mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
Merge remote-tracking branch 'origin/fix_safecrop' into fix_safecrop
# Conflicts: # EyeTrackApp/eye_processor.py
This commit is contained in:
commit
ec83c3831f
@ -4,9 +4,11 @@ from functools import lru_cache
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import cv2
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import cv2
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import numpy as np
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import numpy as np
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from utils.misc_utils import clamp
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from utils.misc_utils import clamp
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from utils.img_utils import safe_crop
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from utils.img_utils import safe_crop
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# from line_profiler_pycharm import profile
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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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video_path = "ezgif.com-gif-maker.avi"
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@ -27,6 +29,180 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
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# step==(x,y)
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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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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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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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# It may be a little slower because a dict named "self" is read for each function call.
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@ -37,42 +213,43 @@ class CvParameters:
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# self.prev_step=step
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# self.prev_step=step
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self._step = step
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self._step = step
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self._hsf = HaarSurroundFeature(radius)
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self._hsf = HaarSurroundFeature(radius)
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def get_rpsh(self):
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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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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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# 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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# return self.radius, self.pad, self.step, self.hsf
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@property
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@property
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def radius(self):
|
def radius(self):
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return self._radius
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return self._radius
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@radius.setter
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@radius.setter
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def radius(self, now_radius):
|
def radius(self, now_radius):
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# self.prev_radius=self._radius
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# self.prev_radius=self._radius
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self._radius = now_radius
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self._radius = now_radius
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self.pad = 2 * now_radius
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self.pad = 2 * now_radius
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self.hsf = now_radius
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self.hsf = now_radius
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@property
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@property
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def step(self):
|
def step(self):
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return self._step
|
return self._step
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@step.setter
|
@step.setter
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def step(self, now_step):
|
def step(self, now_step):
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# self.prev_step=self.step
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# self.prev_step=self.step
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self._step = now_step
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self._step = now_step
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@property
|
@property
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def hsf(self):
|
def hsf(self):
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return self._hsf
|
return self._hsf
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@hsf.setter
|
@hsf.setter
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def hsf(self, now_radius):
|
def hsf(self, now_radius):
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self._hsf = HaarSurroundFeature(now_radius)
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self._hsf = HaarSurroundFeature(now_radius)
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|
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|
|
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class HaarSurroundFeature:
|
class HaarSurroundFeature:
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|
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def __init__(self, r_inner, r_outer=None, val=None):
|
def __init__(self, r_inner, r_outer=None, val=None):
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if r_outer is None:
|
if r_outer is None:
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r_outer = r_inner * 3
|
r_outer = r_inner * 3
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@ -80,33 +257,30 @@ class HaarSurroundFeature:
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r_inner2 = r_inner * r_inner
|
r_inner2 = r_inner * r_inner
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count_inner = r_inner2
|
count_inner = r_inner2
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count_outer = r_outer * r_outer - r_inner2
|
count_outer = r_outer * r_outer - r_inner2
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|
|
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if val is None:
|
if val is None:
|
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val_inner = 1.0 / r_inner2
|
val_inner = 1.0 / r_inner2
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val_outer = -val_inner * count_inner / count_outer
|
val_outer = -val_inner * count_inner / count_outer
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|
|
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else:
|
else:
|
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val_inner = val[0]
|
val_inner = val[0]
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val_outer = val[1]
|
val_outer = val[1]
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|
|
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self.val_in = np.array(val_inner, dtype=np.float64)
|
self.val_in = np.array(val_inner, dtype=np.float64)
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self.val_out = np.array(val_outer, dtype=np.float64)
|
self.val_out = np.array(val_outer, dtype=np.float64)
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self.r_in = r_inner
|
self.r_in = r_inner
|
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self.r_out = r_outer
|
self.r_out = r_outer
|
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|
|
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def get_kernel(self):
|
def get_kernel(self):
|
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# Defined here, but not yet used?
|
# Defined here, but not yet used?
|
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# Create a kernel filled with the value of self.val_out
|
# Create a kernel filled with the value of self.val_out
|
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kernel = (
|
kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
|
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np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64)
|
|
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* self.val_out
|
|
||||||
)
|
|
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|
|
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# Set the values of the inner area of the kernel using array slicing
|
# Set the values of the inner area of the kernel using array slicing
|
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start = self.r_out - self.r_in
|
start = (self.r_out - self.r_in)
|
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end = self.r_out + self.r_in - 1
|
end = (self.r_out + self.r_in - 1)
|
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kernel[start:end, start:end] = self.val_in
