mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
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625 lines
24 KiB
Python
625 lines
24 KiB
Python
'''
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------------------------------------------------------------------------------------------------------
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,@@@@@@
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@@@@@@@@@@@ @@@
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@@@@@@@@@@@@ @@@@@@@@@@@
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@@@@@@@@@@@@@ @@@@@@@@@@@@@@
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@@@@@@@/ ,@@@@@@@@@@@@@
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/@@@@@@@@@@@@@@@ @@@@@@@@
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@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@
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@@@@@@@@ @@@@@
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,@@@ @@@@&
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@@@@@@. @@@@
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@@@ @@@@@@@@@/ @@@@@
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,@@@. @@@@@@((@ @@@@(
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//@@@ ,, @@@@ @@@@@
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@@@( @@@@@@@
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@@@ @ @@@@@@@@#
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@@@@@@@@@@@@@@@@@
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@@@@@@@@@@@@@(
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HSR By: Sean.Denka (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer)
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Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
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Copyright (c) 2022 EyeTrackVR <3
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------------------------------------------------------------------------------------------------------
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'''
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import functools
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import math
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import os
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import sys
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import timeit
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from functools import lru_cache
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import cv2
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import numpy as np
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# from line_profiler_pycharm import profile
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calc_print_enable = True
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save_video = False
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skip_autoradius = False
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skip_blink_detect = False
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# cache param
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lru_maxsize_vvs = 16
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lru_maxsize_vs = 64
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# CV param
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default_radius = 20
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auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
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blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
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# step==(x,y)
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default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
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"""
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Attention.
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If using cv2.filter2D in this code, be careful with the kernel
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https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python
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"""
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def TimeitWrapper(*args, **kwargs):
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"""
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This decorator @TimeitWrapper() prints the function name and execution time in seconds.
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:param args:
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:param kwargs:
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:return:
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"""
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def decorator(function):
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@functools.wraps(function)
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def wrapper(*args, **kwargs):
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start = timeit.default_timer()
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results = function(*args, **kwargs)
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end = timeit.default_timer()
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print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
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return results
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return wrapper
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return decorator
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class TimeitResult(object):
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"""
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from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
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Object returned by the timeit magic with info about the run.
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Contains the following attributes :
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loops: (int) number of loops done per measurement
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repeat: (int) number of times the measurement has been repeated
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best: (float) best execution time / number
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all_runs: (list of float) execution time of each run (in s)
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"""
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def __init__(self, loops, repeat, best, worst, all_runs, precision):
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self.loops = loops
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self.repeat = repeat
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self.best = best
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self.worst = worst
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self.all_runs = all_runs
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self._precision = precision
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self.timings = [dt / self.loops for dt in all_runs]
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@property
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def average(self):
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return math.fsum(self.timings) / len(self.timings)
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@property
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def stdev(self):
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mean = self.average
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return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
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def __str__(self):
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pm = '+-'
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb1'.encode(sys.stdout.encoding)
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pm = u'\xb1'
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except:
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pass
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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(
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pm=pm,
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runs=self.repeat,
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loops=self.loops,
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loop_plural="" if self.loops == 1 else "s",
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run_plural="" if self.repeat == 1 else "s",
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mean=format_time(self.average, self._precision),
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std=format_time(self.stdev, self._precision),
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best=format_time(self.best, self._precision),
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worst=format_time(self.worst, self._precision),
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)
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def _repr_pretty_(self, p, cycle):
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unic = self.__str__()
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p.text(u'<TimeitResult : ' + unic + u'>')
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class FPSResult(object):
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"""
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base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
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"""
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def __init__(self, loops, repeat, best, worst, all_runs, precision):
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self.loops = loops
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self.repeat = repeat
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self.best = 1 / best
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self.worst = 1 / worst
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self.all_runs = all_runs
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self._precision = precision
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self.fps = [1 / dt for dt in all_runs]
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self.unit = "fps"
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@property
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def average(self):
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return math.fsum(self.fps) / len(self.fps)
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@property
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def stdev(self):
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mean = self.average
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return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
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def __str__(self):
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pm = '+-'
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb1'.encode(sys.stdout.encoding)
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pm = u'\xb1'
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except:
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pass
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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(
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pm=pm,
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runs=self.repeat,
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loops=self.loops,
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loop_plural="" if self.loops == 1 else "s",
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run_plural="" if self.repeat == 1 else "s",
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mean="%.*g%s" % (self._precision, self.average, self.unit),
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std="%.*g%s" % (self._precision, self.stdev, self.unit),
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best="%.*g%s" % (self._precision, self.best, self.unit),
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worst="%.*g%s" % (self._precision, self.worst, self.unit),
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)
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def _repr_pretty_(self, p, cycle):
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unic = self.__str__()
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p.text(u'<FPSResult : ' + unic + u'>')
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def format_time(timespan, precision=3):
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"""
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https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
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Formats the timespan in a human readable form
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"""
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if timespan >= 60.0:
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# we have more than a minute, format that in a human readable form
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# Idea from http://snipplr.com/view/5713/
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parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
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time = []
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leftover = timespan
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for suffix, length in parts:
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value = int(leftover / length)
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if value > 0:
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leftover = leftover % length
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time.append(u'%s%s' % (str(value), suffix))
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if leftover < 1:
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break
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return " ".join(time)
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# Unfortunately the unicode 'micro' symbol can cause problems in
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# certain terminals.
