EyeTrackVR/EyeTrackApp/eye_processor.py
2022-12-26 12:52:20 -08:00

569 lines
23 KiB
Python

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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)
Additional Contributors: [Assassin], Summer404NotFound, lorow, ZanzyTHEbar
Copyright (c) 2022 EyeTrackVR <3
------------------------------------------------------------------------------------------------------
'''
from operator import truth
from dataclasses import dataclass
import sys
import asyncio
sys.path.append(".")
from config import EyeTrackCameraConfig
from config import EyeTrackSettingsConfig
from pye3d.camera import CameraModel
from pye3d.detector_3d import Detector3D, DetectorMode
import queue
import threading
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
from osc_calibrate_filter import *
from haar_surround_feature import *
from blob import *
from ransac import *
from hsrac import *
from blink import *
class InformationOrigin(Enum):
RANSAC = 1
BLOB = 2
FAILURE = 3
HSF = 4
HSRAC = 5
bbb = 0
@dataclass
class EyeInformation:
info_type: InformationOrigin
x: float
y: float
pupil_dialation: int
blink: bool
lowb = np.array(0)
def run_once(f):
def wrapper(*args, **kwargs):
if not wrapper.has_run:
wrapper.has_run = True
return f(*args, **kwargs)
wrapper.has_run = False
return wrapper
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)
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 = 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
self.current_image_gray = None
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
self.ymin = 69420
self.cct = 300
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.response_list = [] #This might not be correct.
#HSF
self.cv_mode = ["first_frame", "radius_adjust", "init", "normal"]
self.now_mode = self.cv_mode[0]
self.cvparam = CvParameters(default_radius, default_step)
self.skip_blink_detect = False
self.default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
# self.default_step==(x,y)
self.radius_cand_list = []
self.blink_init_frames = 60 * 3
prev_max_size = 60 * 3 # 60fps*3sec
# response_min=0
self.response_max = None
self.auto_radius_range = (self.settings.gui_HSF_radius - 10, self.settings.gui_HSF_radius + 10)
#blink
self.max_ints = []
self.max_int = 0
self.min_int = 4000000000000
self.frames = 0
self.blinkvalue = False
try:
min_cutoff = float(self.settings.gui_min_cutoff) # 0.0004
beta = float(self.settings.gui_speed_coefficient) # 0.9
except:
print('[WARN] OneEuroFilter values must be a legal number.')
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
)
def output_images_and_update(self, threshold_image, output_information: EyeInformation):
image_stack = np.concatenate(
(
cv2.cvtColor(self.current_image_gray, cv2.COLOR_GRAY2BGR),
cv2.cvtColor(threshold_image, cv2.COLOR_GRAY2BGR),
),
axis=1,
)
self.image_queue_outgoing.put((image_stack, output_information))
self.previous_image = self.current_image
self.previous_rotation = self.config.rotation_angle
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
),
]
except:
# Failure to process frame, reuse previous frame.
self.current_image = self.previous_image
print("[ERROR] Frame capture issue detected.")
try:
# Apply rotation to cropped area. For any rotation area outside of the bounds of the image,
# fill with white.
try:
rows, cols, _ = self.current_image.shape
except:
rows, cols, _ = self.previous_image.shape
img_center = (cols / 2, rows / 2)
rotation_matrix = cv2.getRotationMatrix2D(
img_center, self.config.rotation_angle, 1
)
self.current_image = cv2.warpAffine(
self.current_image,
rotation_matrix,
(cols, rows),
borderMode=cv2.BORDER_CONSTANT,
borderValue=(255, 255, 255),
)
return True
except:
pass
def HSF(self):
frame = self.current_image_gray
if self.now_mode == self.cv_mode[1]:
prev_res_len = len(self.response_list)
# adjustment of radius
if prev_res_len == 1:
# len==1==self.response_list==[self.settings.gui_HSF_radius]
self.cvparam.radius = self.auto_radius_range[0]
elif prev_res_len == 2:
# len==2==self.response_list==[self.settings.gui_HSF_radius, self.auto_radius_range[0]]
self.cvparam.radius = self.auto_radius_range[1]
elif prev_res_len == 3:
# len==3==self.response_list==[self.settings.gui_HSF_radius,self.auto_radius_range[0],self.auto_radius_range[1]]
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
# Extract the radius with the lowest response value
if sort_res[0] == self.settings.gui_HSF_radius:
# If the default value is best, change self.now_mode to init after setting radius to the default value.
self.cvparam.radius = self.settings.gui_HSF_radius
self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
self.response_list = []
elif sort_res[0] == self.auto_radius_range[0]:
self.radius_cand_list = [i for i in range(self.auto_radius_range[0], self.settings.gui_HSF_radius, self.default_step[0])][1:]
# self.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 self.default_step
self.cvparam.radius = self.radius_cand_list.pop()
else:
self.radius_cand_list = [i for i in range(self.settings.gui_HSF_radius, self.auto_radius_range[1], self.default_step[0])][1:]
# self.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 self.default_step
self.cvparam.radius = self.radius_cand_list.pop()
else:
# Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out
# Better make it a binary search.
if len(self.radius_cand_list) == 0:
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
self.cvparam.radius = sort_res[0]
self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
self.response_list = []
else:
self.cvparam.radius = self.radius_cand_list.pop()
radius, pad, step, hsf = self.cvparam.get_rpsh()
# For measuring processing time of image processing
cv_start_time = timeit.default_timer()
gray_frame = frame
# 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)
# 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)
crop_start_time = timeit.default_timer()
# Define the center point and radius
center_x, center_y = center_xy
upper_x = center_x + 25 #TODO make this a setting
lower_x = center_x - 25
upper_y = center_y + 25
lower_y = center_y - 25
# Crop the image using the calculated bounds
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why?
