EyeTrackVR/EyeTrackApp/eye_processor.py
2022-12-22 12:33:49 -08:00

1561 lines
60 KiB
Python

'''
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HSR By: Sean.Denka (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer)
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization)
BLOB By: Prohurtz#0001 (Main App Developer)
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
Additional Contributors: [Assassin], Summer404NotFound, lorow, ZanzyTHEbar
Copyright (c) 2022 EyeTrackVR <3
------------------------------------------------------------------------------------------------------
'''
from operator import truth
from dataclasses import dataclass
import sys
import asyncio
sys.path.append(".")
from config import EyeTrackCameraConfig
from config import EyeTrackSettingsConfig
from pye3d.camera import CameraModel
from pye3d.detector_3d import Detector3D, DetectorMode
import queue
import threading
import numpy as np
import cv2
from enum import Enum
from one_euro_filter import OneEuroFilter
if sys.platform.startswith("win"):
from winsound import PlaySound, SND_FILENAME, SND_ASYNC
import functools
import math
import os
import timeit
from collections import namedtuple
from functools import lru_cache
class InformationOrigin(Enum):
RANSAC = 1
BLOB = 2
FAILURE = 3
HSF = 4
@dataclass
class EyeInformation:
info_type: InformationOrigin
x: float
y: float
pupil_dialation: int
blink: bool
lowb = np.array(0)
def run_once(f):
def wrapper(*args, **kwargs):
if not wrapper.has_run:
wrapper.has_run = True
return f(*args, **kwargs)
wrapper.has_run = False
return wrapper
async def delayed_setting_change(setting, value):
await asyncio.sleep(5)
setting = value
if sys.platform.startswith("win"):
PlaySound('Audio/compleated.wav', SND_FILENAME | SND_ASYNC)
def cal_osc(self, cx, cy):
if self.eye_id == "EyeId.RIGHT":
flipx = self.settings.gui_flip_x_axis_right
else:
flipx = self.settings.gui_flip_x_axis_left
if self.calibration_frame_counter == 0:
self.calibration_frame_counter = None
self.xoff = cx
self.yoff = cy
if sys.platform.startswith("win"):
PlaySound('Audio/compleated.wav', SND_FILENAME | SND_ASYNC)
elif self.calibration_frame_counter != None:
self.settings.gui_recenter_eyes = False
if cx > self.xmax:
self.xmax = cx
if cx < self.xmin:
self.xmin = cx
if cy > self.ymax:
self.ymax = cy
if cy < self.ymin:
self.ymin = cy
self.calibration_frame_counter -= 1
if self.settings.gui_recenter_eyes == True:
self.xoff = cx
self.yoff = cy
if self.ts == 0:
self.settings.gui_recenter_eyes = False
if sys.platform.startswith("win"):
PlaySound('Audio/compleated.wav', SND_FILENAME | SND_ASYNC)
else:
self.ts = self.ts - 1
else:
self.ts = 10
xl = float(
(cx - self.xoff) / (self.xmax - self.xoff)
)
xr = float(
(cx - self.xoff) / (self.xmin - self.xoff)
)
yu = float(
(cy - self.yoff) / (self.ymin - self.yoff)
)
yd = float(
(cy - self.yoff) / (self.ymax - self.yoff)
)
out_x = 0
out_y = 0
if self.settings.gui_flip_y_axis: # check config on flipped values settings and apply accordingly
if yd >= 0:
out_y = max(0.0, min(1.0, yd))
if yu > 0:
out_y = -abs(max(0.0, min(1.0, yu)))
else:
if yd >= 0:
out_y = -abs(max(0.0, min(1.0, yd)))
if yu > 0:
out_y = max(0.0, min(1.0, yu))
if flipx: #TODO Check for working function
if xr >= 0:
out_x = -abs(max(0.0, min(1.0, xr)))
if xl > 0:
out_x = max(0.0, min(1.0, xl))
else:
if xr >= 0:
out_x = max(0.0, min(1.0, xr))
if xl > 0:
out_x = -abs(max(0.0, min(1.0, xl)))
try:
noisy_point = np.array([float(out_x), float(out_y)]) # fliter our values with a One Euro Filter
point_hat = self.one_euro_filter(noisy_point)
out_x = point_hat[0]
out_y = point_hat[1]
except:
pass
return out_x, out_y
#HSF \/
# cache param
lru_maxsize_vvs = 16
lru_maxsize_vs = 64
# CV param
default_radius = 20
auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
# step==(x,y)
default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
response_list = []
"""
Attention.
If using cv2.filter2D in this code, be careful with the kernel
https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python
"""
def TimeitWrapper(*args, **kwargs):
"""
This decorator @TimeitWrapper() prints the function name and execution time in seconds.
