import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-f",
"--face",
required=True,
help="path to where the face cascade resides")
ap.add_argument("-e",
"--eye",
required=True,
help="path to where the eye cascade resides")
ap.add_argument("-v", "--video", help="path to wthe (optional) video file")
args = vars(ap.parse_args())
et = EyeTracker(args["face"], args["eye"])
if not args.get("video", False):
camera = cv2.VideoCapture(0)
else:
camera = cv2.VideoCapture(args["video"])
while True:
(grabbed, frame) = camera.read()
if args.get("video") and not grabbed:
break
frame = image_utils.resize(frame, width=300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
link = "100.1.1.1"
# Initialize QT application
app = QtGui.QApplication(sys.argv)
# Initialize Tracker
eyetracker = EyeTracker(link=link,
dummy_mode=False,
sprate=1000,
thresvel=35,
thresacc=9500,
illumi=2,
caltype='HV5',
dodrift=False,
trackedeye='right',
display_type=display_type,
ptw=app,
bgcol=(127, 127, 127),
distance=550,
resolution=resolution,
winsize=(400, 300),
inner_tgcol=(127, 127, 127),
outer_tgcol=(255, 255, 255),
targetsize_out=1.5,
targetsize_in=0.5)
app.lastWindowClosed.connect(QtCore.QCoreApplication.instance().quit)
# Show application
eyetracker.display.gui.show()
# -*- coding: utf-8 -*-
"""
Created on Fri May 31 19:21:53 2019
@author: David
"""
import cv2
from eyetracker import EyeTracker
camera = cv2.VideoCapture(0)
et = EyeTracker("cascades\\haarcascade_frontalface_default.xml",
"cascades\\haarcascade_eye.xml")
while True:
(grabbed, frame) = camera.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = et.track(gray)
for rect in rects:
cv2.rectangle(frame, (rect[0], rect[1]), (rect[2], rect[3]),
(0, 255, 0), 2)
cv2.imshow("Tracking", frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
camera.release()
cv2.destroyAllWindows()
#faces = []
facesPath = []
currFaces = 0
num_of_images = len(args["images"])
for i,im in enumerate(args["images"]):
if any(x == os.path.splitext(im)[1][1:] for x in ('jpg','jpeg','png','bmp')):
# Read image
image = cv2.imread(im)
#imagecopy = image.copy()
imageName = os.path.dirname(im)[7:]
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# Create eyetracker object
et = EyeTracker(args["faces"],args["eyes"])
# Optimize image size and classify accordingly
image = imutils.optimize(image,lowerLim,upperLim)
if image.shape[0]==lowerLim or image.shape[1]==lowerLim:
allRects = et.track(image,FscaleFactor=1.04,FminNeighbors=3,FminSize=(2,2))
else:
allRects = et.track(image)
# Cycle through all faces and eyes
for (i,rect) in enumerate(allRects):
if i%3==0:
(Fx,Fy,Fw,Fh) = rect
#cv2.rectangle(imagecopy,(Fx,Fy),(Fx+Fw,Fy+Fh),(0,0,255),1)
face = image[Fy:Fy+Fh,Fx:Fx+Fw]
e2c = e1c = (0,0)
core.wait(0.1)
# wait for sometime
core.wait(waittime)
# check for keypresses
if len(event.getKeys()) > 0:
break
experiment_cleanup(win)
event.clearEvents()
def on_data(self, x, y):
eye = visual.Circle(self.win,
pos=(x, y),
fillColor=[0.5, 0.5, 0.5],
size=0.05,
lineWidth=1.5)
eye.draw()
pass
def experiment_cleanup(self, win):
# cleanup
win.close()
core.quit()
exp = Experiment()
et = EyeTracker(exp.initialize_exp, exp.on_data)
#initialize_exp()
import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument('-f',
'--face',
required=True,
help='path to where the face cascade resides')
ap.add_argument('-e',
'--eye',
required=True,
help='Path to where the eye cascade resides')
ap.add_argument('-v', '--video', help='path to the video')
args = vars(ap.parse_args())
et = EyeTracker(args['face'], args['eye'])
if not args.get('video', False):
camera = cv2.VideoCapture(0)
else:
camera = cv2.VideoCapture(args['video'])
while True:
(grabbed, frame) = camera.read()
if args.get('video') and not grabbed:
break
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
raise Exception(
'Oops. If you want to try out the Qt version, you might use example_Qt.py'
)
# From this point, the EyeTracker wrapper class works the same whatever we are using a pygame or a psychopy window
eyetracker = EyeTracker(link=link,
dummy_mode=use_dummy,
sprate=1000,
thresvel=35,
thresacc=9500,
illumi=2,
caltype='HV9',
dodrift=False,
trackedeye='right',
display_type=display_type,
ptw=win,
bgcol=(0, 0, 0),
distance=distance,
resolution=resolution,
winsize=screen_size,
inner_tgcol=(0, 0, 0),
outer_tgcol=(1, 1, 1),
targetsize_out=1.0,
targetsize_in=0.25)
# Checking methods (fixationtest, checkfixation)
checking = Checking(eyetracker, eyetracker.display, radius=2)
# Run Eyelink and do a calibration
eyetracker.run()
ap.add_argument("-f", "--face", required=True,
help="path to where the face cascade resides")
ap.add_argument("-e", "--eye", required=True,
help="path to where the eye cascade resides")
ap.add_argument("-i", "--images", required=True,
help="path to file containing list of images to process")
ap.add_argument("-a", "--aspect", required=False,
help="Define aspect ratio (note this doesn't squash or stretch the image)")
args = vars(ap.parse_args())
# Aspect ratio
target_aspect = args["aspect"]
# Setup face finder and eye tracker
# TODO - Rework to just use Face Detection?
et = EyeTracker(args["face"], args["eye"])
# Get list of images
fileList = open(args["images"]).readlines()
for imageInfo in fileList:
file = imageInfo.split(";")[0]
label = imageInfo.split(";")[1]
# Filename processing
print "Processing file:" + file
dirname, filename = os.path.split(os.path.relpath(file))
# load the image then resize and convert it to grayscale
image = cv2.imread(file)
frame = imgutils.resize(image, width=300)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)