|
kernel[start:end, start:end] = self.val_in
|
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|
|
||||||
return kernel
|
return kernel
|
||||||
|
|
||||||
|
|
||||||
@ -130,10 +304,10 @@ def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset
|
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row -= 1
|
row -= 1
|
||||||
col -= 1
|
col -= 1
|
||||||
x_step, y_step = xysteps
|
x_step, y_step = xysteps
|
||||||
|
|
||||||
# This is not beautiful.
|
# This is not beautiful.
|
||||||
start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad
|
start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad
|
||||||
|
|
||||||
if start_offset is not None:
|
if start_offset is not None:
|
||||||
start_pad_x += start_offset[0]
|
start_pad_x += start_offset[0]
|
||||||
start_pad_y += start_offset[1]
|
start_pad_y += start_offset[1]
|
||||||
@ -142,9 +316,9 @@ def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset
|
|||||||
end_pad_y += end_offset[1]
|
end_pad_y += end_offset[1]
|
||||||
y_np = np.arange(start_pad_y, row - end_pad_y, y_step)
|
y_np = np.arange(start_pad_y, row - end_pad_y, y_step)
|
||||||
x_np = np.arange(start_pad_x, col - end_pad_x, x_step)
|
x_np = np.arange(start_pad_x, col - end_pad_x, x_step)
|
||||||
|
|
||||||
xy_np = (x_np, y_np)
|
xy_np = (x_np, y_np)
|
||||||
|
|
||||||
return xy_np
|
return xy_np
|
||||||
|
|
||||||
|
|
||||||
@ -160,14 +334,8 @@ def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
|
|||||||
p10 = np.empty(len_syx, dtype=frame_int_dtype)
|
p10 = np.empty(len_syx, dtype=frame_int_dtype)
|
||||||
response_list = np.empty(len_syx, dtype=np.float64)
|
response_list = np.empty(len_syx, dtype=np.float64)
|
||||||
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
|
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
|
||||||
frame_conv_stride = frame_conv[:: fcshape[1], :: fcshape[2]]
|
frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
|
||||||
return (
|
return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
|
||||||
(inner_sum, outer_sum),
|
|
||||||
p_temp,
|
|
||||||
(p00, p11, p01, p10),
|
|
||||||
response_list,
|
|
||||||
(frame_conv, frame_conv_stride),
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# @profile
|
# @profile
|
||||||
@ -186,39 +354,30 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
|
|||||||
# padding2 = 2 * padding
|
# padding2 = 2 * padding
|
||||||
f_shape = row - 2 * padding, col - 2 * padding
|
f_shape = row - 2 * padding, col - 2 * padding
|
||||||
r_in = kernel.r_in
|
r_in = kernel.r_in
|
||||||
|
|
||||||
len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
|
len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
|
||||||
inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array(
|
inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1,
|
||||||
(len_sy, len_sx), col + 1, frame_int.dtype, (f_shape, y_step, x_step)
|
frame_int.dtype, (f_shape, y_step, x_step))
|
||||||
)
|
|
||||||
inner_sum, outer_sum = inout_sum
|
inner_sum, outer_sum = inout_sum
|
||||||
p00, p11, p01, p10 = p_list
|
p00, p11, p01, p10 = p_list
|
||||||
frame_conv, frame_conv_stride = frameconvlist
|
frame_conv, frame_conv_stride = frameconvlist
|
||||||
|
|
||||||
y_rin_m = xy_steps_list[1] - r_in
|
y_rin_m = xy_steps_list[1] - r_in
|
||||||
x_rin_m = xy_steps_list[0] - r_in
|
x_rin_m = xy_steps_list[0] - r_in
|
||||||
y_rin_p = xy_steps_list[1] + r_in
|
y_rin_p = xy_steps_list[1] + r_in
|
||||||
x_rin_p = xy_steps_list[0] + r_in
|
x_rin_p = xy_steps_list[0] + r_in
|
||||||
# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
|
# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
|
||||||
inarr_mm = frame_int[
|
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]
|
||||||
y_rin_m[0] : y_rin_m[-1] + 1 : y_step, x_rin_m[0] : x_rin_m[-1] + 1 : x_step
|
inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
|
||||||
]
|
inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
|
||||||
inarr_mp = frame_int[
|
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]
|
||||||
y_rin_m[0] : y_rin_m[-1] + 1 : y_step, x_rin_p[0] : x_rin_p[-1] + 1 : x_step
|
|
||||||
]
|
|
||||||
inarr_pm = frame_int[
|
|
||||||
y_rin_p[0] : y_rin_p[-1] + 1 : y_step, x_rin_m[0] : x_rin_m[-1] + 1 : x_step
|
|
||||||
]
|
|
||||||
inarr_pp = frame_int[
|
|
||||||
y_rin_p[0] : y_rin_p[-1] + 1 : y_step, x_rin_p[0] : x_rin_p[-1] + 1 : x_step
|
|
||||||
]
