/
head_pose_estimation.py
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/
head_pose_estimation.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jul 31 03:00:36 2020
@author: hp
"""
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
Parameters
----------
img : np.unit8
Original Image.
rotation_vector : Array of float64
Rotation Vector obtained from cv2.solvePnP
translation_vector : Array of float64
Translation Vector obtained from cv2.solvePnP
camera_matrix : Array of float64
The camera matrix
rear_size : int, optional
Size of rear box. The default is 300.
rear_depth : int, optional
The default is 0.
front_size : int, optional
Size of front box. The default is 500.
front_depth : int, optional
Front depth. The default is 400.
color : tuple, optional
The color with which to draw annotation box. The default is (255, 255, 0).
line_width : int, optional
line width of lines drawn. The default is 2.
Returns
-------
None.
"""
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
Parameters
----------
img : np.unit8
Original Image.
rotation_vector : Array of float64
Rotation Vector obtained from cv2.solvePnP
translation_vector : Array of float64
Translation Vector obtained from cv2.solvePnP
camera_matrix : Array of float64
The camera matrix
Returns
-------
(x, y) : tuple
Coordinates of line to estimate head pose
"""
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()