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# See bug: https://bugs.launchpad.net/ipython/+bug/348466
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# Try to prevent crashes by being more secure than it needs to
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# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
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units = [u"s", u"ms", u'us', "ns"] # the save value
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb5'.encode(sys.stdout.encoding)
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units = [u"s", u"ms", u'\xb5s', "ns"]
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except:
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pass
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scaling = [1, 1e3, 1e6, 1e9]
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if timespan > 0.0:
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order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
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else:
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order = 3
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return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
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class CvParameters:
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# It may be a little slower because a dict named "self" is read for each function call.
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def __init__(self, radius, step):
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# self.prev_radius=radius
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self._radius = radius
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self.pad = 2 * radius
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# self.prev_step=step
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self._step = step
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self._hsf = HaarSurroundFeature(radius)
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def get_rpsh(self):
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return self._radius, self.pad, self._step, self._hsf
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# Essentially, the following would be preferable, but it would take twice as long to call.
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# return self.radius, self.pad, self.step, self.hsf
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@property
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def radius(self):
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return self._radius
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@radius.setter
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def radius(self, now_radius):
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# self.prev_radius=self._radius
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self._radius = now_radius
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self.pad = 2 * now_radius
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self.hsf = now_radius
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@property
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def step(self):
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return self._step
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@step.setter
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def step(self, now_step):
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# self.prev_step=self.step
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self._step = now_step
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@property
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def hsf(self):
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return self._hsf
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@hsf.setter
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def hsf(self, now_radius):
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self._hsf = HaarSurroundFeature(now_radius)
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class HaarSurroundFeature:
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def __init__(self, r_inner, r_outer=None, val=None):
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if r_outer is None:
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r_outer = r_inner * 3
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r_inner2 = r_inner * r_inner
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count_inner = r_inner2
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count_outer = r_outer * r_outer - r_inner2
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if val is None:
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val_inner = 1.0 / r_inner2
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val_outer = -val_inner * count_inner / count_outer
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else:
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val_inner = val[0]
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val_outer = val[1]
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self.val_in = np.array(val_inner, dtype=np.float64)
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self.val_out = np.array(val_outer, dtype=np.float64)
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self.r_in = r_inner
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self.r_out = r_outer
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def get_kernel(self):
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# Defined here, but not yet used?
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# Create a kernel filled with the value of self.val_out
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kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
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# Set the values of the inner area of the kernel using array slicing
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start = (self.r_out - self.r_in)
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end = (self.r_out + self.r_in - 1)
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kernel[start:end, start:end] = self.val_in
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return kernel
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@lru_cache(maxsize=lru_maxsize_vs)
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def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None):
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"""
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:param imageshape: (height(row),width(col)). row==y,cal==x
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:param xysteps: (x,y)
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:param pad: int
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:param start_offset: (x,y) or None
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:param end_offset: (x,y) or None
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:return: xy_np:tuple(x,y)
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"""
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row, col = imageshape
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row -= 1
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col -= 1
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x_step, y_step = xysteps
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# This is not beautiful.
