HSF is still broke.....

aaaaaaaaaaaaaaaaaaaaah
This commit is contained in:
Prohurtz 2023-01-03 10:06:54 -08:00
parent db1883929a
commit dfe8a9a436
5 changed files with 280 additions and 293 deletions

View File

@ -46,6 +46,7 @@ class EyeTrackSettingsConfig(BaseModel):
gui_HSFP: int = 3
gui_BLOBP: int = 4
gui_skip_autoradius: bool = True
gui_thresh_add: int = 20
class EyeTrackConfig(BaseModel):
version: int = 1

View File

@ -285,9 +285,6 @@ class EyeProcessor:
def ALGOSELECT(self):
if self.failed == 0 and self.firstalgo != None:

View File

@ -465,12 +465,28 @@ response_max = None
response_list = []
def HSF(self):
#global now_mode
#global response_list
#global radius_cand_list
#global response_max
# default_radius = 15
#frame = self.current_image_gray
global now_mode
global response_list
global radius_cand_list
global response_max
# default_radius = 15
global skip_autoradius
global default_radius
global prev_rany
global prev_ranx
global prev_hsfy
global prev_hsfx
skip_autoradius = self.settings.gui_skip_autoradius
default_radius = self.settings.gui_HSF_radius
frame = self.current_image_gray
if now_mode == cv_mode[1]:
prev_res_len = len(response_list)
@ -535,14 +551,13 @@ def HSF(self):
crop_start_time = timeit.default_timer()
# Define the center point and radius
center_x, center_y = center_xy
upper_x = center_x + radius
lower_x = center_x - radius
upper_y = center_y + radius
lower_y = center_y - radius
upper_x = center_x + 20
lower_x = center_x - 20
upper_y = center_y + 20
lower_y = center_y - 20
# Crop the image using the calculated bounds
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
if now_mode == cv_mode[0] or now_mode == cv_mode[1]:
# If mode is first_frame or radius_adjust, record current radius and response
response_list.append((radius, response))
@ -598,6 +613,7 @@ def HSF(self):
try:
self.failed = 0
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
return center_x, center_y, frame
except:

View File

@ -10,285 +10,18 @@ import numpy as np
# from line_profiler_pycharm import profile
#RANSACAHA
thresh_add = 20
class TimeitResult(object):
"""
from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
Object returned by the timeit magic with info about the run.
Contains the following attributes :
loops: (int) number of loops done per measurement
repeat: (int) number of times the measurement has been repeated
best: (float) best execution time / number
all_runs: (list of float) execution time of each run (in s)
"""
def __init__(self, loops, repeat, best, worst, all_runs, precision):
self.loops = loops
self.repeat = repeat
self.best = best
self.worst = worst
self.all_runs = all_runs
self._precision = precision
self.timings = [dt / self.loops for dt in all_runs]
@property
def average(self):
return math.fsum(self.timings) / len(self.timings)
@property
def stdev(self):
mean = self.average
return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
def __str__(self):
pm = '+-'
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
try:
u'\xb1'.encode(sys.stdout.encoding)
pm = u'\xb1'
except:
pass
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
pm=pm,
runs=self.repeat,
loops=self.loops,
loop_plural="" if self.loops == 1 else "s",
run_plural="" if self.repeat == 1 else "s",
mean=format_time(self.average, self._precision),
std=format_time(self.stdev, self._precision),
best=format_time(self.best, self._precision),
worst=format_time(self.worst, self._precision),
)
def _repr_pretty_(self, p, cycle):
unic = self.__str__()
p.text(u'<TimeitResult : ' + unic + u'>')
class FPSResult(object):
"""
base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
"""
def __init__(self, loops, repeat, best, worst, all_runs, precision):
self.loops = loops
self.repeat = repeat
self.best = 1 / best
self.worst = 1 / worst
self.all_runs = all_runs
self._precision = precision
self.fps = [1 / dt for dt in all_runs]
self.unit = "fps"
@property
def average(self):
return math.fsum(self.fps) / len(self.fps)
@property
def stdev(self):
mean = self.average
return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
def __str__(self):
pm = '+-'
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
try:
u'\xb1'.encode(sys.stdout.encoding)
pm = u'\xb1'
except:
pass
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
pm=pm,
runs=self.repeat,
loops=self.loops,
loop_plural="" if self.loops == 1 else "s",
run_plural="" if self.repeat == 1 else "s",
mean="%.*g%s" % (self._precision, self.average, self.unit),
std="%.*g%s" % (self._precision, self.stdev, self.unit),
best="%.*g%s" % (self._precision, self.best, self.unit),
worst="%.*g%s" % (self._precision, self.worst, self.unit),
)
def _repr_pretty_(self, p, cycle):
unic = self.__str__()
p.text(u'<FPSResult : ' + unic + u'>')
def format_time(timespan, precision=3):
"""
https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
Formats the timespan in a human readable form
"""
if timespan >= 60.0:
# we have more than a minute, format that in a human readable form
# Idea from http://snipplr.com/view/5713/
parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
time = []
leftover = timespan
for suffix, length in parts:
value = int(leftover / length)
if value > 0:
leftover = leftover % length
time.append(u'%s%s' % (str(value), suffix))
if leftover < 1:
break
return " ".join(time)
# Unfortunately the unicode 'micro' symbol can cause problems in
# certain terminals.
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
# Try to prevent crashes by being more secure than it needs to
# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
units = [u"s", u"ms", u'us', "ns"] # the save value
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
try:
u'\xb5'.encode(sys.stdout.encoding)
units = [u"s", u"ms", u'\xb5s', "ns"]
except:
pass
scaling = [1, 1e3, 1e6, 1e9]
if timespan > 0.0:
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
else:
order = 3
return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
def ellipse_model(data, y, f):
"""
There is no need to make this process a function, since making the process a function will slow it down a little by calling it.
The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same.
a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4]
:param data:
:param y: np.c_[d, e, a, c, b]
:param f: f == P[4, 0]
:return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ])
"""
return data.dot(y) + f
# @profile
def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80
): # before changing these values, please read up on the ransac algorithm
# However if you want to change any value just know that higher iterations will make processing frames slower
effective_sample = None
# The array contents do not change during the loop, so only one call is needed.
# They say len is faster than shape.
# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
len_data = len(data)
if len_data < sample_num:
return None
# Type of calculation result
ret_dtype = np.float64
# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
# If the array size is less than about 100, this is faster than rng.choice.
rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
# or
# I don't see any advantage to doing this.
# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
# I don't think it looks beautiful.
# x,y,x**2,y**2,x*y,1,-1*x**2
datamod = np.concatenate(
[data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
(-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
dtype=ret_dtype)
datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
datamod_rng = datamod[rng_sample]
datamod_rng6 = datamod_rng[:, :, 6]
datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1))
# These two lines are one of the bottlenecks
datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
# I don't think it looks beautiful.
ellipse_y_arr = np.asarray(
[datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(len(datamod_rng_p)), datamod_rng_p[:, 1], datamod_rng_p[:, 0]], dtype=ret_dtype)
ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
ellipse_data_abs = np.abs(ellipse_data_arr)
ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
effective_data_arr = ellipse_data_arr[ellipse_data_index]
effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr)
# @profile
def fit_rotated_ellipse(data, P):
a = 1.0
b = P[0]
c = P[1]
d = P[2]
e = P[3]
f = P[4]
# The cost of trigonometric functions is high.
theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
theta_sin = np.sin(theta, dtype=np.float64)
theta_cos = np.cos(theta, dtype=np.float64)
tc2 = theta_cos ** 2
ts2 = theta_sin ** 2
b_tcs = b * theta_cos * theta_sin
# Do the calculation only once
cxy = b ** 2 - 4 * a * c
cx = (2 * c * d - b * e) / cxy
cy = (2 * a * e - b * d) / cxy
# I just want to clear things up around here.
cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
wh = np.sqrt(cu / cu_r)
w, h = wh[0], wh[1]
error_sum = np.sum(data)
# print("fitting error = %.3f" % (error_sum))
return (cx, cy, w, h, theta)
# HSF
video_path = "ezgif.com-gif-maker.avi"
imshow_enable = True
calc_print_enable = True
save_video = False
skip_autoradius = True
