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openface/demos/sphere.py

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#!/usr/bin/env python2
# projectS and projectC were written by Gabriele Farina.
import time
start = time.time()
import argparse
import cv2
import os
import dlib
import numpy as np
np.set_printoptions(precision=2)
import openface
from matplotlib import cm
fileDir = os.path.dirname(os.path.realpath(__file__))
modelDir = os.path.join(fileDir, '..', 'models')
dlibModelDir = os.path.join(modelDir, 'dlib')
def getRep(bgrImg):
start = time.time()
if bgrImg is None:
raise Exception("Unable to load image/frame")
rgbImg = cv2.cvtColor(bgrImg, cv2.COLOR_BGR2RGB)
if args.verbose:
print(" + Original size: {}".format(rgbImg.shape))
if args.verbose:
print("Loading the image took {} seconds.".format(time.time() - start))
start = time.time()
# Get all bounding boxes
bb = align.getAllFaceBoundingBoxes(rgbImg)
if bb is None:
# raise Exception("Unable to find a face: {}".format(imgPath))
return None
if args.verbose:
print("Face detection took {} seconds.".format(time.time() - start))
start = time.time()
alignedFaces = []
for box in bb:
alignedFaces.append(
align.align(
args.imgDim,
rgbImg,
box,
landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE))
if alignedFaces is None:
raise Exception("Unable to align the frame")
if args.verbose:
print("Alignment took {} seconds.".format(time.time() - start))
start = time.time()
reps = []
for alignedFace in alignedFaces:
reps.append(net.forward(alignedFace))
if args.verbose:
print("Neural network forward pass took {} seconds.".format(
time.time() - start))
return reps
def projectS(rho, theta, z):
p = np.array([np.sqrt(3.) * rho * (np.cos(theta) + np.sin(theta)) / 2.,
z + 1. + rho * (np.cos(theta) - np.sin(theta)) / 2.])
p += np.array([1.5, 0.5])
p /= 3.
return p
def projectC(x, y, z):
rho = np.sqrt(x**2 + y**2)
if x == 0 and y == 0:
theta = 0
elif x >= 0:
theta = np.arcsin(y / rho)
else:
theta = -np.arcsin(y / rho) + np.pi
return projectS(rho, theta, z)
def draw(pts=[], clrs=[], cSz=400):
def toFrame(x):
return tuple((cSz * x).astype(np.int32))
cFrame = np.full((cSz, cSz, 3), 255, dtype=np.uint8)
for z in np.linspace(-1, 1, 9):
r = np.sqrt(1. - z**2)
last = None
for theta in np.linspace(0, 2 * np.pi, 50):
x = toFrame(projectS(r, theta, z))
if last is not None:
cv2.line(cFrame, x, last, color=(0, 0, 0))
last = x
for x in np.linspace(-1, 1, 9):
last = None
for theta in np.linspace(0, 2 * np.pi, 50):
r = np.sqrt(1. - x**2)
z = r * np.sin(theta)
y = r * np.cos(theta)
# x = toFrame(projectS(r, theta, z))
p = toFrame(projectC(x, y, z))
if last is not None:
cv2.line(cFrame, p, last, color=(0, 0, 0))
last = p
s = 1
x = toFrame(projectC(-s, 0, 0))
y = toFrame(projectC(s, 0, 0))
cv2.line(cFrame, x, y, color=(0, 0, 0), thickness=4)
x = toFrame(projectC(0, -s, 0))
y = toFrame(projectC(0, s, 0))
cv2.line(cFrame, x, y, color=(0, 0, 0), thickness=4)
x = toFrame(projectC(0, 0, -s))
y = toFrame(projectC(0, 0, s))
cv2.line(cFrame, x, y, color=(0, 0, 0), thickness=4)
for pt, c in zip(pts, clrs):
fPt = toFrame(projectC(pt[0], pt[1], pt[2]))
fPt_noz = toFrame(projectC(pt[0], pt[1], 0))
fPt_nozy = toFrame(projectC(pt[0], 0, 0))
fPt_nozx = toFrame(projectC(0, pt[1], 0))
cv2.line(cFrame, fPt, fPt_noz, color=c, thickness=2)
cv2.line(cFrame, fPt_noz, fPt_nozy, color=c, thickness=2)
cv2.line(cFrame, fPt_noz, fPt_nozx, color=c, thickness=2)
cv2.circle(cFrame, fPt, 5, color=c, thickness=-1)
return cFrame
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--dlibFacePredictor',
