mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
Revert "Cleaning+SafeCrop"
This commit is contained in:
parent
f6d3ea0659
commit
a6105a3f51
@ -1,4 +1,4 @@
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"""
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'''
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------------------------------------------------------------------------------------------------------
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,@@@@@@
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@ -19,8 +19,8 @@
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@@@@@@@@@@@@@@@@@
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@@@@@@@@@@@@@(
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HSR By: PallasNeko (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer)
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RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization)
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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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@ -28,7 +28,7 @@ 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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'''
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from operator import truth
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from dataclasses import dataclass
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@ -46,20 +46,19 @@ 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 importlib
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from osc_calibrate_filter import *
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from haar_surround_feature import External_Run_HSF
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from haar_surround_feature import *
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from blob import *
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from ransac import *
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from hsrac import External_Run_HSRACS
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from hsrac import *
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from blink import *
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from intensity_eye_open import *
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from intensity_eye_open import *
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class InformationOrigin(Enum):
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RANSAC = 1
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@ -68,10 +67,7 @@ class InformationOrigin(Enum):
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HSF = 4
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HSRAC = 5
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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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@ -89,7 +85,7 @@ def run_once(f):
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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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@ -98,35 +94,36 @@ 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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PlaySound('Audio/compleated.wav', SND_FILENAME | SND_ASYNC)
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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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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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@ -134,14 +131,14 @@ class EyeProcessor:
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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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@ -150,41 +147,44 @@ class EyeProcessor:
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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.skip_blink_detect = False
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# blink
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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.frames = 0
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self.blinkvalue = False
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self.prev_x = None
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self.prev_y = None
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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("\033[93m[WARN] OneEuroFilter values must be a legal number.\033[0m")
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print('\033[93m[WARN] OneEuroFilter values must be a legal number.\033[0m')
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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, min_cutoff=min_cutoff, beta=beta
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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(
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self, threshold_image, output_information: EyeInformation
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):
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def output_images_and_update(self, threshold_image, output_information: EyeInformation):
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try:
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image_stack = np.concatenate(
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(
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@ -196,32 +196,29 @@ class EyeProcessor:
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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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except: # If this fails it likely means that the images are not the same size for some reason.
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print(
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"\033[91m[ERROR] Size of frames to display are of unequal sizes.\033[0m"
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)
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except: # If this fails it likely means that the images are not the same size for some reason.
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print('\033[91m[ERROR] Size of frames to display are of unequal sizes.\033[0m')
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pass
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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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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("\033[91m[ERROR] Frame capture issue detected.\033[0m")
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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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@ -243,119 +240,96 @@ class EyeProcessor:
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return True
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except:
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pass
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def BLINKM(self):
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self.blinkvalue = BLINK(self)
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def HSRACM(self):
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# temporary implementation
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cx, cy, thresh, gray_frame, uncropframe = External_Run_HSRACS().run(
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self.current_image_gray
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)
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cx, cy, thresh, gray_frame, uncropframe = External_Run_HSRACS.HSRACS(self)
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self.current_image_gray = gray_frame
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if self.prev_x is None:
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if self.prev_x == None:
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self.prev_x = cx
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self.prev_y = cy
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# print(self.prev_x, self.prev_y, cx, cy)
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#print(self.prev_x, self.prev_y, cx, cy)
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# #filter values with too much movement
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# if (cx - self.prev_x) <= 45 and (cy - self.prev_y) <= 45 :
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# self.prev_x = cx
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# self.prev_y = cy
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# if (cx - self.prev_x) <= 45 and (cy - self.prev_y) <= 45 :
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# self.prev_x = cx
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# self.prev_y = cy
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eyeopen = intense(cx, cy, uncropframe)
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out_x, out_y = cal_osc(self, cx, cy)
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if cx == 0:
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self.output_images_and_update(
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thresh,
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EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
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) # update app
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen)) #update app
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else:
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self.output_images_and_update(
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thresh,
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EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
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)
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# else:
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# print("EYE MOVED TOO FAST")
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# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, 0, 0, 0, False))
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen))
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# else:
