mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
1689 lines
65 KiB
Python
1689 lines
65 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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@@@ @ @@@@@@@@#
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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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import _thread
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import functools
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import math
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import os
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import timeit
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import time
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from collections import namedtuple
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from functools import lru_cache
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import xxhash
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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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@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 cal_osc(self, cx, cy):
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if self.eye_id == "EyeId.RIGHT":
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flipx = self.settings.gui_flip_x_axis_right
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else:
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flipx = self.settings.gui_flip_x_axis_left
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if self.calibration_frame_counter == 0:
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self.calibration_frame_counter = None
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self.xoff = cx
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self.yoff = cy
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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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elif self.calibration_frame_counter != None:
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self.settings.gui_recenter_eyes = False
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if cx > self.xmax:
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self.xmax = cx
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if cx < self.xmin:
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self.xmin = cx
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if cy > self.ymax:
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self.ymax = cy
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if cy < self.ymin:
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self.ymin = cy
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self.calibration_frame_counter -= 1
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if self.settings.gui_recenter_eyes == True:
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self.xoff = cx
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self.yoff = cy
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if self.ts == 0:
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self.settings.gui_recenter_eyes = False
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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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else:
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self.ts = self.ts - 1
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else:
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self.ts = 10
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xl = float(
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(cx - self.xoff) / (self.xmax - self.xoff)
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)
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xr = float(
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(cx - self.xoff) / (self.xmin - self.xoff)
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)
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yu = float(
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(cy - self.yoff) / (self.ymin - self.yoff)
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)
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yd = float(
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(cy - self.yoff) / (self.ymax - self.yoff)
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)
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out_x = 0
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out_y = 0
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if self.settings.gui_flip_y_axis: # check config on flipped values settings and apply accordingly
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if yd >= 0:
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out_y = max(0.0, min(1.0, yd))
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if yu > 0:
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out_y = -abs(max(0.0, min(1.0, yu)))
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else:
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if yd >= 0:
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out_y = -abs(max(0.0, min(1.0, yd)))
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if yu > 0:
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out_y = max(0.0, min(1.0, yu))
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if flipx: #TODO Check for working function
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if xr >= 0:
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out_x = -abs(max(0.0, min(1.0, xr)))
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if xl > 0:
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out_x = max(0.0, min(1.0, xl))
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else:
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if xr >= 0:
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out_x = max(0.0, min(1.0, xr))
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if xl > 0:
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out_x = -abs(max(0.0, min(1.0, xl)))
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try:
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noisy_point = np.array([float(out_x), float(out_y)]) # fliter our values with a One Euro Filter
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point_hat = self.one_euro_filter(noisy_point)
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out_x = point_hat[0]
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out_y = point_hat[1]
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except:
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pass
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return out_x, out_y
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#HSF \/
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# cache param
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lru_maxsize_vvs = 16
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lru_maxsize_vs = 64
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lru_maxsize_s = 512
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lru_maxsize_m = 1024
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lru_maxsize_l = 2048 # For functions with a large number of calls and a small amount of output data
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lru_maxsize_vl = 4096 # 8192 #For functions with a very large number of calls and a small amount of output data
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response_list = []
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@lru_cache(maxsize=lru_maxsize_vs)
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def _step2byte(iterable, itemsize):
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"""
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https://github.com/chainer/chainer/blob/a8e15cbe55a90854a3918b8b5a976abbbff9ec94/chainer/functions/array/as_strided.py#L125
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:param iterable:
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:param itemsize:
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:return:
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"""
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return tuple([i * itemsize for i in iterable])
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@lru_cache(maxsize=lru_maxsize_vs)
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def _min_index(shape, strides, storage_offset):
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"""
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https://github.com/chainer/chainer/blob/a8e15cbe55a90854a3918b8b5a976abbbff9ec94/chainer/functions/array/as_strided.py#L125
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Returns the leftest index in the array (in the unit-steps)
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Args:
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shape (tuple of int): The shape of output.
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strides (tuple of int):
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The strides of output, given in the unit of steps.
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storage_offset (int):
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The offset between the head of allocated memory and the pointer of
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first element, given in the unit of steps.
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Returns:
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int: The leftest pointer in the array
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"""
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sh_st_neg = [sh_st for sh_st in zip(shape, strides) if sh_st[1] < 0]
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if not sh_st_neg:
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return storage_offset
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else:
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return storage_offset + functools.reduce(
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lambda base, sh_st: base + (sh_st[0] - 1) * sh_st[1], sh_st_neg, 0)
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@lru_cache(maxsize=lru_maxsize_vs)
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def _max_index(shape, strides, storage_offset):
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"""
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https://github.com/chainer/chainer/blob/a8e15cbe55a90854a3918b8b5a976abbbff9ec94/chainer/functions/array/as_strided.py#L125
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Returns the rightest index in the array
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|
Args:
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shape (tuple of int): The shape of output.
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|
strides (tuple of int): The strides of output, given in unit-steps.
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storage_offset (int):
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The offset between the head of allocated memory and the pointer of
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first element, given in the unit of steps.
