mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
Fixing the call
This commit is contained in:
parent
ec83c3831f
commit
fc31c0c054
@ -29,181 +29,6 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
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# step==(x,y)
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default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
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"""
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Attention.
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If using cv2.filter2D in this code, be careful with the kernel
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https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python
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"""
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def TimeitWrapper(*args, **kwargs):
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"""
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This decorator @TimeitWrapper() prints the function name and execution time in seconds.
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:param args:
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:param kwargs:
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:return:
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"""
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def decorator(function):
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@functools.wraps(function)
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def wrapper(*args, **kwargs):
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start = timeit.default_timer()
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results = function(*args, **kwargs)
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end = timeit.default_timer()
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print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
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return results
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return wrapper
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return decorator
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class TimeitResult(object):
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"""
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from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
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Object returned by the timeit magic with info about the run.
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Contains the following attributes :
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loops: (int) number of loops done per measurement
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repeat: (int) number of times the measurement has been repeated
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best: (float) best execution time / number
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all_runs: (list of float) execution time of each run (in s)
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"""
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def __init__(self, loops, repeat, best, worst, all_runs, precision):
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self.loops = loops
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self.repeat = repeat
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self.best = best
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self.worst = worst
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self.all_runs = all_runs
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self._precision = precision
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self.timings = [dt / self.loops for dt in all_runs]
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@property
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def average(self):
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return math.fsum(self.timings) / len(self.timings)
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@property
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def stdev(self):
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mean = self.average
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return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
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def __str__(self):
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pm = '+-'
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb1'.encode(sys.stdout.encoding)
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pm = u'\xb1'
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except:
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pass
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return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
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pm=pm,
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runs=self.repeat,
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loops=self.loops,
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loop_plural="" if self.loops == 1 else "s",
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run_plural="" if self.repeat == 1 else "s",
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mean=format_time(self.average, self._precision),
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std=format_time(self.stdev, self._precision),
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best=format_time(self.best, self._precision),
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worst=format_time(self.worst, self._precision),
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)
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def _repr_pretty_(self, p, cycle):
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unic = self.__str__()
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p.text(u'<TimeitResult : ' + unic + u'>')
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class FPSResult(object):
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"""
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base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
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"""
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def __init__(self, loops, repeat, best, worst, all_runs, precision):
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self.loops = loops
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self.repeat = repeat
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self.best = 1 / best
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self.worst = 1 / worst
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self.all_runs = all_runs
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self._precision = precision
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self.fps = [1 / dt for dt in all_runs]
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self.unit = "fps"
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@property
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def average(self):
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return math.fsum(self.fps) / len(self.fps)
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@property
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def stdev(self):
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mean = self.average
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return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
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def __str__(self):
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pm = '+-'
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb1'.encode(sys.stdout.encoding)
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pm = u'\xb1'
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except:
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pass
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return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
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pm=pm,
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runs=self.repeat,
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loops=self.loops,
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loop_plural="" if self.loops == 1 else "s",
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run_plural="" if self.repeat == 1 else "s",
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mean="%.*g%s" % (self._precision, self.average, self.unit),
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std="%.*g%s" % (self._precision, self.stdev, self.unit),
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best="%.*g%s" % (self._precision, self.best, self.unit),
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worst="%.*g%s" % (self._precision, self.worst, self.unit),
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)
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def _repr_pretty_(self, p, cycle):
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unic = self.__str__()
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p.text(u'<FPSResult : ' + unic + u'>')
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def format_time(timespan, precision=3):
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"""
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https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
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Formats the timespan in a human readable form
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"""
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if timespan >= 60.0:
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# we have more than a minute, format that in a human readable form
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# Idea from http://snipplr.com/view/5713/
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parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
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time = []
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leftover = timespan
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for suffix, length in parts:
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value = int(leftover / length)
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if value > 0:
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leftover = leftover % length
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time.append(u'%s%s' % (str(value), suffix))
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if leftover < 1:
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break
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return " ".join(time)
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# Unfortunately the unicode 'micro' symbol can cause problems in
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# certain terminals.
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# See bug: https://bugs.launchpad.net/ipython/+bug/348466
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# Try to prevent crashes by being more secure than it needs to
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# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
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units = [u"s", u"ms", u'us', "ns"] # the save value
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb5'.encode(sys.stdout.encoding)
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units = [u"s", u"ms", u'\xb5s', "ns"]
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except:
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pass
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scaling = [1, 1e3, 1e6, 1e9]
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if timespan > 0.0:
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order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
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else:
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order = 3
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return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
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class CvParameters:
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# It may be a little slower because a dict named "self" is read for each function call.
