mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
1222 lines
52 KiB
Python
1222 lines
52 KiB
Python
'''
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,@@@@@@
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@@@@@@@@@@@ @@@
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@@@@@@@@@@@@ @@@@@@@@@@@
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/@@@@@@@@@@@@@@@ @@@@@@@@
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@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@
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@@@@@@@@ @@@@@
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,@@@ @@@@&
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@@@@@@. @@@@
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@@@ @@@@@@@@@/ @@@@@
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,@@@. @@@@@@((@ @@@@(
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//@@@ ,, @@@@ @@@@@
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@@@( @@@@@@@
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@@@ @ @@@@@@@@#
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@@@@@@@@@@@@@@@@@
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@@@@@@@@@@@@@(
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HSR By: Sean.Denka (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer)
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RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization)
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BLOB By: Prohurtz#0001 (Main App Developer)
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Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
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Additional Contributors: [Assassin], Summer404NotFound, lorow, ZanzyTHEbar
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Copyright (c) 2022 EyeTrackVR <3
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------------------------------------------------------------------------------------------------------
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'''
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from operator import truth
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from dataclasses import dataclass
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import sys
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import asyncio
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sys.path.append(".")
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from config import EyeTrackCameraConfig
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from config import EyeTrackSettingsConfig
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from pye3d.camera import CameraModel
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from pye3d.detector_3d import Detector3D, DetectorMode
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import queue
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import threading
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import numpy as np
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import cv2
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from enum import Enum
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from one_euro_filter import OneEuroFilter
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if sys.platform.startswith("win"):
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from winsound import PlaySound, SND_FILENAME, SND_ASYNC
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from osc_calibrate_filter import *
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from haar_surround_feature import *
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class InformationOrigin(Enum):
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RANSAC = 1
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BLOB = 2
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FAILURE = 3
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HSF = 4
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bbb = 0
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@dataclass
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class EyeInformation:
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info_type: InformationOrigin
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x: float
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y: float
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pupil_dialation: int
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blink: bool
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lowb = np.array(0)
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def run_once(f):
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def wrapper(*args, **kwargs):
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if not wrapper.has_run:
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wrapper.has_run = True
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return f(*args, **kwargs)
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wrapper.has_run = False
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return wrapper
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async def delayed_setting_change(setting, value):
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await asyncio.sleep(5)
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setting = value
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if sys.platform.startswith("win"):
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PlaySound('Audio/compleated.wav', SND_FILENAME | SND_ASYNC)
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def ellipse_model(data, y, f):
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"""
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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.
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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.
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a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4]
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:param data:
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:param y: np.c_[d, e, a, c, b]
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:param f: f == P[4, 0]
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:return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ])
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"""
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return data.dot(y) + f
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# @profile
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def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80
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): # before changing these values, please read up on the ransac algorithm
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# However if you want to change any value just know that higher iterations will make processing frames slower
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effective_sample = None
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# The array contents do not change during the loop, so only one call is needed.
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# They say len is faster than shape.
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# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
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len_data = len(data)
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if len_data < sample_num:
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return None
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# Type of calculation result
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ret_dtype = np.float64
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# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
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# If the array size is less than about 100, this is faster than rng.choice.
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rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
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# or
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# I don't see any advantage to doing this.
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# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
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# I don't think it looks beautiful.
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# x,y,x**2,y**2,x*y,1,-1*x**2
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datamod = np.concatenate(
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[data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
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(-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
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dtype=ret_dtype)
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datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
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datamod_rng = datamod[rng_sample]
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datamod_rng6 = datamod_rng[:, :, 6]
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datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
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datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1))
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# These two lines are one of the bottlenecks
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datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
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datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
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datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
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# I don't think it looks beautiful.
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ellipse_y_arr = np.asarray(
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[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)
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ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
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ellipse_data_abs = np.abs(ellipse_data_arr)
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ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
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effective_data_arr = ellipse_data_arr[ellipse_data_index]
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effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
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return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr)
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# @profile
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def fit_rotated_ellipse(data, P):
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a = 1.0
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b = P[0]
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c = P[1]
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d = P[2]
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e = P[3]
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f = P[4]
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# The cost of trigonometric functions is high.
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theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
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theta_sin = np.sin(theta, dtype=np.float64)
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theta_cos = np.cos(theta, dtype=np.float64)
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tc2 = theta_cos ** 2
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ts2 = theta_sin ** 2
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b_tcs = b * theta_cos * theta_sin
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# Do the calculation only once
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cxy = b ** 2 - 4 * a * c
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cx = (2 * c * d - b * e) / cxy
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cy = (2 * a * e - b * d) / cxy
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# I just want to clear things up around here.