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# print("EYE MOVED TOO FAST")
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# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, 0, 0, 0, False))
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def HSFM(self):
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# temporary implementation
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cx, cy, frame = External_Run_HSF().run(self.current_image_gray)
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cx, cy, frame = External_Run_HSF.HSFS(self)
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eyeopen = intense(cx, cy, self.current_image_gray)
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out_x, out_y = cal_osc(self, cx, cy)
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if cx == 0:
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self.output_images_and_update(
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frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, eyeopen)
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) # update app
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, eyeopen)) #update app
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else:
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self.output_images_and_update(
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frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, eyeopen)
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)
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, eyeopen))
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def RANSAC3DM(self):
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cx, cy, thresh = RANSAC3D(self)
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eyeopen = intense(cx, cy, self.current_image_gray)
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out_x, out_y = cal_osc(self, cx, cy)
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if cx == 0:
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self.output_images_and_update(
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thresh,
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EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, eyeopen),
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) # update app
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, eyeopen)) #update app
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else:
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self.output_images_and_update(
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thresh,
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EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, eyeopen),
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)
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, eyeopen))
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def BLOBM(self):
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cx, cy, thresh = BLOB(self)
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eyeopen = intense(cx, cy, self.current_image_gray)
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out_x, out_y = cal_osc(self, cx, cy)
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if cx == 0:
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self.output_images_and_update(
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thresh,
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EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
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) # update app
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen)) #update app
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else:
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self.output_images_and_update(
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thresh,
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EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen),
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)
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def ALGOSELECT(self):
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, eyeopen))
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if self.failed == 0 and self.firstalgo is not None:
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def ALGOSELECT(self):
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if self.failed == 0 and self.firstalgo != None:
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self.firstalgo()
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else:
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self.failed = self.failed + 1
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if (
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self.failed == 1 and self.secondalgo is not None
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): # send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
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if self.failed == 1 and self.secondalgo != None: #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
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self.secondalgo()
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else:
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self.failed = self.failed + 1
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if self.failed == 2 and self.thirdalgo is not None:
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if self.failed == 2 and self.thirdalgo != None:
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self.thirdalgo()
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else:
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self.failed = self.failed + 1
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if self.failed == 3 and self.fourthalgo is not None:
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if self.failed == 3 and self.fourthalgo != None:
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self.fourthalgo()
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else:
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self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
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self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
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def run(self):
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self.firstalgo = None
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self.secondalgo = None
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self.thirdalgo = None
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self.fourthalgo = None
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# set algo priorities
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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
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#set algo priorities
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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
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self.firstalgo = self.HSFM
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 2:
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self.secondalgo = self.HSFM
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@ -363,7 +337,7 @@ class EyeProcessor:
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self.thirdalgo = self.HSFM
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 4:
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self.fourthalgo = self.HSFM
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if self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 1:
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self.firstalgo = self.RANSAC3DM
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 2:
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@ -372,8 +346,8 @@ class EyeProcessor:
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self.thirdalgo = self.RANSAC3DM
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4:
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self.fourthalgo = self.RANSAC3DM
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if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1:
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if self.settings.gui_HSRAC == True and self.settings.gui_HSRACP == 1:
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self.firstalgo = self.HSRACM
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2:
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self.secondalgo = self.HSRACM
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@ -381,7 +355,7 @@ class EyeProcessor:
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self.thirdalgo = self.HSRACM
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 4:
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self.fourthalgo = self.HSRACM
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if self.settings.gui_BLOB and self.settings.gui_BLOBP == 1:
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self.firstalgo = self.BLOBM
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 2:
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@ -390,31 +364,31 @@ class EyeProcessor:
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self.thirdalgo = self.BLOBM
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4:
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self.fourthalgo = self.BLOBM
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f = True
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while True:
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# f = True
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# Check to make sure we haven't been requested to close
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# f = True
|
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# Check to make sure we haven't been requested to close
|
||||
if self.cancellation_event.is_set():
|
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print("\033[94m[INFO] Exiting Tracking thread\033[0m")
|
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return
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||||
|
||||
|
||||
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.