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Returns:
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int: The rightest pointer in the array
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"""
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sh_st_pos = [sh_st for sh_st in zip(shape, strides) if sh_st[1] > 0]
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if not sh_st_pos:
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return storage_offset
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else:
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return storage_offset + functools.reduce(
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lambda base, sh_st: base + (sh_st[0] - 1) * sh_st[1], sh_st_pos, 0)
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|
|
def _get_base_array(array):
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|
"""
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|
https://github.com/chainer/chainer/blob/a8e15cbe55a90854a3918b8b5a976abbbff9ec94/chainer/functions/array/as_strided.py#L125
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Get the founder of :class:`numpy.ndarray`.
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|
Args:
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array (:class:`numpy.ndarray`):
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The view of the base array.
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Returns:
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:class:`numpy.ndarray`:
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The base array.
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"""
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base_array_candidate = array
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while base_array_candidate.base is not None:
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base_array_candidate = base_array_candidate.base
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return base_array_candidate
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def _stride_array(array, shape, strides, storage_offset):
|
|
"""
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|
https://github.com/chainer/chainer/blob/a8e15cbe55a90854a3918b8b5a976abbbff9ec94/chainer/functions/array/as_strided.py#L125
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Wrapper of :func:`numpy.lib.stride_tricks.as_strided`.
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.. note:
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``strides`` and ``storage_offset`` is given in the unit of steps
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instead the unit of bytes. This specification differs from that of
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:func:`numpy.lib.stride_tricks.as_strided`.
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Args:
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array (:class:`numpy.ndarray` of :class:`cupy.ndarray`):
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The base array for the returned view.
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shape (tuple of int):
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|
The shape of the returned view.
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|
strides (tuple of int):
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The strides of the returned view, given in the unit of steps.
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storage_offset (int):
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|
The offset from the leftest pointer of allocated memory to
|
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the first element of returned view, given in the unit of steps.
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|
Returns:
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:class:`numpy.ndarray` or :class:`cupy.ndarray`:
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The new view for the base array.
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"""
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min_index = _min_index(shape, strides, storage_offset)
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max_index = _max_index(shape, strides, storage_offset)
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strides = _step2byte(strides, array.itemsize)
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storage_offset, = _step2byte((storage_offset,), array.itemsize)
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if min_index < 0:
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raise ValueError('Out of buffer: too small index was specified')
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base_array = _get_base_array(array)
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if (max_index + 1) * base_array.itemsize > base_array.nbytes:
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raise ValueError('Out of buffer: too large index was specified')
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return np.ndarray(shape, base_array.dtype, base_array.data,
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storage_offset, strides)
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# From functools
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_CacheInfo2 = namedtuple("CacheInfo", ["hits", "misses", "maxsize", "currsize"])
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class _HashedSeq2(list):
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""" This class guarantees that hash() will be called no more than once
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per element. This is important because the lru_cache() will hash
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the key multiple times on a cache miss.
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"""
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__slots__ = 'hashvalue'
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def __init__(self, tup, hash=hash):
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self[:] = tup
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self.hashvalue = hash(tup)
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def __hash__(self):
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return self.hashvalue
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|
|
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def _make_key2(args, kwds, typed,
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kwd_mark=(object(),),
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fasttypes={int, str},
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tuple=tuple, type=type, len=len):
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"""Make a cache key from optionally typed positional and keyword arguments
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The key is constructed in a way that is flat as possible rather than
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as a nested structure that would take more memory.
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If there is only a single argument and its data type is known to cache
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its hash value, then that argument is returned without a wrapper. This
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saves space and improves lookup speed.
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"""
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# All of code below relies on kwds preserving the order input by the user.
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# Formerly, we sorted() the kwds before looping. The new way is *much*
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# faster; however, it means that f(x=1, y=2) will now be treated as a
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# distinct call from f(y=2, x=1) which will be cached separately.
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key = args
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if kwds:
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key += kwd_mark
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for item in kwds.items():
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key += item
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key = tuple(xxhash.xxh3_128_intdigest(k) if isinstance(k, np.ndarray) else k for k in key)
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if typed:
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key += tuple(type(v) for v in args)
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if kwds:
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key += tuple(type(v) for v in kwds.values())
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elif len(key) == 1 and type(key[0]) in fasttypes:
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return key[0]
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return _HashedSeq2(key)
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|
|
|
|
|
|
def np_lru_cache(maxsize=128, typed=False):
|
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"""Least-recently-used cache decorator.
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If *maxsize* is set to None, the LRU features are disabled and the cache
|
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can grow without bound.
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If *typed* is True, arguments of different types will be cached separately.
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For example, f(3.0) and f(3) will be treated as distinct calls with
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distinct results.
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Arguments to the cached function must be hashable.
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View the cache statistics named tuple (hits, misses, maxsize, currsize)
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with f.cache_info(). Clear the cache and statistics with f.cache_clear().
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Access the underlying function with f.__wrapped__.