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def __init__(self, radius, step):
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@ -421,7 +246,7 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
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return frame_conv, min_response, center
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class Auto_Radius_Calc(object):
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class AutoRadiusCalc(object):
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def __init__(self):
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self.response_list = []
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self.radius_cand_list = []
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@ -524,7 +349,7 @@ class Auto_Radius_Calc(object):
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return None
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class Blink_Detector(object):
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class BlinkDetector(object):
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def __init__(self):
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self.response_list = []
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self.response_max = None
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@ -667,7 +492,7 @@ class CenterCorrection(object):
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return out_x, out_y
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class HSRAC_cls(object):
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class HSF_cls(object):
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def __init__(self):
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# I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble.
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@ -681,9 +506,9 @@ class HSRAC_cls(object):
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self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"]
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self.now_modeo = self.cv_modeo[0]
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self.auto_radius_calc = Auto_Radius_Calc()
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self.blink_detector = Blink_Detector()
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self.center_q1 = Blink_Detector()
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self.auto_radius_calc = AutoRadiusCalc()
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self.blink_detector = BlinkDetector()
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self.center_q1 = BlinkDetector()
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self.center_correct = CenterCorrection()
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self.cap = None
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@ -15,6 +15,10 @@ from utils.misc_utils import clamp
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# from line_profiler_pycharm import profile
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#RANSAC
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thresh_add = 10
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video_path = "ezgif.com-gif-maker.avi"
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imshow_enable = True
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calc_print_enable = True
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@ -33,799 +37,6 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
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# step==(x,y)
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default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
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"""
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Attention.
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If using cv2.filter2D in this code, be careful with the kernel
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https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python
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"""
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def TimeitWrapper(*args, **kwargs):
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"""
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This decorator @TimeitWrapper() prints the function name and execution time in seconds.
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:param args:
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:param kwargs:
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:return:
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"""
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def decorator(function):
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@functools.wraps(function)
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def wrapper(*args, **kwargs):
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start = timeit.default_timer()
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results = function(*args, **kwargs)
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end = timeit.default_timer()
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print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
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return results
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return wrapper
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return decorator
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class TimeitResult(object):
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"""
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from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
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Object returned by the timeit magic with info about the run.
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Contains the following attributes :
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loops: (int) number of loops done per measurement
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repeat: (int) number of times the measurement has been repeated
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best: (float) best execution time / number
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all_runs: (list of float) execution time of each run (in s)
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"""
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def __init__(self, loops, repeat, best, worst, all_runs, precision):
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self.loops = loops
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self.repeat = repeat
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self.best = best
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self.worst = worst
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self.all_runs = all_runs
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self._precision = precision
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self.timings = [dt / self.loops for dt in all_runs]
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@property
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def average(self):
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return math.fsum(self.timings) / len(self.timings)
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@property
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def stdev(self):
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mean = self.average
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return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
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def __str__(self):
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pm = '+-'
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb1'.encode(sys.stdout.encoding)
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pm = u'\xb1'
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except:
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pass
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return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
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pm=pm,
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runs=self.repeat,
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loops=self.loops,
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loop_plural="" if self.loops == 1 else "s",
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run_plural="" if self.repeat == 1 else "s",
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mean=format_time(self.average, self._precision),
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std=format_time(self.stdev, self._precision),
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best=format_time(self.best, self._precision),
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worst=format_time(self.worst, self._precision),
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)
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def _repr_pretty_(self, p, cycle):
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unic = self.__str__()
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p.text(u'<TimeitResult : ' + unic + u'>')
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class FPSResult(object):
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"""
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base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
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"""
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def __init__(self, loops, repeat, best, worst, all_runs, precision):
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self.loops = loops
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self.repeat = repeat
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self.best = 1 / best
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self.worst = 1 / worst
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self.all_runs = all_runs
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self._precision = precision
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self.fps = [1 / dt for dt in all_runs]
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self.unit = "fps"
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@property
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def average(self):
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return math.fsum(self.fps) / len(self.fps)
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@property
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def stdev(self):
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mean = self.average
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return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
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def __str__(self):
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pm = '+-'
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
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try:
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u'\xb1'.encode(sys.stdout.encoding)
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pm = u'\xb1'
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except:
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pass
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return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
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pm=pm,
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runs=self.repeat,
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loops=self.loops,
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loop_plural="" if self.loops == 1 else "s",
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run_plural="" if self.repeat == 1 else "s",
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mean="%.*g%s" % (self._precision, self.average, self.unit),
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std="%.*g%s" % (self._precision, self.stdev, self.unit),
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best="%.*g%s" % (self._precision, self.best, self.unit),
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worst="%.*g%s" % (self._precision, self.worst, self.unit),
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)
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def _repr_pretty_(self, p, cycle):
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unic = self.__str__()
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p.text(u'<FPSResult : ' + unic + u'>')
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def format_time(timespan, precision=3):
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"""
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https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
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Formats the timespan in a human readable form
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"""
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if timespan >= 60.0:
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# we have more than a minute, format that in a human readable form
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# Idea from http://snipplr.com/view/5713/
|
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parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
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time = []
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leftover = timespan
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for suffix, length in parts:
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value = int(leftover / length)
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if value > 0:
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leftover = leftover % length
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time.append(u'%s%s' % (str(value), suffix))
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if leftover < 1:
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break
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return " ".join(time)
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# Unfortunately the unicode 'micro' symbol can cause problems in
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# certain terminals.