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start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad
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if start_offset is not None:
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start_pad_x += start_offset[0]
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start_pad_y += start_offset[1]
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if end_offset is not None:
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end_pad_x += end_offset[0]
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end_pad_y += end_offset[1]
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y_np = np.arange(start_pad_y, row - end_pad_y, y_step)
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x_np = np.arange(start_pad_x, col - end_pad_x, x_step)
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xy_np = (x_np, y_np)
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return xy_np
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@lru_cache(maxsize=lru_maxsize_vvs)
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def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
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# Function to reduce array allocation by providing an empty array first and recycling it with lru
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inner_sum = np.empty(len_syx, dtype=frame_int_dtype)
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outer_sum = np.empty(len_syx, dtype=frame_int_dtype)
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p_temp = np.empty((len_syx[0], frameint_x), dtype=frame_int_dtype)
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p00 = np.empty(len_syx, dtype=frame_int_dtype)
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p11 = np.empty(len_syx, dtype=frame_int_dtype)
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p01 = np.empty(len_syx, dtype=frame_int_dtype)
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p10 = np.empty(len_syx, dtype=frame_int_dtype)
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response_list = np.empty(len_syx, dtype=np.float64)
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frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
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frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
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return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
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# @profile
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def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
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"""
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:param frame_int:
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:param kernel: hsf
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:param step: (x,y)
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:param padding: int
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:return:
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"""
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row, col = frame_int.shape
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row -= 1
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col -= 1
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x_step, y_step = xy_step
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# padding2 = 2 * padding
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f_shape = row - 2 * padding, col - 2 * padding
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r_in = kernel.r_in
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len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
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inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1,
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frame_int.dtype, (f_shape, y_step, x_step))
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inner_sum, outer_sum = inout_sum
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p00, p11, p01, p10 = p_list
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frame_conv, frame_conv_stride = frameconvlist
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y_rin_m = xy_steps_list[1] - r_in
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x_rin_m = xy_steps_list[0] - r_in
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y_rin_p = xy_steps_list[1] + r_in
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x_rin_p = xy_steps_list[0] + r_in
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# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
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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]
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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]
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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]
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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]
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# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
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inner_sum[:, :] = inarr_mm
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inner_sum += inarr_pp
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inner_sum -= inarr_mp
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inner_sum -= inarr_pm
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# Bottleneck here, I want to make it smarter. Someone do it.
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# (y,x)
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# p00=max(y_ro_m,0),max(x_ro_m,0)
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# p11=min(y_ro_p,ylim),min(x_ro_p,xlim)
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# p01=max(y_ro_m,0),min(x_ro_p,xlim)
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# p10=min(y_ro_p,ylim),max(x_ro_m,0)
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y_ro_m = xy_steps_list[1] - kernel.r_out
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x_ro_m = xy_steps_list[0] - kernel.r_out
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y_ro_p = xy_steps_list[1] + kernel.r_out
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x_ro_p = xy_steps_list[0] + kernel.r_out
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# p00 calc
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np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp)
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np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00)
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# p01 calc
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np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01)
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# p11 calc
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np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp)
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np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11)
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# p10 calc
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np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10)
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# the point is this
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# p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
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# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
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# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
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# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
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outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
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np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
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response_list += kernel.val_out * outer_sum
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# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
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min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
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center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding))
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frame_conv_stride[:, :] = response_list
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# or
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# frame_conv_stride[:, :] = response_list.astype(np.uint8)
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return frame_conv, min_response, center
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# @profile
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timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []}
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# I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble.
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# For measuring total processing time
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main_start_time = timeit.default_timer()
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rng = np.random.default_rng()
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cvparam = CvParameters(default_radius, default_step)
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cv_mode = ["first_frame", "radius_adjust", "init", "normal"]
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now_mode = cv_mode[0]
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radius_cand_list = []
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# response_min=0
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response_max = None
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response_list = []
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def HSF(self):
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#global now_mode
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#global response_list
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#global radius_cand_list
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#global response_max
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# default_radius = 15
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#frame = self.current_image_gray
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global now_mode
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global response_list
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global radius_cand_list
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global response_max
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global skip_autoradius
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global default_radius
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global prev_rany
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global prev_ranx
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global prev_hsfy
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global prev_hsfx
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skip_autoradius = self.settings.gui_skip_autoradius
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default_radius = self.settings.gui_HSF_radius
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frame = self.current_image_gray
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if now_mode == cv_mode[1]:
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prev_res_len = len(response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==response_list==[default_radius]
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cvparam.radius = auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==response_list==[default_radius, auto_radius_range[0]]
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cvparam.radius = auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
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sort_res = sorted(response_list, key=lambda x: x[1])[0]
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# Extract the radius with the lowest response value
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if sort_res[0] == default_radius:
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# If the default value is best, change now_mode to init after setting radius to the default value.