if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
# If mode is first_frame or radius_adjust, record current radius and response
self.response_list.append((radius, response))
elif self.now_mode == self.cv_mode[2]:
# Statistics for blink detection
if len(self.response_list) < self.blink_init_frames:
# Record the average value of cropped_image
self.response_list.append(cv2.mean(cropped_image)[0])
else:
# Calculate self.response_max by computing interquartile range, IQR
# Change self.cv_mode to normal
self.response_list = np.array(self.response_list)
# 25%,75%
# This value may need to be adjusted depending on the environment.
quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
iqr = quartile_3 - quartile_1
# response_min = quartile_1 - (iqr * 1.5)
self.response_max = quartile_3 + (iqr * 1.5)
self.now_mode = self.cv_mode[3]
else:
if 0 in cropped_image.shape:
# If shape contains 0, it is not detected well.
print("[WARN] HSF: Something's wrong.")
else:
# If the average value of cropped_image is greater than self.response_max
# (i.e., if the cropimage is whitish
if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max:
# blink
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
# If you want to update self.response_max. it may be more cost-effective to rewrite self.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
out_x, out_y = cal_osc(self, center_x, center_y)
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
# print(center_x, center_y)
try:
if self.settings.gui_BLINK: #tbh this is redundant, the algo already has blink detection built in
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
else:
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
self.failed = 0
except:
if self.settings.gui_BLINK: #tbh this is redundant, the algo already has blink detection built in
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, self.blinkvalue))
else:
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, False))
self.failed = self.failed + 1
if self.now_mode != self.cv_mode[0] and self.now_mode != self.cv_mode[1]:
if cropped_image.size < 400:
pass
if self.now_mode == self.cv_mode[0]:
self.now_mode = self.cv_mode[1]
return
#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False))
# return
#self.output_images_and_update(larger_threshold,EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False),)
# return
#self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True))
def ALGOSELECT(self):
if self.failed == 0 and self.firstalgo != None:
print('first')
self.firstalgo()
else:
self.failed = self.failed + 1
if self.failed == 1 and self.secondalgo != None:
print('2nd')
self.secondalgo() #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
else:
self.failed = self.failed + 1
if self.failed == 2 and self.thirdalgo != None:
print('3rd')
self.thirdalgo()
else:
self.failed = self.failed + 1
if self.failed == 3 and self.fourthalgo != None:
print('4th')
self.fourthalgo()
else:
self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
print(self.failed)
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
self.firstalgo = self.HSF
elif self.settings.gui_HSF and self.settings.gui_HSFP == 2:
self.secondalgo = self.HSF
elif self.settings.gui_HSF and self.settings.gui_HSFP == 3:
self.thirdalgo = self.HSF
elif self.settings.gui_HSF and self.settings.gui_HSFP == 4:
self.fourthalgo = self.HSF
if self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 1:
self.firstalgo = self.RANSAC3D
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 2:
self.secondalgo = self.RANSAC3D
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 3:
self.thirdalgo = self.RANSAC3D
elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4:
self.fourthalgo = self.RANSAC3D
if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1:
self.firstalgo = self.HSRAC
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2:
self.secondalgo = self.HSRAC
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 3:
self.thirdalgo = self.HSRAC
elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 4:
self.fourthalgo = self.HSRAC
if self.settings.gui_BLOB and self.settings.gui_BLOBP == 1:
self.firstalgo = self.BLOB
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 2:
self.secondalgo = self.BLOB
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 3:
self.thirdalgo = self.BLOB
elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4:
self.fourthalgo = self.BLOB
"""
# if self.settings.gui_BLOBP
# if self.settings.gui_HSFP
# if self.settings.gui_RANSAC3DP
f = True
while True:
# 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 != (
self.config.roi_window_w,
self.config.roi_window_h,
)
):
self.camera_model = CameraModel(
focal_length=self.config.focal_length,
resolution=(self.config.roi_window_w, self.config.roi_window_h),
)
self.detector_3d = Detector3D(
camera=self.camera_model, long_term_mode=DetectorMode.blocking
)
try:
if self.capture_queue_incoming.empty():
self.capture_event.set()
# Wait a bit for images here. If we don't get one, just try again.
(
self.current_image,
self.current_frame_number,
self.current_fps,
) = self.capture_queue_incoming.get(block=True, timeout=0.2)
except queue.Empty:
# print("No image available")
continue
if not self.capture_crop_rotate_image():
continue
self.current_image_gray = cv2.cvtColor(
self.current_image, cv2.COLOR_BGR2GRAY
)
self.current_image_gray_clean = self.current_image_gray.copy() #copy this frame to have a clean image for blink algo
# 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))
# 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