:param args:
:param kwargs:
:return:
"""
def decorator(function):
@functools.wraps(function)
def wrapper(*args, **kwargs):
start = timeit.default_timer()
results = function(*args, **kwargs)
end = timeit.default_timer()
print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
return results
return wrapper
return decorator
class TimeitResult(object):
"""
from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
Object returned by the timeit magic with info about the run.
Contains the following attributes :
loops: (int) number of loops done per measurement
repeat: (int) number of times the measurement has been repeated
best: (float) best execution time / number
all_runs: (list of float) execution time of each run (in s)
"""
def __init__(self, loops, repeat, best, worst, all_runs, precision):
self.loops = loops
self.repeat = repeat
self.best = best
self.worst = worst
self.all_runs = all_runs
self._precision = precision
self.timings = [dt / self.loops for dt in all_runs]
@property
def average(self):
return math.fsum(self.timings) / len(self.timings)
@property
def stdev(self):
mean = self.average
return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
def __str__(self):
pm = '+-'
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
try:
u'\xb1'.encode(sys.stdout.encoding)
pm = u'\xb1'
except:
pass
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
pm=pm,
runs=self.repeat,
loops=self.loops,
loop_plural="" if self.loops == 1 else "s",
run_plural="" if self.repeat == 1 else "s",
mean=format_time(self.average, self._precision),
std=format_time(self.stdev, self._precision),
best=format_time(self.best, self._precision),
worst=format_time(self.worst, self._precision),
)
def _repr_pretty_(self, p, cycle):
unic = self.__str__()
p.text(u'<TimeitResult : ' + unic + u'>')
class FPSResult(object):
"""
base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
"""
def __init__(self, loops, repeat, best, worst, all_runs, precision):
self.loops = loops
self.repeat = repeat
self.best = 1 / best
self.worst = 1 / worst
self.all_runs = all_runs
self._precision = precision
self.fps = [1 / dt for dt in all_runs]
self.unit = "fps"
@property
def average(self):
return math.fsum(self.fps) / len(self.fps)
@property
def stdev(self):
mean = self.average
return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
def __str__(self):
pm = '+-'
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
try:
u'\xb1'.encode(sys.stdout.encoding)
pm = u'\xb1'
except:
pass
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
pm=pm,
runs=self.repeat,
loops=self.loops,
loop_plural="" if self.loops == 1 else "s",
run_plural="" if self.repeat == 1 else "s",
mean="%.*g%s" % (self._precision, self.average, self.unit),
std="%.*g%s" % (self._precision, self.stdev, self.unit),
best="%.*g%s" % (self._precision, self.best, self.unit),
worst="%.*g%s" % (self._precision, self.worst, self.unit),
)
def _repr_pretty_(self, p, cycle):
unic = self.__str__()
p.text(u'<FPSResult : ' + unic + u'>')
def format_time(timespan, precision=3):
"""
https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
Formats the timespan in a human readable form
"""
if timespan >= 60.0:
# we have more than a minute, format that in a human readable form
# Idea from http://snipplr.com/view/5713/
parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
time = []
leftover = timespan
for suffix, length in parts:
value = int(leftover / length)
if value > 0:
leftover = leftover % length
time.append(u'%s%s' % (str(value), suffix))
if leftover < 1:
break
return " ".join(time)
# Unfortunately the unicode 'micro' symbol can cause problems in
# certain terminals.
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
# Try to prevent crashes by being more secure than it needs to
# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
units = [u"s", u"ms", u'us', "ns"] # the save value
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
try:
u'\xb5'.encode(sys.stdout.encoding)
units = [u"s", u"ms", u'\xb5s', "ns"]
except:
pass
scaling = [1, 1e3, 1e6, 1e9]
if timespan > 0.0:
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
else:
order = 3
return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
class CvParameters:
# It may be a little slower because a dict named "self" is read for each function call.
def __init__(self, radius, step):
# self.prev_radius=radius
self._radius = radius
self.pad = 2 * radius
# self.prev_step=step
self._step = step
self._hsf = HaarSurroundFeature(radius)
def get_rpsh(self):