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cvparam.radius = default_radius
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now_mode = cv_mode[2] if not skip_blink_detect else cv_mode[3]
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response_list = []
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elif sort_res[0] == auto_radius_range[0]:
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radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, default_step[0])][1:]
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# default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
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# It should be no problem to set it to anything other than default_step
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cvparam.radius = radius_cand_list.pop()
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else:
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radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], default_step[0])][1:]
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# default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
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# It should be no problem to set it to anything other than default_step
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cvparam.radius = radius_cand_list.pop()
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else:
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# Try the contents of the radius_cand_list in order until the radius_cand_list runs out
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# Better make it a binary search.
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if len(radius_cand_list) == 0:
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sort_res = sorted(response_list, key=lambda x: x[1])[0]
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cvparam.radius = sort_res[0]
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now_mode = cv_mode[2] if not skip_blink_detect else cv_mode[3]
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response_list = []
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else:
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cvparam.radius = radius_cand_list.pop()
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radius, pad, step, hsf = cvparam.get_rpsh()
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# For measuring processing time of image processing
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cv_start_time = timeit.default_timer()
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gray_frame = frame
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timedict["to_gray"].append(timeit.default_timer() - cv_start_time)
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# Calculate the integral image of the frame
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int_start_time = timeit.default_timer()
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# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
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frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
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frame_int = cv2.integral(frame_pad)
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timedict["int_img"].append(timeit.default_timer() - int_start_time)
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# Convolve the feature with the integral image
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conv_int_start_time = timeit.default_timer()
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xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
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frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
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timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time)
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crop_start_time = timeit.default_timer()
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# Define the center point and radius
|
|
center_x, center_y = center_xy
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upper_x = center_x + 20
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lower_x = center_x - 20
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upper_y = center_y + 20
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lower_y = center_y - 20
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|
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# Crop the image using the calculated bounds
|
|
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
|
|
if now_mode == cv_mode[0] or now_mode == cv_mode[1]:
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|
# If mode is first_frame or radius_adjust, record current radius and response
|
|
response_list.append((radius, response))
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elif now_mode == cv_mode[2]:
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# Statistics for blink detection
|
|
if len(response_list) < blink_init_frames:
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# Record the average value of cropped_image
|
|
response_list.append(cv2.mean(cropped_image)[0])
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else:
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|
# Calculate response_max by computing interquartile range, IQR
|
|
# Change cv_mode to normal
|
|
response_list = np.array(response_list)
|
|
# 25%,75%
|
|
# This value may need to be adjusted depending on the environment.
|
|
quartile_1, quartile_3 = np.percentile(response_list, [25, 75])
|
|
iqr = quartile_3 - quartile_1
|
|
# response_min = quartile_1 - (iqr * 1.5)
|
|
response_max = quartile_3 + (iqr * 1.5)
|
|
now_mode = 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 response_max
|
|
# (i.e., if the cropimage is whitish
|
|
if response_max is not None and cv2.mean(cropped_image)[0] > response_max:
|
|
# blink
|
|
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
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|
|
|
# If you want to update response_max. it may be more cost-effective to rewrite 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
|
|
|
|
cv_end_time = timeit.default_timer()
|
|
timedict["crop"].append(cv_end_time - crop_start_time)
|
|
timedict["total_cv"].append(cv_end_time - cv_start_time)
|
|
|
|
# the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly
|
|
print('Kernel response:', response)
|
|
print('Pixel position:', center_xy)
|
|
|
|
|
|
if now_mode == cv_mode[0]:
|
|
# Moving from first_frame to the next mode
|
|
if skip_autoradius and skip_blink_detect:
|
|
now_mode = cv_mode[3]
|
|
response_list = []
|
|
elif skip_autoradius:
|
|
now_mode = cv_mode[2]
|
|
response_list = []
|
|
else:
|
|
now_mode = cv_mode[1]
|
|
|
|
try:
|
|
self.failed = 0
|
|
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
|
|
return center_x, center_y, frame
|
|
|
|
except:
|
|
self.failed = self.failed + 1
|
|
return 0, 0, frame
|
|
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