mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
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740 lines
29 KiB
Python
740 lines
29 KiB
Python
import cv2
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import numpy as np
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import timeit
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from functools import lru_cache
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import os
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import sys
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import functools
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import math
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#HSF \/
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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 = 15
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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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response_list = []
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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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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(self.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[:, :] = self.response_list.astype(np.uint8)
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return frame_conv, min_response, center
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#RANSAC \/
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def ellipse_model(data, y, f):
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"""
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There is no need to make this process a function, since making the process a function will slow it down a little by calling it.
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The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same.
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a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4]
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:param data:
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:param y: np.c_[d, e, a, c, b]
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:param f: f == P[4, 0]
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:return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ])
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"""
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return data.dot(y) + f
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# @profile
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def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80
|
|
): # before changing these values, please read up on the ransac algorithm
|
|
# However if you want to change any value just know that higher iterations will make processing frames slower
|
|
effective_sample = None
|
|
|
|
# The array contents do not change during the loop, so only one call is needed.
|
|
# They say len is faster than shape.
|
|
# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
|
|
len_data = len(data)
|
|
|
|
if len_data < sample_num:
|
|
return None
|
|
|
|
# Type of calculation result
|
|
ret_dtype = np.float64
|
|
|
|
# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
|
|
# If the array size is less than about 100, this is faster than rng.choice.
|
|
rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
|
|
# or
|
|
# I don't see any advantage to doing this.
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|
# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
|
|
|
|
# I don't think it looks beautiful.
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|
# x,y,x**2,y**2,x*y,1,-1*x**2
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|
datamod = np.concatenate(
|
|
[data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
|
|
(-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
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|
dtype=ret_dtype)
|
|
|
|
datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
|
|
|
|
datamod_rng = datamod[rng_sample]
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|
datamod_rng6 = datamod_rng[:, :, 6]
|
|
datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
|
|
datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1))
|
|
|
|
# These two lines are one of the bottlenecks
|
|
datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
|
|
datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
|
|
|
|
datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
|
|
|
|
# I don't think it looks beautiful.
|
|
ellipse_y_arr = np.asarray(
|
|
[datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(len(datamod_rng_p)), datamod_rng_p[:, 1], datamod_rng_p[:, 0]], dtype=ret_dtype)
|
|
|
|
ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
|
|
ellipse_data_abs = np.abs(ellipse_data_arr)
|
|
ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
|
|
effective_data_arr = ellipse_data_arr[ellipse_data_index]
|
|
effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
|
|
|
|
return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr)
|
|
|
|
|
|
# @profile
|
|
def fit_rotated_ellipse(data, P):
|
|
a = 1.0
|
|
b = P[0]
|
|
c = P[1]
|
|
d = P[2]
|
|
e = P[3]
|
|
f = P[4]
|
|
# The cost of trigonometric functions is high.
|
|
theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
|
|
theta_sin = np.sin(theta, dtype=np.float64)
|
|
theta_cos = np.cos(theta, dtype=np.float64)
|
|
tc2 = theta_cos ** 2
|
|
ts2 = theta_sin ** 2
|
|
b_tcs = b * theta_cos * theta_sin
|
|
|
|
# Do the calculation only once
|
|
cxy = b ** 2 - 4 * a * c
|
|
cx = (2 * c * d - b * e) / cxy
|
|
cy = (2 * a * e - b * d) / cxy
|
|
|
|
# I just want to clear things up around here.
|
|
cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
|
|
cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
|
|
wh = np.sqrt(cu / cu_r)
|
|
|
|
w, h = wh[0], wh[1]
|
|
|
|
error_sum = np.sum(data)
|
|
# print("fitting error = %.3f" % (error_sum))
|
|
|
|
return (cx, cy, w, h, theta)
|
|
|
|
|
|
|
|
|
|
def HSRAC(self):
|
|
|
|
default_radius = self.settings.gui_HSF_radius
|
|
auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
|
|
frame = self.current_image_gray
|
|
if self.now_mode == self.cv_mode[1]:
|
|
|
|
|
|
prev_res_len = len(self.response_list)
|
|
# adjustment of radius
|
|
if prev_res_len == 1:
|
|
# len==1==self.response_list==[default_radius]
|
|
self.cvparam.radius = self.auto_radius_range[0]
|
|
elif prev_res_len == 2:
|
|
# len==2==self.response_list==[default_radius, self.auto_radius_range[0]]
|
|
self.cvparam.radius = self.auto_radius_range[1]
|
|
elif prev_res_len == 3:
|
|
# len==3==self.response_list==[default_radius,self.auto_radius_range[0],self.auto_radius_range[1]]
|
|
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
|
# Extract the radius with the lowest response value
|
|
if sort_res[0] == default_radius:
|
|
# If the default value is best, change self.now_mode to init after setting radius to the default value.
|
|
self.cvparam.radius = default_radius
|
|
self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
|
|
self.response_list = []
|
|
elif sort_res[0] == self.auto_radius_range[0]:
|
|
self.radius_cand_list = [i for i in range(self.auto_radius_range[0], default_radius, self.default_step[0])][1:]
|
|
# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
|
|
# It should be no problem to set it to anything other than self.default_step
|
|
self.cvparam.radius = self.radius_cand_list.pop()
|
|
else:
|
|
self.radius_cand_list = [i for i in range(default_radius, self.auto_radius_range[1], self.default_step[0])][1:]
|
|
# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
|
|
# It should be no problem to set it to anything other than self.default_step
|
|
self.cvparam.radius = self.radius_cand_list.pop()
|
|
else:
|
|
# Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out
|
|
# Better make it a binary search.
|
|
if len(self.radius_cand_list) == 0:
|
|
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
|
self.cvparam.radius = sort_res[0]
|
|
self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
|
|
self.response_list = []
|
|
else:
|
|
self.cvparam.radius = self.radius_cand_list.pop()
|
|
|
|
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
|
|
|
gray_frame = frame
|
|
try:
|
|
# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
|
|
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
|
|
frame_int = cv2.integral(frame_pad)
|
|
|
|
# Convolve the feature with the integral image
|
|
conv_int_start_time = timeit.default_timer()
|
|
xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
|
frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
|
|
|
|
crop_start_time = timeit.default_timer()
|
|
# Define the center point and radius
|
|
center_x, center_y = center_xy
|
|
upper_x = center_x + 25 #TODO make this a setting
|
|
lower_x = center_x - 25
|
|
upper_y = center_y + 25
|
|
lower_y = center_y - 25
|
|
|
|
# Crop the image using the calculated bounds
|
|
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why?
|
|
|
|
if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
|
|
# If mode is first_frame or radius_adjust, record current radius and response
|
|
self.response_list.append((radius, response))
|
|
elif self.now_mode == self.cv_mode[2]:
|
|
# Statistics for blink detection
|
|
if len(self.response_list) < self.blink_init_frames:
|
|
# Record the average value of cropped_image
|
|
self.response_list.append(cv2.mean(cropped_image)[0])
|
|
else:
|
|
# Calculate self.response_max by computing interquartile range, IQR
|
|
# Change self.cv_mode to normal
|
|
self.response_list = np.array(self.response_list)
|
|
# 25%,75%
|
|
# This value may need to be adjusted depending on the environment.
|
|
quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
|
iqr = quartile_3 - quartile_1
|
|
# response_min = quartile_1 - (iqr * 1.5)
|
|
self.response_max = quartile_3 + (iqr * 1.5)
|
|
self.now_mode = self.cv_mode[3]
|
|
else:
|
|
if 0 in cropped_image.shape:
|
|
# If shape contains 0, it is not detected well.
|
|
print("Something's wrong.")
|
|
else:
|
|
# If the average value of cropped_image is greater than self.response_max
|
|
# (i.e., if the cropimage is whitish
|
|
if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max:
|
|
# blink
|
|
|
|
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
|
|
# If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way
|
|
# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
|
|
|
|
except:
|
|
return 0, 0, frame
|
|
#run ransac on the HSF crop\
|
|
try:
|
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
|
thresh_add = 10
|
|
rng = np.random.default_rng()
|
|
|
|
f = False
|
|
|
|
# Convert the image to grayscale, and set up thresholding. Thresholds here are basically a
|
|
# low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user
|
|
# configurable in this utility as we're dealing with variable lighting amounts/placement, as
|
|
# well as camera positioning and lensing. Therefore everyone's cutoff may be different.
|
|
#
|
|
# The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we
|
|
# crop the image earlier; it gives us less possible dark area to get confused about in the
|
|
# next step.
|
|
frame = cropped_image
|
|
# For measuring processing time of image processing
|
|
# Crop first to reduce the amount of data to process.
|
|
|
|
#frame = frame[0:len(frame) - 5, :]
|
|
|
|
# To reduce the processing data, first convert to 1-channel and then blur.
|
|
# The processing results were the same when I swapped the order of blurring and 1-channelization.
|
|
frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
|
|
|
|
|
|
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m
|
|
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray)
|
|
|
|
maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
|
|
|
|
# crop 15% sqare around min_loc
|
|
# frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf,
|
|
# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
|
|
|
|
threshold_value = min_val + thresh_add
|
|
_, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY)
|
|
try:
|
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
|
th_frame = 255 - closing
|
|
except:
|
|
# I want to eliminate try here because try tends to be slow in execution.
|
|
th_frame = 255 - frame_gray
|
|
|
|
|
|
detect_start_time = timeit.default_timer()
|
|
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
|
hull = []
|
|
# This way is faster than contours[i]
|
|
# But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
|
|
for cnt in contours:
|
|
hull.append(cv2.convexHull(cnt, False))
|
|
if not hull:
|
|
# If empty, go to next loop
|
|
pass
|
|
try:
|
|
|
|
cnt = sorted(hull, key=cv2.contourArea)
|
|
maxcnt = cnt[-1]
|
|
# ellipse = cv2.fitEllipse(maxcnt)
|
|
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), rng)
|
|
if ransac_data is None:
|
|
# ransac_data is None==maxcnt.shape[0]<sample_num
|
|
# go to next loop
|
|
pass
|
|
|
|
crop_start_time = timeit.default_timer()
|
|
cx, cy, w, h, theta = ransac_data
|
|
|
|
csx = frame.shape[0]
|
|
csy = frame.shape[1]
|
|
|
|
cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF
|
|
cy = center_y - (csy - cy)
|
|
out_x, out_y = cx, cy
|
|
|
|
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
|
|
|
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
|
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
|
|
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
|
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
|
|
|
|
#img = newImage2[y1:y2, x1:x2]
|
|
except:
|
|
pass
|
|
|
|
self.current_image_gray = frame
|
|
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
|
|
-1) # the point of the darkest area in the image
|
|
try:
|
|
# print(radius)
|
|
return out_x, out_y, thresh
|
|
|
|
except:
|
|
return 0, 0, thresh
|
|
|
|
|
|
except:
|
|
return center_x, center_y, self.current_image_gray
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|