skip_autoradius = False
skip_blink_detect = False
# cache param
lru_maxsize_vvs = 16
lru_maxsize_vs = 64
# CV param
default_radius = 10
default_radius = 20
auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
# step==(x,y)
@ -518,7 +251,7 @@ class HaarSurroundFeature:
def __init__(self, r_inner, r_outer=None, val=None):
if r_outer is None:
r_outer = r_inner * 3
print(r_outer)
r_inner2 = r_inner * r_inner
count_inner = r_inner2
count_outer = r_outer * r_outer - r_inner2
@ -549,6 +282,12 @@ class HaarSurroundFeature:
return kernel
def to_gray(frame):
# Faster by quitting checking if the input image is already grayscale
# Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function
return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
@lru_cache(maxsize=lru_maxsize_vs)
def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None):
"""
@ -684,8 +423,227 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
# @profile
class
# 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
main_start_time = timeit.default_timer()
rng = np.random.default_rng()
cvparam = CvParameters(default_radius, default_step)
cv_modeo = ["first_frame", "radius_adjust", "init", "normal"]
now_modeo = cv_modeo[0]
radius_cand_listo = []
# response_min=0
response_maxo = None
response_listo = []
def HSRAC(self):
## default_radius = 14
frame = self.current_image_gray
if now_modeo == cv_modeo[1]:
prev_res_len = len(response_listo)
# adjustment of radius
if prev_res_len == 1:
# len==1==response_list==[default_radius]
cvparam.radius = auto_radius_range[0]
elif prev_res_len == 2:
# len==2==response_list==[default_radius, auto_radius_range[0]]
cvparam.radius = auto_radius_range[1]
elif prev_res_len == 3:
# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
sort_res = sorted(response_listo, key=lambda x: x[1])[0]
# Extract the radius with the lowest response value
if sort_res[0] == default_radius:
# If the default value is best, change now_mode to init after setting radius to the default value.
cvparam.radius = default_radius
now_modeo = cv_modeo[2] if not skip_blink_detect else cv_modeo[3]
response_listo = []
elif sort_res[0] == auto_radius_range[0]:
radius_cand_listo = [i for i in range(auto_radius_range[0], default_radius, default_step[0])][1:]
# default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
# It should be no problem to set it to anything other than default_step
cvparam.radius = radius_cand_listo.pop()
else:
radius_cand_listo = [i for i in range(default_radius, auto_radius_range[1], default_step[0])][1:]
# default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
# It should be no problem to set it to anything other than default_step
cvparam.radius = radius_cand_listo.pop()
else:
# Try the contents of the radius_cand_list in order until the radius_cand_list runs out
# Better make it a binary search.
if len(radius_cand_listo) == 0:
sort_res = sorted(response_listo, key=lambda x: x[1])[0]
cvparam.radius = sort_res[0]
now_modeo = cv_modeo[2] if not skip_blink_detect else cv_modeo[3]
response_listo = []
else:
cvparam.radius = radius_cand_listo.pop()
radius, pad, step, hsf = cvparam.get_rpsh()
# For measuring processing time of image processing
cv_start_time = timeit.default_timer()
gray_frame = frame
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_int = cv2.integral(frame_pad)
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)
frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
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
upper_x = center_x + radius
lower_x = center_x - radius
upper_y = center_y + radius
lower_y = center_y - radius
# Crop the image using the calculated bounds
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
if now_modeo == cv_modeo[0] or now_modeo == cv_modeo[1]:
# If mode is first_frame or radius_adjust, record current radius and response
response_listo.append((radius, response))
elif now_modeo == cv_modeo[2]:
# Statistics for blink detection
if len(response_listo) < blink_init_frames:
# Record the average value of cropped_image
response_listo.append(cv2.mean(cropped_image)[0])
else:
# Calculate response_max by computing interquartile range, IQR
# Change cv_mode to normal
response_listo = np.array(response_listo)
# 25%,75%
# This value may need to be adjusted depending on the environment.
quartile_1, quartile_3 = np.percentile(response_listo, [25, 75])
iqr = quartile_3 - quartile_1
# response_min = quartile_1 - (iqr * 1.5)
response_maxo = quartile_3 + (iqr * 1.5)
now_modeo = cv_modeo[3]
else:
if 0 in cropped_image.shape:
# If shape contains 0, it is not detected well.
print("Something's wrong.")
else:
# If the average value of cropped_image is greater than response_max
# (i.e., if the cropimage is whitish
if response_maxo is not None and cv2.mean(cropped_image)[0] > response_maxo:
# blink
pass
# if imshow_enable or save_video:
# cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -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()
timedict["crop"].append(cv_end_time - crop_start_time)
timedict["total_cv"].append(cv_end_time - cv_start_time)
if calc_print_enable:
# the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly
print('Kernel response:', response)
print('Pixel position:', center_xy)
if imshow_enable:
if now_modeo != cv_modeo[0] and now_modeo != cv_modeo[1]:
if 0 in cropped_image.shape:
# If shape contains 0, it is not detected well.
pass
else:
cv2.imshow("crop", cropped_image)
if cv2.waitKey(1) & 0xFF == ord("q"):
pass
if now_modeo == cv_modeo[0]:
# Moving from first_frame to the next mode
if skip_autoradius and skip_blink_detect:
now_modeo = cv_modeo[3]
response_list = []
elif skip_autoradius:
now_modeo = cv_modeo[2]
response_list = []
else:
now_modeo = cv_modeo[1]
return center_x, center_y, frame
'''
timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []}
# I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble.
@ -715,14 +673,17 @@ def HSRAC(self):
global response_list
global radius_cand_list
global response_max
global skip_autoradius
global default_radius
global prev_rany
global prev_ranx
global prev_hsfy
global prev_hsfx
skip_autoradius = self.settings.gui_skip_autoradius
default_radius = self.settings.gui_HSF_radius
thresh_add = self.settings.gui_thresh_add
frame = self.current_image_gray
if now_mode == cv_mode[1]:
@ -851,10 +812,7 @@ def HSRAC(self):
# try:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
thresh_add = 10
rng = np.random.default_rng()
f = False
# Convert the image to grayscale, and set up thresholding. Thresholds here are basically a
# low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user
@ -955,20 +913,21 @@ def HSRAC(self):
except:
xoff = prev_hsfx - prev_ranx
yoff = prev_hsfy - prev_rany
return (center_x + xoff), (center_y + yoff), thresh
return (xoff), (yoff), thresh
except:
self.current_image_gray = frame #cv2.resize(frame, (150, 150), interpolation = cv2.INTER_AREA)
xoff = prev_hsfx - prev_ranx
yoff = prev_hsfy - prev_rany
return (center_x + xoff), (center_y + yoff), thresh
xoff = prev_hsfx - 28
yoff = prev_hsfy - 28
print(prev_hsfx, prev_ranx)
return (xoff), (yoff), thresh
'''
try:
self.failed = 0

