Merge branch 'HSF-and-new-algos-feature-branch' into fix_safecrop

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PallasNeko 2023-02-02 00:37:58 +09:00 committed by GitHub
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5 changed files with 1361 additions and 514 deletions

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@ -1,4 +1,4 @@
"""
'''
------------------------------------------------------------------------------------------------------
,@@@@@@
@ -19,8 +19,8 @@
@@@@@@@@@@@@@@@@@
@@@@@@@@@@@@@(
HSR By: PallasNeko (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer)
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization)
HSR By: Sean.Denka (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer)
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization)
BLOB By: Prohurtz#0001 (Main App Developer)
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
@ -28,7 +28,7 @@ Additional Contributors: [Assassin], Summer404NotFound, lorow, ZanzyTHEbar
Copyright (c) 2022 EyeTrackVR <3
------------------------------------------------------------------------------------------------------
"""
'''
from operator import truth
from dataclasses import dataclass
@ -46,20 +46,19 @@ import numpy as np
import cv2
from enum import Enum
from one_euro_filter import OneEuroFilter
if sys.platform.startswith("win"):
from winsound import PlaySound, SND_FILENAME, SND_ASYNC
import importlib
from osc_calibrate_filter import *
from haar_surround_feature import External_Run_HSF
from haar_surround_feature import *
from blob import *
from ransac import *
from hsrac import External_Run_HSRACS
from hsrac import *
from blink import *
from intensity_eye_open import *
from intensity_eye_open import *
class InformationOrigin(Enum):
RANSAC = 1
@ -68,10 +67,7 @@ class InformationOrigin(Enum):
HSF = 4
HSRAC = 5
bbb = 0
@dataclass
class EyeInformation:
info_type: InformationOrigin
@ -98,7 +94,8 @@ async def delayed_setting_change(setting, value):
await asyncio.sleep(5)
setting = value
if sys.platform.startswith("win"):
PlaySound("Audio/compleated.wav", SND_FILENAME | SND_ASYNC)
PlaySound('Audio/compleated.wav', SND_FILENAME | SND_ASYNC)
class EyeProcessor:
@ -150,6 +147,7 @@ class EyeProcessor:
self.cccs = False
self.ts = 10
self.previous_rotation = self.config.rotation_angle
self.calibration_frame_counter
self.camera_model = None
self.detector_3d = None
@ -170,21 +168,23 @@ class EyeProcessor:
self.prev_x = None
self.prev_y = None
try:
min_cutoff = float(self.settings.gui_min_cutoff) # 0.0004
beta = float(self.settings.gui_speed_coefficient) # 0.9
except:
print("\033[93m[WARN] OneEuroFilter values must be a legal number.\033[0m")
print('\033[93m[WARN] OneEuroFilter values must be a legal number.\033[0m')
min_cutoff = 0.0004
beta = 0.9
noisy_point = np.array([1, 1])
self.one_euro_filter = OneEuroFilter(
noisy_point, min_cutoff=min_cutoff, beta=beta
noisy_point,
min_cutoff=min_cutoff,
beta=beta
)
def output_images_and_update(
self, threshold_image, output_information: EyeInformation
):
def output_images_and_update(self, threshold_image, output_information: EyeInformation):
try:
image_stack = np.concatenate(
(
@ -197,12 +197,9 @@ class EyeProcessor:
self.previous_image = self.current_image
self.previous_rotation = self.config.rotation_angle
except: # If this fails it likely means that the images are not the same size for some reason.
print(
"\033[91m[ERROR] Size of frames to display are of unequal sizes.\033[0m"
)
print('\033[91m[ERROR] Size of frames to display are of unequal sizes.\033[0m')
pass
def capture_crop_rotate_image(self):
# Get our current frame
@ -245,15 +242,13 @@ class EyeProcessor:
pass
def BLINKM(self):
self.blinkvalue = BLINK(self)
self.eyeoffx = BLINK(self)
def HSRACM(self):
# temporary implementation
cx, cy, thresh, gray_frame, uncropframe = External_Run_HSRACS().run(
self.current_image_gray
)
cx, cy, thresh, gray_frame, uncropframe = External_Run_HSRACS.HSRACS(self)
self.current_image_gray = gray_frame
if self.prev_x is None:
if self.prev_x == None:
self.prev_x = cx
self.prev_y = cy
#print(self.prev_x, self.prev_y, cx, cy)
@ -261,92 +256,71 @@ class EyeProcessor:
# if (cx - self.prev_x) <= 45 and (cy - self.prev_y) <= 45 :
# self.prev_x = cx
# self.prev_y = cy
eyeopen = intense(cx, cy, uncropframe)
self.eyeopen = intense(cx, cy, uncropframe)
out_x, out_y = cal_osc(self, cx, cy)
if cx == 0:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
) # update app
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.eyeopen)) #update app
else:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
)
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.eyeopen))
# else:
# print("EYE MOVED TOO FAST")
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, 0, 0, 0, False))
