Revert "Cleaning+SafeCrop"

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Prohurtz 2023-01-31 12:02:25 -08:00 committed by GitHub
parent f6d3ea0659
commit a6105a3f51
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7 changed files with 1387 additions and 794 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
@ -89,7 +85,7 @@ def run_once(f):
if not wrapper.has_run:
wrapper.has_run = True
return f(*args, **kwargs)
wrapper.has_run = False
return wrapper
@ -98,35 +94,36 @@ 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:
def __init__(
self,
config: "EyeTrackCameraConfig",
settings: "EyeTrackSettingsConfig",
cancellation_event: "threading.Event",
capture_event: "threading.Event",
capture_queue_incoming: "queue.Queue",
image_queue_outgoing: "queue.Queue",
eye_id,
self,
config: "EyeTrackCameraConfig",
settings: "EyeTrackSettingsConfig",
cancellation_event: "threading.Event",
capture_event: "threading.Event",
capture_queue_incoming: "queue.Queue",
image_queue_outgoing: "queue.Queue",
eye_id,
):
self.config = config
self.settings = settings
# Cross-thread communication management
self.capture_queue_incoming = capture_queue_incoming
self.image_queue_outgoing = image_queue_outgoing
self.cancellation_event = cancellation_event
self.capture_event = capture_event
self.eye_id = eye_id
# Cross algo state
self.lkg_projected_sphere = None
self.xc = None
self.yc = None
# Image state
self.previous_image = None
self.current_image = None
@ -134,14 +131,14 @@ class EyeProcessor:
self.current_frame_number = None
self.current_fps = None
self.threshold_image = None
# Calibration Values
self.xoff = 1
self.yoff = 1
# Keep large in order to recenter correctly
self.calibration_frame_counter = None
self.eyeoffx = 1
self.xmax = -69420
self.xmin = 69420
self.ymax = -69420
@ -150,41 +147,44 @@ 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
self.camera_model = None
self.detector_3d = None
self.failed = 0
self.skip_blink_detect = False
# blink
#blink
self.max_ints = []
self.max_int = 0
self.min_int = 4000000000000
self.frames = 0
self.frames = 0
self.blinkvalue = False
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(
(
@ -196,32 +196,29 @@ class EyeProcessor:
self.image_queue_outgoing.put((image_stack, output_information))
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"
)
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')
pass
def capture_crop_rotate_image(self):
# Get our current frame
try:
# Get frame from capture source, crop to ROI
self.current_image = self.current_image[
int(self.config.roi_window_y): int(
self.config.roi_window_y + self.config.roi_window_h
),
int(self.config.roi_window_x): int(
self.config.roi_window_x + self.config.roi_window_w
),
]
int(self.config.roi_window_y): int(
self.config.roi_window_y + self.config.roi_window_h
),
int(self.config.roi_window_x): int(
self.config.roi_window_x + self.config.roi_window_w
),
]
except:
# Failure to process frame, reuse previous frame.
self.current_image = self.previous_image
print("\033[91m[ERROR] Frame capture issue detected.\033[0m")
try:
# Apply rotation to cropped area. For any rotation area outside of the bounds of the image,
# fill with white.
@ -243,119 +240,96 @@ class EyeProcessor:
return True
except:
pass
def BLINKM(self):
self.blinkvalue = 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)
#print(self.prev_x, self.prev_y, cx, cy)
# #filter values with too much movement
# if (cx - self.prev_x) <= 45 and (cy - self.prev_y) <= 45 :
# self.prev_x = cx
# self.prev_y = cy
# 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)
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, eyeopen)) #update app
else:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
)
# else:
# print("EYE MOVED TOO FAST")
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, 0, 0, 0, False))
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, 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)
cx, cy, frame = External_Run_HSF.HSFS(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, 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, eyeopen))