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See: https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_recently_used_(LRU)
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"""
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# Users should only access the lru_cache through its public API:
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|
# cache_info, cache_clear, and f.__wrapped__
|
|
# The internals of the lru_cache are encapsulated for thread safety and
|
|
# to allow the implementation to change (including a possible C version).
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|
|
|
if isinstance(maxsize, int):
|
|
# Negative maxsize is treated as 0
|
|
if maxsize < 0:
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|
maxsize = 0
|
|
elif callable(maxsize) and isinstance(typed, bool):
|
|
# The user_function was passed in directly via the maxsize argument
|
|
user_function, maxsize = maxsize, 128
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|
wrapper = _np_lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo2)
|
|
wrapper.cache_parameters = lambda: {'maxsize': maxsize, 'typed': typed}
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|
return functools.update_wrapper(wrapper, user_function)
|
|
elif maxsize is not None:
|
|
raise TypeError(
|
|
'Expected first argument to be an integer, a callable, or None')
|
|
|
|
def decorating_function(user_function):
|
|
wrapper = _np_lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo2)
|
|
wrapper.cache_parameters = lambda: {'maxsize': maxsize, 'typed': typed}
|
|
return functools.update_wrapper(wrapper, user_function)
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|
|
|
return decorating_function
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|
|
|
|
|
|
|
def _np_lru_cache_wrapper(user_function, maxsize, typed, _CacheInfo):
|
|
# Constants shared by all lru cache instances:
|
|
sentinel = object() # unique object used to signal cache misses
|
|
make_key = _make_key2 # build a key from the function arguments
|
|
PREV, NEXT, KEY, RESULT = 0, 1, 2, 3 # names for the link fields
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|
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|
cache = {}
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|
hits = misses = 0
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|
full = False
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|
cache_get = cache.get # bound method to lookup a key or return None
|
|
cache_len = cache.__len__ # get cache size without calling len()
|
|
lock = _thread.RLock() # because linkedlist updates aren't threadsafe
|
|
root = [] # root of the circular doubly linked list
|
|
root[:] = [root, root, None, None] # initialize by pointing to self
|
|
|
|
if maxsize == 0:
|
|
|
|
def wrapper(*args, **kwds):
|
|
# No caching -- just a statistics update
|
|
nonlocal misses
|
|
misses += 1
|
|
result = user_function(*args, **kwds)
|
|
return result
|
|
|
|
elif maxsize is None:
|
|
|
|
def wrapper(*args, **kwds):
|
|
# Simple caching without ordering or size limit
|
|
nonlocal hits, misses
|
|
key = make_key(args, kwds, typed)
|
|
result = cache_get(key, sentinel)
|
|
if result is not sentinel:
|
|
hits += 1
|
|
return result
|
|
misses += 1
|
|
result = user_function(*args, **kwds)
|
|
cache[key] = result
|
|
return result
|
|
|
|
else:
|
|
|
|
def wrapper(*args, **kwds):
|
|
# Size limited caching that tracks accesses by recency
|
|
nonlocal root, hits, misses, full
|
|
key = make_key(args, kwds, typed)
|
|
with lock:
|
|
link = cache_get(key)
|
|
if link is not None:
|
|
# Move the link to the front of the circular queue
|
|
link_prev, link_next, _key, result = link
|
|
link_prev[NEXT] = link_next
|
|
link_next[PREV] = link_prev
|
|
last = root[PREV]
|
|
last[NEXT] = root[PREV] = link
|
|
link[PREV] = last
|
|
link[NEXT] = root
|
|
hits += 1
|
|
return result
|
|
misses += 1
|
|
result = user_function(*args, **kwds)
|
|
with lock:
|
|
if key in cache:
|
|
# Getting here means that this same key was added to the
|
|
# cache while the lock was released. Since the link
|
|
# update is already done, we need only return the
|
|
# computed result and update the count of misses.
|
|
pass
|
|
elif full:
|
|
# Use the old root to store the new key and result.
|
|
oldroot = root
|
|
oldroot[KEY] = key
|
|
oldroot[RESULT] = result
|
|
# Empty the oldest link and make it the new root.
|
|
# Keep a reference to the old key and old result to
|
|
# prevent their ref counts from going to zero during the
|
|
# update. That will prevent potentially arbitrary object
|
|
# clean-up code (i.e. __del__) from running while we're
|
|
# still adjusting the links.
|
|
root = oldroot[NEXT]
|
|
oldkey = root[KEY]
|
|
oldresult = root[RESULT]
|
|
root[KEY] = root[RESULT] = None
|
|
# Now update the cache dictionary.
|
|
del cache[oldkey]
|
|
# Save the potentially reentrant cache[key] assignment
|
|
# for last, after the root and links have been put in
|
|
# a consistent state.
|
|
cache[key] = oldroot
|
|
else:
|
|
# Put result in a new link at the front of the queue.
|
|
last = root[PREV]
|
|
link = [last, root, key, result]
|
|
last[NEXT] = root[PREV] = cache[key] = link
|
|
# Use the cache_len bound method instead of the len() function
|
|
# which could potentially be wrapped in an lru_cache itself.
|
|
full = (cache_len() >= maxsize)
|
|
return result
|
|
|
|
def cache_info():
|
|
"""Report cache statistics"""
|
|
with lock:
|
|
return _CacheInfo(hits, misses, maxsize, cache_len())
|
|
|
|
def cache_clear():
|
|
"""Clear the cache and cache statistics"""
|
|
nonlocal hits, misses, full
|
|
with lock:
|
|
cache.clear()
|
|
root[:] = [root, root, None, None]
|
|
hits = misses = 0
|
|
full = False
|
|
|
|
wrapper.cache_info = cache_info
|
|
wrapper.cache_clear = cache_clear
|
|
return wrapper
|
|
|
|
|
|
class CvParameters:
|
|
# It may be a little slower because a dict named "self" is read for each function call.
|
|
def __init__(self, radius, step):
|
|
# self.prev_radius=radius
|
|
self._radius = radius
|
|
self.pad = 2 * radius
|
|
# self.prev_step=step
|
|
self._step = step
|
|
self._hsf = HaarSurroundFeature(radius)
|
|
# self._imagesize = None
|
|
|
|
# @lru_cache(maxsize=lru_maxsize_vs)
|
|
def get_rpsh(self):
|
|
return self.radius, self.pad, self.step, self.hsf
|
|
|
|
@property
|
|
def radius(self):
|
|
return self._radius
|
|
|
|
@radius.setter
|
|
def radius(self, now_radius):
|
|
# self.prev_radius=self._radius
|
|
self._radius = now_radius
|
|
self.pad = 2 * now_radius
|
|
self.hsf = now_radius
|
|
|
|
@property
|
|
def step(self):
|
|
return self._step
|
|
|
|
@step.setter
|
|
def step(self, now_step):
|
|
# self.prev_step=self.step
|
|
self._step = now_step
|
|
|
|
@property
|
|
def hsf(self):
|
|
return self._hsf
|
|
|
|
@hsf.setter
|
|
def hsf(self, now_radius):
|
|
self._hsf = HaarSurroundFeature(now_radius)
|
|
|
|
|
|
class HaarSurroundFeature:
|
|
|
|
def __init__(self, r_inner, r_outer=None, val=None):
|
|
if r_outer is None:
|
|
r_outer = r_inner * 3
|
|
|
|
r_inner2 = r_inner * r_inner
|
|
count_inner = r_inner2
|
|
count_outer = r_outer * r_outer - r_inner2
|
|
|
|
if val is None:
|
|
val_inner = 1.0 / r_inner2
|
|
val_outer = -val_inner * count_inner / count_outer
|
|
|
|
else:
|
|
val_inner = val[0]
|
|
val_outer = val[1]
|
|
|
|
self.val_in = np.array(val_inner, dtype=np.float64)
|
|
self.val_out = np.array(val_outer, dtype=np.float64)
|
|
self.r_in = r_inner
|
|
self.r_out = r_outer
|
|
|
|
def get_kernel(self):
|
|
# Defined here, but not yet used?
|
|
# Create a kernel filled with the value of self.val_out
|
|
kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
|
|
|
|
# Set the values of the inner area of the kernel using array slicing
|
|
start = (self.r_out - self.r_in)
|
|
end = (self.r_out + self.r_in - 1)
|
|
kernel[start:end, start:end] = self.val_in
|
|
|
|
return kernel
|
|
|
|
def to_gray(frame):
|
|
frame_len = len(frame.shape)
|
|
if frame_len == 2:
|
|
return frame
|
|
if frame_len == 3:
|
|
frame_s2 = frame.shape[2]
|
|
if frame_s2 == 3:
|
|
return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
elif frame_s2 == 4:
|
|
return cv2.cvtColor(frame, cv2.COLOR_BGRA2GRAY)
|
|
raise ValueError('Unsupported number of channels')
|
|
|
|
|
|
@lru_cache(maxsize=lru_maxsize_vs)
|
|
def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None):
|
|
"""
|
|
|
|
:param imageshape: (height(row),width(col),channel) or (height(row),width(col)). row==y,cal==x
|
|
:param xysteps: (x,y)
|
|
:param pad: int
|
|
:param start_offset: (x,y) or None
|
|
:param end_offset: (x,y) or None
|
|
:return: xy_np:tuple(x,y), xy_min:tuple(x,y), xy_rin_pm:tuple(x+rin,y+rin,x-rin,y-rin), xy_rout_pm:tuple(x+rout,y+rout,x-rout,y-rout)
|
|
"""
|
|
if len(imageshape) == 2:
|
|
row, col = imageshape
|
|
else:
|
|
row, col = imageshape[0], imageshape[1]
