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https://github.com/EyeTrackVR/EyeTrackVR.git
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856 lines
33 KiB
Python
856 lines
33 KiB
Python
import functools
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import math
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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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video_path = "ezgif.com-gif-maker.avi"
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imshow_enable = True
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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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auto_radius_step = 1
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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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# print(r_outer)
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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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def to_gray(frame):
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# Faster by quitting checking if the input image is already grayscale
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# Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function
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return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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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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class Auto_Radius_Calc(object):
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def __init__(self):
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self.response_list = []
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self.radius_cand_list = []
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self.adj_comp_flag = False
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self.radius_middle_index = None
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self.left_item = None
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self.right_item = None
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self.left_index = None
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self.right_index = None
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def get_radius(self):
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prev_res_len = len(self.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]
|
|
self.adj_comp_flag = False
|
|
return auto_radius_range[0]
|
|
elif prev_res_len == 2:
|
|
# len==2==response_list==[default_radius, auto_radius_range[0]]
|
|
self.adj_comp_flag = False
|
|
return auto_radius_range[1]
|
|
elif prev_res_len == 3:
|
|
# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
|
|
if self.response_list[1][1] < self.response_list[2][1]:
|
|
self.left_item = self.response_list[1]
|
|
self.right_item = self.response_list[0]
|
|
else:
|
|
self.left_item = self.response_list[0]
|
|
self.right_item = self.response_list[2]
|
|
self.radius_cand_list = [i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)]
|
|
self.left_index = 0
|
|
self.right_index = len(self.radius_cand_list) - 1
|
|
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
|
self.adj_comp_flag = False
|
|
return self.radius_cand_list[self.radius_middle_index]
|
|
else:
|
|
if self.left_index <= self.right_index and self.left_index != self.radius_middle_index:
|
|
if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]):
|
|
self.right_item = self.response_list[-1]
|
|
self.right_index = self.radius_middle_index - 1
|
|
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
|
self.adj_comp_flag = False
|
|
return self.radius_cand_list[self.radius_middle_index]
|
|
if (self.left_item[1] + self.response_list[-1][1]) > (self.right_item[1] + self.response_list[-1][1]):
|
|
self.left_item = self.response_list[-1]
|
|
self.left_index = self.radius_middle_index + 1
|
|
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
|
self.adj_comp_flag = False
|
|
return self.radius_cand_list[self.radius_middle_index]
|
|
self.adj_comp_flag = True
|
|
return self.radius_cand_list[self.radius_middle_index]
|
|
|
|
def get_radius_base(self):
|
|
"""
|
|
Use it when the new version doesn't work well.
|
|
:return:
|
|
"""
|
|
|
|
prev_res_len = len(self.response_list)
|
|
# adjustment of radius
|
|
if prev_res_len == 1:
|
|
# len==1==response_list==[default_radius]
|
|
self.adj_comp_flag = False
|
|
return auto_radius_range[0]
|
|
elif prev_res_len == 2:
|
|
# len==2==response_list==[default_radius, auto_radius_range[0]]
|
|
self.adj_comp_flag = False
|
|
return auto_radius_range[1]
|
|
elif prev_res_len == 3:
|
|
# len==3==response_list==[default_radius,auto_radius_range[0],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] == default_radius:
|
|
# If the default value is best, change now_mode to init after setting radius to the default value.
|
|
self.adj_comp_flag = True
|
|
return default_radius
|
|
elif sort_res[0] == auto_radius_range[0]:
|
|
self.radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, auto_radius_step)][1:]
|
|
self.adj_comp_flag = False
|
|
return self.radius_cand_list.pop()
|
|
else:
|
|
self.radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], auto_radius_step)][1:]
|
|
self.adj_comp_flag = False
|
|
return self.radius_cand_list.pop()
|
|
else:
|
|
# Try the contents of the radius_cand_list in order until the 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.adj_comp_flag = True
|
|
return sort_res[0]
|
|
else:
|
|
self.adj_comp_flag = False
|
|
return self.radius_cand_list.pop()
|
|
|
|
def add_response(self, radius, response):
|
|
self.response_list.append((radius, response))
|
|
return None
|
|
|
|
|
|
class Blink_Detector(object):
|
|
def __init__(self):
|
|
self.response_list = []
|
|
self.response_max = None
|
|
self.enable_detect_flg = False
|
|
self.quartile_1 = None
|
|
|
|
def calc_thresh(self):