|
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# Sleep a bit while we wait.
|
||||
if self.cancellation_event.wait(0.1):
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||||
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
|
||||
!= (
|
||||
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,
|
||||
@ -423,7 +397,7 @@ class EyeProcessor:
|
||||
self.detector_3d = Detector3D(
|
||||
camera=self.camera_model, long_term_mode=DetectorMode.blocking
|
||||
)
|
||||
|
||||
|
||||
try:
|
||||
if self.capture_queue_incoming.empty():
|
||||
self.capture_event.set()
|
||||
@ -439,35 +413,41 @@ class EyeProcessor:
|
||||
|
||||
if not self.capture_crop_rotate_image():
|
||||
continue
|
||||
|
||||
|
||||
self.current_image_gray = cv2.cvtColor(
|
||||
self.current_image, cv2.COLOR_BGR2GRAY
|
||||
self.current_image, cv2.COLOR_BGR2GRAY
|
||||
)
|
||||
self.current_image_gray_clean = (
|
||||
self.current_image_gray.copy()
|
||||
) # copy this frame to have a clean image for blink algo
|
||||
# print(self.settings.gui_RANSAC3D)
|
||||
self.current_image_gray_clean = self.current_image_gray.copy() #copy this frame to have a clean image for blink algo
|
||||
# print(self.settings.gui_RANSAC3D)
|
||||
|
||||
# BLINK(self)
|
||||
|
||||
# cx, cy, thresh = HSRAC(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# if cx == 0:
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app
|
||||
# else:
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
|
||||
|
||||
# BLINK(self)
|
||||
|
||||
# cx, cy, thresh = RANSAC3D(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False)) #update app
|
||||
|
||||
|
||||
# cx, cy, larger_threshold = BLOB(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, False)) #update app
|
||||
|
||||
#center_x, center_y, frame = HSF(self) #run algo
|
||||
#out_x, out_y = cal_osc(self, center_x, center_y) #filter and calibrate
|
||||
#self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False)) #update app
|
||||
|
||||
# cx, cy, thresh = HSRAC(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# if cx == 0:
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app
|
||||
# else:
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
|
||||
|
||||
# cx, cy, thresh = RANSAC3D(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False)) #update app
|
||||
|
||||
# cx, cy, larger_threshold = BLOB(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, False)) #update app
|
||||
|
||||
# center_x, center_y, frame = HSF(self) #run algo
|
||||
# out_x, out_y = cal_osc(self, center_x, center_y) #filter and calibrate
|
||||
# self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False)) #update app
|
||||
|
||||
self.ALGOSELECT() # run our algos in priority order set in settings
|
||||
self.ALGOSELECT() #run our algos in priority order set in settings
|
||||
self.BLINKM()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -7,8 +7,6 @@ from functools import lru_cache
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
from EyeTrackApp.img_utils import safe_crop
|
||||
|
||||
# from line_profiler_pycharm import profile
|
||||
|
||||
video_path = "ezgif.com-gif-maker.avi"
|
||||
@ -29,6 +27,180 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
|
||||
# step==(x,y)
|
||||
default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
|
||||
|
||||
"""
|
||||
Attention.
|
||||
If using cv2.filter2D in this code, be careful with the kernel
|
||||
https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python
|
||||
"""
|
||||
|
||||
|
||||
def TimeitWrapper(*args, **kwargs):
|
||||
"""
|
||||
This decorator @TimeitWrapper() prints the function name and execution time in seconds.
|
||||
:param args:
|
||||
:param kwargs:
|
||||
:return:
|
||||
"""
|
||||
|
||||
def decorator(function):
|
||||
@functools.wraps(function)
|
||||
def wrapper(*args, **kwargs):
|
||||
start = timeit.default_timer()
|
||||
results = function(*args, **kwargs)
|
||||
end = timeit.default_timer()
|
||||
print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
|
||||
return results
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
class TimeitResult(object):
|
||||
"""
|
||||
from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
|
||||
Object returned by the timeit magic with info about the run.