|
|
row -= 1
|
|
col -= 1
|
|
x_step, y_step = xysteps
|
|
|
|
# This is not beautiful.
|
|
start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad
|
|
|
|
if start_offset is not None:
|
|
start_pad_x += start_offset[0]
|
|
start_pad_y += start_offset[1]
|
|
if end_offset is not None:
|
|
end_pad_x += end_offset[0]
|
|
end_pad_y += end_offset[1]
|
|
y_np = np.arange(start_pad_y, row - end_pad_y, y_step)
|
|
x_np = np.arange(start_pad_x, col - end_pad_x, x_step)
|
|
|
|
xy_np = (x_np, y_np)
|
|
|
|
return xy_np
|
|
|
|
@lru_cache(maxsize=lru_maxsize_vvs)
|
|
def get_emp_p_array(len_sxy, frameint_x, frame_int_dtype, fcshape):
|
|
len_sx, len_sy = len_sxy
|
|
inner_sum = np.empty((len_sy, len_sx), dtype=frame_int_dtype)
|
|
outer_sum = np.empty((len_sy, len_sx), dtype=frame_int_dtype)
|
|
p_temp = np.empty((len_sy, frameint_x), dtype=frame_int_dtype)
|
|
p00 = np.empty((len_sy, len_sx), dtype=frame_int_dtype)
|
|
p11 = np.empty((len_sy, len_sx), dtype=frame_int_dtype)
|
|
p01 = np.empty((len_sy, len_sx), dtype=frame_int_dtype)
|
|
p10 = np.empty((len_sy, len_sx), dtype=frame_int_dtype)
|
|
response_list = np.empty((len_sy, len_sx), dtype=np.float64)
|
|
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
|
|
frame_conv_stride = _stride_array(frame_conv, shape=(len_sy, len_sx), strides=(fcshape[1], fcshape[2]),
|
|
storage_offset=0)
|
|
return (inner_sum, outer_sum), p_temp, (
|
|
p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
|
|
|
|
|
|
# @profile
|
|
def conv_int(frame_int, kernel, step, padding, xy_step):
|
|
"""
|
|
|
|
:param frame_int:
|
|
:param kernel: hsf
|
|
:param step: (x,y)
|
|
:param padding: int
|
|
:return:
|
|
"""
|
|
# Init
|
|
row_b, col_b = frame_int.shape
|
|
row, col = row_b, col_b
|
|
row -= 1
|
|
col -= 1
|
|
x_step, y_step = step
|
|
padding2 = 2 * padding
|
|
f_shape = row - padding2, col - padding2
|
|
r_in = kernel.r_in
|
|
r_in3 = r_in * 3
|
|
|
|
len_sx, len_sy = len(xy_step[0]), len(xy_step[1])
|
|
col_rin = col_b * kernel.r_in
|
|
col_padrin = col_b * (padding + r_in)
|
|
col_ystep = col_b * y_step
|
|
|
|
inout_sum, p_temp, p_list, response_list, frameconvlist = get_emp_p_array((len_sx, len_sy), col_b,
|
|
frame_int.dtype, (f_shape, f_shape[1] * y_step, x_step))
|
|
inner_sum, outer_sum = inout_sum
|
|
p00, p11, p01, p10 = p_list
|
|
frame_conv, frame_conv_stride = frameconvlist
|
|
|
|
inarr_mm = _stride_array(frame_int, shape=(len_sy, len_sx), strides=(col_ystep, x_step), storage_offset=col_rin + r_in)
|
|
inarr_mp = _stride_array(frame_int, shape=(len_sy, len_sx), strides=(col_ystep, x_step), storage_offset=col_rin + r_in3)
|
|
inarr_pm = _stride_array(frame_int, shape=(len_sy, len_sx), strides=(col_ystep, x_step), storage_offset=(col_padrin + r_in))
|
|
inarr_pp = _stride_array(frame_int, shape=(len_sy, len_sx), strides=(col_ystep, x_step), storage_offset=(col_padrin + r_in3))
|
|
|
|
# inner_sum[:, :] = inarr_mm + inarr_pp - inarr_mp - inarr_pm
|
|
inner_sum[:, :] = inarr_mm
|
|
inner_sum += inarr_pp
|
|
inner_sum -= inarr_mp
|
|
inner_sum -= inarr_pm
|
|
|
|
y_ro_m = xy_step[1] - kernel.r_out
|
|
x_ro_m = xy_step[0] - kernel.r_out
|
|
y_ro_p = xy_step[1] + kernel.r_out
|
|
x_ro_p = xy_step[0] + kernel.r_out
|
|
|
|
# y,x
|
|
# p00=max(y_ro_m,0),max(x_ro_m,0)
|
|
# p11=min(y_ro_p,ylim),min(x_ro_p,xlim)
|
|
# p01=max(y_ro_m,0),min(x_ro_p,xlim)
|
|
# p10=min(y_ro_p,ylim),max(x_ro_m,0)
|
|
|
|
# Bottleneck here, I want to make it smarter. Someone do it.
|
|
# p00 calc
|
|
np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp)
|
|
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00)
|
|
|
|
# p01 calc
|
|
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01)
|
|
|
|
# p11 calc
|
|
np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp)
|
|
np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11)
|
|
|
|
# p10 calk
|
|
np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10)
|
|
|
|
# p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
|
# p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
|
# p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip")
|
|
# p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip")
|
|
|
|
outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum
|
|
|
|
np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
|
|
response_list += kernel.val_out * outer_sum
|
|
|
|
# min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list)
|
|
min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
|
|
|
|
center = ((xy_step[0][min_loc[0]] - padding), (xy_step[1][min_loc[1]] - padding))
|
|
|
|
frame_conv_stride[:, :] = response_list
|
|
# or
|
|
# frame_conv_stride[:, :] = response_list.astype(np.uint8)
|
|
|
|
return frame_conv, min_response, center
|
|
|
|
|
|
def ellipse_model(data, y, f):
|
|
"""
|
|
There is no need to make this process a function, since making the process a function will slow it down a little by calling it.
|
|
The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same.
|
|
a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4]
|
|
:param data:
|
|
:param y: np.c_[d, e, a, c, b]
|
|
:param f: f == P[4, 0]
|
|
:return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ])
|
|
"""
|
|
return data.dot(y) + f
|
|
|
|
|
|
def fit_rotated_ellipse_ransac(data: np.ndarray, 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
|
|
count_max = 0
|
|
effective_sample = None
|
|
rng = np.random.default_rng()
|
|
|
|
# The array contents do not change during the loop, so only one call is needed.
|
|
# They say len is faster than shape.
|
|
# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
|
|
len_data = len(data)
|
|
|
|
if len_data < sample_num:
|
|
return None
|
|
|
|
# Type of calculation result
|
|
# Although the accuracy may be lower, I feel that float32 is better considering the memory used.
|
|
# Whether float32 or float64 is faster depends on the execution environment.
|
|
ret_dtype = np.float64
|
|
|
|
# Declare this number only once, since it is immutable.
|
|
a = np.array(1.0, dtype=ret_dtype)
|
|
|
|
# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
|
|
# If the array size is less than about 100, this is faster than rng.choice.
|
|
rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
|
|
# or
|
|
# I don't see any advantage to doing this.
|
|
# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
|
|
|
|
# I don't think it looks beautiful.
|
|
# x,y,x**2,y**2,x*y,1,-1*x**2
|
|
datamod = np.concatenate(
|
|
[data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
|
|
(-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
|
|
dtype=ret_dtype)
|
|
|
|
datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
|
|
|
|
datamod_rng = datamod[rng_sample]
|
|
|
|
P5x5 = np.empty((5, 5), dtype=ret_dtype)
|
|
P5xSmp = np.empty((5, sample_num), dtype=ret_dtype)
|
|
P = np.empty(5, dtype=ret_dtype)
|
|
|
|
for data_smp in datamod_rng:
|
|
|
|
# np.random.choice is slow
|
|
# data_smp = datamod[sample]
|
|
# xs, ys, xs2, ys2, xy, smp_ones = data_smp[:, 0], data_smp[:, 1], data_smp[:, 2], data_smp[:, 3], data_smp[:, 4], data_smp[:, 5]
|
|
J = data_smp[:, [4, 3, 0, 1, 5]]
|
|
|
|
# Y = -1 * xs2
|
|
Y = data_smp[:, 6]
|
|
|
|
J_T = J.T
|
|
# I don't know which is faster, this or np.dot.
|
|
J_T.dot(J, out=P5x5)
|
|
np.linalg.inv(P5x5).dot(J_T, out=P5xSmp)
|
|
P5xSmp.dot(Y, out=P)
|
|
|
|
# fitter a*x**2 + b*x*y + c*y**2 + d*x + e*y + f = 0
|
|
# b,c,d,e,f = P[0],P[1],P[2],P[3],P[4] # It looks like they are making copies of these and I want to remove it.
|
|
|
|
ellipse_y = np.asarray([P[2], P[3], a, P[1], P[0]], dtype=ret_dtype)
|
|
ellipse_data = np.abs(ellipse_model(datamod_slim, ellipse_y, P[4]))
|
|
|
|
# threshold
|
|
ran_sample = datamod[ellipse_data < offset]
|
|
|
|
# Reduce one function call by using a variable.
|
|
len_ran = len(ran_sample)
|
|
|
|
if len_ran > count_max:
|
|
count_max = len_ran
|
|
effective_sample = ran_sample
|
|
|
|
return fit_rotated_ellipse(effective_sample)
|
|
|
|
|
|
def fit_rotated_ellipse(data):
|
|
J = data[:, [4, 3, 0, 1, 5]]
|
|
|
|
# Y = -1 * xs2
|
|
Y = data[:, 6]
|
|
J_T = J.T
|
|
|
|
P = np.linalg.inv(J_T.dot(J)).dot(J_T).dot(Y)
|
|
|
|
a = 1.0
|
|
b = P[0]
|
|
c = P[1]
|
|
d = P[2]
|
|
e = P[3]
|
|
f = P[4]
|
|
|
|
theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
|
|
# The cost of trigonometric functions is high.
|
|
theta_sin = np.sin(theta, dtype=np.float64)
|
|
theta_cos = np.cos(theta, dtype=np.float64)
|
|
tc2 = theta_cos ** 2
|
|
ts2 = theta_sin ** 2
|
|
b_tcs = b * theta_cos * theta_sin
|
|
|
|
# Do the calculation only once
|
|
cxy = b ** 2 - 4 * a * c
|
|
cx = (2 * c * d - b * e) / cxy
|
|
cy = (2 * a * e - b * d) / cxy
|
|
|
|
# I just want to clear things up around here.
|
|
cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
|
|
cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
|
|
wh = np.sqrt(cu / cu_r)
|
|
w, h = wh[0], wh[1]
|
|
|
|
ellipse_y = np.asarray([d, e, a, c, b], dtype=np.float64)
|
|
|
|
error_sum = np.sum(ellipse_model(data[:, :5], ellipse_y, f))
|
|
# print("fitting error = %.3f" % (error_sum))
|
|
|
|
return (cx, cy, w, h, theta)
|
|
|
|
|
|
|
|
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.response_list = []
|
|
#HSF
|
|
|
|
|
|
self.cv_mode = ["first_frame", "radius_adjust", "init", "normal"]
|
|
self.now_mode = self.cv_mode[0]
|
|
self.default_radius = 15
|
|
self.default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
|
|
# default_step==(x,y)
|
|
self.radius_cand_list = []
|
|
self.prev_max_size = 60 * 3 # 60fps*3sec
|
|
# response_min=0
|
|
self.response_max = 0
|
|
|
|
|
|
#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 BLOB(self):
|
|
|
|
# define circle
|
|
if self.config.gui_circular_crop:
|
|
if self.cct == 0:
|
|
try:
|
|
ht, wd = self.current_image_gray.shape[:2]
|
|
|
|
radius = int(float(self.lkg_projected_sphere["axes"][0]))
|
|
|
|
# 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
|
|
_, larger_threshold = cv2.threshold(self.current_image_gray, int(self.config.threshold + 12), 255, cv2.THRESH_BINARY)
|
|
|
|
|
|
#try:
|
|
# Try rebuilding our contours
|
|
contours, _ = cv2.findContours(
|
|
larger_threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
|
|
)