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cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
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cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
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wh = np.sqrt(cu / cu_r)
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w, h = wh[0], wh[1]
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error_sum = np.sum(data)
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# print("fitting error = %.3f" % (error_sum))
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return (cx, cy, w, h, theta)
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class EyeProcessor:
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def __init__(
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self,
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config: "EyeTrackCameraConfig",
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settings: "EyeTrackSettingsConfig",
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cancellation_event: "threading.Event",
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capture_event: "threading.Event",
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capture_queue_incoming: "queue.Queue",
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image_queue_outgoing: "queue.Queue",
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eye_id,
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):
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self.config = config
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self.settings = settings
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# Cross-thread communication management
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self.capture_queue_incoming = capture_queue_incoming
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self.image_queue_outgoing = image_queue_outgoing
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self.cancellation_event = cancellation_event
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self.capture_event = capture_event
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self.eye_id = eye_id
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# Cross algo state
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self.lkg_projected_sphere = None
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self.xc = None
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self.yc = None
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# Image state
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self.previous_image = None
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self.current_image = None
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self.current_image_gray = None
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self.current_frame_number = None
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self.current_fps = None
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self.threshold_image = None
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# Calibration Values
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self.xoff = 1
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self.yoff = 1
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# Keep large in order to recenter correctly
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self.calibration_frame_counter = None
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self.eyeoffx = 1
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self.xmax = -69420
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self.xmin = 69420
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self.ymax = -69420
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self.ymin = 69420
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self.cct = 300
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self.cccs = False
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self.ts = 10
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self.previous_rotation = self.config.rotation_angle
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self.calibration_frame_counter
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self.camera_model = None
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self.detector_3d = None
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self.camera_model = None
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self.detector_3d = None
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self.failed = 0
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self.response_list = [] #This might not be correct.
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#HSF
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self.cv_mode = ["first_frame", "radius_adjust", "init", "normal"]
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self.now_mode = self.cv_mode[0]
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self.cvparam = CvParameters(default_radius, default_step)
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self.skip_blink_detect = False
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self.default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
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# self.default_step==(x,y)
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self.radius_cand_list = []
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self.blink_init_frames = 60 * 3
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prev_max_size = 60 * 3 # 60fps*3sec
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# response_min=0
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self.response_max = None
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self.auto_radius_range = (self.settings.gui_HSF_radius - 10, self.settings.gui_HSF_radius + 10)
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#blink
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self.max_ints = []
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self.max_int = 0
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self.min_int = 4000000000000
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self.frames = 0
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self.blinkvalue = False
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try:
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min_cutoff = float(self.settings.gui_min_cutoff) # 0.0004
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beta = float(self.settings.gui_speed_coefficient) # 0.9
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except:
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print('[WARN] OneEuroFilter values must be a legal number.')
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min_cutoff = 0.0004
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beta = 0.9
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noisy_point = np.array([1, 1])
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self.one_euro_filter = OneEuroFilter(
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noisy_point,
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min_cutoff=min_cutoff,
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beta=beta
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)
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def output_images_and_update(self, threshold_image, output_information: EyeInformation):
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image_stack = np.concatenate(
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(
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cv2.cvtColor(self.current_image_gray, cv2.COLOR_GRAY2BGR),
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cv2.cvtColor(threshold_image, cv2.COLOR_GRAY2BGR),
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),
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axis=1,
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)
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self.image_queue_outgoing.put((image_stack, output_information))
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self.previous_image = self.current_image
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self.previous_rotation = self.config.rotation_angle
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def capture_crop_rotate_image(self):
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# Get our current frame
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try:
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# Get frame from capture source, crop to ROI
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self.current_image = self.current_image[
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int(self.config.roi_window_y): int(
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self.config.roi_window_y + self.config.roi_window_h
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),
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int(self.config.roi_window_x): int(
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self.config.roi_window_x + self.config.roi_window_w
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),
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]
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except:
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# Failure to process frame, reuse previous frame.
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self.current_image = self.previous_image
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print("[ERROR] Frame capture issue detected.")
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try:
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# Apply rotation to cropped area. For any rotation area outside of the bounds of the image,
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# fill with white.