def HSFM(self):
# temporary implementation
cx, cy, frame = External_Run_HSF().run(self.current_image_gray)
eyeopen = intense(cx, cy, self.current_image_gray)
cx, cy, frame = External_Run_HSF.HSFS(self)
self.eyeopen = intense(cx, cy, self.current_image_gray)
out_x, out_y = cal_osc(self, cx, cy)
if cx == 0:
self.output_images_and_update(
frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, eyeopen)
) # update app
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.eyeopen)) #update app
else:
self.output_images_and_update(
frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, eyeopen)
)
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.eyeopen))
def RANSAC3DM(self):
cx, cy, thresh = RANSAC3D(self)
eyeopen = intense(cx, cy, self.current_image_gray)
self.eyeopen = intense(cx, cy, self.current_image_gray)
out_x, out_y = cal_osc(self, cx, cy)
if cx == 0:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, eyeopen),
) # update app
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.eyeopen)) #update app
else:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, eyeopen),
)
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.eyeopen))
def BLOBM(self):
cx, cy, thresh = BLOB(self)
eyeopen = intense(cx, cy, self.current_image_gray)
self.eyeopen = intense(cx, cy, self.current_image_gray)
out_x, out_y = cal_osc(self, cx, cy)
if cx == 0:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
) # update app
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.eyeopen)) #update app
else:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
)
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.eyeopen))
def ALGOSELECT(self):
if self.failed == 0 and self.firstalgo is not None:
if self.failed == 0 and self.firstalgo != None:
self.firstalgo()
else:
self.failed = self.failed + 1
if (
self.failed == 1 and self.secondalgo is not None
): # send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
if self.failed == 1 and self.secondalgo != None: #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
self.secondalgo()
else:
self.failed = self.failed + 1
if self.failed == 2 and self.thirdalgo is not None:
if self.failed == 2 and self.thirdalgo != None:
self.thirdalgo()
else:
self.failed = self.failed + 1
if self.failed == 3 and self.fourthalgo is not None:
if self.failed == 3 and self.fourthalgo != None:
self.fourthalgo()
else:
self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
def run(self):
self.firstalgo = None
@ -355,7 +329,7 @@ class EyeProcessor:
self.fourthalgo = None
#set algo priorities
if (self.settings.gui_HSF and self.settings.gui_HSFP == 1): # I feel like this is super innefficient though it only runs at startup and no solution is coming to me atm
if self.settings.gui_HSF and self.settings.gui_HSFP == 1: #I feel like this is super innefficient though it only runs at startup and no solution is coming to me atm
self.firstalgo = self.HSFM
elif self.settings.gui_HSF and self.settings.gui_HSFP == 2:
self.secondalgo = self.HSFM
@ -373,7 +347,7 @@ class EyeProcessor:
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4:
self.fourthalgo = self.RANSAC3DM
if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1:
if self.settings.gui_HSRAC == True and self.settings.gui_HSRACP == 1:
self.firstalgo = self.HSRACM
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2:
self.secondalgo = self.HSRACM
@ -406,12 +380,12 @@ class EyeProcessor:
return
continue
# If our ROI configuration has changed, reset our model and detector
if (
self.camera_model is None
if (self.camera_model is None
or self.detector_3d is None
or self.camera_model.resolution
!= (
or self.camera_model.resolution != (
self.config.roi_window_w,
self.config.roi_window_h,
)
@ -440,12 +414,11 @@ class EyeProcessor:
if not self.capture_crop_rotate_image():
continue
self.current_image_gray = cv2.cvtColor(
self.current_image, cv2.COLOR_BGR2GRAY
)
self.current_image_gray_clean = (
self.current_image_gray.copy()
) # copy this frame to have a clean image for blink algo
self.current_image_gray_clean = self.current_image_gray.copy() #copy this frame to have a clean image for blink algo
# print(self.settings.gui_RANSAC3D)
# BLINK(self)
@ -457,10 +430,12 @@ class EyeProcessor:
# else:
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
# cx, cy, thresh = RANSAC3D(self)
# out_x, out_y = cal_osc(self, cx, cy)
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False)) #update app
# cx, cy, larger_threshold = BLOB(self)
# out_x, out_y = cal_osc(self, cx, cy)
# self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, False)) #update app
@ -471,3 +446,8 @@ class EyeProcessor:
self.ALGOSELECT() #run our algos in priority order set in settings
self.BLINKM()