|
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# See bug: https://bugs.launchpad.net/ipython/+bug/348466
|
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# Try to prevent crashes by being more secure than it needs to
|
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# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
|
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units = [u"s", u"ms", u'us', "ns"] # the save value
|
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if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
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try:
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u'\xb5'.encode(sys.stdout.encoding)
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units = [u"s", u"ms", u'\xb5s', "ns"]
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except:
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pass
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scaling = [1, 1e3, 1e6, 1e9]
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if timespan > 0.0:
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order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
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else:
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order = 3
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return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
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class CvParameters:
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# It may be a little slower because a dict named "self" is read for each function call.
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def __init__(self, radius, step):
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# self.prev_radius=radius
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self._radius = radius
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self.pad = 2 * radius
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# self.prev_step=step
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self._step = step
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self._hsf = HaarSurroundFeature(radius)
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def get_rpsh(self):
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return self._radius, self.pad, self._step, self._hsf
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# Essentially, the following would be preferable, but it would take twice as long to call.
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# return self.radius, self.pad, self.step, self.hsf
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@property
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def radius(self):
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return self._radius
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@radius.setter
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def radius(self, now_radius):
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# self.prev_radius=self._radius
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self._radius = now_radius
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self.pad = 2 * now_radius
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self.hsf = now_radius
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@property
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def step(self):
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return self._step
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@step.setter
|
||||
def step(self, now_step):
|
||||
# self.prev_step=self.step
|
||||
self._step = now_step
|
||||
|
||||
@property
|
||||
def hsf(self):
|
||||
return self._hsf
|
||||
|
||||
@hsf.setter
|
||||
def hsf(self, now_radius):
|
||||
self._hsf = HaarSurroundFeature(now_radius)
|
||||
|
||||
|
||||
class HaarSurroundFeature:
|
||||
|
||||
def __init__(self, r_inner, r_outer=None, val=None):
|
||||
if r_outer is None:
|
||||
r_outer = r_inner * 3
|
||||
# print(r_outer)
|
||||
r_inner2 = r_inner * r_inner
|
||||
count_inner = r_inner2
|
||||
count_outer = r_outer * r_outer - r_inner2
|
||||
|
||||
if val is None:
|
||||
val_inner = 1.0 / r_inner2
|
||||
val_outer = -val_inner * count_inner / count_outer
|
||||
|
||||
else:
|
||||
val_inner = val[0]
|
||||
val_outer = val[1]
|
||||
|
||||
self.val_in = np.array(val_inner, dtype=np.float64)
|
||||
self.val_out = np.array(val_outer, dtype=np.float64)
|
||||
self.r_in = r_inner
|
||||
self.r_out = r_outer
|
||||
|
||||
def get_kernel(self):
