mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
1632 lines
66 KiB
Python
1632 lines
66 KiB
Python
'''
|
|
------------------------------------------------------------------------------------------------------
|
|
|
|
,@@@@@@
|
|
@@@@@@@@@@@ @@@
|
|
@@@@@@@@@@@@ @@@@@@@@@@@
|
|
@@@@@@@@@@@@@ @@@@@@@@@@@@@@
|
|
@@@@@@@/ ,@@@@@@@@@@@@@
|
|
/@@@@@@@@@@@@@@@ @@@@@@@@
|
|
@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@
|
|
@@@@@@@@ @@@@@
|
|
,@@@ @@@@&
|
|
@@@@@@. @@@@
|
|
@@@ @@@@@@@@@/ @@@@@
|
|
,@@@. @@@@@@((@ @@@@(
|
|
//@@@ ,, @@@@ @@@@@
|
|
@@@( @@@@@@@
|
|
@@@ @ @@@@@@@@#
|
|
@@@@@@@@@@@@@@@@@
|
|
@@@@@@@@@@@@@(
|
|
|
|
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
|
|
|
|
from osc_calibrate_filter import *
|
|
|
|
class InformationOrigin(Enum):
|
|
RANSAC = 1
|
|
BLOB = 2
|
|
FAILURE = 3
|
|
HSF = 4
|
|
|
|
bbb = 0
|
|
@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)
|
|
|
|
#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.failed = 0
|
|
|
|
|
|
self.response_list = [] #This might not be correct.
|
|
#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.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.auto_radius_range = (self.settings.gui_HSF_radius - 10, self.settings.gui_HSF_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.settings.gui_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.failed = self.failed + 1
|
|
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))
|
|
self.failed = 0
|
|
return
|
|
|
|
self.failed = self.failed + 1
|
|
self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, 0, 0, 0, self.blinkvalue))
|
|
|
|
|
|
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.settings.gui_HSF_radius]
|
|
self.cvparam.radius = self.auto_radius_range[0]
|
|
elif prev_res_len == 2:
|
|
# len==2==self.response_list==[self.settings.gui_HSF_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.settings.gui_HSF_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.settings.gui_HSF_radius:
|
|
# If the default value is best, change self.now_mode to init after setting radius to the default value.
|
|
self.cvparam.radius = self.settings.gui_HSF_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.settings.gui_HSF_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.settings.gui_HSF_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("[WARN] HSF: 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
|
|
|
|
|
|
|
|
|
|
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))
|
|
self.failed = 0
|
|
|
|
except:
|
|
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, 0, 0, 0, self.blinkvalue))
|
|
else:
|
|
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, False))
|
|
self.failed = self.failed + 1
|
|
|
|
|
|
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
|
|
#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))
|
|
|
|
|
|
|
|
|
|
|
|
def HSRAC(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.settings.gui_HSF_radius]
|
|
self.cvparam.radius = self.auto_radius_range[0]
|
|
elif prev_res_len == 2:
|
|
# len==2==self.response_list==[self.settings.gui_HSF_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.settings.gui_HSF_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.settings.gui_HSF_radius:
|
|
# If the default value is best, change self.now_mode to init after setting radius to the default value.
|
|
self.cvparam.radius = self.settings.gui_HSF_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.settings.gui_HSF_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.settings.gui_HSF_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
|
|
|
|
|
|
#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
|
|
|
|
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)
|
|
|
|
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
|
|
|
cv2.drawContours(frame, contours, -1, (255, 0, 0), 1)
|
|
cv2.circle(frame, (cx, cy), 2, (0, 0, 255), -1)
|
|
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
|
cv2.ellipse(frame, (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 abs(self.settings.gui_HSFP - self.settings.gui_HSRACP) < 2: #at this point we have successfully tan HSF, if ransac fails and HSF is the next algo, just send HSF values and continue
|
|
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
|
|
else: #HSF must not be next algo, so fail and move to the next one.
|
|
self.failed = self.failed + 1
|
|
#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))
|
|
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))
|
|
self.failed = 0 # we have succeded, continue with this
|
|
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))
|
|
self.failed = self.failed + 1 #we have failed, move onto next algo
|
|
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 ALGOSELECT(self):
|
|
|
|
if self.failed == 0 and self.firstalgo != None:
|
|
print('first')
|
|
self.firstalgo()
|
|
|
|
else:
|
|
self.failed = self.failed + 1
|
|
|
|
if self.failed == 1 and self.secondalgo != None:
|
|
print('2nd')
|
|
self.secondalgo() #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
|
|
|
|
else:
|
|
self.failed = self.failed + 1
|
|
|
|
if self.failed == 2 and self.thirdalgo != None:
|
|
print('3rd')
|
|
self.thirdalgo()
|
|
|
|
else:
|
|
self.failed = self.failed + 1
|
|
|
|
if self.failed == 3 and self.fourthalgo != None:
|
|
print('4th')
|
|
self.fourthalgo()
|
|
|
|
else:
|
|
self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
|
|
print(self.failed)
|
|
|
|
|
|
|
|
def run(self):
|
|
|
|
print("running")
|
|
self.firstalgo = None
|
|
self.secondalgo = None
|
|
self.thirdalgo = None
|
|
self.fourthalgo = None
|
|
#set algo priorities
|
|
if self.settings.gui_HSF and self.settings.gui_HSFP == 1: #I feel like this is super innefficient though it only runs at startup and no solution is coming to me atm
|
|
self.firstalgo = self.HSF
|
|
elif self.settings.gui_HSF and self.settings.gui_HSFP == 2:
|
|
self.secondalgo = self.HSF
|
|
elif self.settings.gui_HSF and self.settings.gui_HSFP == 3:
|
|
self.thirdalgo = self.HSF
|
|
elif self.settings.gui_HSF and self.settings.gui_HSFP == 4:
|
|
self.fourthalgo = self.HSF
|
|
|
|
if self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 1:
|
|
self.firstalgo = self.RANSAC3D
|
|
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 2:
|
|
self.secondalgo = self.RANSAC3D
|
|
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 3:
|
|
self.thirdalgo = self.RANSAC3D
|
|
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4:
|
|
self.fourthalgo = self.RANSAC3D
|
|
|
|
if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1:
|
|
self.firstalgo = self.HSRAC
|
|
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2:
|
|
self.secondalgo = self.HSRAC
|
|
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 3:
|
|
self.thirdalgo = self.HSRAC
|
|
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 4:
|
|
self.fourthalgo = self.HSRAC
|
|
|
|
if self.settings.gui_BLOB and self.settings.gui_BLOBP == 1:
|
|
self.firstalgo = self.BLOB
|
|
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 2:
|
|
self.secondalgo = self.BLOB
|
|
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 3:
|
|
self.thirdalgo = self.BLOB
|
|
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4:
|
|
self.fourthalgo = self.BLOB
|
|
|
|
|
|
# if self.settings.gui_BLOBP
|
|
# if self.settings.gui_HSFP
|
|
# if self.settings.gui_RANSAC3DP
|
|
|
|
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)
|
|
|
|
self.ALGOSELECT() #run our algos in priority order set in settings
|
|
|
|
|
|
|
|
|
|
|
|
|