EyeTrackVR/RANSAC/pupiltest.py
2022-04-25 20:52:44 -05:00

307 lines
9.4 KiB
Python

from tkinter import E
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from time import time
import sys
from pythonosc import udp_client
import torch
#model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt',force_reload=True)
#model.conf = 0.25 # NMS confidence threshold
#model.iou = 0.45 # NMS IoU threshold
#model.agnostic = False # NMS class-agnostic
#model.multi_label = False # NMS multiple labels per box
#model.max_det = 1 # maximum number of detections per image
#model.amp = False # Automatic Mixed Precision (AMP) inference
cx = 0.5
cy = 0.5
def vc():
vc.lidmax = 1
vc.lidmin = 6969 #( ͡° ͜ʖ ͡°) yes i know im stupid
vc.cfc = 1
vc.cc = 1
vc.cu = 0
vc.cd = 0
vc.cl = 0
vc.cr = 0
vc.fc = 0
vc.el = 2
vc.eyelidv = 1
vc.src = '1'
vc()
OSCip="127.0.0.1"
OSCport=9000 #VR Chat OSC port
client = udp_client.SimpleUDPClient(OSCip, OSCport)
def fit_rotated_ellipse_ransac(
data, iter=90, sample_num=10, offset=80.0
): # before changing these values, please read up on the ransac algorithm
# However if you want to change any value just know that higher iterations will make processing frames slower
count_max = 0
effective_sample = None
for i in range(iter):
sample = np.random.choice(len(data), sample_num, replace=False)
xs = data[sample][:, 0].reshape(-1, 1)
ys = data[sample][:, 1].reshape(-1, 1)
J = np.mat(
np.hstack((xs * ys, ys**2, xs, ys, np.ones_like(xs, dtype=np.float)))
)
Y = np.mat(-1 * xs**2)
P = (J.T * J).I * J.T * Y
# fitter a*x**2 + b*x*y + c*y**2 + d*x + e*y + f = 0
a = 1.0
b = P[0, 0]
c = P[1, 0]
d = P[2, 0]
e = P[3, 0]
f = P[4, 0]
ellipse_model = (
lambda x, y: a * x**2 + b * x * y + c * y**2 + d * x + e * y + f
)
# threshold
ran_sample = np.array(
[[x, y] for (x, y) in data if np.abs(ellipse_model(x, y)) < offset]
)
if len(ran_sample) > count_max:
count_max = len(ran_sample)
effective_sample = ran_sample
return fit_rotated_ellipse(effective_sample)
def fit_rotated_ellipse(data):
xs = data[:, 0].reshape(-1, 1)
ys = data[:, 1].reshape(-1, 1)
J = np.mat(np.hstack((xs * ys, ys**2, xs, ys, np.ones_like(xs, dtype=np.float))))
Y = np.mat(-1 * xs**2)
P = (J.T * J).I * J.T * Y
a = 1.0
b = P[0, 0]
c = P[1, 0]
d = P[2, 0]
e = P[3, 0]
f = P[4, 0]
theta = 0.5 * np.arctan(b / (a - c))
cx = (2 * c * d - b * e) / (b**2 - 4 * a * c)
cy = (2 * a * e - b * d) / (b**2 - 4 * a * c)
cu = a * cx**2 + b * cx * cy + c * cy**2 - f
w = np.sqrt(
cu
/ (
a * np.cos(theta) ** 2
+ b * np.cos(theta) * np.sin(theta)
+ c * np.sin(theta) ** 2
)
)
h = np.sqrt(
cu
/ (
a * np.sin(theta) ** 2
- b * np.cos(theta) * np.sin(theta)
+ c * np.cos(theta) ** 2
)
)
ellipse_model = lambda x, y: a * x**2 + b * x * y + c * y**2 + d * x + e * y + f
error_sum = np.sum([ellipse_model(x, y) for x, y in data])
print("fitting error = %.3f" % (error_sum))
return (cx, cy, w, h, theta)
def increase_brightness(img, value):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
h, s, v = cv2.split(hsv)
lim = 255 - value
v[v > lim] = 255
v[v <= lim] += value
final_hsv = cv2.merge((h, s, v))
img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)
return img
cap = cv2.VideoCapture("http://192.168.0.202:81/stream")
#cap = cv2.VideoCapture("http://192.168.1.177:4747/video")
# change this to the video you want to test
if cap.isOpened() == False:
print("Error opening video stream or file")
while cap.isOpened():
with open("config.txt") as calibratefl:
lines = calibratefl.readlines()