View File

@ -37,7 +37,7 @@ class SettingsWidget:
self.gui_RANSAC3DP = f"-RANSAC3DP{widget_id}-"
self.gui_HSFP = f"-HSFP{widget_id}-"
self.gui_BLOBP = f"-BLOBP{widget_id}-"
self.gui_thresh_add = f"-THRESHADD{widget_id}-"
self.gui_threshold_slider = f"-BLOBTHRESHOLD{widget_id}-"
self.main_config = main_config
@ -195,6 +195,16 @@ class SettingsWidget:
tooltip = "Adjusts the radius paramater for HSF. Only adjust if you are having tracking issues.",
),
],
[sg.Text("RANSAC Thresh Add", background_color='#424042'),
sg.Slider(
range=(1, 50),
default_value=self.config.gui_thresh_add,
orientation="h",
key=self.gui_thresh_add,
background_color='#424042',
tooltip = "Adjusts the ammount of threshold to add to RANSAC. Usefull for fine tuning your setup.",
),
],
[
sg.Text("Blob Threshold", background_color='#424042'), #TODO make this for right and left eyes? I dont know how vital that is..
sg.Slider(
@ -435,6 +445,10 @@ class SettingsWidget:
self.config.gui_threshold = int(values[self.gui_threshold_slider])
changed = True
if self.config.gui_thresh_add != values[self.gui_thresh_add]:
self.config.gui_thresh_add = int(values[self.gui_thresh_add])
changed = True
if self.config.gui_eye_falloff != values[self.gui_eye_falloff]:
self.config.gui_eye_falloff = values[self.gui_eye_falloff]
changed = True