|
||||
# Defined here, but not yet used?
|
||||
# Create a kernel filled with the value of self.val_out
|
||||
kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
|
||||
|
||||
# Set the values of the inner area of the kernel using array slicing
|
||||
start = (self.r_out - self.r_in)
|
||||
end = (self.r_out + self.r_in - 1)
|
||||
kernel[start:end, start:end] = self.val_in
|
||||
|
||||
return kernel
|
||||
|
||||
|
||||
def to_gray(frame):
|
||||
# Faster by quitting checking if the input image is already grayscale
|
||||
# Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function
|
||||
return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
|
||||
|
||||
@lru_cache(maxsize=lru_maxsize_vs)
|
||||
def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None):
|
||||
"""
|
||||
:param imageshape: (height(row),width(col)). row==y,cal==x
|
||||
:param xysteps: (x,y)
|
||||
:param pad: int
|
||||
:param start_offset: (x,y) or None
|
||||
:param end_offset: (x,y) or None
|
||||
:return: xy_np:tuple(x,y)
|
||||
"""
|
||||
row, col = imageshape
|
||||
row -= 1
|
||||
col -= 1
|
||||
x_step, y_step = xysteps
|
||||
|
||||
# This is not beautiful.
|
||||
start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad
|
||||
|
||||
if start_offset is not None:
|
||||
start_pad_x += start_offset[0]
|
||||
start_pad_y += start_offset[1]
|
||||
if end_offset is not None:
|
||||
end_pad_x += end_offset[0]
|
||||
end_pad_y += end_offset[1]
|
||||
y_np = np.arange(start_pad_y, row - end_pad_y, y_step)
|
||||
x_np = np.arange(start_pad_x, col - end_pad_x, x_step)
|
||||
|
||||
xy_np = (x_np, y_np)
|
||||
|
||||
return xy_np
|
||||
|
||||
|
||||
@lru_cache(maxsize=lru_maxsize_vvs)
|
||||
def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
|
||||
# Function to reduce array allocation by providing an empty array first and recycling it with lru
|
||||
inner_sum = np.empty(len_syx, dtype=frame_int_dtype)
|
||||
outer_sum = np.empty(len_syx, dtype=frame_int_dtype)
|
||||
p_temp = np.empty((len_syx[0], frameint_x), dtype=frame_int_dtype)
|
||||
p00 = np.empty(len_syx, dtype=frame_int_dtype)
|
||||
p11 = np.empty(len_syx, dtype=frame_int_dtype)
|
||||
p01 = np.empty(len_syx, dtype=frame_int_dtype)
|
||||
p10 = np.empty(len_syx, dtype=frame_int_dtype)
|
||||
response_list = np.empty(len_syx, dtype=np.float64)
|
||||
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
|
||||
frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
|
||||
return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
|
||||
|
||||
|
||||
# @profile
|
||||
def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
"""
|
||||
:param frame_int:
|
||||
:param kernel: hsf
|
||||
:param step: (x,y)
|
||||
:param padding: int
|
||||
:return:
|
||||
"""
|
||||
row, col = frame_int.shape
|
||||
row -= 1
|
||||
col -= 1
|
||||
x_step, y_step = xy_step
|
||||
# padding2 = 2 * padding
|
||||
f_shape = row - 2 * padding, col - 2 * padding
|
||||
r_in = kernel.r_in
|
||||
|
||||
len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
|
||||
inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1,
|
||||
frame_int.dtype, (f_shape, y_step, x_step))