|
|
||||||
|
|
||||||
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
|
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
|
||||||
inner_sum[:, :] = inarr_mm
|
inner_sum[:, :] = inarr_mm
|
||||||
inner_sum += inarr_pp
|
inner_sum += inarr_pp
|
||||||
inner_sum -= inarr_mp
|
inner_sum -= inarr_mp
|
||||||
inner_sum -= inarr_pm
|
inner_sum -= inarr_pm
|
||||||
|
|
||||||
# Bottleneck here, I want to make it smarter. Someone do it.
|
# Bottleneck here, I want to make it smarter. Someone do it.
|
||||||
# (y,x)
|
# (y,x)
|
||||||
# p00=max(y_ro_m,0),max(x_ro_m,0)
|
# p00=max(y_ro_m,0),max(x_ro_m,0)
|
||||||
@ -244,40 +403,37 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
|
|||||||
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
||||||
# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
||||||
# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
||||||
|
|
||||||
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
|
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
|
||||||
|
|
||||||
np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
|
np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
|
||||||
response_list += kernel.val_out * outer_sum
|
response_list += kernel.val_out * outer_sum
|
||||||
|
|
||||||
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
|
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
|
||||||
min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
|
min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
|
||||||
|
|
||||||
center = (
|
center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding))
|
||||||
(xy_steps_list[0][min_loc[0]] - padding),
|
|
||||||
(xy_steps_list[1][min_loc[1]] - padding),
|
|
||||||
)
|
|
||||||
|
|
||||||
frame_conv_stride[:, :] = response_list
|
frame_conv_stride[:, :] = response_list
|
||||||
# or
|
# or
|
||||||
# frame_conv_stride[:, :] = response_list.astype(np.uint8)
|
# frame_conv_stride[:, :] = response_list.astype(np.uint8)
|
||||||
|
|
||||||
return frame_conv, min_response, center
|
return frame_conv, min_response, center
|
||||||
|
|
||||||
|
|
||||||
class AutoRadiusCalc(object):
|
class Auto_Radius_Calc(object):
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.response_list = []
|
self.response_list = []
|
||||||
self.radius_cand_list = []
|
self.radius_cand_list = []
|
||||||
self.adj_comp_flag = False
|
self.adj_comp_flag = False
|
||||||
|
|
||||||
self.radius_middle_index = None
|
self.radius_middle_index = None
|
||||||
|
|
||||||
self.left_item = None
|
self.left_item = None
|
||||||
self.right_item = None
|
self.right_item = None
|
||||||
self.left_index = None
|
self.left_index = None
|
||||||
self.right_index = None
|
self.right_index = None
|
||||||
|
|
||||||
def get_radius(self):
|
def get_radius(self):
|
||||||
prev_res_len = len(self.response_list)
|
prev_res_len = len(self.response_list)
|
||||||
# adjustment of radius
|
# adjustment of radius
|
||||||
@ -297,35 +453,21 @@ class AutoRadiusCalc(object):
|
|||||||
else:
|
else:
|
||||||
self.left_item = self.response_list[0]
|
self.left_item = self.response_list[0]
|
||||||
self.right_item = self.response_list[2]
|
self.right_item = self.response_list[2]
|
||||||
self.radius_cand_list = [
|
self.radius_cand_list = [i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)]
|
||||||
i
|
|
||||||
for i in range(
|
|
||||||
self.left_item[0],
|
|
||||||
self.right_item[0] + auto_radius_step,
|
|
||||||
auto_radius_step,
|
|
||||||
)
|
|
||||||
]
|
|
||||||
self.left_index = 0
|
self.left_index = 0
|
||||||
self.right_index = len(self.radius_cand_list) - 1
|
self.right_index = len(self.radius_cand_list) - 1
|
||||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||||
self.adj_comp_flag = False
|
self.adj_comp_flag = False
|
||||||
return self.radius_cand_list[self.radius_middle_index]
|
return self.radius_cand_list[self.radius_middle_index]
|
||||||
else:
|
else:
|
||||||
if (
|
if self.left_index <= self.right_index and self.left_index != self.radius_middle_index:
|
||||||
self.left_index <= self.right_index
|
if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]):
|
||||||
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_item = self.response_list[-1]
|
||||||
self.right_index = self.radius_middle_index - 1
|
self.right_index = self.radius_middle_index - 1
|
||||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||||
self.adj_comp_flag = False
|
self.adj_comp_flag = False
|
||||||
return self.radius_cand_list[self.radius_middle_index]
|
return self.radius_cand_list[self.radius_middle_index]
|
||||||
if (self.left_item[1] + self.response_list[-1][1]) > (
|
if (self.left_item[1] + self.response_list[-1][1]) > (self.right_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_item = self.response_list[-1]
|
||||||
self.left_index = self.radius_middle_index + 1
|
self.left_index = self.radius_middle_index + 1
|
||||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||||
@ -333,13 +475,13 @@ class AutoRadiusCalc(object):
|
|||||||
return self.radius_cand_list[self.radius_middle_index]
|
return self.radius_cand_list[self.radius_middle_index]
|
||||||
self.adj_comp_flag = True
|
self.adj_comp_flag = True
|
||||||
return self.radius_cand_list[self.radius_middle_index]
|
return self.radius_cand_list[self.radius_middle_index]
|
||||||
|
|
||||||
def get_radius_base(self):
|
def get_radius_base(self):
|
||||||
"""
|
"""
|
||||||
Use it when the new version doesn't work well.
|
Use it when the new version doesn't work well.