return self._radius, self.pad, self._step, self._hsf
# Essentially, the following would be preferable, but it would take twice as long to call.
# return self.radius, self.pad, self.step, self.hsf
@property
def radius(self):
return self._radius
@radius.setter
def radius(self, now_radius):
# self.prev_radius=self._radius
self._radius = now_radius
self.pad = 2 * now_radius
self.hsf = now_radius
@property
def step(self):
return self._step
@step.setter
def step(self, now_step):
# self.prev_step=self.step
self._step = now_step
@property
def hsf(self):
return self._hsf
@hsf.setter
def hsf(self, now_radius):
self._hsf = HaarSurroundFeature(now_radius)
class HaarSurroundFeature:
def __init__(self, r_inner, r_outer=None, val=None):
if r_outer is None:
r_outer = r_inner * 3
r_inner2 = r_inner * r_inner
count_inner = r_inner2
count_outer = r_outer * r_outer - r_inner2
if val is None:
val_inner = 1.0 / r_inner2
val_outer = -val_inner * count_inner / count_outer
else:
val_inner = val[0]
val_outer = val[1]
self.val_in = np.array(val_inner, dtype=np.float64)
self.val_out = np.array(val_outer, dtype=np.float64)
self.r_in = r_inner
self.r_out = r_outer
def get_kernel(self):
# Defined here, but not yet used?
# Create a kernel filled with the value of self.val_out
kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
# Set the values of the inner area of the kernel using array slicing
start = (self.r_out - self.r_in)
end = (self.r_out + self.r_in - 1)
kernel[start:end, start:end] = self.val_in
return kernel
def to_gray(frame):
# Faster by quitting checking if the input image is already grayscale
# Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function
return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
@lru_cache(maxsize=lru_maxsize_vs)
def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None):
"""
:param imageshape: (height(row),width(col)). row==y,cal==x
:param xysteps: (x,y)
:param pad: int
:param start_offset: (x,y) or None
:param end_offset: (x,y) or None
:return: xy_np:tuple(x,y)
"""
row, col = imageshape
row -= 1
col -= 1
x_step, y_step = xysteps
# This is not beautiful.
start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad
if start_offset is not None:
start_pad_x += start_offset[0]
start_pad_y += start_offset[1]
if end_offset is not None:
end_pad_x += end_offset[0]
end_pad_y += end_offset[1]
y_np = np.arange(start_pad_y, row - end_pad_y, y_step)
x_np = np.arange(start_pad_x, col - end_pad_x, x_step)
xy_np = (x_np, y_np)
return xy_np
@lru_cache(maxsize=lru_maxsize_vvs)
def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
# Function to reduce array allocation by providing an empty array first and recycling it with lru
inner_sum = np.empty(len_syx, dtype=frame_int_dtype)
outer_sum = np.empty(len_syx, dtype=frame_int_dtype)
p_temp = np.empty((len_syx[0], frameint_x), dtype=frame_int_dtype)
p00 = np.empty(len_syx, dtype=frame_int_dtype)
p11 = np.empty(len_syx, dtype=frame_int_dtype)
p01 = np.empty(len_syx, dtype=frame_int_dtype)
p10 = np.empty(len_syx, dtype=frame_int_dtype)
response_list = np.empty(len_syx, dtype=np.float64)
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
# @profile
def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
"""
:param frame_int:
:param kernel: hsf
:param step: (x,y)
:param padding: int
:return:
"""
row, col = frame_int.shape
row -= 1
col -= 1
x_step, y_step = xy_step
# padding2 = 2 * padding
f_shape = row - 2 * padding, col - 2 * padding
r_in = kernel.r_in
len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1,
frame_int.dtype, (f_shape, y_step, x_step))
inner_sum, outer_sum = inout_sum
p00, p11, p01, p10 = p_list
frame_conv, frame_conv_stride = frameconvlist
y_rin_m = xy_steps_list[1] - r_in
x_rin_m = xy_steps_list[0] - r_in
y_rin_p = xy_steps_list[1] + r_in
x_rin_p = xy_steps_list[0] + r_in
# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
inarr_mm = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
inarr_pp = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
inner_sum[:, :] = inarr_mm
inner_sum += inarr_pp
inner_sum -= inarr_mp
inner_sum -= inarr_pm
# Bottleneck here, I want to make it smarter. Someone do it.
# (y,x)
# p00=max(y_ro_m,0),max(x_ro_m,0)
# p11=min(y_ro_p,ylim),min(x_ro_p,xlim)
# p01=max(y_ro_m,0),min(x_ro_p,xlim)
# p10=min(y_ro_p,ylim),max(x_ro_m,0)
y_ro_m = xy_steps_list[1] - kernel.r_out
x_ro_m = xy_steps_list[0] - kernel.r_out
y_ro_p = xy_steps_list[1] + kernel.r_out
x_ro_p = xy_steps_list[0] + kernel.r_out
# p00 calc
np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp)
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00)
# p01 calc
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01)
# p11 calc
np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp)
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11)
# p10 calc
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10)
# the point is this
# p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
response_list += kernel.val_out * outer_sum
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(self.response_list)
min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding))
frame_conv_stride[:, :] = response_list
# or
# frame_conv_stride[:, :] = self.response_list.astype(np.uint8)
return frame_conv, min_response, center
def ellipse_model(data, y, f):
"""
There is no need to make this process a function, since making the process a function will slow it down a little by calling it.