|
||||
inner_sum, outer_sum = inout_sum
|
||||
p00, p11, p01, p10 = p_list
|
||||
frame_conv, frame_conv_stride = frameconvlist
|
||||
|
||||
y_rin_m = xy_steps_list[1] - r_in
|
||||
x_rin_m = xy_steps_list[0] - r_in
|
||||
y_rin_p = xy_steps_list[1] + r_in
|
||||
x_rin_p = xy_steps_list[0] + r_in
|
||||
# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
|
||||
inarr_mm = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
|
||||
inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
|
||||
inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
|
||||
inarr_pp = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
|
||||
|
||||
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
|
||||
inner_sum[:, :] = inarr_mm
|
||||
inner_sum += inarr_pp
|
||||
inner_sum -= inarr_mp
|
||||
inner_sum -= inarr_pm
|
||||
|
||||
# Bottleneck here, I want to make it smarter. Someone do it.
|
||||
# (y,x)
|
||||
# p00=max(y_ro_m,0),max(x_ro_m,0)
|
||||
# p11=min(y_ro_p,ylim),min(x_ro_p,xlim)
|
||||
# p01=max(y_ro_m,0),min(x_ro_p,xlim)
|
||||
# p10=min(y_ro_p,ylim),max(x_ro_m,0)
|
||||
y_ro_m = xy_steps_list[1] - kernel.r_out
|
||||
x_ro_m = xy_steps_list[0] - kernel.r_out
|
||||
y_ro_p = xy_steps_list[1] + kernel.r_out
|
||||
x_ro_p = xy_steps_list[0] + kernel.r_out
|
||||
# p00 calc
|
||||
np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp)
|
||||
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00)
|
||||
# p01 calc
|
||||
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01)
|
||||
# p11 calc
|
||||
np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp)
|
||||
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11)
|
||||
# p10 calc
|
||||
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10)
|
||||
# the point is this
|
||||
# p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
||||
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
||||
# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
||||
# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
||||
|
||||
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
|
||||
|
||||
np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
|
||||
response_list += kernel.val_out * outer_sum
|
||||
|
||||
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
|
||||
min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
|
||||
|
||||
center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding))
|
||||
|
||||
frame_conv_stride[:, :] = response_list
|
||||
# or
|
||||
# frame_conv_stride[:, :] = response_list.astype(np.uint8)
|
||||
|
||||
return frame_conv, min_response, center
|
||||
|
||||
|
||||
class Auto_Radius_Calc(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.radius_cand_list = []
|
||||
self.adj_comp_flag = False
|
||||
|
||||
self.radius_middle_index = None
|
||||
|
||||
self.left_item = None
|
||||
self.right_item = None
|
||||
self.left_index = None
|
||||
self.right_index = None
|
||||
|
||||
def get_radius(self):
|
||||
prev_res_len = len(self.response_list)
|
||||
# adjustment of radius
|
||||
if prev_res_len == 1:
|
||||
# len==1==response_list==[default_radius]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[0]
|
||||
elif prev_res_len == 2:
|
||||
# len==2==response_list==[default_radius, auto_radius_range[0]]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[1]
|
||||
elif prev_res_len == 3:
|
||||
# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
|
||||
if self.response_list[1][1] < self.response_list[2][1]:
|
||||
self.left_item = self.response_list[1]
|
||||
self.right_item = self.response_list[0]
|
||||
else:
|
||||
self.left_item = self.response_list[0]
|
||||
self.right_item = self.response_list[2]
|
||||
self.radius_cand_list = [i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)]
|
||||
self.left_index = 0
|
||||
self.right_index = len(self.radius_cand_list) - 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
else:
|
||||
if self.left_index <= self.right_index and self.left_index != self.radius_middle_index:
|
||||
if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]):
|
||||
self.right_item = self.response_list[-1]
|
||||
self.right_index = self.radius_middle_index - 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
if (self.left_item[1] + self.response_list[-1][1]) > (self.right_item[1] + self.response_list[-1][1]):
|
||||
self.left_item = self.response_list[-1]
|
||||
self.left_index = self.radius_middle_index + 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
self.adj_comp_flag = True
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
|
||||
def get_radius_base(self):
|
||||
"""
|
||||
Use it when the new version doesn't work well.
|
||||
:return:
|
||||
"""
|
||||
|
||||
prev_res_len = len(self.response_list)
|
||||
# adjustment of radius
|
||||
if prev_res_len == 1:
|
||||
# len==1==response_list==[default_radius]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[0]
|
||||
elif prev_res_len == 2:
|
||||
# len==2==response_list==[default_radius, auto_radius_range[0]]
|
||||
self.adj_comp_flag = False
|
||||
return auto_radius_range[1]
|
||||
elif prev_res_len == 3:
|
||||
# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
|
||||
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
||||
# Extract the radius with the lowest response value
|
||||
if sort_res[0] == default_radius:
|
||||
# If the default value is best, change now_mode to init after setting radius to the default value.
|
||||
self.adj_comp_flag = True
|
||||
return default_radius
|
||||
elif sort_res[0] == auto_radius_range[0]:
|
||||
self.radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, auto_radius_step)][1:]
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
else:
|
||||
self.radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], auto_radius_step)][1:]
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
else:
|
||||
# Try the contents of the radius_cand_list in order until the radius_cand_list runs out
|
||||
# Better make it a binary search.
|
||||
if len(self.radius_cand_list) == 0:
|
||||
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
||||
self.adj_comp_flag = True
|
||||
return sort_res[0]
|
||||
else:
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
|
||||
def add_response(self, radius, response):
|
||||
self.response_list.append((radius, response))
|
||||
return None
|
||||
|
||||
|
||||
class Blink_Detector(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.response_max = None
|
||||
self.enable_detect_flg = False
|
||||
self.quartile_1 = None
|
||||
|
||||
def calc_thresh(self):