def RANSAC3DM(self):
cx, cy, thresh = RANSAC3D(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, 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, eyeopen))
def BLOBM(self):
cx, cy, thresh = BLOB(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, eyeopen)) #update app
else:
self.output_images_and_update(
thresh,
EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
)
def ALGOSELECT(self):
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen))
if self.failed == 0 and self.firstalgo is not None:
def ALGOSELECT(self):
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
self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
def run(self):
self.firstalgo = None
self.secondalgo = None
self.thirdalgo = None
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
#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
self.firstalgo = self.HSFM
elif self.settings.gui_HSF and self.settings.gui_HSFP == 2:
self.secondalgo = self.HSFM
@ -363,7 +337,7 @@ class EyeProcessor:
self.thirdalgo = self.HSFM
elif self.settings.gui_HSF and self.settings.gui_HSFP == 4:
self.fourthalgo = self.HSFM
if self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 1:
self.firstalgo = self.RANSAC3DM
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 2:
@ -372,8 +346,8 @@ class EyeProcessor:
self.thirdalgo = self.RANSAC3DM
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
@ -381,7 +355,7 @@ class EyeProcessor:
self.thirdalgo = self.HSRACM
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 4:
self.fourthalgo = self.HSRACM
if self.settings.gui_BLOB and self.settings.gui_BLOBP == 1:
self.firstalgo = self.BLOBM
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 2:
@ -390,31 +364,31 @@ class EyeProcessor:
self.thirdalgo = self.BLOBM
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4:
self.fourthalgo = self.BLOBM
f = True
while True:
# f = True
# Check to make sure we haven't been requested to close
# f = True
# Check to make sure we haven't been requested to close
if self.cancellation_event.is_set():
print("\033[94m[INFO] Exiting Tracking thread\033[0m")
return
if self.config.roi_window_w <= 0 or self.config.roi_window_h <= 0:
# At this point, we're waiting for the user to set up the ROI window in the GUI.
# Sleep a bit while we wait.
if self.cancellation_event.wait(0.1):
return
continue
# If our ROI configuration has changed, reset our model and detector
if (
self.camera_model is None
or self.detector_3d is None
or self.camera_model.resolution
!= (
if (self.camera_model is None
or self.detector_3d is None
or self.camera_model.resolution != (
self.config.roi_window_w,
self.config.roi_window_h,
)
)
):
self.camera_model = CameraModel(
focal_length=self.config.focal_length,
@ -423,7 +397,7 @@ class EyeProcessor:
self.detector_3d = Detector3D(
camera=self.camera_model, long_term_mode=DetectorMode.blocking
)
try:
if self.capture_queue_incoming.empty():
self.capture_event.set()
@ -439,35 +413,41 @@ 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, cv2.COLOR_BGR2GRAY
)
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)
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)
# cx, cy, thresh = HSRAC(self)
# 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, True)) #update app
# else:
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
# BLINK(self)
# 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
#center_x, center_y, frame = HSF(self) #run algo
#out_x, out_y = cal_osc(self, center_x, center_y) #filter and calibrate
#self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False)) #update app
# cx, cy, thresh = HSRAC(self)
# 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, True)) #update app
# 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
# center_x, center_y, frame = HSF(self) #run algo
# out_x, out_y = cal_osc(self, center_x, center_y) #filter and calibrate
# self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False)) #update app
self.ALGOSELECT() # run our algos in priority order set in settings
self.ALGOSELECT() #run our algos in priority order set in settings
self.BLINKM()

