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ex_dlib_pnp_head_pose_estimation_video.py
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ex_dlib_pnp_head_pose_estimation_video.py
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#!/usr/bin/env python
##
# Massimiliano Patacchiola, Plymouth University 2016
#
# This is an example of head pose estimation with solvePnP and dlib face detector.
# It uses the dlib library and openCV.
# To use this example you have to provide an input video file
# and an output path:
# python ex_pnp_pose_estimation_video.py /home/video.mpg ./output.avi
#
import numpy
import cv2
import sys
import os
from deepgaze.face_landmark_detection import faceLandmarkDetection
#For the frontal face detector
import dlib
#Antropometric constant values of the human head.
#Found on wikipedia and on:
# "Head-and-Face Anthropometric Survey of U.S. Respirator Users"
#
#X-Y-Z with X pointing forward and Y on the left.
#The X-Y-Z coordinates used are like the standard
# coordinates of ROS (robotic operative system)
P3D_RIGHT_SIDE = numpy.float32([-100.0, -77.5, -5.0]) #0
P3D_GONION_RIGHT = numpy.float32([-110.0, -77.5, -85.0]) #4
P3D_MENTON = numpy.float32([0.0, 0.0, -122.7]) #8
P3D_GONION_LEFT = numpy.float32([-110.0, 77.5, -85.0]) #12
P3D_LEFT_SIDE = numpy.float32([-100.0, 77.5, -5.0]) #16
P3D_FRONTAL_BREADTH_RIGHT = numpy.float32([-20.0, -56.1, 10.0]) #17
P3D_FRONTAL_BREADTH_LEFT = numpy.float32([-20.0, 56.1, 10.0]) #26
P3D_SELLION = numpy.float32([0.0, 0.0, 0.0]) #27
P3D_NOSE = numpy.float32([21.1, 0.0, -48.0]) #30
P3D_SUB_NOSE = numpy.float32([5.0, 0.0, -52.0]) #33
P3D_RIGHT_EYE = numpy.float32([-20.0, -65.5,-5.0]) #36
P3D_RIGHT_TEAR = numpy.float32([-10.0, -40.5,-5.0]) #39
P3D_LEFT_TEAR = numpy.float32([-10.0, 40.5,-5.0]) #42
P3D_LEFT_EYE = numpy.float32([-20.0, 65.5,-5.0]) #45
#P3D_LIP_RIGHT = numpy.float32([-20.0, 65.5,-5.0]) #48
#P3D_LIP_LEFT = numpy.float32([-20.0, 65.5,-5.0]) #54
P3D_STOMION = numpy.float32([10.0, 0.0, -75.0]) #62
#The points to track
#These points are the ones used by PnP
# to estimate the 3D pose of the face
TRACKED_POINTS = (0, 4, 8, 12, 16, 17, 26, 27, 30, 33, 36, 39, 42, 45, 62)
ALL_POINTS = list(range(0,68)) #Used for debug only
def main():
#Check if some argumentshave been passed
#pass the path of a video
if(len(sys.argv) > 2):
file_path = sys.argv[1]
if(os.path.isfile(file_path)==False):
print("ex_pnp_head_pose_estimation: the file specified does not exist.")
return
else:
#Open the video file
video_capture = cv2.VideoCapture(file_path)
if(video_capture.isOpened() == True): print("ex_pnp_head_pose_estimation: the video source has been opened correctly...")
# Define the codec and create VideoWriter object
#fourcc = cv2.VideoWriter_fourcc(*'XVID')
output_path = sys.argv[2]
fourcc = cv2.cv.CV_FOURCC(*'XVID')
out = cv2.VideoWriter(output_path, fourcc, 20.0, (1280,720))
else:
print("You have to pass as argument the path to a video file and the path to the output file to produce, for example: \n python ex_pnp_pose_estimation_video.py /home/video.mpg ./output.avi")
return
#Create the main window and move it
cv2.namedWindow('Video')
cv2.moveWindow('Video', 20, 20)
#Obtaining the CAM dimension
cam_w = int(video_capture.get(3))
cam_h = int(video_capture.get(4))
#Defining the camera matrix.
