EyeTrackVR/EyeTrackApp/ransac.py
2025-02-18 14:29:47 -06:00

484 lines
19 KiB
Python

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RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization)
Algorithm App Implementations By: Prohurtz, qdot (Initial App Creator)
Copyright (c) 2025 EyeTrackVR <3
LICENSE: Summer Software Distribution License 1.0
------------------------------------------------------------------------------------------------------
"""
import cv2
import numpy as np
from eye import EyeId
from utils.img_utils import safe_crop
from utils.misc_utils import clamp
import os
import psutil
import sys
process = psutil.Process(os.getpid()) # set process priority to low
try: # medium chance this does absolutely nothing but eh
sys.getwindowsversion()
except AttributeError:
process.nice(0) # UNIX: 0 low 10 high
process.nice()
else:
process.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS) # Windows
process.nice()
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=45,
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)])
if cu > 1: # negatives can get thrown which cause errors, just ignore them
wh = np.sqrt(cu / cu_r)
else:
pass
w, h = wh[0], wh[1]
error_sum = np.sum(data)
# print("fitting error = %.3f" % (error_sum))
return (cx, cy, w, h, theta)
def get_center_noclamp(center_xy, 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
ransac_upper_x = center_x + max(20, radius)
ransac_lower_x = center_x - max(20, radius)
ransac_upper_y = center_y + max(20, radius)
ransac_lower_y = center_y - max(20, radius)
ransac_xy_offset = (ransac_lower_x, ransac_lower_y)
return (
center_x,
center_y,
upper_x,
lower_x,
upper_y,
lower_y,
ransac_lower_x,
ransac_lower_y,
ransac_upper_x,
ransac_upper_y,
ransac_xy_offset,
)
cct = 300
def RANSAC3D(self, hsrac_en):
f = False
ranf = False
blink = 0.8
angle = 0
if hsrac_en:
(
center_x,
center_y,
upper_x,
lower_x,
upper_y,
lower_y,
ransac_lower_x,
ransac_lower_y,
ransac_upper_x,
ransac_upper_y,
ransac_xy_offset,
) = get_center_noclamp((self.rawx, self.rawy), self.radius)
frame = safe_crop(
self.current_image_gray_clean,
int(ransac_lower_x),
int(ransac_lower_y),
int(ransac_upper_x),
int(ransac_upper_y),
1,
)
else:
frame = self.current_image_gray_clean
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
rng = np.random.default_rng()
newFrame2 = self.current_image_gray.copy()
# 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
# configurable in this utility as we're dealing with variable lighting amounts/placement, as
# well as camera positioning and lensing. Therefore, everyone's cutoff may be different.
#
# The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we
# crop the image earlier; it gives us less possible dark area to get confused about in the
# next step.
# Crop first to reduce the amount of data to process.
# frame = self.current_image_gray
# For measuring processing time of image processing
# Crop first to reduce the amount of data to process.
# frame = frame[0:len(frame) - 5, :]
# To reduce the processing data, blur.
if frame is None:
print("[WARN] Frame is empty")
self.failed = self.failed + 1 # we have failed, move onto next algo
return 0, 0, 0, frame, blink, 0, 0
else:
frame_gray = cv2.GaussianBlur(frame, (9, 9), 10)
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray)
maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
# crop 15% sqare around min_loc
# frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf,
# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
if self.settings.gui_legacy_ransac:
if self.eye_id in [EyeId.LEFT]:
threshold_value = self.settings.gui_legacy_ransac_thresh_left
else:
threshold_value = self.settings.gui_legacy_ransac_thresh_right
else:
threshold_value = min_val + self.settings.gui_thresh_add
_, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY)
try:
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
th_frame = 255 - closing
except:
# I want to eliminate try here because try tends to be slow in execution.
th_frame = 255 - frame_gray
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
hull = []
# print(contours)
# This way is faster than contours[i]
# But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
for cnt in contours:
hull.append(cv2.convexHull(cnt, False))
if not hull:
# If empty, go to next loop
pass
try:
cnt = sorted(hull, key=cv2.contourArea)
maxcnt = cnt[-1]
# ellipse = cv2.fitEllipse(maxcnt)
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), rng)
if ransac_data is None:
# ransac_data is None==maxcnt.shape[0]<sample_num
# go to next loop
pass
cx, cy, w, h, theta = ransac_data
# print(cx, cy)
# cxi, cyi, wi, hi = int(cx), int(cy), int(w), int(h)
# cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
# cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
# cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
# img = newImage2[y1:y2, x1:x2]
except:
ranf = True
pass
self.current_image_gray = frame
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255), -1) # the point of the darkest area in the image
# However eyes are annoyingly three dimensional, so we need to take this ellipse and turn it
# into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
# ophthalmologist about possible issues with astigmatism.
try:
# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
# from somewhat to completely magic, as most of it happens in native libraries (hence passing
# via dicts).
result_2d = {}
result_2d_final = {}
result_2d["center"] = (cx, cy)
result_2d["axes"] = (w, h)
result_2d["angle"] = theta * 180.0 / np.pi
angle = result_2d["angle"]
result_2d_final["ellipse"] = result_2d
result_2d_final["diameter"] = w
result_2d_final["location"] = (cx, cy)
result_2d_final["confidence"] = 0.99
result_2d_final["timestamp"] = self.current_frame_number / self.current_fps
# Black magic happens here, but after this we have our reprojected pupil/eye, and all we had
# to do was sell our soul to satan and/or C++.
result_3d = self.detector_3d.update_and_detect(result_2d_final, self.current_image_gray)
# Now we have our pupil
ellipse_3d = result_3d["ellipse"]
# And our eyeball that the pupil is on the surface of
self.lkg_projected_sphere = result_3d["projected_sphere"]
# Record our pupil center
exm = ellipse_3d["center"][0]
eym = ellipse_3d["center"][1]
# print(result_2d["angle"])
d = result_3d["diameter_3d"]
self.cc_radius = int(float(self.lkg_projected_sphere["axes"][0]))
self.xc = int(float(self.lkg_projected_sphere["center"][0]))
self.yc = int(float(self.lkg_projected_sphere["center"][1]))
except:
f = True
csy = newFrame2.shape[0]
csx = newFrame2.shape[1]
if hsrac_en:
if ranf:
cx = self.rawx
cy = self.rawy
else:
# print(int(cx), int(clamp(cx + ransac_lower_x, 0, csx)), ransac_lower_x, csx, "y", int(cy), int(clamp(cy + ransac_lower_y, 0, csy)), ransac_lower_y, csy)
cx = int(clamp(cx + ransac_lower_x, 0, csx)) # dunno why this is being weird
cy = int(clamp(cy + ransac_lower_y, 0, csy))
# print(contours)
for cnt in contours:
(x, y, w, h) = cv2.boundingRect(cnt)
perscalarw = w / csx
perscalarh = h / csy
# print(abs(perscalarw-perscalarh))
# if abs(perscalarw-perscalarh) >= 0.2: # TODO setting
# blink = 0.0
if self.settings.gui_RANSACBLINK:
if self.ran_blink_check_for_file:
if self.eye_id in [EyeId.LEFT]:
file_path = "RANSAC_blink_LEFT.cfg"
if self.eye_id in [EyeId.RIGHT]:
file_path = "RANSAC_blink_RIGHT.cfg"
else:
file_path = "RANSAC_blink_RIGHT.cfg"
if os.path.exists(file_path):
with open(file_path, "r") as file:
self.blink_list = [float(line.strip()) for line in file]
else:
print(
f"\033[93m[INFO] RANSAC Blink Config '{file_path}' not found. Waiting for calibration.\033[0m"
)
self.ran_blink_check_for_file = False
if len(self.blink_list) == 10000: # self calibrate ransac blink IN TESTING
if self.eye_id in [EyeId.LEFT]:
with open("RANSAC_BLINK_LEFT.cfg", "w") as file:
for item in self.blink_list:
file.write(str(item) + "\n")
if self.eye_id in [EyeId.RIGHT]:
with open("RANSAC_BLINK_RIGHT.cfg", "w") as file:
for item in self.blink_list:
file.write(str(item) + "\n")
# print("SAVE")
# self.blink_list.pop(0)
self.blink_list.append(abs(perscalarw - perscalarh))
elif len(self.blink_list) < 10000:
self.blink_list.append(abs(perscalarw - perscalarh))
if abs(perscalarw - perscalarh) >= np.percentile(self.blink_list, 92):
blink = 0.0
try:
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1) # TODO: fix visualizations with HSRAC
cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
except:
pass
# try: #for some reason the pye3d visualizations are wack, im going to just not visualize it for now..
# cv2.ellipse(
# self.current_image_gray,
# tuple(int(v) for v in ellipse_3d["center"]),
# tuple(int(v) for v in ellipse_3d["axes"]),
# ellipse_3d["angle"],
# 0,
# 360, # start/end angle for drawing
# (0, 255, 0), # color (BGR): red
# )
# except Exception:
# Sometimes we get bogus axes and trying to draw this throws. Ideally we should check for
# validity beforehand, but for now just pass. It usually fixes itself on the next frame.
# pass
try:
# print(self.lkg_projected_sphere["angle"], self.lkg_projected_sphere["axes"], self.lkg_projected_sphere["center"])
cv2.ellipse(
newFrame2,
tuple(int(v) for v in self.lkg_projected_sphere["center"]),
tuple(int(v) for v in self.lkg_projected_sphere["axes"]),
self.lkg_projected_sphere["angle"],
0,
360, # start/end angle for drawing
(0, 255, 0), # color (BGR): red
)
# draw line from center of eyeball to center of pupil
cv2.line(
self.current_image_gray,
tuple(int(v) for v in self.lkg_projected_sphere["center"]),
tuple(int(v) for v in ellipse_3d["center"]),
(0, 255, 0), # color (BGR): red
)
except:
pass
self.current_image_gray = newFrame2
y, x = self.current_image_gray.shape
thresh = cv2.resize(thresh, (x, y))
try:
self.failed = 0 # we have succeded, continue with this
return cx, cy, angle, thresh, blink, w, h
except:
self.failed = self.failed + 1 # we have failed, move onto next algo
return 0, 0, 0, thresh, blink, 0, 0