View File

@ -7,8 +7,6 @@ from functools import lru_cache
import cv2
import numpy as np
from EyeTrackApp.img_utils import safe_crop
# from line_profiler_pycharm import profile
video_path = "ezgif.com-gif-maker.avi"
@ -29,6 +27,180 @@ 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.
@ -39,42 +211,43 @@ class CvParameters:
# self.prev_step=step
self._step = step
self._hsf = HaarSurroundFeature(radius)
def get_rpsh(self):
return self._radius, self.pad, self._step, self._hsf
# Essentially, the following would be preferable, but it would take twice as long to call.
# return self.radius, self.pad, self.step, self.hsf
@property
def radius(self):
return self._radius
@radius.setter
def radius(self, now_radius):
# self.prev_radius=self._radius
self._radius = now_radius
self.pad = 2 * now_radius
self.hsf = now_radius
@property
def step(self):
return self._step
@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
@ -82,33 +255,30 @@ class HaarSurroundFeature:
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
)
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
@ -132,10 +302,10 @@ def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset
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]
@ -144,9 +314,9 @@ def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset
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
@ -162,14 +332,8 @@ def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
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),
)
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
@ -188,39 +352,30 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
# 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)
)
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 = 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)
@ -246,40 +401,37 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
# 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),
)
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 AutoRadiusCalc(object):
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
@ -299,35 +451,21 @@ 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
@ -335,13 +473,13 @@ class AutoRadiusCalc(object):
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:
@ -361,21 +499,11 @@ 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:
@ -388,19 +516,19 @@ class AutoRadiusCalc(object):
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 BlinkDetector(object):
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)
@ -409,28 +537,28 @@ class BlinkDetector(object):
# 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)
@ -441,7 +569,7 @@ class CenterCorrection(object):
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
@ -449,14 +577,12 @@ 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))
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)
@ -465,44 +591,34 @@ class CenterCorrection(object):
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)
# 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)
)
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.
base_x, base_y = center_x, center_y
@ -511,54 +627,36 @@ 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:
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
):
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)
#
@ -567,36 +665,29 @@ 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.
# For measuring total processing time
self.main_start_time = timeit.default_timer()
self.rng = np.random.default_rng()
self.cvparam = CvParameters(default_radius, default_step)
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
cap = cv2.VideoCapture(video_path)
@ -604,7 +695,7 @@ class HSF_cls(object):
raise IOError("Error opening video stream or file")
self.cap = cap
return True
def read_frame(self):
# Temporary implementation to run
if not self.cap.isOpened():
@ -615,53 +706,47 @@ class HSF_cls(object):
self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
return True
return False
def single_run(self):
# Temporary implementation to run
## default_radius = 14
frame = self.current_image_gray
if self.now_modeo == self.cv_modeo[1]:
# adjustment of radius
# debug print
# if calc_print_enable:
# temp_radius = self.auto_radius_calc.get_radius()
# print('Now radius:', temp_radius)
# self.cvparam.radius = temp_radius
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()
# For measuring processing time of image processing
cv_start_time = timeit.default_timer()
gray_frame = frame
self.timedict["to_gray"].append(timeit.default_timer() - cv_start_time)
# 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)
crop_start_time = timeit.default_timer()
# Define the center point and radius
center_x, center_y = center_xy
@ -669,10 +754,10 @@ class HSF_cls(object):
lower_x = center_x - radius
upper_y = center_y + radius
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)
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
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)
@ -680,19 +765,15 @@ class HSF_cls(object):
# Statistics for blink detection
if self.blink_detector.response_len() < blink_init_frames:
self.blink_detector.add_response(cv2.mean(cropped_image)[0])
upper_x = center_x + self.center_correct.center_q1_radius
lower_x = center_x - self.center_correct.center_q1_radius
upper_y = center_y + self.center_correct.center_q1_radius
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))[
0
]
)
self.center_q1.add_response(cv2.mean(gray_frame[lower_y:upper_y, lower_x:upper_x])[0])
else:
self.blink_detector.calc_thresh()
self.center_q1.calc_thresh()
self.now_modeo = self.cv_modeo[3]
@ -709,15 +790,11 @@ class HSF_cls(object):
# blink
pass
else:
# pass
# pass
if not self.center_correct.setup_comp:
self.center_correct.init_array(
gray_frame.shape, self.center_q1.quartile_1, radius
)
center_x, center_y = self.center_correct.correction(
gray_frame, center_x, center_y
)
self.center_correct.init_array(gray_frame.shape, self.center_q1.quartile_1, radius)
center_x, center_y = self.center_correct.correction(gray_frame, center_x, center_y)
# Define the center point and radius
center_xy = (center_x, center_y)
upper_x = center_x + radius
@ -725,29 +802,24 @@ class HSF_cls(object):
upper_y = center_y + radius
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
)
# 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)
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
# 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
cv_end_time = timeit.default_timer()
self.timedict["crop"].append(cv_end_time - crop_start_time)
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
# 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)
# 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
@ -756,7 +828,7 @@ class HSF_cls(object):
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
if skip_autoradius and skip_blink_detect:
@ -765,22 +837,20 @@ class HSF_cls(object):
self.now_modeo = self.cv_modeo[2]
else:
self.now_modeo = self.cv_modeo[1]
return center_x, center_y, frame
class External_Run_HSF:
class External_Run_HSF(object):
def __init__(self):
self.algo = HSF_cls()
hsrac = HSRAC_cls()
def run(self, current_image_gray):
self.algo.current_image_gray = current_image_gray
center_x, center_y, frame = self.algo.single_run()
def HSFS(self):
External_Run_HSF.hsrac.current_image_gray = self.current_image_gray
center_x, center_y, frame = External_Run_HSF.hsrac.single_run()
return center_x, center_y, frame
if __name__ == "__main__":
hsf = HSF_cls()
hsf.open_video(video_path)
while hsf.read_frame():
_ = hsf.single_run()
if __name__ == '__main__':
hsrac = HSRAC_cls()
hsrac.open_video(video_path)
while hsrac.read_frame():
_ = hsrac.single_run()

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@ -1,12 +0,0 @@
import cv2
def safe_crop(img, x, y, x2, y2):
# The order of the arguments can be reconsidered.
img_h, img_w = img.shape[1::-1]
outimg = img[max(0, y) : min(img_h, y2), max(0, x) : min(img_w, x2)].copy()
reqsize_x, reqsize_y = abs(x2 - x), abs(y2 - y)
if outimg.shape[1::-1] != (reqsize_y, reqsize_x):
# If the size is different from the expected size (smaller by the amount that is out of range)
outimg = cv2.resize(outimg, (reqsize_x, reqsize_y))
return outimg

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

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@ -1,171 +0,0 @@
import functools
import math
import sys
import timeit
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])

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@ -1,2 +0,0 @@
def clamp(x, low, high):
return max(low, min(x, high))