fix: left eye ransac thresh set to right

This commit is contained in:
Prohurtz 2023-09-28 13:19:12 -05:00
parent d11f94f5af
commit c6fbfe8c70

View File

@ -1,4 +1,4 @@
''' """
------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------
,@@@@@@ ,@@@@@@
@ -24,7 +24,7 @@ Algorithm App Implementations By: Prohurtz, qdot (Initial App Creator)
Copyright (c) 2023 EyeTrackVR <3 Copyright (c) 2023 EyeTrackVR <3
------------------------------------------------------------------------------------------------------ ------------------------------------------------------------------------------------------------------
''' """
import cv2 import cv2
import numpy as np import numpy as np
from enum import IntEnum from enum import IntEnum
@ -44,11 +44,14 @@ else:
process.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS) # Windows process.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS) # Windows
process.nice() process.nice()
class EyeId(IntEnum): class EyeId(IntEnum):
RIGHT = 0 RIGHT = 0
LEFT = 1 LEFT = 1
BOTH = 2 BOTH = 2
SETTINGS = 3 SETTINGS = 3
def ellipse_model(data, y, f): 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. 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.
@ -61,9 +64,15 @@ def ellipse_model(data, y, f):
""" """
return data.dot(y) + f return data.dot(y) + f
# @profile # @profile
def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80 def fit_rotated_ellipse_ransac(
): # before changing these values, please read up on the ransac algorithm 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 # However if you want to change any value just know that higher iterations will make processing frames slower
effective_sample = None effective_sample = None
@ -88,9 +97,16 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=
# I don't think it looks beautiful. # I don't think it looks beautiful.
# x,y,x**2,y**2,x*y,1,-1*x**2 # x,y,x**2,y**2,x*y,1,-1*x**2
datamod = np.concatenate( 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, data,
dtype=ret_dtype) 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_slim = np.array(datamod[:, :5], dtype=ret_dtype)
@ -101,15 +117,29 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=
# These two lines are one of the bottlenecks # These two lines are one of the bottlenecks
datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap) 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_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)) datamod_rng_p = np.matmul(
datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]
).reshape((-1, 5))
# I don't think it looks beautiful. # I don't think it looks beautiful.
ellipse_y_arr = np.asarray( 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) [
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_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_abs = np.abs(ellipse_data_arr)
ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0) 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_data_arr = ellipse_data_arr[ellipse_data_index]
@ -130,19 +160,19 @@ def fit_rotated_ellipse(data, P):
theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64) theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
theta_sin = np.sin(theta, dtype=np.float64) theta_sin = np.sin(theta, dtype=np.float64)
theta_cos = np.cos(theta, dtype=np.float64) theta_cos = np.cos(theta, dtype=np.float64)
tc2 = theta_cos ** 2 tc2 = theta_cos**2
ts2 = theta_sin ** 2 ts2 = theta_sin**2
b_tcs = b * theta_cos * theta_sin b_tcs = b * theta_cos * theta_sin
# Do the calculation only once # Do the calculation only once
cxy = b ** 2 - 4 * a * c cxy = b**2 - 4 * a * c
cx = (2 * c * d - b * e) / cxy cx = (2 * c * d - b * e) / cxy
cy = (2 * a * e - b * d) / cxy cy = (2 * a * e - b * d) / cxy
# I just want to clear things up around here. # I just want to clear things up around here.
cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f 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)]) 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 if cu > 1: # negatives can get thrown which cause errors, just ignore them
wh = np.sqrt(cu / cu_r) wh = np.sqrt(cu / cu_r)
else: else:
pass pass
@ -155,7 +185,6 @@ def fit_rotated_ellipse(data, P):
return (cx, cy, w, h, theta) return (cx, cy, w, h, theta)
def get_center_noclamp(center_xy, radius): def get_center_noclamp(center_xy, radius):
center_x, center_y = center_xy center_x, center_y = center_xy
upper_x = center_x + radius upper_x = center_x + radius
@ -168,22 +197,52 @@ def get_center_noclamp(center_xy, radius):
ransac_upper_y = center_y + max(20, radius) ransac_upper_y = center_y + max(20, radius)
ransac_lower_y = center_y - max(20, radius) ransac_lower_y = center_y - max(20, radius)
ransac_xy_offset = (ransac_lower_x, ransac_lower_y) 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 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 cct = 300
def RANSAC3D(self, hsrac_en): def RANSAC3D(self, hsrac_en):
f = False f = False
ranf = False ranf = False
blink = 0.7 blink = 0.7
if hsrac_en: 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) 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, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_y, 1) frame = safe_crop(
self.current_image_gray_clean,
ransac_lower_x,
ransac_lower_y,
ransac_upper_x,
ransac_upper_y,
1,
)
else: else:
frame = self.current_image_gray_clean frame = self.current_image_gray_clean
@ -201,8 +260,6 @@ def RANSAC3D(self, hsrac_en):