|
||||
Contains the following attributes :
|
||||
loops: (int) number of loops done per measurement
|
||||
repeat: (int) number of times the measurement has been repeated
|
||||
best: (float) best execution time / number
|
||||
all_runs: (list of float) execution time of each run (in s)
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = best
|
||||
self.worst = worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.timings = [dt / self.loops for dt in all_runs]
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.timings) / len(self.timings)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean=format_time(self.average, self._precision),
|
||||
std=format_time(self.stdev, self._precision),
|
||||
best=format_time(self.best, self._precision),
|
||||
worst=format_time(self.worst, self._precision),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<TimeitResult : ' + unic + u'>')
|
||||
|
||||
|
||||
class FPSResult(object):
|
||||
"""
|
||||
base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = 1 / best
|
||||
self.worst = 1 / worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.fps = [1 / dt for dt in all_runs]
|
||||
self.unit = "fps"
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.fps) / len(self.fps)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean="%.*g%s" % (self._precision, self.average, self.unit),
|
||||
std="%.*g%s" % (self._precision, self.stdev, self.unit),
|
||||
best="%.*g%s" % (self._precision, self.best, self.unit),
|
||||
worst="%.*g%s" % (self._precision, self.worst, self.unit),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<FPSResult : ' + unic + u'>')
|
||||
|
||||
|
||||
def format_time(timespan, precision=3):
|
||||
"""
|
||||
https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
|
||||
Formats the timespan in a human readable form
|
||||
"""
|
||||
|
||||
if timespan >= 60.0:
|
||||
# we have more than a minute, format that in a human readable form
|
||||
# Idea from http://snipplr.com/view/5713/
|
||||
parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
|
||||
time = []
|
||||
leftover = timespan
|
||||
for suffix, length in parts:
|
||||
value = int(leftover / length)
|
||||
if value > 0:
|
||||
leftover = leftover % length
|
||||
time.append(u'%s%s' % (str(value), suffix))
|
||||
if leftover < 1:
|
||||
break
|
||||
return " ".join(time)
|
||||
|
||||
# Unfortunately the unicode 'micro' symbol can cause problems in
|
||||
# certain terminals.
|
||||
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
|
||||
# Try to prevent crashes by being more secure than it needs to
|
||||
# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
|
||||
units = [u"s", u"ms", u'us', "ns"] # the save value
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb5'.encode(sys.stdout.encoding)
|
||||
units = [u"s", u"ms", u'\xb5s', "ns"]
|
||||
except:
|
||||
pass
|
||||
scaling = [1, 1e3, 1e6, 1e9]
|
||||
|
||||
if timespan > 0.0:
|
||||
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
|
||||
else:
|
||||
order = 3
|
||||
return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
|
||||
|
||||
|
||||
class CvParameters:
|
||||
# It may be a little slower because a dict named "self" is read for each function call.
|
||||
@ -39,42 +211,43 @@ class CvParameters:
|
||||
# self.prev_step=step
|
||||
self._step = step
|
||||
self._hsf = HaarSurroundFeature(radius)
|
||||
|
||||
|
||||
def get_rpsh(self):