|
|
# Calculate response_max by computing interquartile range, IQR
|
|
# self.response_listo = np.array(self.response_listo)
|
|
# 25%,75%
|
|
# This value may need to be adjusted depending on the environment.
|
|
# quartile_1, quartile_3 = np.percentile(self.response_listo, [25, 75])
|
|
# iqr = quartile_3 - quartile_1
|
|
# self.response_maxo = quartile_3 + (iqr * 1.5)
|
|
|
|
# quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
|
# or
|
|
quartile_1, quartile_3 = np.percentile(np.array(self.response_list), [25, 75])
|
|
self.quartile_1 = quartile_1
|
|
iqr = quartile_3 - quartile_1
|
|
# response_min = quartile_1 - (iqr * 1.5)
|
|
|
|
self.response_max = float(quartile_3 + (iqr * 1.5))
|
|
# or
|
|
# self.response_max = quartile_3 + (iqr * 1.5)
|
|
|
|
self.enable_detect_flg = True
|
|
return None
|
|
|
|
def detect(self, now_response):
|
|
return now_response > self.response_max
|
|
|
|
def add_response(self, response):
|
|
self.response_list.append(response)
|
|
return None
|
|
|
|
def response_len(self):
|
|
return len(self.response_list)
|
|
|
|
|
|
class CenterCorrection(object):
|
|
def __init__(self):
|
|
# Tunable parameters
|
|
kernel_size = 7 # 3 or 5 or 7
|
|
self.hist_thr = float(4) # 4%
|
|
self.center_q1_radius = 20
|
|
|
|
self.setup_comp = False
|
|
self.quartile_1 = None
|
|
self.radius = None
|
|
self.frame_shape = None
|
|
self.frame_mask = None
|
|
self.frame_bin = None
|
|
self.frame_final = None
|
|
self.morph_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
|
|
self.morph_kernel2 = np.ones((3, 3))
|
|
self.hist_index = np.arange(256)
|
|
self.hist = np.empty((256, 1))
|
|
self.hist_norm = np.empty((256, 1))
|
|
|
|
def init_array(self, gray_shape, quartile_1, radius):
|
|
self.frame_shape = gray_shape
|
|
self.frame_mask = np.empty(gray_shape, dtype=np.uint8)
|
|
self.frame_bin = np.empty(gray_shape, dtype=np.uint8)
|
|
self.frame_final = np.empty(gray_shape, dtype=np.uint8)
|
|
self.quartile_1 = quartile_1
|
|
self.radius = radius
|
|
self.setup_comp = True
|
|
|
|
# def reset_array(self):
|
|
# self.frame_mask.fill(0)
|
|
|
|
def correction(self, gray_frame, orig_x, orig_y):
|
|
center_x, center_y = orig_x, orig_y
|
|
self.frame_mask.fill(0)
|
|
|
|
# cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1)
|
|
|
|
# bottleneck
|
|
cv2.calcHist([gray_frame], [0], None, [256], [0, 256], hist=self.hist)
|
|
|
|
cv2.normalize(self.hist, self.hist_norm, alpha=100.0, norm_type=cv2.NORM_L1)
|
|
hist_per = self.hist_norm.cumsum()
|
|
hist_index_list = self.hist_index[hist_per >= self.hist_thr]
|
|
frame_thr = hist_index_list[0] if len(hist_index_list) else np.percentile(cv2.bitwise_or(255 - self.frame_mask, gray_frame), 4)
|
|
|
|
# bottleneck
|
|
self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[1]
|
|
cropped_x, cropped_y, cropped_w, cropped_h = cv2.boundingRect(self.frame_bin)
|
|
|
|
self.frame_final = cv2.bitwise_and(self.frame_bin, self.frame_mask)
|
|
|
|
# bottleneck
|
|
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel)
|
|
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_OPEN, self.morph_kernel)
|
|
|
|
if (cropped_h, cropped_w) == self.frame_shape:
|
|
# Not detected.
|
|
base_x, base_y = center_x, center_y
|
|
else:
|
|
base_x = cropped_x + cropped_w // 2
|
|
base_y = cropped_y + cropped_h // 2
|
|
if self.frame_final[base_y, base_x] != 1:
|
|
if self.frame_final[center_y, center_x] != 1:
|
|
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_DILATE, self.morph_kernel2, iterations=3)
|
|
else:
|
|
base_x, base_y = center_x, center_y
|
|
|
|
contours, _ = cv2.findContours(self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
|
contours_box = [cv2.boundingRect(cnt) for cnt in contours]
|
|
contours_dist = np.array(
|
|
[abs(base_x - (cnt_x + cnt_w / 2)) + abs(base_y - (cnt_y + cnt_h / 2)) for cnt_x, cnt_y, cnt_w, cnt_h in contours_box])
|
|
|
|
if len(contours_box):
|
|
cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[contours_dist.argmin()]
|
|
x = cropped_x2 + cropped_w2 // 2
|
|
y = cropped_y2 + cropped_h2 // 2
|
|
else:
|
|
x = center_x
|
|
y = center_y
|
|
|
|
# if imshow_enable:
|
|
# cv2.circle(frame, (orig_x, orig_y), 10, (255, 0, 0), -1)
|
|
# cv2.circle(frame, (x, y), 7, (0, 0, 255), -1)
|
|
|
|
#
|
|
# out_x = center_x if abs(x - center_x) > radius else x
|
|
# out_y = center_y if abs(y - center_y) > radius else y
|
|
out_x, out_y = orig_x, orig_y
|
|
if gray_frame[int(max(y - 5, 0)):int(min(y + 5, self.frame_shape[0])),
|
|
int(max(x - 5, 0)):int(min(x + 5, self.frame_shape[1]))].min() < self.quartile_1:
|
|
out_x = x
|
|
out_y = y
|
|
|
|
# if imshow_enable:
|
|
# cv2.circle(frame, (out_x, out_y), 5, (0, 255, 0), -1)
|
|
#
|
|
# cv2.imshow("frame_bin", self.frame_bin * 255)
|
|
# cv2.imshow("frame_final", self.frame_final * 255)
|
|
return out_x, out_y
|
|
|
|
|
|
class HSRAC_cls(object):
|
|
def __init__(self):
|
|
# I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble.
|
|
|
|
# For measuring total processing time
|
|
|
|
self.main_start_time = timeit.default_timer()
|
|
|
|
self.rng = np.random.default_rng()
|
|
self.cvparam = CvParameters(default_radius, default_step)
|
|
|
|
self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"]
|
|
self.now_modeo = self.cv_modeo[0]
|
|
|
|
self.auto_radius_calc = Auto_Radius_Calc()
|
|
self.blink_detector = Blink_Detector()
|
|
self.center_q1 = Blink_Detector()
|
|
self.center_correct = CenterCorrection()
|
|
|
|
self.cap = None
|
|
|
|
self.timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []}
|
|
|
|
def open_video(self, video_path):
|
|
# Temporary implementation to run
|
|
cap = cv2.VideoCapture(video_path)
|
|
if not cap.isOpened():
|
|
raise IOError("Error opening video stream or file")
|
|
self.cap = cap
|
|
return True
|
|
|
|
def read_frame(self):
|
|
# Temporary implementation to run
|
|
if not self.cap.isOpened():
|
|
return False
|
|
ret, frame = self.cap.read()
|
|
if ret:
|
|
# I have set it to grayscale (1ch) just in case, but if the frame is 1ch, this line can be commented out.
|
|
self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
return True
|
|
return False
|
|
|
|
def single_run(self):
|
|
# Temporary implementation to run
|
|
|
|
## default_radius = 14
|
|
|
|
frame = self.current_image_gray
|
|
if self.now_modeo == self.cv_modeo[1]:
|
|
# adjustment of radius
|
|
|
|
# debug print
|
|
# if calc_print_enable:
|
|
# temp_radius = self.auto_radius_calc.get_radius()
|
|
# print('Now radius:', temp_radius)
|
|
# self.cvparam.radius = temp_radius
|
|
|
|
self.cvparam.radius = self.auto_radius_calc.get_radius()
|
|
if self.auto_radius_calc.adj_comp_flag:
|
|
self.now_modeo = self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
|
|
|
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
|
|
|
# For measuring processing time of image processing
|
|
cv_start_time = timeit.default_timer()
|
|
|
|
gray_frame = frame
|
|
self.timedict["to_gray"].append(timeit.default_timer() - cv_start_time)
|
|
|
|
# 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)
|
|
self.timedict["int_img"].append(timeit.default_timer() - int_start_time)
|
|
|
|
# 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)
|
|
self.timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time)
|
|
|
|
crop_start_time = timeit.default_timer()
|
|
# Define the center point and radius
|
|
center_x, center_y = center_xy
|
|
upper_x = center_x + radius
|
|
lower_x = center_x - radius
|
|
upper_y = center_y + radius
|
|
lower_y = center_y - radius
|
|
|
|
# Crop the image using the calculated bounds
|
|
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
|
|
|
|
if self.now_modeo == self.cv_modeo[0] or self.now_modeo == self.cv_modeo[1]:
|
|
# If mode is first_frame or radius_adjust, record current radius and response
|
|
self.auto_radius_calc.add_response(radius, response)
|
|
elif self.now_modeo == self.cv_modeo[2]:
|
|
# Statistics for blink detection
|
|
if self.blink_detector.response_len() < blink_init_frames:
|
|
self.blink_detector.add_response(cv2.mean(cropped_image)[0])
|
|
|
|
upper_x = center_x + self.center_correct.center_q1_radius
|
|
lower_x = center_x - self.center_correct.center_q1_radius
|
|
upper_y = center_y + self.center_correct.center_q1_radius
|
|
lower_y = center_y - self.center_correct.center_q1_radius
|
|
self.center_q1.add_response(cv2.mean(gray_frame[lower_y:upper_y, lower_x:upper_x])[0])
|
|
|
|
else:
|
|
|
|
self.blink_detector.calc_thresh()
|
|
self.center_q1.calc_thresh()
|
|
self.now_modeo = self.cv_modeo[3]
|
|
else:
|
|
if 0 in cropped_image.shape:
|
|
# If shape contains 0, it is not detected well.
|
|
print("Something's wrong.")
|
|
else:
|
|
orig_x, orig_y = center_x, center_y
|
|
if self.blink_detector.enable_detect_flg:
|
|
# If the average value of cropped_image is greater than response_max
|
|
# (i.e., if the cropimage is whitish
|
|
if self.blink_detector.detect(cv2.mean(cropped_image)[0]):
|
|
# blink
|
|
pass
|
|
else:
|
|
# pass
|
|
if not self.center_correct.setup_comp:
|
|
self.center_correct.init_array(gray_frame.shape, self.center_q1.quartile_1, radius)
|
|
|
|
center_x, center_y = self.center_correct.correction(gray_frame, center_x, center_y)
|
|
# Define the center point and radius
|
|
center_xy = (center_x, center_y)
|
|
upper_x = center_x + radius
|
|
lower_x = center_x - radius
|
|
upper_y = center_y + radius
|
|
lower_y = center_y - radius
|
|
# Crop the image using the calculated bounds
|
|
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
|
|
# if imshow_enable or save_video:
|
|
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
|
# cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1)
|
|
# 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()
|
|
self.timedict["crop"].append(cv_end_time - crop_start_time)
|
|
self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
|
|
|
# if calc_print_enable:
|
|
# 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 imshow_enable:
|
|
if self.now_modeo != self.cv_modeo[0] and self.now_modeo != self.cv_modeo[1]:
|
|
if 0 in cropped_image.shape:
|
|
# If shape contains 0, it is not detected well.
|
|
pass
|
|
else:
|
|
cv2.imshow("crop", cropped_image)
|
|
cv2.imshow("frame", frame)
|
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
|
pass
|
|
|
|
if self.now_modeo == self.cv_modeo[0]:
|
|
# Moving from first_frame to the next mode
|
|
if skip_autoradius and skip_blink_detect:
|
|
self.now_modeo = self.cv_modeo[3]
|
|
elif skip_autoradius:
|
|
self.now_modeo = self.cv_modeo[2]
|
|
else:
|
|
self.now_modeo = self.cv_modeo[1]
|
|
|
|
return center_x, center_y, frame
|
|
|
|
class External_Run_HSF:
|
|
|
|
hsrac = HSRAC_cls()
|
|
|
|
def HSFS(self):
|
|
External_Run_HSF.hsrac.current_image_gray = self.current_image_gray
|
|
center_x, center_y, frame = External_Run_HSF.hsrac.single_run()
|
|
return center_x, center_y, frame
|
|
|
|
if __name__ == '__main__':
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hsrac = HSRAC_cls()
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hsrac.open_video(video_path)
|
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while hsrac.read_frame():
|
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_ = hsrac.single_run() |