#To have better result it is necessary to find the focal
# lenght of the camera. fx/fy are the focal lengths (in pixels)
# and cx/cy are the optical centres. These values can be obtained
# roughly by approximation, for example in a 640x480 camera:
# cx = 640/2 = 320
# cy = 480/2 = 240
# fx = fy = cx/tan(60/2 * pi / 180) = 554.26
c_x = cam_w / 2
c_y = cam_h / 2
f_x = c_x / numpy.tan(60/2 * numpy.pi / 180)
f_y = f_x
#Estimated camera matrix values.
camera_matrix = numpy.float32([[f_x, 0.0, c_x],
[0.0, f_y, c_y],
[0.0, 0.0, 1.0] ])
print("Estimated camera matrix: \n" + str(camera_matrix) + "\n")
#These are the camera matrix values estimated on my webcam with
# the calibration code (see: src/calibration):
#camera_matrix = numpy.float32([[602.10618226, 0.0, 320.27333589],
#[ 0.0, 603.55869786, 229.7537026],
#[ 0.0, 0.0, 1.0] ])
#Distortion coefficients
camera_distortion = numpy.float32([0.0, 0.0, 0.0, 0.0, 0.0])
#Distortion coefficients estimated by calibration
#camera_distortion = numpy.float32([ 0.06232237, -0.41559805, 0.00125389, -0.00402566, 0.04879263])
#This matrix contains the 3D points of the
# 11 landmarks we want to find. It has been
# obtained from antrophometric measurement
# on the human head.
landmarks_3D = numpy.float32([P3D_RIGHT_SIDE,
P3D_GONION_RIGHT,
P3D_MENTON,
P3D_GONION_LEFT,
P3D_LEFT_SIDE,
P3D_FRONTAL_BREADTH_RIGHT,
P3D_FRONTAL_BREADTH_LEFT,
P3D_SELLION,
P3D_NOSE,
P3D_SUB_NOSE,
P3D_RIGHT_EYE,
P3D_RIGHT_TEAR,
P3D_LEFT_TEAR,
P3D_LEFT_EYE,
P3D_STOMION])
#Declaring the two classifiers
#my_cascade = haarCascade("../etc/haarcascade_frontalface_alt.xml", "../etc/haarcascade_profileface.xml")
dlib_landmarks_file = "./shape_predictor_68_face_landmarks.dat"
if(os.path.isfile(dlib_landmarks_file)==False):
print("The dlib landmarks file is missing! Use the following commands to download and unzip: ")
print(">> wget dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2")
print(">> bzip2 -d shape_predictor_68_face_landmarks.dat.bz2")
return
my_detector = faceLandmarkDetection(dlib_landmarks_file)
my_face_detector = dlib.get_frontal_face_detector()
while(True):
# Capture frame-by-frame
ret, frame = video_capture.read()
#gray = cv2.cvtColor(frame[roi_y1:roi_y2, roi_x1:roi_x2], cv2.COLOR_BGR2GRAY)
faces_array = my_face_detector(frame, 1)
print("Total Faces: " + str(len(faces_array)))
for i, pos in enumerate(faces_array):
face_x1 = pos.left()
face_y1 = pos.top()
face_x2 = pos.right()
face_y2 = pos.bottom()
text_x1 = face_x1
text_y1 = face_y1 - 3
cv2.putText(frame, "FACE " + str(i+1), (text_x1,text_y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 1);
cv2.rectangle(frame,
(face_x1, face_y1),
(face_x2, face_y2),
(0, 255, 0),
2)
landmarks_2D = my_detector.returnLandmarks(frame, face_x1, face_y1, face_x2, face_y2, points_to_return=TRACKED_POINTS)
for point in landmarks_2D:
cv2.circle(frame,( point[0], point[1] ), 2, (0,0,255), -1)
#Applying the PnP solver to find the 3D pose
# of the head from the 2D position of the
# landmarks.
#retval - bool
#rvec - Output rotation vector that, together with tvec, brings
# points from the model coordinate system to the camera coordinate system.
#tvec - Output translation vector.
retval, rvec, tvec = cv2.solvePnP(landmarks_3D,
landmarks_2D,
camera_matrix, camera_distortion)
#Now we project the 3D points into the image plane
#Creating a 3-axis to be used as reference in the image.
axis = numpy.float32([[50,0,0],
[0,50,0],
[0,0,50]])
imgpts, jac = cv2.projectPoints(axis, rvec, tvec, camera_matrix, camera_distortion)
#Drawing the three axis on the image frame.
#The opencv colors are defined as BGR colors such as:
# (a, b, c) >> Blue = a, Green = b and Red = c
#Our axis/color convention is X=R, Y=G, Z=B
sellion_xy = (landmarks_2D[7][0], landmarks_2D[7][1])
cv2.line(frame, sellion_xy, tuple(imgpts[1].ravel()), (0,255,0), 3) #GREEN
cv2.line(frame, sellion_xy, tuple(imgpts[2].ravel()), (255,0,0), 3) #BLUE
cv2.line(frame, sellion_xy, tuple(imgpts[0].ravel()), (0,0,255), 3) #RED
#Writing in the output file
out.write(frame)
#Showing the frame and waiting
# for the exit command
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'): break
#Release the camera
video_capture.release()
print("Bye...")
if __name__ == "__main__":
main()