The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same.
a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4]
:param data:
:param y: np.c_[d, e, a, c, b]
:param f: f == P[4, 0]
:return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ])
"""
return data.dot(y) + f
# @profile
def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80
): # 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
effective_sample = None
# The array contents do not change during the loop, so only one call is needed.
# They say len is faster than shape.
# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
len_data = len(data)
if len_data < sample_num:
return None
# Type of calculation result
ret_dtype = np.float64
# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
# If the array size is less than about 100, this is faster than rng.choice.
rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
# or
# I don't see any advantage to doing this.
# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
# I don't think it looks beautiful.
# x,y,x**2,y**2,x*y,1,-1*x**2
datamod = np.concatenate(
[data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
(-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
dtype=ret_dtype)
datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
datamod_rng = datamod[rng_sample]
datamod_rng6 = datamod_rng[:, :, 6]
datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1))
# These two lines are one of the bottlenecks
datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
# I don't think it looks beautiful.
ellipse_y_arr = np.asarray(
[datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(len(datamod_rng_p)), datamod_rng_p[:, 1], datamod_rng_p[:, 0]], dtype=ret_dtype)
ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
ellipse_data_abs = np.abs(ellipse_data_arr)
ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
effective_data_arr = ellipse_data_arr[ellipse_data_index]
effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr)
# @profile
def fit_rotated_ellipse(data, P):
a = 1.0
b = P[0]
c = P[1]
d = P[2]
e = P[3]
f = P[4]
# The cost of trigonometric functions is high.
theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
theta_sin = np.sin(theta, dtype=np.float64)
theta_cos = np.cos(theta, dtype=np.float64)
tc2 = theta_cos ** 2
ts2 = theta_sin ** 2
b_tcs = b * theta_cos * theta_sin
# Do the calculation only once
cxy = b ** 2 - 4 * a * c
cx = (2 * c * d - b * e) / cxy
cy = (2 * a * e - b * d) / cxy
# I just want to clear things up around here.
cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
wh = np.sqrt(cu / cu_r)
w, h = wh[0], wh[1]
error_sum = np.sum(data)
# print("fitting error = %.3f" % (error_sum))
return (cx, cy, w, h, theta)
class EyeProcessor:
def __init__(
self,
config: "EyeTrackCameraConfig",
settings: "EyeTrackSettingsConfig",
cancellation_event: "threading.Event",
capture_event: "threading.Event",
capture_queue_incoming: "queue.Queue",
image_queue_outgoing: "queue.Queue",
eye_id,
):
self.config = config
self.settings = settings
# Cross-thread communication management
self.capture_queue_incoming = capture_queue_incoming
self.image_queue_outgoing = image_queue_outgoing
self.cancellation_event = cancellation_event
self.capture_event = capture_event
self.eye_id = eye_id
# Cross algo state
self.lkg_projected_sphere = None
self.xc = None
self.yc = None
# Image state
self.previous_image = None
self.current_image = None
self.current_image_gray = None
self.current_frame_number = None
self.current_fps = None
self.threshold_image = None
# Calibration Values
self.xoff = 1
self.yoff = 1
# Keep large in order to recenter correctly
self.calibration_frame_counter = None
self.eyeoffx = 1
self.xmax = -69420
self.xmin = 69420
self.ymax = -69420
self.ymin = 69420
self.cct = 300
self.cccs = False
self.ts = 10
self.previous_rotation = self.config.rotation_angle
self.calibration_frame_counter
self.camera_model = None
self.detector_3d = None
self.camera_model = None
self.detector_3d = None
self.response_list = [] #TODO we need to unify this?
#HSF
self.cv_mode = ["first_frame", "radius_adjust", "init", "normal"]
self.now_mode = self.cv_mode[0]
self.cvparam = CvParameters(default_radius, default_step)
self.default_radius = 15
self.skip_blink_detect = False
self.default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
# self.default_step==(x,y)
self.radius_cand_list = []
self.blink_init_frames = 60 * 3
prev_max_size = 60 * 3 # 60fps*3sec
# response_min=0
self.response_max = None
self.default_radius = 20
self.auto_radius_range = (self.default_radius - 10, self.default_radius + 10)
#blink
self.max_ints = []
self.max_int = 0
self.min_int = 4000000000000
self.frames = 0
self.blinkvalue = False
try:
min_cutoff = float(self.settings.gui_min_cutoff) # 0.0004
beta = float(self.settings.gui_speed_coefficient) # 0.9
except:
print('[WARN] OneEuroFilter values must be a legal number.')