# crop the image earlier; it gives us less possible dark area to get confused about in the # crop the image earlier; it gives us less possible dark area to get confused about in the
# next step. # next step.
# Crop first to reduce the amount of data to process. # Crop first to reduce the amount of data to process.
# frame = self.current_image_gray # frame = self.current_image_gray
@ -212,7 +269,6 @@ def RANSAC3D(self, hsrac_en):
# To reduce the processing data, blur. # To reduce the processing data, blur.
frame_gray = cv2.GaussianBlur(frame, (5, 5), 0) frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m # 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) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray)
@ -260,22 +316,22 @@ def RANSAC3D(self, hsrac_en):
cx, cy, w, h, theta = ransac_data cx, cy, w, h, theta = ransac_data
# print(cx, cy) # print(cx, cy)
#cxi, cyi, wi, hi = int(cx), int(cy), int(w), int(h) # 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.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
# cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -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)) # 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, ) # 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] # img = newImage2[y1:y2, x1:x2]
except: except:
ranf = True ranf = True
pass pass
self.current_image_gray = frame self.current_image_gray = frame
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255), cv2.circle(
-1) # the point of the darkest area in the image 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 # 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 # into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
@ -323,17 +379,17 @@ def RANSAC3D(self, hsrac_en):
csx = newFrame2.shape[1] csx = newFrame2.shape[1]
if hsrac_en: if hsrac_en:
if ranf: if ranf:
cx = self.rawx cx = self.rawx
cy = self.rawy cy = self.rawy
else: 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) # 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 cx = int(
clamp(cx + ransac_lower_x, 0, csx)
) # dunno why this is being weird
cy = int(clamp(cy + ransac_lower_y, 0, csy)) cy = int(clamp(cy + ransac_lower_y, 0, csy))
# print(contours)
#print(contours)
for cnt in contours: for cnt in contours:
(x, y, w, h) = cv2.boundingRect(cnt) (x, y, w, h) = cv2.boundingRect(cnt)
perscalarw = w / csx perscalarw = w / csx
@ -346,48 +402,46 @@ def RANSAC3D(self, hsrac_en):
if self.ran_blink_check_for_file: if self.ran_blink_check_for_file:
if self.eye_id in [EyeId.LEFT]: if self.eye_id in [EyeId.LEFT]:
file_path = 'RANSAC_blink_LEFT.cfg' file_path = "RANSAC_blink_LEFT.cfg"
if self.eye_id in [EyeId.RIGHT]: if self.eye_id in [EyeId.RIGHT]:
file_path = 'RANSAC_blink_RIGHT.cfg' file_path = "RANSAC_blink_RIGHT.cfg"
else: else:
file_path = 'RANSAC_blink_RIGHT.cfg' file_path = "RANSAC_blink_RIGHT.cfg"
if os.path.exists(file_path): if os.path.exists(file_path):
with open(file_path, 'r') as file: with open(file_path, "r") as file:
self.blink_list = [float(line.strip()) for line in file] self.blink_list = [float(line.strip()) for line in file]
else: else:
print(f"\033[93m[INFO] RANSAC Blink Config '{file_path}' not found. Waiting for calibration.\033[0m") print(
f"\033[93m[INFO] RANSAC Blink Config '{file_path}' not found. Waiting for calibration.\033[0m"
)
self.ran_blink_check_for_file = False self.ran_blink_check_for_file = False
if len(self.blink_list) == 10000: # self calibrate ransac blink IN TESTING if len(self.blink_list) == 10000: # self calibrate ransac blink IN TESTING
if self.eye_id in [EyeId.LEFT]: if self.eye_id in [EyeId.LEFT]:
with open("RANSAC_BLINK_LEFT.cfg", 'w') as file: with open("RANSAC_BLINK_LEFT.cfg", "w") as file:
for item in self.blink_list: for item in self.blink_list:
file.write(str(item) + '\n') file.write(str(item) + "\n")
if self.eye_id in [EyeId.RIGHT]: if self.eye_id in [EyeId.RIGHT]:
with open("RANSAC_BLINK_RIGHT.cfg", 'w') as file: with open("RANSAC_BLINK_RIGHT.cfg", "w") as file:
for item in self.blink_list: for item in self.blink_list:
file.write(str(item) + '\n') file.write(str(item) + "\n")
print('SAVE') print("SAVE")
# self.blink_list.pop(0) # self.blink_list.pop(0)
self.blink_list.append(abs(perscalarw-perscalarh)) self.blink_list.append(abs(perscalarw - perscalarh))
elif len(self.blink_list) < 10000: elif len(self.blink_list) < 10000:
self.blink_list.append(abs(perscalarw-perscalarh)) self.blink_list.append(abs(perscalarw - perscalarh))
if abs(perscalarw - perscalarh) >= np.percentile(self.blink_list, 94):
if abs(perscalarw-perscalarh) >= np.percentile(
self.blink_list, 94
):
blink = 0.0 blink = 0.0
try: try:
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1) # TODO: fix visualizations with HSRAC 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) cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
except: except:
pass pass
@ -430,14 +484,12 @@ def RANSAC3D(self, hsrac_en):
except: except:
pass pass
self.current_image_gray = newFrame2 self.current_image_gray = newFrame2
y, x = self.current_image_gray.shape y, x = self.current_image_gray.shape
thresh = cv2.resize(thresh, (x,y)) thresh = cv2.resize(thresh, (x, y))
try: try:
self.failed = 0 # we have succeded, continue with this self.failed = 0 # we have succeded, continue with this
return cx, cy, thresh, blink return cx, cy, thresh, blink
except: except:
self.failed = self.failed + 1 #we have failed, move onto next algo self.failed = self.failed + 1 # we have failed, move onto next algo
return 0, 0, thresh, blink return 0, 0, thresh, blink