|
|
contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
|
|
|
|
# If we have no contours, we have nothing to blob track. Fail here.
|
|
if len(contours) == 0:
|
|
raise RuntimeError("No contours found for image")
|
|
# except:
|
|
# self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False))
|
|
# return
|
|
|
|
rows, cols = larger_threshold.shape
|
|
|
|
for cnt in contours:
|
|
(x, y, w, h) = cv2.boundingRect(cnt)
|
|
|
|
# if our blob width/height are within suitable (yet arbitrary) boundaries, call that good.
|
|
#
|
|
# TODO This should be scaled based on camera resolution.
|
|
|
|
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:
|
|
continue
|
|
|
|
cx = x + int(w / 2)
|
|
|
|
cy = y + int(h / 2)
|
|
|
|
cv2.line(
|
|
self.current_image_gray,
|
|
(x + int(w / 2), 0),
|
|
(x + int(w / 2), rows),
|
|
(255, 0, 0),
|
|
1,
|
|
) # visualizes eyetracking on thresh
|
|
cv2.line(
|
|
self.current_image_gray,
|
|
(0, y + int(h / 2)),
|
|
(cols, y + int(h / 2)),
|
|
(255, 0, 0),
|
|
1,
|
|
)
|
|
cv2.drawContours(self.current_image_gray, [cnt], -1, (255, 0, 0), 3)
|
|
cv2.rectangle(
|
|
self.current_image_gray, (x, y), (x + w, y + h), (255, 0, 0), 2
|
|
)
|
|
|
|
out_x, out_y = cal_osc(self, cx, cy) #filter and calibrate values
|
|
|
|
|
|
|
|
|
|
|
|
self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, self.blinkvalue))
|
|
f = False
|
|
return f
|
|
|
|
# self.output_images_and_update(
|
|
# larger_threshold, EyeInformation(InformationOrigin.BLOB, 0, 0, 0, True)
|
|
# )
|
|
# print("[INFO] BLINK Detected.")
|
|
f = True
|
|
return f
|
|
|
|
|
|
def HSF(self):
|
|
|
|
if self.now_mode == self.cv_mode[1]:
|
|
prev_res_len = len(self.response_list)
|
|
# adjustment of radius
|
|
if prev_res_len == 1:
|
|
self.cvparam.radius = self.radius_range[0]
|
|
elif prev_res_len == 2:
|
|
self.cvparam.radius = self.radius_range[1]
|
|
elif prev_res_len == 3:
|
|
# response_list==[default_radius,self.radius_range[0],self.radius_range[1]]
|
|
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
|
if sort_res[0] == self.default_radius:
|
|
self.cvparam.radius = self.default_radius
|
|
self.now_mode = self.cv_mode[2]
|
|
response_list = []
|
|
elif sort_res[0] == self.radius_range[0]:
|
|
self.radius_cand_list = [i for i in range(self.radius_range[0], self.default_radius, self.default_step[0])][1:]
|
|
self.cvparam.radius = self.radius_cand_list.pop()
|
|
else:
|
|
self.radius_cand_list = [i for i in range(self.default_radius, self.radius_range[1], self.default_step[0])][1:]
|
|
self.cvparam.radius = self.radius_cand_list.pop()
|
|
else:
|
|
# 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]
|
|
self.response_list = []
|
|
else:
|
|
self.cvparam.radius = self.radius_cand_list.pop()
|
|
|
|
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
|
|
|
gray_frame = to_gray(self.current_image_gray) #pretty sure we do no need this step, should already be receiving gray frame
|
|
frame = self.current_image_gray
|
|
# Calculate the integral image of the frame
|
|
|
|
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT) #cv2.BORDER_REPLICATE
|
|
frame_int = cv2.integral(frame_pad)
|
|
|
|
# Convolve the feature with the integral image
|
|
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)
|
|
|
|
# Define the center point and radius
|
|
# center_y, center_x = center
|
|
center_x, center_y = center_xy
|
|
upper_x = center_x + 20
|
|
lower_x = center_x - 20
|
|
upper_y = center_y + 20
|
|
lower_y = center_y - 20
|
|
|
|
# Crop the image using the calculated bounds
|
|
# cropped_image = gray_frame[lower_x:upper_x, lower_y:upper_y]
|
|
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
|
|
|
|
if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
|
|
self.response_list.append((radius, response)) # , center_x, center_y))
|
|
elif self.now_mode == self.cv_mode[2]:
|
|
if len(self.response_list) < self.prev_max_size:
|
|
self.response_list.append(cropped_image.mean())
|
|
else:
|
|
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 cropped_image.size < 400:
|
|
print("Something's wrong.")