min_cutoff = 0.0004
beta = 0.9
noisy_point = np.array([1, 1])
self.one_euro_filter = OneEuroFilter(
noisy_point,
min_cutoff=min_cutoff,
beta=beta
)
def output_images_and_update(self, threshold_image, output_information: EyeInformation):
image_stack = np.concatenate(
(
cv2.cvtColor(self.current_image_gray, cv2.COLOR_GRAY2BGR),
cv2.cvtColor(threshold_image, cv2.COLOR_GRAY2BGR),
),
axis=1,
)
self.image_queue_outgoing.put((image_stack, output_information))
self.previous_image = self.current_image
self.previous_rotation = self.config.rotation_angle
def capture_crop_rotate_image(self):
# Get our current frame
try:
# Get frame from capture source, crop to ROI
self.current_image = self.current_image[
int(self.config.roi_window_y): int(
self.config.roi_window_y + self.config.roi_window_h
),
int(self.config.roi_window_x): int(
self.config.roi_window_x + self.config.roi_window_w
),
]
except:
# Failure to process frame, reuse previous frame.
self.current_image = self.previous_image
print("[ERROR] Frame capture issue detected.")
try:
# Apply rotation to cropped area. For any rotation area outside of the bounds of the image,
# fill with white.
try:
rows, cols, _ = self.current_image.shape
except:
rows, cols, _ = self.previous_image.shape
img_center = (cols / 2, rows / 2)
rotation_matrix = cv2.getRotationMatrix2D(
img_center, self.config.rotation_angle, 1
)
self.current_image = cv2.warpAffine(
self.current_image,
rotation_matrix,
(cols, rows),
borderMode=cv2.BORDER_CONSTANT,
borderValue=(255, 255, 255),
)
return True
except:
pass
def BLOB(self):
# define circle
if self.config.gui_circular_crop:
if self.cct == 0:
try:
ht, wd = self.current_image_gray.shape[:2]
radius = int(float(self.lkg_projected_sphere["axes"][0]))
# draw filled circle in white on black background as mask
mask = np.zeros((ht, wd), dtype=np.uint8)
mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
# create white colored background
color = np.full_like(self.current_image_gray, (255))
# apply mask to image
masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
# apply inverse mask to colored image
masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
# combine the two masked images
self.current_image_gray = cv2.add(masked_img, masked_color)
except:
pass
else:
self.cct = self.cct - 1
_, larger_threshold = cv2.threshold(self.current_image_gray, int(self.config.threshold + 12), 255, cv2.THRESH_BINARY)
#try:
# Try rebuilding our contours
contours, _ = cv2.findContours(
larger_threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
)
contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
# If we have no contours, we have nothing to blob track. Fail here.
if len(contours) == 0:
raise RuntimeError("No contours found for image")
# except:
# self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False))
# return
rows, cols = larger_threshold.shape
for cnt in contours:
(x, y, w, h) = cv2.boundingRect(cnt)
# if our blob width/height are within suitable (yet arbitrary) boundaries, call that good.
#
# TODO This should be scaled based on camera resolution.
if not self.settings.gui_blob_minsize <= h <= self.settings.gui_blob_maxsize or not self.settings.gui_blob_minsize <= w <= self.settings.gui_blob_maxsize:
continue
cx = x + int(w / 2)
cy = y + int(h / 2)
cv2.line(
self.current_image_gray,
(x + int(w / 2), 0),
(x + int(w / 2), rows),
(255, 0, 0),
1,
) # visualizes eyetracking on thresh
cv2.line(
self.current_image_gray,
(0, y + int(h / 2)),
(cols, y + int(h / 2)),
(255, 0, 0),
1,
)
cv2.drawContours(self.current_image_gray, [cnt], -1, (255, 0, 0), 3)
cv2.rectangle(
self.current_image_gray, (x, y), (x + w, y + h), (255, 0, 0), 2
)
out_x, out_y = cal_osc(self, cx, cy) #filter and calibrate values
self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, self.blinkvalue))
f = False
return f
# self.output_images_and_update(
# larger_threshold, EyeInformation(InformationOrigin.BLOB, 0, 0, 0, True)
# )
# print("[INFO] BLINK Detected.")
f = True
return f
def HSF(self):
frame = self.current_image_gray
if self.now_mode == self.cv_mode[1]:
prev_res_len = len(self.response_list)
# adjustment of radius
if prev_res_len == 1:
# len==1==self.response_list==[self.default_radius]
self.cvparam.radius = self.auto_radius_range[0]
elif prev_res_len == 2:
# len==2==self.response_list==[self.default_radius, self.auto_radius_range[0]]
self.cvparam.radius = self.auto_radius_range[1]
elif prev_res_len == 3:
# len==3==self.response_list==[self.default_radius,self.auto_radius_range[0],self.auto_radius_range[1]]
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
# Extract the radius with the lowest response value
if sort_res[0] == self.default_radius:
# If the default value is best, change self.now_mode to init after setting radius to the default value.
self.cvparam.radius = self.default_radius
self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
self.response_list = []
elif sort_res[0] == self.auto_radius_range[0]:
self.radius_cand_list = [i for i in range(self.auto_radius_range[0], self.default_radius, self.default_step[0])][1:]
# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
# It should be no problem to set it to anything other than self.default_step
self.cvparam.radius = self.radius_cand_list.pop()
else:
self.radius_cand_list = [i for i in range(self.default_radius, self.auto_radius_range[1], self.default_step[0])][1:]
# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
# It should be no problem to set it to anything other than self.default_step
self.cvparam.radius = self.radius_cand_list.pop()
else:
# Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out
# Better make it a binary search.
if len(self.radius_cand_list) == 0:
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
self.cvparam.radius = sort_res[0]
self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
self.response_list = []
else:
self.cvparam.radius = self.radius_cand_list.pop()
radius, pad, step, hsf = self.cvparam.get_rpsh()
# For measuring processing time of image processing
cv_start_time = timeit.default_timer()
gray_frame = frame
# Calculate the integral image of the frame
int_start_time = timeit.default_timer()
# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
frame_int = cv2.integral(frame_pad)
# Convolve the feature with the integral image
conv_int_start_time = timeit.default_timer()
xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
crop_start_time = timeit.default_timer()
# Define the center point and radius
center_x, center_y = center_xy
upper_x = center_x + 25 #TODO make this a setting
lower_x = center_x - 25
upper_y = center_y + 25
lower_y = center_y - 25
# Crop the image using the calculated bounds
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why?
if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
# If mode is first_frame or radius_adjust, record current radius and response
self.response_list.append((radius, response))
elif self.now_mode == self.cv_mode[2]:
# Statistics for blink detection
if len(self.response_list) < self.blink_init_frames:
# Record the average value of cropped_image
self.response_list.append(cv2.mean(cropped_image)[0])
else:
# Calculate self.response_max by computing interquartile range, IQR
# Change self.cv_mode to normal
self.response_list = np.array(self.response_list)
# 25%,75%
# This value may need to be adjusted depending on the environment.
quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
iqr = quartile_3 - quartile_1
# response_min = quartile_1 - (iqr * 1.5)
self.response_max = quartile_3 + (iqr * 1.5)
self.now_mode = self.cv_mode[3]
else:
if 0 in cropped_image.shape:
# If shape contains 0, it is not detected well.
print("Something's wrong.")
else:
# If the average value of cropped_image is greater than self.response_max
# (i.e., if the cropimage is whitish
if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max:
# blink
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
# If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way
# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
if not self.settings.gui_HSRAC:
out_x, out_y = cal_osc(self, center_x, center_y)
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
# print(center_x, center_y)
try:
if self.settings.gui_BLINK: #tbh this is redundant, the algo already has blink detection built in
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
else:
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
f = False
except:
pass
if self.now_mode != self.cv_mode[0] and self.now_mode != self.cv_mode[1]:
if cropped_image.size < 400:
pass
if self.now_mode == self.cv_mode[0]:
self.now_mode = self.cv_mode[1]
return f
#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False))
# return
#self.output_images_and_update(larger_threshold,EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False),)
# return
#self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True))
else: #run ransac on the HSF crop\
try:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
thresh_add = 10
rng = np.random.default_rng()
f = False
# Convert the image to grayscale, and set up thresholding. Thresholds here are basically a
# low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user
# configurable in this utility as we're dealing with variable lighting amounts/placement, as
# well as camera positioning and lensing. Therefore everyone's cutoff may be different.
#
# The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we
# crop the image earlier; it gives us less possible dark area to get confused about in the
# next step.
frame = cropped_image
# For measuring processing time of image processing
# Crop first to reduce the amount of data to process.
frame = frame[0:len(frame) - 5, :]
# To reduce the processing data, first convert to 1-channel and then blur.
# The processing results were the same when I swapped the order of blurring and 1-channelization.
frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray)
maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
# crop 15% sqare around min_loc
# frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf,
# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
threshold_value = min_val + thresh_add
_, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY)
try:
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
th_frame = 255 - closing
except:
# I want to eliminate try here because try tends to be slow in execution.
th_frame = 255 - frame_gray
detect_start_time = timeit.default_timer()
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
hull = []
# This way is faster than contours[i]
# But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
for cnt in contours:
hull.append(cv2.convexHull(cnt, False))
if not hull:
# If empty, go to next loop
pass
try:
cnt = sorted(hull, key=cv2.contourArea)
maxcnt = cnt[-1]
# ellipse = cv2.fitEllipse(maxcnt)
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), rng)
if ransac_data is None:
# ransac_data is None==maxcnt.shape[0]<sample_num
# go to next loop
pass
crop_start_time = timeit.default_timer()
cx, cy, w, h, theta = ransac_data
csx = frame.shape[0]
csy = frame.shape[1]
cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF
cy = center_y - (csy - cy)
out_x, out_y = cal_osc(self, cx, cy)
#print()
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
#img = newImage2[y1:y2, x1:x2]
except:
pass
self.current_image_gray = frame
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
-1) # the point of the darkest area in the image
try:
if self.settings.gui_BLINK:
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
else:
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
f = False
except:
pass
except:
try:
if self.settings.gui_BLINK:
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
else:
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
f = False
except:
pass
def RANSAC3D(self):
f = False
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
thresh_add = 10
rng = np.random.default_rng()
f = False
self.capture_crop_rotate_image()
# Convert the image to grayscale, and set up thresholding. Thresholds here are basically a
# low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user
# configurable in this utility as we're dealing with variable lighting amounts/placement, as
# well as camera positioning and lensing. Therefore everyone's cutoff may be different.
#
# The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we
# crop the image earlier; it gives us less possible dark area to get confused about in the
# next step.
if self.config.gui_circular_crop == True:
if self.cct == 0:
try:
ht, wd = self.current_image_gray.shape[:2]
radius = int(float(self.lkg_projected_sphere["axes"][0]))
self.xc = int(float(self.lkg_projected_sphere["center"][0]))
self.yc = int(float(self.lkg_projected_sphere["center"][1]))
# draw filled circle in white on black background as mask
mask = np.zeros((ht, wd), dtype=np.uint8)
mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
# create white colored background
color = np.full_like(self.current_image_gray, (255))
# apply mask to image
masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
# apply inverse mask to colored image
masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
# combine the two masked images
self.current_image_gray = cv2.add(masked_img, masked_color)
except:
pass
else:
self.cct = self.cct - 1
else:
self.cct = 300
# Crop first to reduce the amount of data to process.
newFrame2 = self.current_image_gray.copy()
frame = self.current_image_gray
# For measuring processing time of image processing
# Crop first to reduce the amount of data to process.
frame = frame[0:len(frame) - 5, :]
# To reduce the processing data, first convert to 1-channel and then blur.
# The processing results were the same when I swapped the order of blurring and 1-channelization.
frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray)
maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
# crop 15% sqare around min_loc
# frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf,
# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
threshold_value = min_val + thresh_add
_, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY)
try:
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
th_frame = 255 - closing
except:
# I want to eliminate try here because try tends to be slow in execution.
th_frame = 255 - frame_gray
detect_start_time = timeit.default_timer()
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
hull = []
# This way is faster than contours[i]
# But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
for cnt in contours:
hull.append(cv2.convexHull(cnt, False))
if not hull:
# If empty, go to next loop
pass
try:
cnt = sorted(hull, key=cv2.contourArea)
maxcnt = cnt[-1]
# ellipse = cv2.fitEllipse(maxcnt)
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), rng)
if ransac_data is None:
# ransac_data is None==maxcnt.shape[0]<sample_num
# go to next loop
pass
crop_start_time = timeit.default_timer()
cx, cy, w, h, theta = ransac_data
out_x, out_y = cal_osc(self, cx, cy)
# print(cx, cy)
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
# once a pupil is found, crop 100x100 around it
x1 = cx - 50
x2 = cx + 50
y1 = cy - 50
y2 = cy + 50
cropped_image = newFrame2[y1:y2, x1:x2]
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
#img = newImage2[y1:y2, x1:x2]
except:
pass
self.current_image_gray = frame
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
-1) # the point of the darkest area in the image
# However eyes are annoyingly three dimensional, so we need to take this ellipse and turn it
# into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
# ophthalmologist about possible issues with astigmatism.
try:
# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
# from somewhat to completely magic, as most of it happens in native libraries (hence passing
# via dicts).
result_2d = {}
result_2d_final = {}
result_2d["center"] = (cx, cy)
result_2d["axes"] = (w, h)
result_2d["angle"] = theta * 180.0 / np.pi
result_2d_final["ellipse"] = result_2d
result_2d_final["diameter"] = w
result_2d_final["location"] = (cx, cy)
result_2d_final["confidence"] = 0.99
result_2d_final["timestamp"] = self.current_frame_number / self.current_fps
# Black magic happens here, but after this we have our reprojected pupil/eye, and all we had
# to do was sell our soul to satan and/or C++.
result_3d = self.detector_3d.update_and_detect(
result_2d_final, self.current_image_gray
)
# Now we have our pupil
ellipse_3d = result_3d["ellipse"]
# And our eyeball that the pupil is on the surface of
self.lkg_projected_sphere = result_3d["projected_sphere"]
# Record our pupil center
exm = ellipse_3d["center"][0]
eym = ellipse_3d["center"][1]
d = result_3d["diameter_3d"]
except:
f = True
# Draw our image and stack it for visual output
try:
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
except:
pass
# try: #for some reason the pye3d visualizations are wack, im going to just not visualize it for now..
# cv2.ellipse(
# self.current_image_gray,
# tuple(int(v) for v in ellipse_3d["center"]),
# tuple(int(v) for v in ellipse_3d["axes"]),
# ellipse_3d["angle"],
# 0,
# 360, # start/end angle for drawing
# (0, 255, 0), # color (BGR): red
# )
# except Exception:
# Sometimes we get bogus axes and trying to draw this throws. Ideally we should check for
# validity beforehand, but for now just pass. It usually fixes itself on the next frame.
# pass
try:
# print(self.lkg_projected_sphere["angle"], self.lkg_projected_sphere["axes"], self.lkg_projected_sphere["center"])
cv2.ellipse(
self.current_image_gray,
tuple(int(v) for v in self.lkg_projected_sphere["center"]),
tuple(int(v) for v in self.lkg_projected_sphere["axes"]),
self.lkg_projected_sphere["angle"],
0,
360, # start/end angle for drawing
(0, 255, 0), # color (BGR): red
)
# draw line from center of eyeball to center of pupil
# cv2.line(
# self.current_image_gray,
# tuple(int(v) for v in self.lkg_projected_sphere["center"]),
# tuple(int(v) for v in ellipse_3d["center"]),
# (0, 255, 0), # color (BGR): red
# )
except:
pass
try:
if self.settings.gui_BLINK:
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
else:
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False))
f = False
except:
if self.settings.gui_BLINK:
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, self.blinkvalue))
else:
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, True))
f = True
pass
# Shove a concatenated image out to the main GUI thread for rendering
#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0 ,0, 0, False))
#self.output_images_and_update(thresh, output_info)
#except:
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
return f
def BLINK(self):
intensity = np.sum(self.current_image_gray)
self.frames = self.frames + 1
if intensity > self.max_int:
self.max_int = intensity
if self.frames > 200:
self.max_ints.append(self.max_int)
if intensity < self.min_int:
self.min_int = intensity
if len(self.max_ints) > 1:
if intensity > min(self.max_ints):
print("Blink")
self.blinkvalue = True
else:
self.blinkvalue = False
print(self.blinkvalue)
def run(self):
cvparam = CvParameters(self.default_radius, self.default_step)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
thresh_add = 10
rng = np.random.default_rng()
f = True
while True:
# f = True
# Check to make sure we haven't been requested to close
if self.cancellation_event.is_set():
print("\033[94m[INFO] Exiting Tracking thread\033[0m")
return
if self.config.roi_window_w <= 0 or self.config.roi_window_h <= 0:
# At this point, we're waiting for the user to set up the ROI window in the GUI.
# Sleep a bit while we wait.
if self.cancellation_event.wait(0.1):
return
continue
# If our ROI configuration has changed, reset our model and detector
if (self.camera_model is None
or self.detector_3d is None
or self.camera_model.resolution != (
self.config.roi_window_w,
self.config.roi_window_h,
)
):
self.camera_model = CameraModel(
focal_length=self.config.focal_length,
resolution=(self.config.roi_window_w, self.config.roi_window_h),
)
self.detector_3d = Detector3D(
camera=self.camera_model, long_term_mode=DetectorMode.blocking
)
try:
if self.capture_queue_incoming.empty():
self.capture_event.set()
# Wait a bit for images here. If we don't get one, just try again.
(
self.current_image,
self.current_frame_number,
self.current_fps,
) = self.capture_queue_incoming.get(block=True, timeout=0.2)
except queue.Empty:
# print("No image available")
continue
if not self.capture_crop_rotate_image():
continue
self.current_image_gray = cv2.cvtColor(
self.current_image, cv2.COLOR_BGR2GRAY
)
# print(self.settings.gui_RANSAC3D)
"""try:
if self.settings.gui_RANSAC3D == True: #for now ransac goes first
f == self.RANSAC3D()
if f and self.settings.gui_HSF == True: #if a fail has been reported and other algo is enabled, use it.
f == self.HSF()
if f and self.settings.gui_BLOB == True:
f == self.BLOB()
except:
pass
""" #print("[WARN] ALL ALGORITHIMS HAVE FAILED OR ARE DISABLED.")
# self.RANSAC3D()
#self.BLINK()
self.HSF()
# f == self.RANSAC3D()'''
#FLOW MOCK
#if PYE3D
#RUN PYE
#receive values, if fail reported, go to next method
#IF HSF
#RUN HSF
#receive values, if fail reported, go to next method
#IF BLOB
#RUN BLOB (ew tbh)
#receive values, if fail reported, end here in complete fail.