type=str,
help="Path to dlib's face predictor.",
default=os.path.join(
dlibModelDir,
"shape_predictor_68_face_landmarks.dat"))
parser.add_argument(
'--networkModel',
type=str,
help="Path to Torch network model.",
default='nn4.small2.3d.v1.t7')
# Download the 3D model from:
# https://storage.cmusatyalab.org/openface-models/nn4.small2.3d.v1.t7
parser.add_argument('--imgDim', type=int,
help="Default image dimension.", default=96)
parser.add_argument(
'--captureDevice',
type=int,
default=0,
help='Capture device. 0 for latop webcam and 1 for usb webcam')
# parser.add_argument('--width', type=int, default=640)
# parser.add_argument('--height', type=int, default=480)
parser.add_argument('--width', type=int, default=1280)
parser.add_argument('--height', type=int, default=800)
parser.add_argument('--scale', type=int, default=0.25)
parser.add_argument('--threshold', type=float, default=0.5)
parser.add_argument('--cuda', action='store_true')
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
align = openface.AlignDlib(args.dlibFacePredictor)
net = openface.TorchNeuralNet(
args.networkModel,
imgDim=args.imgDim,
cuda=args.cuda)
# Capture device. Usually 0 will be webcam and 1 will be usb cam.
video_capture = cv2.VideoCapture(args.captureDevice)
video_capture.set(3, args.width)
video_capture.set(4, args.height)
cv2.namedWindow('video', cv2.WINDOW_NORMAL)
class Tracker:
def __init__(self, img, bb, rep):
self.t = dlib.correlation_tracker()
self.t.start_track(img, bb)
self.rep = rep
self.bb = bb
self.pings = 0
def updateRep(self, rep):
self.pings = 0
alpha = 0.9
self.rep = alpha * self.rep + (1. - alpha) * rep
return self.rep
def overlap(self, bb):
p = float(self.bb.intersect(bb).area()) / float(self.bb.area())
return p > 0.3
def ping(self):
self.pings += 1
trackers = []
while True:
ret, frame = video_capture.read()
frame = cv2.flip(frame, 1)
frameSmall = cv2.resize(frame, (int(args.width * args.scale),
int(args.height * args.scale)))
bbs = align.getAllFaceBoundingBoxes(frameSmall)
pts, clrs = [], []
for i, bb in enumerate(bbs):
alignedFace = align.align(96, frameSmall, bb,
landmarkIndices=openface.AlignDlib.INNER_EYES_AND_BOTTOM_LIP)
rep = net.forward(alignedFace)
center = bb.center()
centerI = 0.7 * center.x * center.y / \
(args.scale * args.scale * args.width * args.height)
color_np = cm.Set1(centerI)
color_cv = list(np.multiply(color_np[:3], 255))
bl = (int(bb.left() / args.scale), int(bb.bottom() / args.scale))
tr = (int(bb.right() / args.scale), int(bb.top() / args.scale))
cv2.rectangle(frame, bl, tr, color=color_cv, thickness=3)
tracked = False
for i in xrange(len(trackers) - 1, -1, -1):
t = trackers[i]
t.t.update(frame)
if t.overlap(bb):
rep = t.updateRep(rep)
pts.append(rep)
clrs.append(color_cv)
tracked = True
break
if not tracked:
trackers.append(Tracker(frame, bb, rep))
pts.append(rep)
clrs.append(color_cv)
for i in xrange(len(trackers) - 1, -1, -1):
t = trackers[i]
t.ping()
if t.pings > 10:
del trackers[i]
continue
for j in range(i):
if t.t.get_position().intersect(trackers[j].t.get_position()).area() / \
t.t.get_position().area() > 0.4:
del trackers[i]
continue
cSz = 450
sphere = np.copy(frame)
sphere[0:cSz, 0:cSz, :] = draw(pts, clrs, cSz)
alpha = 0.25
beta = 1. - alpha
cv2.putText(sphere, "CMU OpenFace", (50, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 2.,
(0, 0, 0), 1, cv2.cv.CV_AA)
cv2.addWeighted(frame, alpha, sphere, beta, 0.0, frame)
cv2.imshow('video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
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