|
||||
# Calculate response_max by computing interquartile range, IQR
|
||||
# self.response_listo = np.array(self.response_listo)
|
||||
# 25%,75%
|
||||
# This value may need to be adjusted depending on the environment.
|
||||
# quartile_1, quartile_3 = np.percentile(self.response_listo, [25, 75])
|
||||
# iqr = quartile_3 - quartile_1
|
||||
# self.response_maxo = quartile_3 + (iqr * 1.5)
|
||||
|
||||
# quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
||||
# or
|
||||
quartile_1, quartile_3 = np.percentile(np.array(self.response_list), [25, 75])
|
||||
self.quartile_1 = quartile_1
|
||||
iqr = quartile_3 - quartile_1
|
||||
# response_min = quartile_1 - (iqr * 1.5)
|
||||
|
||||
self.response_max = float(quartile_3 + (iqr * 1.5))
|
||||
# or
|
||||
# self.response_max = quartile_3 + (iqr * 1.5)
|
||||
|
||||
self.enable_detect_flg = True
|
||||
return None
|
||||
|
||||
def detect(self, now_response):
|
||||
return now_response > self.response_max
|
||||
|
||||
def add_response(self, response):
|
||||
self.response_list.append(response)
|
||||
return None
|
||||
|
||||
def response_len(self):
|
||||
return len(self.response_list)
|
||||
|
||||
|
||||
class CenterCorrection(object):
|
||||
def __init__(self):
|
||||
# Tunable parameters
|
||||
kernel_size = 7 # 3 or 5 or 7
|
||||
self.hist_thr = float(4) # 4%
|
||||
self.center_q1_radius = 20
|
||||
|
||||
self.setup_comp = False
|
||||
self.quartile_1 = None
|
||||
self.radius = None
|
||||
self.frame_shape = None
|
||||
self.frame_mask = None
|
||||
self.frame_bin = None
|
||||
self.frame_final = None
|
||||
self.morph_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
|
||||
self.morph_kernel2 = np.ones((3, 3))
|
||||
self.hist_index = np.arange(256)
|
||||
self.hist = np.empty((256, 1))
|
||||
self.hist_norm = np.empty((256, 1))
|
||||
|
||||
def init_array(self, gray_shape, quartile_1, radius):
|
||||
self.frame_shape = gray_shape
|
||||
self.frame_mask = np.empty(gray_shape, dtype=np.uint8)
|
||||
self.frame_bin = np.empty(gray_shape, dtype=np.uint8)
|
||||
self.frame_final = np.empty(gray_shape, dtype=np.uint8)
|
||||
self.quartile_1 = quartile_1
|
||||
self.radius = radius
|
||||
self.setup_comp = True
|
||||
|
||||
# def reset_array(self):
|
||||
# self.frame_mask.fill(0)
|
||||
|
||||
def correction(self, gray_frame, orig_x, orig_y):
|
||||
center_x, center_y = orig_x, orig_y
|
||||
self.frame_mask.fill(0)
|
||||
|
||||
# cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1)
|
||||
|
||||
# bottleneck
|
||||
cv2.calcHist([gray_frame], [0], None, [256], [0, 256], hist=self.hist)
|
||||
|
||||
cv2.normalize(self.hist, self.hist_norm, alpha=100.0, norm_type=cv2.NORM_L1)
|
||||
hist_per = self.hist_norm.cumsum()
|
||||
hist_index_list = self.hist_index[hist_per >= self.hist_thr]
|
||||
frame_thr = hist_index_list[0] if len(hist_index_list) else np.percentile(cv2.bitwise_or(255 - self.frame_mask, gray_frame), 4)
|
||||
|
||||
# bottleneck
|
||||
self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[1]
|
||||
cropped_x, cropped_y, cropped_w, cropped_h = cv2.boundingRect(self.frame_bin)
|
||||
|
||||
self.frame_final = cv2.bitwise_and(self.frame_bin, self.frame_mask)
|
||||
|
||||
# bottleneck
|
||||
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel)
|
||||
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_OPEN, self.morph_kernel)
|
||||
|
||||
if (cropped_h, cropped_w) == self.frame_shape:
|
||||
# Not detected.
|
||||
base_x, base_y = center_x, center_y
|
||||
else:
|
||||
base_x = cropped_x + cropped_w // 2
|
||||
base_y = cropped_y + cropped_h // 2
|
||||
if self.frame_final[base_y, base_x] != 1:
|
||||
if self.frame_final[center_y, center_x] != 1:
|
||||
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_DILATE, self.morph_kernel2, iterations=3)
|
||||
else:
|
||||
base_x, base_y = center_x, center_y
|
||||
|
||||
contours, _ = cv2.findContours(self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
||||
contours_box = [cv2.boundingRect(cnt) for cnt in contours]
|
||||
contours_dist = np.array(
|
||||
[abs(base_x - (cnt_x + cnt_w / 2)) + abs(base_y - (cnt_y + cnt_h / 2)) for cnt_x, cnt_y, cnt_w, cnt_h in contours_box])
|
||||
|
||||
if len(contours_box):
|
||||
cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[contours_dist.argmin()]
|
||||
x = cropped_x2 + cropped_w2 // 2
|
||||
y = cropped_y2 + cropped_h2 // 2
|
||||
else:
|
||||
x = center_x
|
||||
y = center_y
|
||||
|
||||
# if imshow_enable:
|
||||
# cv2.circle(frame, (orig_x, orig_y), 10, (255, 0, 0), -1)
|
||||
# cv2.circle(frame, (x, y), 7, (0, 0, 255), -1)
|
||||
|
||||
#
|
||||
# out_x = center_x if abs(x - center_x) > radius else x
|
||||
# out_y = center_y if abs(y - center_y) > radius else y
|
||||
out_x, out_y = orig_x, orig_y
|
||||
if gray_frame[int(max(y - 5, 0)):int(min(y + 5, self.frame_shape[0])),
|
||||
int(max(x - 5, 0)):int(min(x + 5, self.frame_shape[1]))].min() < self.quartile_1:
|
||||
out_x = x
|
||||
out_y = y
|
||||
|
||||
# if imshow_enable:
|
||||
# cv2.circle(frame, (out_x, out_y), 5, (0, 255, 0), -1)
|
||||
#
|
||||
# cv2.imshow("frame_bin", self.frame_bin * 255)
|
||||
# cv2.imshow("frame_final", self.frame_final * 255)
|
||||
return out_x, out_y
|
||||
|
||||
|
||||
#RANSAC
|
||||
|
||||
|
||||
video_path = "demo2.mp4"
|
||||
imshow_enable = True
|
||||
save_video = False
|
||||
|
||||
thresh_add = 10
|
||||
|
||||
|
||||
class TimeitResult(object):
|
||||
"""
|
||||
from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
|
||||
Object returned by the timeit magic with info about the run.
|
||||
Contains the following attributes :
|
||||
loops: (int) number of loops done per measurement
|
||||
repeat: (int) number of times the measurement has been repeated
|
||||
best: (float) best execution time / number
|
||||
all_runs: (list of float) execution time of each run (in s)
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = best
|
||||
self.worst = worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.timings = [dt / self.loops for dt in all_runs]
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.timings) / len(self.timings)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean=format_time(self.average, self._precision),
|
||||
std=format_time(self.stdev, self._precision),
|
||||
best=format_time(self.best, self._precision),
|
||||
worst=format_time(self.worst, self._precision),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<TimeitResult : ' + unic + u'>')
|
||||
|
||||
|
||||
class FPSResult(object):
|
||||
"""
|
||||
base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = 1 / best
|
||||
self.worst = 1 / worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.fps = [1 / dt for dt in all_runs]
|
||||
self.unit = "fps"
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.fps) / len(self.fps)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean="%.*g%s" % (self._precision, self.average, self.unit),
|
||||
std="%.*g%s" % (self._precision, self.stdev, self.unit),
|
||||
best="%.*g%s" % (self._precision, self.best, self.unit),
|
||||
worst="%.*g%s" % (self._precision, self.worst, self.unit),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<FPSResult : ' + unic + u'>')
|
||||
|
||||
|
||||
def format_time(timespan, precision=3):
|
||||
"""
|
||||
https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
|
||||
Formats the timespan in a human readable form
|
||||
"""
|
||||
|
||||
if timespan >= 60.0:
|
||||
# we have more than a minute, format that in a human readable form
|
||||
# Idea from http://snipplr.com/view/5713/
|
||||
parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
|
||||
time = []
|
||||
leftover = timespan
|
||||
for suffix, length in parts:
|
||||
value = int(leftover / length)
|
||||
if value > 0:
|
||||
leftover = leftover % length
|
||||
time.append(u'%s%s' % (str(value), suffix))
|
||||
if leftover < 1:
|
||||
break
|
||||
return " ".join(time)