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@ -7,9 +7,11 @@ from functools import lru_cache
import cv2
import numpy as np
from utils.misc_utils import clamp
from utils.img_utils import safe_crop
# from line_profiler_pycharm import profile
video_path = "ezgif.com-gif-maker.avi"
@ -30,6 +32,180 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
# step==(x,y)
default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
"""
Attention.
If using cv2.filter2D in this code, be careful with the kernel
https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python
"""
def TimeitWrapper(*args, **kwargs):
"""
This decorator @TimeitWrapper() prints the function name and execution time in seconds.
:param args:
:param kwargs:
:return:
"""
def decorator(function):
@functools.wraps(function)
def wrapper(*args, **kwargs):
start = timeit.default_timer()
results = function(*args, **kwargs)
end = timeit.default_timer()
print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
return results
return wrapper
return decorator
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])
class CvParameters:
# It may be a little slower because a dict named "self" is read for each function call.
@ -76,6 +252,7 @@ class CvParameters:
class HaarSurroundFeature:
def __init__(self, r_inner, r_outer=None, val=None):
if r_outer is None:
r_outer = r_inner * 3
@ -100,14 +277,11 @@ class HaarSurroundFeature:
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
)
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
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
@ -164,13 +338,7 @@ def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
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),
)
return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
# @profile
@ -191,9 +359,8 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
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)
)
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
@ -203,18 +370,10 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
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 = 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
@ -256,10 +415,7 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
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),
)
center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding))
frame_conv_stride[:, :] = response_list
# or
@ -268,7 +424,7 @@ 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 = []
@ -300,35 +456,21 @@ 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
@ -362,21 +504,11 @@ 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:
@ -395,7 +527,7 @@ class AutoRadiusCalc(object):
return None
class BlinkDetector(object):
class Blink_Detector(object):
def __init__(self):
self.response_list = []
self.response_max = None
@ -450,9 +582,7 @@ 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))
@ -482,27 +612,17 @@ 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.
@ -512,30 +632,17 @@ 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:
@ -550,13 +657,8 @@ 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
@ -568,8 +670,7 @@ 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.
@ -583,20 +684,14 @@ 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
@ -620,10 +715,12 @@ 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
@ -636,9 +733,7 @@ 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()
@ -651,17 +746,13 @@ 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)
@ -674,11 +765,13 @@ 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)
@ -691,12 +784,14 @@ 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()
@ -746,6 +841,7 @@ 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
@ -759,10 +855,7 @@ 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
@ -781,12 +874,11 @@ 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()

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View File

@ -1,11 +1,17 @@
import pandas as pd
import numpy as np
import time
from enum import IntEnum
#higher intensity means more closed/ more white/less pupil
#Hm I need an acronym for this, any ideas?
#IBO Intensity Based Openess
class EyeId(IntEnum):
RIGHT = 0
LEFT = 1
BOTH = 2
SETTINGS = 3
# HOW THIS WORKS:
# we get the intensity of pupil area from HSF crop, When the eyelid starts to close, the pupil starts being obstructed by skin which is generally lighter than the pupil.
@ -13,8 +19,14 @@ import time
# ex. when you look up there is less pupil visible, which results in an uncalculated change in intensity even though the eyelid has not moved in a meaningful way.
# We compare the darkest intensity of that area, to the lightest (global) intensity to find the appropriate openness state via a float.
fname = "IBO.csv" #TODO Expose as setting
if EyeId.RIGHT:
fname = "IBO_RIGHT.csv"
if EyeId.LEFT:
fname = "IBO_LEFT.csv"
lct = time.time()
try:
data = pd.read_csv(fname, sep=",")
except:
@ -27,15 +39,18 @@ except:
def intense(x, y, frame):
global lct
upper_x = int(x) + 25 #TODO make this a setting
lower_x = int(x) - 25
upper_y = int(y) + 25
lower_y = int(y) - 25
frame = frame[lower_y:upper_y, lower_x:upper_x]
print(x, y, int(x), int(y), upper_x, upper_y, lower_x, lower_y)
try:
xy = int(str(int(x)) + str(int(y)) + str(int(x)+int(y)))
intensity = np.sum(frame)
intensity = np.sum(frame) #why is this outputting 0s?
# print(intensity, upper_x, upper_y, lower_x, lower_y)
except:
return 0.0 #TODO find how on earth a hyphen gets thrown into this
@ -78,9 +93,10 @@ def intense(x, y, frame):
#eyeopen = (intensity - minp) / (maxp - minp)
eyeopen = (intensity - maxp) / (minp - maxp)
eyeopen = 1 - eyeopen
eyeopen = eyeopen - 0.2
# print(intensity, maxp, minp, x, y)
print(f"EYEOPEN: {eyeopen}")
# print(f"EYEOPEN: {eyeopen}")
# print(int(x), int(y), eyeopen, maxp, minp)
except:
print('[INFO] Something went wrong, assuming blink.')

View File

@ -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