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try:
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rows, cols, _ = self.current_image.shape
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except:
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rows, cols, _ = self.previous_image.shape
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img_center = (cols / 2, rows / 2)
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rotation_matrix = cv2.getRotationMatrix2D(
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img_center, self.config.rotation_angle, 1
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)
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self.current_image = cv2.warpAffine(
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self.current_image,
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rotation_matrix,
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(cols, rows),
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borderMode=cv2.BORDER_CONSTANT,
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borderValue=(255, 255, 255),
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)
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return True
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except:
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pass
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def BLOB(self):
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# define circle
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if self.config.gui_circular_crop:
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if self.cct == 0:
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try:
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ht, wd = self.current_image_gray.shape[:2]
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radius = int(float(self.lkg_projected_sphere["axes"][0]))
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# draw filled circle in white on black background as mask
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mask = np.zeros((ht, wd), dtype=np.uint8)
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mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
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# create white colored background
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color = np.full_like(self.current_image_gray, (255))
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# apply mask to image
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masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
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# apply inverse mask to colored image
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masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
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# combine the two masked images
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self.current_image_gray = cv2.add(masked_img, masked_color)
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except:
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pass
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else:
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self.cct = self.cct - 1
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_, larger_threshold = cv2.threshold(self.current_image_gray, int(self.settings.gui_threshold + 12), 255, cv2.THRESH_BINARY)
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try:
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# Try rebuilding our contours
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contours, _ = cv2.findContours(
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larger_threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
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)
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contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
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# If we have no contours, we have nothing to blob track. Fail here.
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if len(contours) == 0:
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raise RuntimeError("No contours found for image")
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except:
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self.failed = self.failed + 1
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self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False))
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return
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rows, cols = larger_threshold.shape
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for cnt in contours:
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(x, y, w, h) = cv2.boundingRect(cnt)
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# if our blob width/height are within suitable (yet arbitrary) boundaries, call that good.
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#
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# TODO This should be scaled based on camera resolution.
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if not self.settings.gui_blob_minsize <= h <= self.settings.gui_blob_maxsize or not self.settings.gui_blob_minsize <= w <= self.settings.gui_blob_maxsize:
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continue
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cx = x + int(w / 2)
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cy = y + int(h / 2)
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# cv2.line(
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# self.current_image_gray,
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# (x + int(w / 2), 0),
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# (x + int(w / 2), rows),
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# (255, 0, 0),
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# 1,
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# ) # visualizes eyetracking on thresh
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# cv2.line(
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# self.current_image_gray,
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# (0, y + int(h / 2)),
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# (cols, y + int(h / 2)),
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# (255, 0, 0),
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# 1,
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# )
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cv2.drawContours(self.current_image_gray, [cnt], -1, (255, 0, 0), 3)
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cv2.rectangle(
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self.current_image_gray, (x, y), (x + w, y + h), (255, 0, 0), 2
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)
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out_x, out_y = cal_osc(self, cx, cy) #filter and calibrate values
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self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, self.blinkvalue))
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self.failed = 0
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return
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self.failed = self.failed + 1
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self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, 0, 0, 0, self.blinkvalue))
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def HSF(self):
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frame = self.current_image_gray
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if self.now_mode == self.cv_mode[1]:
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prev_res_len = len(self.response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==self.response_list==[self.settings.gui_HSF_radius]
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self.cvparam.radius = self.auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==self.response_list==[self.settings.gui_HSF_radius, self.auto_radius_range[0]]
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self.cvparam.radius = self.auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==self.response_list==[self.settings.gui_HSF_radius,self.auto_radius_range[0],self.auto_radius_range[1]]
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sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
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# Extract the radius with the lowest response value
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if sort_res[0] == self.settings.gui_HSF_radius:
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# If the default value is best, change self.now_mode to init after setting radius to the default value.
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self.cvparam.radius = self.settings.gui_HSF_radius
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self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
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self.response_list = []
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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 HSRAC(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("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
|
|
|
|
|
|
#run ransac on the HSF crop\
|
|
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
|
|
# 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.
|
|
frame = cropped_image
|
|
# 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, first convert to 1-channel and then blur.
|
|
# The processing results were the same when I swapped the order of blurring and 1-channelization.
|
|
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
|
|
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]
|
|
|
|
threshold_value = min_val + 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
|
|
|
|
|
|
detect_start_time = timeit.default_timer()
|
|
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
|
hull = []
|
|
# 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
|
|
|
|
csx = frame.shape[0]
|
|
csy = frame.shape[1]
|
|
|
|
cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF
|
|
cy = center_y - (csy - cy)
|
|
out_x, out_y = cal_osc(self, cx, cy)
|
|
|
|
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
|
|
|
cv2.drawContours(frame, contours, -1, (255, 0, 0), 1)
|
|
cv2.circle(frame, (cx, cy), 2, (0, 0, 255), -1)
|
|
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
|
cv2.ellipse(frame, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
|
|
|
|
#img = newImage2[y1:y2, x1:x2]
|
|
except:
|
|
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
|
|
try:
|
|
if self.settings.gui_BLINK:
|
|
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
|
|
else:
|
|
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
|
|
f = False
|
|
except:
|
|
pass
|
|
|
|
|
|
except:
|
|
try:
|
|
if abs(self.settings.gui_HSFP - self.settings.gui_HSRACP) < 2: #at this point we have successfully tan HSF, if ransac fails and HSF is the next algo, just send HSF values and continue
|
|
if self.settings.gui_BLINK:
|
|
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))
|
|
f = False
|
|
else: #HSF must not be next algo, so fail and move to the next one.