|
||||
|
||||
# Unfortunately the unicode 'micro' symbol can cause problems in
|
||||
# certain terminals.
|
||||
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
|
||||
# Try to prevent crashes by being more secure than it needs to
|
||||
# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
|
||||
units = [u"s", u"ms", u'us', "ns"] # the save value
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb5'.encode(sys.stdout.encoding)
|
||||
units = [u"s", u"ms", u'\xb5s', "ns"]
|
||||
except:
|
||||
pass
|
||||
scaling = [1, 1e3, 1e6, 1e9]
|
||||
|
||||
if timespan > 0.0:
|
||||
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
|
||||
else:
|
||||
order = 3
|
||||
return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
|
||||
|
||||
|
||||
def ellipse_model(data, y, f):
|
||||
"""
|
||||
@ -931,11 +142,6 @@ def fit_rotated_ellipse(data, P):
|
||||
return (cx, cy, w, h, theta)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class HSRAC_cls(object):
|
||||
def __init__(self):
|
||||
# I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble.
|
||||
@ -1090,21 +296,21 @@ class HSRAC_cls(object):
|
||||
self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
||||
|
||||
# if calc_print_enable:
|
||||
# the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly
|
||||
# print('Kernel response:', response)
|
||||
# print('Pixel position:', center_xy)
|
||||
# the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly
|
||||
# print('Kernel response:', response)
|
||||
# print('Pixel position:', center_xy)
|
||||
|
||||
|
||||
# if imshow_enable:
|
||||
# if self.now_modeo != self.cv_modeo[0] and self.now_modeo != self.cv_modeo[1]:
|
||||
# if 0 in cropped_image.shape:
|
||||
# If shape contains 0, it is not detected well.
|
||||
pass
|
||||
else:
|
||||
cv2.imshow("crop", cropped_image)
|
||||
cv2.imshow("frame", frame)
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
pass
|
||||
#
|
||||
# if imshow_enable:
|
||||
# if self.now_modeo != self.cv_modeo[0] and self.now_modeo != self.cv_modeo[1]:
|
||||
# if 0 in cropped_image.shape:
|
||||
# If shape contains 0, it is not detected well.
|
||||
# pass
|
||||
# else:
|
||||
# cv2.imshow("crop", cropped_image)
|
||||
# cv2.imshow("frame", frame)
|
||||
# if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
# pass
|
||||
|
||||
if self.now_modeo == self.cv_modeo[0]:
|
||||
# Moving from first_frame to the next mode
|
||||
@ -1119,7 +325,7 @@ class HSRAC_cls(object):
|
||||
# For measuring processing time of image processing
|
||||
cv_start_time = timeit.default_timer()
|
||||
# Crop first to reduce the amount of data to process.
|
||||
# frame = cropped_image[0:len(cropped_image) - 10, :]
|
||||
# frame = cropped_image[0:len(cropped_image) - 10, :]
|
||||
# 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)
|
||||
@ -1196,16 +402,13 @@ class HSRAC_cls(object):
|
||||
|
||||
crop_start_time = timeit.default_timer()
|
||||
cx, cy, w, h, theta = ransac_data
|
||||
# print(cx, cy)
|
||||
# print(cx, cy)
|
||||
if w >= 2.1 * h: #new blink detection algo lmao this works pretty good actually
|
||||
print("RAN BLINK")
|
||||
#return center_x, center_y, frame, frame, True
|
||||
|
||||
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)
|
||||
# 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)
|
||||
|
||||
# csy = frame.shape[0]
|
||||
# csx = frame.shape[1]
|
||||
@ -1264,9 +467,9 @@ class HSRAC_cls(object):
|
||||
|
||||
|
||||
|
||||
class External_Run_HSRACS:
|
||||
|
||||
hsrac = HSRAC_cls()
|
||||
class External_Run_HSRACS(object):
|
||||
def __init__(self):
|
||||
self.algo = HSRAC_cls()
|
||||
|
||||
def run(self, current_image_gray):
|
||||
self.algo.current_image_gray = current_image_gray
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
@@@@@@@@@@@@@@@@@
|
||||
@@@@@@@@@@@@@(
|
||||
|
||||
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization)
|
||||
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization)
|
||||
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
|
||||
|
||||
Copyright (c) 2022 EyeTrackVR <3
|
||||
|
||||
Loading…
Reference in New Issue
Block a user