|
||||||
:return:
|
:return:
|
||||||
"""
|
"""
|
||||||
|
|
||||||
prev_res_len = len(self.response_list)
|
prev_res_len = len(self.response_list)
|
||||||
# adjustment of radius
|
# adjustment of radius
|
||||||
if prev_res_len == 1:
|
if prev_res_len == 1:
|
||||||
@ -359,21 +501,11 @@ class AutoRadiusCalc(object):
|
|||||||
self.adj_comp_flag = True
|
self.adj_comp_flag = True
|
||||||
return default_radius
|
return default_radius
|
||||||
elif sort_res[0] == auto_radius_range[0]:
|
elif sort_res[0] == auto_radius_range[0]:
|
||||||
self.radius_cand_list = [
|
self.radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, auto_radius_step)][1:]
|
||||||
i
|
|
||||||
for i in range(
|
|
||||||
auto_radius_range[0], default_radius, auto_radius_step
|
|
||||||
)
|
|
||||||
][1:]
|
|
||||||
self.adj_comp_flag = False
|
self.adj_comp_flag = False
|
||||||
return self.radius_cand_list.pop()
|
return self.radius_cand_list.pop()
|
||||||
else:
|
else:
|
||||||
self.radius_cand_list = [
|
self.radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], auto_radius_step)][1:]
|
||||||
i
|
|
||||||
for i in range(
|
|
||||||
default_radius, auto_radius_range[1], auto_radius_step
|
|
||||||
)
|
|
||||||
][1:]
|
|
||||||
self.adj_comp_flag = False
|
self.adj_comp_flag = False
|
||||||
return self.radius_cand_list.pop()
|
return self.radius_cand_list.pop()
|
||||||
else:
|
else:
|
||||||
@ -386,19 +518,19 @@ class AutoRadiusCalc(object):
|
|||||||
else:
|
else:
|
||||||
self.adj_comp_flag = False
|
self.adj_comp_flag = False
|
||||||
return self.radius_cand_list.pop()
|
return self.radius_cand_list.pop()
|
||||||
|
|
||||||
def add_response(self, radius, response):
|
def add_response(self, radius, response):
|
||||||
self.response_list.append((radius, response))
|
self.response_list.append((radius, response))
|
||||||
return None
|
return None
|
||||||
|
|
||||||
|
|
||||||
class BlinkDetector(object):
|
class Blink_Detector(object):
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.response_list = []
|
self.response_list = []
|
||||||
self.response_max = None
|
self.response_max = None
|
||||||
self.enable_detect_flg = False
|
self.enable_detect_flg = False
|
||||||
self.quartile_1 = None
|
self.quartile_1 = None
|
||||||
|
|
||||||
def calc_thresh(self):
|
def calc_thresh(self):
|
||||||
# Calculate response_max by computing interquartile range, IQR
|
# Calculate response_max by computing interquartile range, IQR
|
||||||
# self.response_listo = np.array(self.response_listo)
|
# self.response_listo = np.array(self.response_listo)
|
||||||
@ -407,28 +539,28 @@ class BlinkDetector(object):
|
|||||||
# quartile_1, quartile_3 = np.percentile(self.response_listo, [25, 75])
|
# quartile_1, quartile_3 = np.percentile(self.response_listo, [25, 75])
|
||||||
# iqr = quartile_3 - quartile_1
|
# iqr = quartile_3 - quartile_1
|
||||||
# self.response_maxo = quartile_3 + (iqr * 1.5)
|
# self.response_maxo = quartile_3 + (iqr * 1.5)
|
||||||
|
|
||||||
# quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
# quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
||||||
# or
|
# or
|
||||||
quartile_1, quartile_3 = np.percentile(np.array(self.response_list), [25, 75])
|
quartile_1, quartile_3 = np.percentile(np.array(self.response_list), [25, 75])
|
||||||
self.quartile_1 = quartile_1
|
self.quartile_1 = quartile_1
|
||||||
iqr = quartile_3 - quartile_1
|
iqr = quartile_3 - quartile_1
|
||||||
# response_min = quartile_1 - (iqr * 1.5)
|
# response_min = quartile_1 - (iqr * 1.5)
|
||||||
|
|
||||||
self.response_max = float(quartile_3 + (iqr * 1.5))
|
self.response_max = float(quartile_3 + (iqr * 1.5))
|
||||||
# or
|
# or
|
||||||
# self.response_max = quartile_3 + (iqr * 1.5)
|
# self.response_max = quartile_3 + (iqr * 1.5)
|
||||||
|
|
||||||
self.enable_detect_flg = True
|
self.enable_detect_flg = True
|
||||||
return None
|
return None
|
||||||
|
|
||||||
def detect(self, now_response):
|
def detect(self, now_response):
|
||||||
return now_response > self.response_max
|
return now_response > self.response_max
|
||||||
|
|
||||||
def add_response(self, response):