vx = float(lines[0].strip())
vy = float(lines[1].strip())
vxl = float(lines[2].strip())
vyl = float(lines[3].strip())
rv = float(lines[4].strip())
calibratefl.close()
# try:
ret, img = cap.read()
img = img[int(vxl): int(float(vy)), int(vyl): int(float(vx))]
if ret == True:
newImage2 = img.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
image_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(
image_gray, 125, 255, cv2.THRESH_BINARY
) # this will need to be adjusted everytime hardwere is changed (brightness of IR, Camera postion, etc)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
image = 255 - closing
contours, hierarchy = cv2.findContours(
image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
)
hull = []
for i in range(len(contours)):
hull.append(cv2.convexHull(contours[i], False))
try:
cv2.drawContours(img, contours, -1, (255, 0, 0), 1)
cnt = sorted(hull, key=cv2.contourArea)
maxcnt = cnt[-1]
ellipse = cv2.fitEllipse(maxcnt)
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
print(cx, cy)
cv2.circle(newImage2, (int(cx), int(cy)), 2, (0, 0, 255), -1)
cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
cv2.ellipse(
newImage2,
(int(cx), int(cy)),
(int(w), int(h)),
theta * 180.0 / np.pi,
0.0,
360.0,
(50, 250, 200),
1,
)
except:
pass
if vc.el == 2:
print('here')
vc.el = 5
#results = model(img) # inference
#for box in results.xyxy[0]: # box is a list of 4 numbers
# if box[5]==0: # if the confidence is 0, then skip
# xB = int(box[2]) # xB is the x coordinate of the bottom right corner
# xA = int(box[0]) # xA is the x coordinate of the top left corner
# yB = int(box[3]) # yB is the y coordinate of the bottom right corner
# yA = int(box[1]) # yA is the y coordinate of the top left corner
# cv2.rectangle(img, (xA, yA), (xB, yB), (0, 255, 0), 2) # draw a rectangle around the detected object
#cv2.circle(img, (int((xA+xB)/2), int((yA+yB)/2)), 2, (0, 0, 255), -1)
#cv2.imshow('EYEMODEL',img)
print('shown')
vc.el = vc.el - 1
print(vc.el)
if vc.cfc == 1:
try:
with open("eyeconfig.cfg") as eyecalib:
lines = eyecalib.readlines()
calibcenterx = float(lines[0].strip())
calibcentery = float(lines[1].strip())
calibrightx = float(lines[2].strip())
calibleftx = float(lines[3].strip())
calibupy = float(lines[4].strip())
calibdowny = float(lines[5].strip())
eyecalib.close()
vc.cfc = 2
except:
print('eror')
sys.exit()
#percentage = (((input - min) * 100) / (max - min)) / 100 only for reference because im dum and forget stuff
xr = float((((cx - calibcenterx) * 100) / (calibrightx - calibcenterx)) / 100)
xl = float((((cx - calibcenterx) * 100) / (calibleftx - calibcenterx)) / 100)
yu = float((((cy - calibcentery) * 100) / (calibupy - calibcentery)) / 100)
yd = float((((cy - calibcentery) * 100) / (calibdowny - calibcentery)) / 100)
if xr > 0:
if xr > 1:
xr = 1.0
client.send_message("/avatar/parameters/RightEyeX", xr)
client.send_message("/avatar/parameters/LeftEyeX", xr)
print('XR', xr)
if xl > 0:
if xl > 1:
xl = 1.0
client.send_message("/avatar/parameters/RightEyeX", -abs(xl))
client.send_message("/avatar/parameters/LeftEyeX", -abs(xl))
print('XL', xl)
if yd > 0:
if yd > 1:
yd = 1.0
client.send_message("/avatar/parameters/EyesY", -abs(yd))
# print('YD', yd)
if yu > 0:
if yu > 1:
yu = 1.0
client.send_message("/avatar/parameters/EyesY", yu)
#print('YU', yu)
cv2.imshow("Ransac", newImage2)
cv2.imshow("gray", image_gray)
cv2.imshow("thresh", thresh)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
#except:
# print('error')