv2.VideoCapture(0 + cv2.CAP_DSHOW)
else:
camera = cv2.VideoCapture(0)
_, sample_img = camera.read() # grab one frame, for getting resolution
height, width, channel = sample_img.shape # resolution & color channel (1 or None = GrayScale, 3 = BGR colors)
# 3D model points.
model_points = np.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye left corner
(225.0, 170.0, -135.0), # Right eye right corne
(-150.0, -150.0, -125.0), # Left Mouth corner
(150.0, -150.0, -125.0) # Right mouth corner
])
# Camera internals
focal_length = width
center = (width/2, height/2)
camera_matrix = np.array([
[focal_length, 0, center[0]],
[0, focal_length, center[1]],
[0, 0, 1]
], dtype='double')
# Lens distance coefficient: assuming no lens distortion
dist_coefficient = np.zeros((4, 1))
while True:
# start time stamp this frame
time_start = datetime.now()
# 1. capture image (1 frame)
ret, frame = camera.read()
if ret:
# 2. detect faces
faces = face_detector.find_faces(frame, face_model)
if faces:
if debug:
face_detector.draw_faces(frame, faces)
for face in faces:
# 3. detect face landmarks
try:
landmarks = face_landmarks.detect_marks(frame, landmark_model, face)
except cv2.error:
# Skip this round if failed to detect landmarks.
break
if debug:
face_landmarks.draw_marks(frame, landmarks, color=(0, 255, 0))
frame_points = np.array([
landmarks[30], # Nose tip
landmarks[8], # Chin
landmarks[36], # Left eye left corner
landmarks[45], # Right eye right corne
landmarks[48], # Left Mouth corner
landmarks[54] # Right mouth corner
], dtype='double')
# 4. get rotation & translation vector
success, rotation_vector, translation_vector = cv2.solvePnP(
model_points, frame_points, camera_matrix, dist_coefficient, flags=cv2.SOLVEPNP_UPNP
)
if debug:
print(' ' * 13, 'solvePnP:',
success, rotation_vector.tolist(), translation_vector.tolist(), end='\r')
# TODO: calculate iris, mouth and head's roll pitch yaw
# TODO: create socket client, send calculated data to unity
cv2.imshow('debug_window', frame)
# end time stamp of this frame
time_delta = datetime.now() - time_start
# calculate Frame Per Second
fps = 1e6 / time_delta.microseconds
print(' FPS:', fps, end='\r')
if cv2.waitKey(1) & 0xFF in (27, ord('q')):
break
if debug:
cv2.destroyAllWindows()
camera.release()
def get_head_position(File_name):
face_model = get_face_detector()
landmark_model = get_landmark_model()
cap = cv2.VideoCapture(File_name)
ret, img = cap.read()
size = img.shape
font = cv2.FONT_HERSHEY_SIMPLEX
# 3D model points.
model_points = np.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye left corner
(225.0, 170.0, -135.0), # Right eye right corne
(-150.0, -150.0, -125.0), # Left Mouth corner
(150.0, -150.0, -125.0) # Right mouth corner
])
# Camera internals
focal_length = size[1]
center = (size[1] / 2, size[0] / 2)
camera_matrix = np.array([[focal_length, 0, center[0]],
[0, focal_length, center[1]], [0, 0, 1]],
dtype="double")
head_directions = [0] * 4 # 아래, 위, 오른쪽, 왼쪽
while True:
ret, img = cap.read()
if ret == True:
faces = find_faces(img, face_model)
for face in faces:
marks = detect_marks(img, landmark_model, face)
# mark_detector.draw_marks(img, marks, color=(0, 255, 0))
image_points = np.array(
[
marks[30], # Nose tip
marks[8], # Chin
marks[36], # Left eye left corner
marks[45], # Right eye right corne
marks[48], # Left Mouth corner
marks[54] # Right mouth corner
],
dtype="double")
dist_coeffs = np.zeros((4, 1)) # Assuming no lens distortion
(success, rotation_vector,
translation_vector) = cv2.solvePnP(model_points,
image_points,
camera_matrix,
dist_coeffs,
flags=cv2.SOLVEPNP_UPNP)
# Project a 3D point (0, 0, 1000.0) onto the image plane.
# We use this to draw a line sticking out of the nose
(nose_end_point2D,
jacobian) = cv2.projectPoints(np.array([
(0.0, 0.0, 1000.0)
]), rotation_vector, translation_vector, camera_matrix,
dist_coeffs)
for p in image_points:
cv2.circle(img, (int(p[0]), int(p[1])), 3, (0, 0, 255), -1)
p1 = (int(image_points[0][0]), int(image_points[0][1]))
p2 = (int(nose_end_point2D[0][0][0]),
int(nose_end_point2D[0][0][1]))
x1, x2 = head_pose_points(img, rotation_vector,
translation_vector, camera_matrix)
cv2.line(img, p1, p2, (0, 255, 255), 2)
cv2.line(img, tuple(x1), tuple(x2), (255, 255, 0), 2)
# for (x, y) in marks:
# cv2.circle(img, (x, y), 4, (255, 255, 0), -1)
# cv2.putText(img, str(p1), p1, font, 1, (0, 255, 255), 1)
try:
m = (p2[1] - p1[1]) / (p2[0] - p1[0])
ang1 = int(math.degrees(math.atan(m)))
except:
ang1 = 90
try:
m = (x2[1] - x1[1]) / (x2[0] - x1[0])
ang2 = int(math.degrees(math.atan(-1 / m)))
except:
ang2 = 90
# print('div by zero error')
if ang1 >= 48:
head_directions[0] += 1
# print('Head down')
# cv2.putText(img, 'Head down', (30, 30), font, 2, (255, 255, 128), 3)
elif ang1 <= -48:
head_directions[1] += 1
# print('Head up')
# cv2.putText(img, 'Head up', (30, 30), font, 2, (255, 255, 128), 3)
if ang2 >= 48:
head_directions[2] += 1
# print('Head right')
# cv2.putText(img, 'Head right', (90, 30), font, 2, (255, 255, 128), 3)
elif ang2 <= -48:
head_directions[3] += 1
# print('Head left')
# cv2.putText(img, 'Head left', (90, 30), font, 2, (255, 255, 128), 3)
# cv2.putText(img, str(ang1), tuple(p1), font, 2, (128, 255, 255), 3)
# cv2.putText(img, str(ang2), tuple(x1), font, 2, (255, 255, 128), 3)
# cv2.imshow('img', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cv2.destroyAllWindows()
cap.release()
return head_directions