|
def add_response(self, response):
|
||||||
self.response_list.append(response)
|
self.response_list.append(response)
|
||||||
return None
|
return None
|
||||||
|
|
||||||
def response_len(self):
|
def response_len(self):
|
||||||
return len(self.response_list)
|
return len(self.response_list)
|
||||||
|
|
||||||
@ -439,7 +571,7 @@ class CenterCorrection(object):
|
|||||||
kernel_size = 7 # 3 or 5 or 7
|
kernel_size = 7 # 3 or 5 or 7
|
||||||
self.hist_thr = float(4) # 4%
|
self.hist_thr = float(4) # 4%
|
||||||
self.center_q1_radius = 20
|
self.center_q1_radius = 20
|
||||||
|
|
||||||
self.setup_comp = False
|
self.setup_comp = False
|
||||||
self.quartile_1 = None
|
self.quartile_1 = None
|
||||||
self.radius = None
|
self.radius = None
|
||||||
@ -447,14 +579,12 @@ class CenterCorrection(object):
|
|||||||
self.frame_mask = None
|
self.frame_mask = None
|
||||||
self.frame_bin = None
|
self.frame_bin = None
|
||||||
self.frame_final = None
|
self.frame_final = None
|
||||||
self.morph_kernel = cv2.getStructuringElement(
|
self.morph_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
|
||||||
cv2.MORPH_RECT, (kernel_size, kernel_size)
|
|
||||||
)
|
|
||||||
self.morph_kernel2 = np.ones((3, 3))
|
self.morph_kernel2 = np.ones((3, 3))
|
||||||
self.hist_index = np.arange(256)
|
self.hist_index = np.arange(256)
|
||||||
self.hist = np.empty((256, 1))
|
self.hist = np.empty((256, 1))
|
||||||
self.hist_norm = np.empty((256, 1))
|
self.hist_norm = np.empty((256, 1))
|
||||||
|
|
||||||
def init_array(self, gray_shape, quartile_1, radius):
|
def init_array(self, gray_shape, quartile_1, radius):
|
||||||
self.frame_shape = gray_shape
|
self.frame_shape = gray_shape
|
||||||
self.frame_mask = np.empty(gray_shape, dtype=np.uint8)
|
self.frame_mask = np.empty(gray_shape, dtype=np.uint8)
|
||||||
@ -463,44 +593,34 @@ class CenterCorrection(object):
|
|||||||
self.quartile_1 = quartile_1
|
self.quartile_1 = quartile_1
|
||||||
self.radius = radius
|
self.radius = radius
|
||||||
self.setup_comp = True
|
self.setup_comp = True
|
||||||
|
|
||||||
# def reset_array(self):
|
# def reset_array(self):
|
||||||
# self.frame_mask.fill(0)
|
# self.frame_mask.fill(0)
|
||||||
|
|
||||||
def correction(self, gray_frame, orig_x, orig_y):
|
def correction(self, gray_frame, orig_x, orig_y):
|
||||||
center_x, center_y = orig_x, orig_y
|
center_x, center_y = orig_x, orig_y
|
||||||
self.frame_mask.fill(0)
|
self.frame_mask.fill(0)
|
||||||
|
|
||||||
# cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1)
|
# cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1)
|
||||||
|
|
||||||
# bottleneck
|
# bottleneck
|
||||||
cv2.calcHist([gray_frame], [0], None, [256], [0, 256], hist=self.hist)
|
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)
|
cv2.normalize(self.hist, self.hist_norm, alpha=100.0, norm_type=cv2.NORM_L1)
|
||||||
hist_per = self.hist_norm.cumsum()
|
hist_per = self.hist_norm.cumsum()
|
||||||
hist_index_list = self.hist_index[hist_per >= self.hist_thr]
|
hist_index_list = self.hist_index[hist_per >= self.hist_thr]
|
||||||
frame_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)
|
||||||
hist_index_list[0]
|
|
||||||
if len(hist_index_list)
|
|
||||||
else np.percentile(cv2.bitwise_or(255 - self.frame_mask, gray_frame), 4)
|
|
||||||
)
|
|
||||||
|
|
||||||
# bottleneck
|
# bottleneck
|
||||||
self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[
|
self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[1]
|
||||||
1
|
|
||||||
]
|
|
||||||
cropped_x, cropped_y, cropped_w, cropped_h = cv2.boundingRect(self.frame_bin)
|
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)
|
self.frame_final = cv2.bitwise_and(self.frame_bin, self.frame_mask)
|
||||||
|
|
||||||
# bottleneck
|
# bottleneck
|
||||||
self.frame_final = cv2.morphologyEx(
|
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel)
|
||||||
self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel
|
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_OPEN, 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:
|
if (cropped_h, cropped_w) == self.frame_shape:
|
||||||
# Not detected.
|
# Not detected.
|
||||||
base_x, base_y = center_x, center_y
|
base_x, base_y = center_x, center_y
|
||||||
@ -509,54 +629,36 @@ class CenterCorrection(object):
|
|||||||
base_y = cropped_y + cropped_h // 2
|
base_y = cropped_y + cropped_h // 2
|
||||||
if self.frame_final[base_y, base_x] != 1:
|
if self.frame_final[base_y, base_x] != 1:
|
||||||
if self.frame_final[center_y, center_x] != 1:
|
if self.frame_final[center_y, center_x] != 1:
|
||||||
self.frame_final = cv2.morphologyEx(
|
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_DILATE, self.morph_kernel2, iterations=3)
|
||||||
self.frame_final,
|
|
||||||
cv2.MORPH_DILATE,
|
|
||||||
self.morph_kernel2,
|
|
||||||
iterations=3,
|
|
||||||
)
|
|
||||||
else:
|
else:
|
||||||
base_x, base_y = center_x, center_y
|
base_x, base_y = center_x, center_y
|
||||||
|
|
||||||
contours, _ = cv2.findContours(
|
contours, _ = cv2.findContours(self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
||||||
self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
|
|
||||||
)
|
|
||||||
contours_box = [cv2.boundingRect(cnt) for cnt in contours]
|
contours_box = [cv2.boundingRect(cnt) for cnt in contours]
|
||||||
contours_dist = np.array(
|
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])
|
||||||
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):
|
if len(contours_box):
|
||||||
cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[
|
cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[contours_dist.argmin()]
|
||||||
contours_dist.argmin()
|
|
||||||
]
|
|
||||||
x = cropped_x2 + cropped_w2 // 2
|
x = cropped_x2 + cropped_w2 // 2
|
||||||
y = cropped_y2 + cropped_h2 // 2
|
y = cropped_y2 + cropped_h2 // 2
|
||||||
else:
|
else:
|
||||||
x = center_x
|
x = center_x
|
||||||
y = center_y
|
y = center_y
|
||||||
|
|
||||||
# if imshow_enable:
|
# if imshow_enable:
|
||||||
# cv2.circle(frame, (orig_x, orig_y), 10, (255, 0, 0), -1)
|
# cv2.circle(frame, (orig_x, orig_y), 10, (255, 0, 0), -1)
|
||||||
# cv2.circle(frame, (x, y), 7, (0, 0, 255), -1)
|
# cv2.circle(frame, (x, y), 7, (0, 0, 255), -1)
|
||||||
|
|
||||||
#
|
#
|
||||||
# out_x = center_x if abs(x - center_x) > radius else x
|
# 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_y = center_y if abs(y - center_y) > radius else y
|
||||||
out_x, out_y = orig_x, orig_y
|
out_x, out_y = orig_x, orig_y
|
||||||
if (
|
if gray_frame[int(max(y - 5, 0)):int(min(y + 5, self.frame_shape[0])),
|
||||||
gray_frame[
|
int(max(x - 5, 0)):int(min(x + 5, self.frame_shape[1]))].min() < self.quartile_1:
|
||||||
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_x = x
|
||||||
out_y = y
|
out_y = y
|
||||||
|
|
||||||
# if imshow_enable:
|
# if imshow_enable:
|
||||||
# cv2.circle(frame, (out_x, out_y), 5, (0, 255, 0), -1)
|
# cv2.circle(frame, (out_x, out_y), 5, (0, 255, 0), -1)
|
||||||
#
|
#
|
||||||
@ -565,36 +667,29 @@ class CenterCorrection(object):
|
|||||||
return out_x, out_y
|
return out_x, out_y
|
||||||
|
|
||||||
|
|
||||||
# temporary name
|
class HSRAC_cls(object):
|
||||||
class HSF_cls(object):
|
|
||||||
def __init__(self):
|
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.
|
# 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
|
# For measuring total processing time
|
||||||
|
|
||||||
self.main_start_time = timeit.default_timer()
|
self.main_start_time = timeit.default_timer()
|
||||||
|
|
||||||
self.rng = np.random.default_rng()
|
self.rng = np.random.default_rng()
|
||||||
self.cvparam = CvParameters(default_radius, default_step)
|
self.cvparam = CvParameters(default_radius, default_step)
|
||||||
|
|
||||||
self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"]
|
self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"]
|
||||||
self.now_modeo = self.cv_modeo[0]
|
self.now_modeo = self.cv_modeo[0]
|
||||||
|
|
||||||
self.auto_radius_calc = AutoRadiusCalc()
|
self.auto_radius_calc = Auto_Radius_Calc()
|
||||||
self.blink_detector = BlinkDetector()
|
self.blink_detector = Blink_Detector()
|
||||||
self.center_q1 = BlinkDetector()
|
self.center_q1 = Blink_Detector()
|
||||||
self.center_correct = CenterCorrection()
|
self.center_correct = CenterCorrection()
|
||||||
|
|
||||||
self.cap = None
|
self.cap = None
|
||||||
|
|
||||||
self.timedict = {
|
self.timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []}
|
||||||
"to_gray": [],
|
|
||||||
"int_img": [],
|
|
||||||
"conv_int": [],
|
|
||||||
"crop": [],
|
|
||||||
"total_cv": [],
|
|
||||||
}
|
|
||||||
|
|
||||||
def open_video(self, video_path):
|
def open_video(self, video_path):
|
||||||
# Temporary implementation to run
|
# Temporary implementation to run
|
||||||
cap = cv2.VideoCapture(video_path)
|
cap = cv2.VideoCapture(video_path)
|
||||||
@ -602,7 +697,7 @@ class HSF_cls(object):
|
|||||||
raise IOError("Error opening video stream or file")
|
raise IOError("Error opening video stream or file")
|