|
|
else:
|
|
if cropped_image.mean() > self.response_max: # or cropped_image.mean() < response_min:
|
|
# blink
|
|
print("BLINK")
|
|
cv2.circle(frame, (center_x, center_y), 20, (0, 0, 255), -1)
|
|
self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True))
|
|
f = False
|
|
|
|
#self.output_images_and_update(frame,EyeInformation(InformationOrigin.HSF, 0, 0, 0, self.blinkvalue))
|
|
# If you want to update self.response_max. it may be more cost-effective to rewrite response_list in the following way
|
|
# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
|
|
|
|
|
|
hsfandransac = True
|
|
if not hsfandransac:
|
|
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))
|
|
f = False
|
|
except:
|
|
pass
|
|
|
|
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 f
|
|
#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))
|
|
|
|
else: #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
|
|
|
|
newImage2 = cropped_image.copy()
|
|
# Crop first to reduce the amount of data to process.
|
|
|
|
# img = self.current_image_gray[0:len(self.current_image_gray) - 10, :]
|
|
|
|
# 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.
|
|
# image_gray = self.current_image_gray
|
|
image_gray = cv2.GaussianBlur(cropped_image, (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(image_gray)
|
|
|
|
maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
|
|
|
|
# crop 15% sqare around min_loc
|
|
# image_gray = image_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
|
|
th_ret, thresh = cv2.threshold(image_gray, threshold_value, 255, cv2.THRESH_BINARY)
|
|
try:
|
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
|
image = 255 - closing
|
|
except:
|
|
# I want to eliminate try here because try tends to be slow in execution.
|
|
image = 255 - image_gray
|
|
contours, hierarchy = cv2.findContours(image, 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:
|
|
self.current_image_gray = cropped_image
|
|
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
|
cnt = sorted(hull, key=cv2.contourArea)
|
|
maxcnt = cnt[-1]
|
|
ellipse = cv2.fitEllipse(maxcnt)
|
|
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
|
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
|
|
|
|
|
|
ocx = center_x - cx
|
|
ocy = center_y - cy
|
|
print(ocx, ocy)
|
|
out_x, out_y = cal_osc(self, ocx, ocy)
|
|
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
|
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, )
|
|
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
|
|
except:
|
|
pass
|
|
#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, 0, 0, 0, False))
|
|
except:
|
|
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))
|
|
f = 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
|
|
|
|
|
|
|
|
|
|
newImage2 = self.current_image_gray.copy()
|
|
# Crop first to reduce the amount of data to process.
|
|
|
|
img = self.current_image_gray[0:len(self.current_image_gray) - 10, :]
|
|
|
|
# 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.
|
|
# image_gray = self.current_image_gray
|
|
image_gray = cv2.GaussianBlur(self.current_image_gray, (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(image_gray)
|
|
|
|
maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
|
|
|
|
# crop 15% sqare around min_loc
|
|
# image_gray = image_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
|
|
th_ret, thresh = cv2.threshold(image_gray, threshold_value, 255, cv2.THRESH_BINARY)
|
|
try:
|
|
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
|
|
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
|
|
image = 255 - closing
|
|
except:
|
|
# I want to eliminate try here because try tends to be slow in execution.
|
|
image = 255 - image_gray
|
|
contours, hierarchy = cv2.findContours(image, 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:
|
|
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
|
cnt = sorted(hull, key=cv2.contourArea)
|
|
maxcnt = cnt[-1]
|
|
ellipse = cv2.fitEllipse(maxcnt)
|
|
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
|
|
if ransac_data is None:
|
|
# ransac_data is None==maxcnt.shape[0]<sample_num
|
|
# go to next loop
|
|
pass
|
|
cx, cy, w, h, theta = ransac_data
|
|
print(cx, cy)
|
|
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
|
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, )
|
|
|
|
# once a pupil is found, crop 100x100 around it
|
|
x1 = cx - 50
|
|
x2 = cx + 50
|
|
y1 = cy - 50
|
|
y2 = cy + 50
|
|
out_x, out_y = cal_osc(self, cx, cy)
|
|
|
|
#img = newImage2[y1:y2, x1:x2]
|
|
except:
|
|
pass
|
|
|
|
|
|
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))
|
|
f = False
|
|
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))
|
|
f = True
|
|
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 run(self):
|
|
|
|
self.radius_range = (self.default_radius - 10, self.default_radius + 10) # (10,30)
|
|
self.cvparam = CvParameters(self.default_radius, self.default_step)
|
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
|
thresh_add = 10
|
|
rng = np.random.default_rng()
|
|
|
|
f = True
|
|
while True:
|
|
# f = True
|
|
# Check to make sure we haven't been requested to close
|
|
if self.cancellation_event.is_set():
|
|
print("Exiting Tracking thread")
|
|
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)
|
|
|
|
"""try:
|
|
if self.settings.gui_RANSAC3D == True: #for now ransac goes first
|
|
f == self.RANSAC3D()
|
|
|
|
if f and self.settings.gui_HSF == True: #if a fail has been reported and other algo is enabled, use it.
|
|
f == self.HSF()
|
|
if f and self.settings.gui_BLOB == True:
|
|
f == self.BLOB()
|
|
|
|
except:
|
|
pass
|
|
|
|
""" #print("[WARN] ALL ALGORITHIMS HAVE FAILED OR ARE DISABLED.")
|
|
#self.RANSAC3D()
|
|
#self.BLINK()
|
|
self.HSF()
|
|
# f == self.RANSAC3D()'''
|
|
|
|
#FLOW MOCK
|
|
|
|
#if PYE3D
|
|
#RUN PYE
|
|
#receive values, if fail reported, go to next method
|
|
|
|
#IF HSF
|
|
#RUN HSF
|
|
#receive values, if fail reported, go to next method
|
|
|
|
#IF BLOB
|
|
#RUN BLOB (ew tbh)
|
|
#receive values, if fail reported, end here in complete fail.
|
|
|
|
|
|
|
|
|
|
|
|
|