|
|
self.failed = self.failed + 1
|
|
#if self.settings.gui_BLINK:
|
|
# 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))
|
|
except:
|
|
pass
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def RANSAC3D(self):
|
|
f = False
|
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
|
thresh_add = 10
|
|
rng = np.random.default_rng()
|
|
|
|
f = False
|
|
self.capture_crop_rotate_image()
|
|
|
|
# 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.
|
|
|
|
if self.config.gui_circular_crop == True:
|
|
if self.cct == 0:
|
|
try:
|
|
ht, wd = self.current_image_gray.shape[:2]
|
|
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]))
|
|
# draw filled circle in white on black background as mask
|
|
mask = np.zeros((ht, wd), dtype=np.uint8)
|
|
mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
|
|
# create white colored background
|
|
color = np.full_like(self.current_image_gray, (255))
|
|
# apply mask to image
|
|
masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
|
|
# apply inverse mask to colored image
|
|
masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
|
|
# combine the two masked images
|
|
self.current_image_gray = cv2.add(masked_img, masked_color)
|
|
except:
|
|
pass
|
|
else:
|
|
self.cct = self.cct - 1
|
|
else:
|
|
self.cct = 300
|
|
|
|
|
|
# Crop first to reduce the amount of data to process.
|
|
newFrame2 = self.current_image_gray.copy()
|
|
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, first convert to 1-channel and then blur.
|
|
# The processing results were the same when I swapped the order of blurring and 1-channelization.
|
|
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
|
|
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]
|
|
|
|
threshold_value = min_val + 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
|
|
|
|
|
|
detect_start_time = timeit.default_timer()
|
|
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
|
|
hull = []
|
|
# 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
|
|
|
|
crop_start_time = timeit.default_timer()
|
|
cx, cy, w, h, theta = ransac_data
|
|
out_x, out_y = cal_osc(self, cx, cy)
|
|
# print(cx, cy)
|
|
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
|
# once a pupil is found, crop 100x100 around it
|
|
x1 = cx - 50
|
|
x2 = cx + 50
|
|
y1 = cy - 50
|
|
y2 = cy + 50
|
|
cropped_image = newFrame2[y1:y2, x1:x2]
|
|
|
|
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:
|
|
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
|
|
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]
|
|
|
|
d = result_3d["diameter_3d"]
|
|
|
|
except:
|
|
f = True
|
|
# Draw our image and stack it for visual output
|
|
try:
|
|
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
|
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(
|
|
self.current_image_gray,
|
|
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
|
|
|
|
try:
|
|
if self.settings.gui_BLINK:
|
|
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
|
|
else:
|
|
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False))
|
|
self.failed = 0 # we have succeded, continue with this
|
|
except:
|
|
if self.settings.gui_BLINK:
|
|
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, self.blinkvalue))
|
|
else:
|
|
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, True))
|
|
self.failed = self.failed + 1 #we have failed, move onto next algo
|
|
pass
|
|
# Shove a concatenated image out to the main GUI thread for rendering
|
|
#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0 ,0, 0, False))
|
|
#self.output_images_and_update(thresh, output_info)
|
|
#except:
|
|
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
|
|
return f
|
|
|
|
|
|
|
|
def BLINK(self):
|
|
|
|
intensity = np.sum(self.current_image_gray)
|
|
self.frames = self.frames + 1
|
|
|
|
if intensity > self.max_int:
|
|
self.max_int = intensity
|
|
if self.frames > 200:
|
|
self.max_ints.append(self.max_int)
|
|
if intensity < self.min_int:
|
|
self.min_int = intensity
|
|
|
|
if len(self.max_ints) > 1:
|
|
if intensity > min(self.max_ints):
|
|
print("Blink")
|
|
self.blinkvalue = True
|
|
else:
|
|
self.blinkvalue = False
|
|
print(self.blinkvalue)
|
|
|
|
|
|
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):
|
|
|
|
print("running")
|
|
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
|
|
)
|
|
# print(self.settings.gui_RANSAC3D)
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
|
|
|
|