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 numpy as np
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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 line_profiler_pycharm import profile
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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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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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@ -73,6 +249,7 @@ class CvParameters:
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class HaarSurroundFeature:
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def __init__(self, r_inner, r_outer=None, val=None):
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if r_outer is None:
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r_outer = r_inner * 3
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@ -97,14 +274,11 @@ class HaarSurroundFeature:
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def get_kernel(self):
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# Defined here, but not yet used?
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# Create a kernel filled with the value of self.val_out
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kernel = (
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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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kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
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# Set the values of the inner area of the kernel using array slicing
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start = self.r_out - self.r_in
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end = self.r_out + self.r_in - 1
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start = (self.r_out - self.r_in)
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end = (self.r_out + self.r_in - 1)
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kernel[start:end, start:end] = self.val_in
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return kernel
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@ -160,14 +334,8 @@ def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
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p10 = np.empty(len_syx, dtype=frame_int_dtype)
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response_list = np.empty(len_syx, dtype=np.float64)
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frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
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frame_conv_stride = frame_conv[:: fcshape[1], :: fcshape[2]]
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return (
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(inner_sum, outer_sum),
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p_temp,
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(p00, p11, p01, p10),
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response_list,
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(frame_conv, frame_conv_stride),
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)
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frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
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return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
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# @profile
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@ -188,9 +356,8 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
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r_in = kernel.r_in
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len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
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inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array(
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(len_sy, len_sx), col + 1, frame_int.dtype, (f_shape, y_step, x_step)
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)
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inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1,
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frame_int.dtype, (f_shape, y_step, x_step))
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inner_sum, outer_sum = inout_sum
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p00, p11, p01, p10 = p_list
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frame_conv, frame_conv_stride = frameconvlist
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@ -200,18 +367,10 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
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y_rin_p = xy_steps_list[1] + r_in
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x_rin_p = xy_steps_list[0] + r_in
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# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
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inarr_mm = frame_int[
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y_rin_m[0] : y_rin_m[-1] + 1 : y_step, x_rin_m[0] : x_rin_m[-1] + 1 : x_step
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]
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inarr_mp = frame_int[
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y_rin_m[0] : y_rin_m[-1] + 1 : y_step, x_rin_p[0] : x_rin_p[-1] + 1 : x_step
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]
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inarr_pm = frame_int[
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y_rin_p[0] : y_rin_p[-1] + 1 : y_step, x_rin_m[0] : x_rin_m[-1] + 1 : x_step
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]
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inarr_pp = frame_int[
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y_rin_p[0] : y_rin_p[-1] + 1 : y_step, x_rin_p[0] : x_rin_p[-1] + 1 : x_step
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||||
]
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inarr_mm = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
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inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
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inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
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inarr_pp = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
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|
||||
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
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inner_sum[:, :] = inarr_mm
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@ -253,10 +412,7 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
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min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
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||||
|
||||
center = (
|
||||
(xy_steps_list[0][min_loc[0]] - padding),
|
||||
(xy_steps_list[1][min_loc[1]] - padding),
|
||||
)
|
||||
center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding))
|
||||
|
||||
frame_conv_stride[:, :] = response_list
|
||||
# or
|
||||
@ -265,7 +421,7 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
return frame_conv, min_response, center
|
||||
|
||||
|
||||
class AutoRadiusCalc(object):
|
||||
class Auto_Radius_Calc(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.radius_cand_list = []
|
||||
@ -297,35 +453,21 @@ class AutoRadiusCalc(object):
|
||||
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.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]
|
||||
):
|
||||
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]
|
||||
):
|
||||
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
|
||||
@ -359,21 +501,11 @@ class AutoRadiusCalc(object):
|
||||
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.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.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:
|
||||
@ -392,7 +524,7 @@ class AutoRadiusCalc(object):
|
||||
return None
|
||||
|
||||
|
||||
class BlinkDetector(object):
|
||||
class Blink_Detector(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.response_max = None
|
||||
@ -447,9 +579,7 @@ class CenterCorrection(object):
|
||||
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_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))
|
||||
@ -479,27 +609,17 @@ class CenterCorrection(object):
|
||||
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)
|
||||
)
|
||||
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
|
||||
]
|
||||
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
|
||||
)
|
||||
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.
|
||||
@ -509,30 +629,17 @@ class CenterCorrection(object):
|
||||
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,
|
||||
)
|
||||
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, _ = 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
|
||||
]
|
||||
)
|
||||
[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()
|
||||
]
|
||||
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:
|
||||
@ -547,13 +654,8 @@ class CenterCorrection(object):
|
||||
# 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
|
||||
):
|
||||
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
|
||||
|
||||
@ -565,8 +667,7 @@ class CenterCorrection(object):
|
||||
return out_x, out_y
|
||||
|
||||
|
||||
# temporary name
|
||||
class HSF_cls(object):
|
||||
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.
