Automated-Proctoring/head_pose_estimation.py at main · Sandeep9975/Automated-Proctoring · GitHub

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import cv2
import numpy as np
import math
from face_detector import get_face_detector, find_faces
from face_landmarks import get_landmark_model, detect_marks

def get_2d_points(img, rotation_vector, translation_vector, camera_matrix, val):
    """Return the 3D points present as 2D for making annotation box"""
    point_3d = []
    dist_coeffs = np.zeros((4,1))
    rear_size = val[0]
    rear_depth = val[1]
    point_3d.append((-rear_size, -rear_size, rear_depth))
    point_3d.append((-rear_size, rear_size, rear_depth))
    point_3d.append((rear_size, rear_size, rear_depth))
    point_3d.append((rear_size, -rear_size, rear_depth))
    point_3d.append((-rear_size, -rear_size, rear_depth))

    front_size = val[2]
    front_depth = val[3]
    point_3d.append((-front_size, -front_size, front_depth))
    point_3d.append((-front_size, front_size, front_depth))
    point_3d.append((front_size, front_size, front_depth))
    point_3d.append((front_size, -front_size, front_depth))
    point_3d.append((-front_size, -front_size, front_depth))
    point_3d = np.array(point_3d, dtype=np.float).reshape(-1, 3)

    # Map to 2d img points
    (point_2d, _) = cv2.projectPoints(point_3d,
                                      rotation_vector,
                                      translation_vector,
                                      camera_matrix,
                                      dist_coeffs)
    point_2d = np.int32(point_2d.reshape(-1, 2))
    return point_2d

def draw_annotation_box(img, rotation_vector, translation_vector, camera_matrix,
                        rear_size=300, rear_depth=0, front_size=500, front_depth=400,
                        color=(255, 255, 0), line_width=2):
    """ Draw a 3D anotation box on the face for head pose estimation """

    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)
    # # Draw all the lines
    cv2.polylines(img, [point_2d], True, color, line_width, cv2.LINE_AA)
    cv2.line(img, tuple(point_2d[1]), tuple(
        point_2d[6]), color, line_width, cv2.LINE_AA)
    cv2.line(img, tuple(point_2d[2]), tuple(
        point_2d[7]), color, line_width, cv2.LINE_AA)
    cv2.line(img, tuple(point_2d[3]), tuple(
        point_2d[8]), color, line_width, cv2.LINE_AA)


def head_pose_points(img, rotation_vector, translation_vector, camera_matrix):
    """ Get the points to estimate head pose sideways    """
    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
                        ])

# 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"
                         )
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:
                print('Head down')
                cv2.putText(img, 'Head down', (30, 30), font, 2, (255, 255, 128), 3)
            elif ang1 <= -48:
                print('Head up')
                cv2.putText(img, 'Head up', (30, 30), font, 2, (255, 255, 128), 3)

            if ang2 >= 48:
                print('Head right')
                cv2.putText(img, 'Head right', (90, 30), font, 2, (255, 255, 128), 3)
            elif ang2 <= -48:
                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()