||||||
self.cap = cap
|
self.cap = cap
|
||||||
return True
|
return True
|
||||||
|
|
||||||
def read_frame(self):
|
def read_frame(self):
|
||||||
# Temporary implementation to run
|
# Temporary implementation to run
|
||||||
if not self.cap.isOpened():
|
if not self.cap.isOpened():
|
||||||
@ -613,55 +708,51 @@ class HSF_cls(object):
|
|||||||
self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||||
return True
|
return True
|
||||||
return False
|
return False
|
||||||
|
|
||||||
def single_run(self):
|
def single_run(self):
|
||||||
# Temporary implementation to run
|
# Temporary implementation to run
|
||||||
|
|
||||||
# default_radius = 14
|
|
||||||
|
## default_radius = 14
|
||||||
|
|
||||||
# cropbox=[] # debug code
|
# cropbox=[] # debug code
|
||||||
|
|
||||||
|
|
||||||
frame = self.current_image_gray
|
frame = self.current_image_gray
|
||||||
if self.now_modeo == self.cv_modeo[1]:
|
if self.now_modeo == self.cv_modeo[1]:
|
||||||
# adjustment of radius
|
# adjustment of radius
|
||||||
|
|
||||||
# debug print
|
# debug print
|
||||||
# if calc_print_enable:
|
# if calc_print_enable:
|
||||||
# temp_radius = self.auto_radius_calc.get_radius()
|
# temp_radius = self.auto_radius_calc.get_radius()
|
||||||
# print('Now radius:', temp_radius)
|
# print('Now radius:', temp_radius)
|
||||||
# self.cvparam.radius = temp_radius
|
# self.cvparam.radius = temp_radius
|
||||||
|
|
||||||
self.cvparam.radius = self.auto_radius_calc.get_radius()
|
self.cvparam.radius = self.auto_radius_calc.get_radius()
|
||||||
if self.auto_radius_calc.adj_comp_flag:
|
if self.auto_radius_calc.adj_comp_flag:
|
||||||
self.now_modeo = (
|
self.now_modeo = self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
||||||
self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
|
||||||
)
|
|
||||||
|
|
||||||
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
||||||
|
|
||||||
# For measuring processing time of image processing
|
# For measuring processing time of image processing
|
||||||
cv_start_time = timeit.default_timer()
|
cv_start_time = timeit.default_timer()
|
||||||
|
|
||||||
gray_frame = frame
|
gray_frame = frame
|
||||||
self.timedict["to_gray"].append(timeit.default_timer() - cv_start_time)
|
self.timedict["to_gray"].append(timeit.default_timer() - cv_start_time)
|
||||||
|
|
||||||
# Calculate the integral image of the frame
|
# Calculate the integral image of the frame
|
||||||
int_start_time = timeit.default_timer()
|
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.
|
# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
|
||||||
frame_pad = cv2.copyMakeBorder(
|
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
|
||||||
gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT
|
|
||||||
)
|
|
||||||
frame_int = cv2.integral(frame_pad)
|
frame_int = cv2.integral(frame_pad)
|
||||||
self.timedict["int_img"].append(timeit.default_timer() - int_start_time)
|
self.timedict["int_img"].append(timeit.default_timer() - int_start_time)
|
||||||
|
|
||||||
# Convolve the feature with the integral image
|
# Convolve the feature with the integral image
|
||||||
conv_int_start_time = timeit.default_timer()
|
conv_int_start_time = timeit.default_timer()
|
||||||
xy_step = frameint_get_xy_step(
|
xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
||||||
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)
|
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)
|
self.timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time)
|
||||||
|
|
||||||
crop_start_time = timeit.default_timer()
|
crop_start_time = timeit.default_timer()
|
||||||
# Define the center point and radius
|
# Define the center point and radius
|
||||||
center_x, center_y = center_xy
|
center_x, center_y = center_xy
|
||||||
@ -669,12 +760,14 @@ class HSF_cls(object):
|
|||||||
lower_x = center_x - radius
|
lower_x = center_x - radius
|
||||||
upper_y = center_y + radius
|
upper_y = center_y + radius
|
||||||
lower_y = center_y - radius
|
lower_y = center_y - radius
|
||||||
|
|
||||||
# Crop the image using the calculated bounds
|
# Crop the image using the calculated bounds
|
||||||
|
|
||||||
cropped_image = safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y)
|
cropped_image = safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y)
|
||||||
|
|
||||||
# cropbox = [clamp(val, 0, gray_frame.shape[i]) for i, val in
|
# cropbox = [clamp(val, 0, gray_frame.shape[i]) for i, val in
|
||||||
# zip([1, 0, 1, 0], [lower_x, lower_y, upper_x, upper_y])] # debug code
|
# zip([1, 0, 1, 0], [lower_x, lower_y, upper_x, upper_y])] # debug code
|
||||||
|
|
||||||
|
|
||||||
if self.now_modeo == self.cv_modeo[0] or self.now_modeo == self.cv_modeo[1]:
|
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
|
# If mode is first_frame or radius_adjust, record current radius and response
|
||||||
@ -683,19 +776,21 @@ class HSF_cls(object):
|
|||||||