|
||||
|
||||
@ -580,20 +681,14 @@ class HSF_cls(object):
|
||||
self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"]
|
||||
self.now_modeo = self.cv_modeo[0]
|
||||
|
||||
self.auto_radius_calc = AutoRadiusCalc()
|
||||
self.blink_detector = BlinkDetector()
|
||||
self.center_q1 = BlinkDetector()
|
||||
self.auto_radius_calc = Auto_Radius_Calc()
|
||||
self.blink_detector = Blink_Detector()
|
||||
self.center_q1 = Blink_Detector()
|
||||
self.center_correct = CenterCorrection()
|
||||
|
||||
self.cap = None
|
||||
|
||||
self.timedict = {
|
||||
"to_gray": [],
|
||||
"int_img": [],
|
||||
"conv_int": [],
|
||||
"crop": [],
|
||||
"total_cv": [],
|
||||
}
|
||||
self.timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []}
|
||||
|
||||
def open_video(self, video_path):
|
||||
# Temporary implementation to run
|
||||
@ -617,10 +712,12 @@ class HSF_cls(object):
|
||||
def single_run(self):
|
||||
# Temporary implementation to run
|
||||
|
||||
# default_radius = 14
|
||||
|
||||
## default_radius = 14
|
||||
|
||||
# cropbox=[] # debug code
|
||||
|
||||
|
||||
frame = self.current_image_gray
|
||||
if self.now_modeo == self.cv_modeo[1]:
|
||||
# adjustment of radius
|
||||
@ -633,9 +730,7 @@ class HSF_cls(object):
|
||||
|
||||
self.cvparam.radius = self.auto_radius_calc.get_radius()
|
||||
if self.auto_radius_calc.adj_comp_flag:
|
||||
self.now_modeo = (
|
||||
self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
||||
)
|
||||
self.now_modeo = self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
||||
|
||||
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
||||
|
||||
@ -648,17 +743,13 @@ class HSF_cls(object):
|
||||
# Calculate the integral image of the frame
|
||||
int_start_time = timeit.default_timer()
|
||||
# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
|
||||
frame_pad = cv2.copyMakeBorder(
|
||||
gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT
|
||||
)
|
||||
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
|
||||
frame_int = cv2.integral(frame_pad)
|
||||
self.timedict["int_img"].append(timeit.default_timer() - int_start_time)
|
||||
|
||||
# Convolve the feature with the integral image
|
||||
conv_int_start_time = timeit.default_timer()
|
||||
xy_step = frameint_get_xy_step(
|
||||
frame_int.shape, step, pad, start_offset=None, end_offset=None
|
||||
)
|
||||
xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
||||
frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
|
||||
self.timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time)
|
||||
|
||||
@ -671,11 +762,13 @@ class HSF_cls(object):
|
||||
lower_y = center_y - radius
|
||||
|
||||
# Crop the image using the calculated bounds
|
||||
|
||||
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
|
||||
# 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 mode is first_frame or radius_adjust, record current radius and response
|
||||
self.auto_radius_calc.add_response(radius, response)
|
||||
@ -688,12 +781,14 @@ class HSF_cls(object):
|
||||
lower_x = center_x - self.center_correct.center_q1_radius
|
||||
upper_y = center_y + self.center_correct.center_q1_radius
|
||||
lower_y = center_y - self.center_correct.center_q1_radius
|
||||
|
||||
self.center_q1.add_response(
|
||||
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y,keepsize=False))[
|
||||
0
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
else:
|
||||
|
||||
self.blink_detector.calc_thresh()
|
||||
@ -743,6 +838,7 @@ class HSF_cls(object):
|
||||
# if imshow_enable or save_video:
|
||||
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
||||
# cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1)
|
||||
|
||||
# If you want to update response_max. it may be more cost-effective to rewrite response_list in the following way
|
||||
# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
|
||||
|
||||
@ -756,10 +852,7 @@ class HSF_cls(object):
|
||||
# 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 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
|
||||
@ -778,12 +871,11 @@ class HSF_cls(object):
|
||||
else:
|
||||
self.now_modeo = self.cv_modeo[1]
|
||||
|
||||
|
||||
# debug code
|
||||
# return center_x,center_y,cropbox,frame
|
||||
return center_x, center_y, frame
|
||||
|
||||
|
||||
|
||||
class External_Run_HSF(object):
|
||||
def __init__(self):
|
||||
self.algo = HSF_cls()
|
||||
|
||||
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)
|
||||
|
||||
Copyright (c) 2022 EyeTrackVR <3
|
||||
|
||||
Loading…
Reference in New Issue
Block a user