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标题: 怎么计算脸部姿势 with cv2.solvePnP in python3 [打印本页]

作者: dahe_1984 时间: 2018-03-21 10:38
标题: 怎么计算脸部姿势 with cv2.solvePnP in python3
我用shape_predictor_68_face_landmarks.dat计算出脸部得68个点，想得到脸部姿势，通过这个函数计算在两个人得图片上计算，发现[float(roll), float(pitch), float(yaw)]值接近得两张图，脸得姿势并不一样，谁用过这个函数，帮忙给点意见。
def face_orientation(frame, landmarks):
size = frame.shape #(height, width, color_channel)

# image_points = np.array([
                        # (landmarks[4], landmarks[5]),    # Nose tip
                        # (landmarks[10], landmarks[11]), # Chin
                        # (landmarks[0], landmarks[1]),    # Left eye left corner
                        # (landmarks[2], landmarks[3]),    # Right eye right corne
                        # (landmarks[6], landmarks[7]),    # Left Mouth corner
                        # (landmarks[8], landmarks[9])    # Right mouth corner
                     # ], dtype="double")

# center_righteyes = (landmarks[36] + landmarks[39]) // 2
# center_lefteyes  = (landmarks[42] + landmarks[45]) // 2
# image_points = numpy.array([
                        # landmarks[31],    # Nose tip
                        # landmarks[8], # Chin
                        # center_lefteyes,    # Left eye left corner
                        # center_righteyes,    # Right eye right corne
                        # landmarks[48],    # Left Mouth corner
                        # landmarks[54]    # Right mouth corner
                     # ], dtype="double")

image_points = numpy.array([
      (landmarks[33, 0], landmarks[33, 1]),    # Nose tip
      (landmarks[8, 0], landmarks[8, 1]),    # Chin
      (landmarks[36, 0], landmarks[36, 1]),    # Left eye left corner
      (landmarks[45, 0], landmarks[45, 1]),    # Right eye right corner
      (landmarks[48, 0], landmarks[48, 1]),    # Left Mouth corner
      (landmarks[54, 0], landmarks[54, 1])    # Right mouth corner
      ], dtype="double")


model_points = numpy.array([
                        (0.0, 0.0, 0.0),          # Nose tip
                        (0.0, -330.0, -65.0),       # Chin
                        (-165.0, 170.0, -135.0),    # Left eye left corner
                        (165.0, 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

center = (size[1]/2, size[0]/2)
focal_length = center[0] / numpy.tan(60/2 * numpy.pi / 180)
camera_matrix = numpy.array(
                     [[focal_length, 0, center[0]],
                     [0, focal_length, center[1]],
                     [0, 0, 1]], dtype = "double"
                     )

dist_coeffs = numpy.zeros((4,1)) # Assuming no lens distortion
#(success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.CV_ITERATIVE)
(success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE)

axis = numpy.float32([[500,0,0],
                        [0,500,0],
                        [0,0,500]])

imgpts, jac = cv2.projectPoints(axis, rotation_vector, translation_vector, camera_matrix, dist_coeffs)
modelpts, jac2 = cv2.projectPoints(model_points, rotation_vector, translation_vector, camera_matrix, dist_coeffs)
rvec_matrix = cv2.Rodrigues(rotation_vector)[0]

proj_matrix = numpy.hstack((rvec_matrix, translation_vector))
eulerAngles = cv2.decomposeProjectionMatrix(proj_matrix)[6]

pitch, yaw, roll = [math.radians(_) for _ in eulerAngles]

pitch = math.degrees(math.asin(math.sin(pitch)))
roll = -math.degrees(math.asin(math.sin(roll)))
yaw = math.degrees(math.asin(math.sin(yaw)))

#return imgpts, modelpts, (str(int(roll)), str(int(pitch)), str(int(yaw))), (landmarks[4], landmarks[5])
return numpy.array([float(roll), float(pitch), float(yaw)], dtype = "double")

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