# Statistics for blink detection
|
# Statistics for blink detection
|
||||||
if self.blink_detector.response_len() < blink_init_frames:
|
if self.blink_detector.response_len() < blink_init_frames:
|
||||||
self.blink_detector.add_response(cv2.mean(cropped_image)[0])
|
self.blink_detector.add_response(cv2.mean(cropped_image)[0])
|
||||||
|
|
||||||
upper_x = center_x + self.center_correct.center_q1_radius
|
upper_x = center_x + self.center_correct.center_q1_radius
|
||||||
lower_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
|
upper_y = center_y + self.center_correct.center_q1_radius
|
||||||
lower_y = center_y - self.center_correct.center_q1_radius
|
lower_y = center_y - self.center_correct.center_q1_radius
|
||||||
|
|
||||||
self.center_q1.add_response(
|
self.center_q1.add_response(
|
||||||
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y,keepsize=False))[
|
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y,keepsize=False))[
|
||||||
0
|
0
|
||||||
]
|
]
|
||||||
)
|
)
|
||||||
|
|
||||||
else:
|
|
||||||
|
|
||||||
|
else:
|
||||||
|
|
||||||
self.blink_detector.calc_thresh()
|
self.blink_detector.calc_thresh()
|
||||||
self.center_q1.calc_thresh()
|
self.center_q1.calc_thresh()
|
||||||
self.now_modeo = self.cv_modeo[3]
|
self.now_modeo = self.cv_modeo[3]
|
||||||
@ -712,7 +807,7 @@ class HSF_cls(object):
|
|||||||
# blink
|
# blink
|
||||||
pass
|
pass
|
||||||
else:
|
else:
|
||||||
# pass
|
# pass
|
||||||
if not self.center_correct.setup_comp:
|
if not self.center_correct.setup_comp:
|
||||||
self.center_correct.init_array(
|
self.center_correct.init_array(
|
||||||
gray_frame.shape, self.center_q1.quartile_1, radius
|
gray_frame.shape, self.center_q1.quartile_1, radius
|
||||||
@ -743,23 +838,21 @@ class HSF_cls(object):
|
|||||||
# if imshow_enable or save_video:
|
# if imshow_enable or save_video:
|
||||||
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
||||||
# cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -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
|
# 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
|
# 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()
|
cv_end_time = timeit.default_timer()
|
||||||
self.timedict["crop"].append(cv_end_time - crop_start_time)
|
self.timedict["crop"].append(cv_end_time - crop_start_time)
|
||||||
self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
||||||
|
|
||||||
# if calc_print_enable:
|
# 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
|
# 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('Kernel response:', response)
|
||||||
# print('Pixel position:', center_xy)
|
# print('Pixel position:', center_xy)
|
||||||
|
|
||||||
if imshow_enable:
|
if imshow_enable:
|
||||||
if (
|
if self.now_modeo != self.cv_modeo[0] and self.now_modeo != self.cv_modeo[1]:
|
||||||
self.now_modeo != self.cv_modeo[0]
|
|
||||||
and self.now_modeo != self.cv_modeo[1]
|
|
||||||
):
|
|
||||||
if 0 in cropped_image.shape:
|
if 0 in cropped_image.shape:
|
||||||
# If shape contains 0, it is not detected well.
|
# If shape contains 0, it is not detected well.
|
||||||
pass
|
pass
|
||||||
@ -768,7 +861,7 @@ class HSF_cls(object):
|
|||||||
cv2.imshow("frame", frame)
|
cv2.imshow("frame", frame)
|
||||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||||
pass
|
pass
|
||||||
|
|
||||||
if self.now_modeo == self.cv_modeo[0]:
|
if self.now_modeo == self.cv_modeo[0]:
|
||||||
# Moving from first_frame to the next mode
|
# Moving from first_frame to the next mode
|
||||||
if skip_autoradius and skip_blink_detect:
|
if skip_autoradius and skip_blink_detect:
|
||||||
@ -777,13 +870,12 @@ class HSF_cls(object):
|
|||||||
self.now_modeo = self.cv_modeo[2]
|
self.now_modeo = self.cv_modeo[2]
|
||||||
else:
|
else:
|
||||||
self.now_modeo = self.cv_modeo[1]
|
self.now_modeo = self.cv_modeo[1]
|
||||||
|
|
||||||
|
|
||||||
# debug code
|
# debug code
|
||||||
# return center_x,center_y,cropbox,frame
|
# return center_x,center_y,cropbox,frame
|
||||||
return center_x, center_y, frame
|
return center_x, center_y, frame
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
class External_Run_HSF(object):
|
class External_Run_HSF(object):
|
||||||
def __init__(self):
|
def __init__(self):
|
||||||
self.algo = HSF_cls()
|
self.algo = HSF_cls()
|
||||||
|
|||||||
1001
EyeTrackApp/hsrac.py
1001
EyeTrackApp/hsrac.py
File diff suppressed because it is too large
Load Diff
@ -19,7 +19,7 @@
|
|||||||
@@@@@@@@@@@@@@@@@
|
@@@@@@@@@@@@@@@@@
|
||||||
@@@@@@@@@@@@@(
|
@@@@@@@@@@@@@(
|
||||||
|
|
||||||
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization)
|
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization)
|
||||||
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
|
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
|
||||||
|
|
||||||
Copyright (c) 2022 EyeTrackVR <3
|
Copyright (c) 2022 EyeTrackVR <3
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user