|
||||
return self._radius, self.pad, self._step, self._hsf
|
||||
# Essentially, the following would be preferable, but it would take twice as long to call.
|
||||
# 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
|
||||
@ -82,33 +255,30 @@ class HaarSurroundFeature:
|
||||
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
|
||||
)
|
||||
|
||||
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
|
||||
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
|
||||
|
||||
|
||||
@ -132,10 +302,10 @@ def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset
|
||||
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]
|
||||
@ -144,9 +314,9 @@ def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset
|
||||
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
|
||||
|
||||
|
||||
@ -162,14 +332,8 @@ def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape):
|
||||
p10 = np.empty(len_syx, dtype=frame_int_dtype)
|
||||
response_list = np.empty(len_syx, dtype=np.float64)
|
||||
frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8)
|
||||
frame_conv_stride = frame_conv[:: fcshape[1], :: fcshape[2]]
|
||||
return (
|
||||
(inner_sum, outer_sum),
|
||||
p_temp,
|
||||
(p00, p11, p01, p10),
|
||||
response_list,
|
||||
(frame_conv, frame_conv_stride),
|
||||
)
|
||||
frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]]
|
||||
return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride)
|
||||
|
||||
|
||||
# @profile
|
||||
@ -188,39 +352,30 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
# padding2 = 2 * padding
|
||||
f_shape = row - 2 * padding, col - 2 * padding
|
||||
r_in = kernel.r_in
|
||||
|
||||
|
||||
len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1])
|
||||
inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array(
|
||||
(len_sy, len_sx), col + 1, frame_int.dtype, (f_shape, y_step, x_step)
|
||||
)
|
||||
inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1,
|
||||
frame_int.dtype, (f_shape, y_step, x_step))
|
||||
inner_sum, outer_sum = inout_sum
|
||||
p00, p11, p01, p10 = p_list
|
||||
frame_conv, frame_conv_stride = frameconvlist
|
||||
|
||||
|
||||
y_rin_m = xy_steps_list[1] - r_in
|
||||
x_rin_m = xy_steps_list[0] - r_in
|
||||
y_rin_p = xy_steps_list[1] + r_in
|
||||
x_rin_p = xy_steps_list[0] + r_in
|
||||
# xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-)
|
||||
inarr_mm = frame_int[
|
||||
y_rin_m[0] : y_rin_m[-1] + 1 : y_step, x_rin_m[0] : x_rin_m[-1] + 1 : x_step
|
||||
]
|
||||
inarr_mp = frame_int[
|
||||
y_rin_m[0] : y_rin_m[-1] + 1 : y_step, x_rin_p[0] : x_rin_p[-1] + 1 : x_step
|
||||
]
|
||||
inarr_pm = frame_int[
|
||||
y_rin_p[0] : y_rin_p[-1] + 1 : y_step, x_rin_m[0] : x_rin_m[-1] + 1 : x_step
|
||||
]
|
||||
inarr_pp = frame_int[
|
||||
y_rin_p[0] : y_rin_p[-1] + 1 : y_step, x_rin_p[0] : x_rin_p[-1] + 1 : x_step
|
||||
]
|
||||
|
||||
inarr_mm = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
|
||||
inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
|
||||
inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step]
|
||||
inarr_pp = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step]
|
||||
|
||||
# == inarr_mm + inarr_pp - inarr_mp - inarr_pm
|
||||
inner_sum[:, :] = inarr_mm
|
||||
inner_sum += inarr_pp
|
||||
inner_sum -= inarr_mp
|
||||
inner_sum -= inarr_pm
|
||||
|
||||
|
||||
# Bottleneck here, I want to make it smarter. Someone do it.
|
||||
# (y,x)
|
||||
# p00=max(y_ro_m,0),max(x_ro_m,0)
|
||||
@ -246,40 +401,37 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
|
||||
# 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_steps_list[0][min_loc[0]] - padding),
|
||||
(xy_steps_list[1][min_loc[1]] - padding),
|
||||
)
|
||||
|
||||
|
||||
center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[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
|
||||
|
||||
|
||||
class AutoRadiusCalc(object):
|
||||
class Auto_Radius_Calc(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.radius_cand_list = []
|
||||
self.adj_comp_flag = False
|
||||
|
||||
|
||||
self.radius_middle_index = None
|
||||
|
||||
|
||||
self.left_item = None
|
||||
self.right_item = None
|
||||
self.left_index = None
|
||||
self.right_index = None
|
||||
|
||||
|
||||
def get_radius(self):
|
||||
prev_res_len = len(self.response_list)
|
||||
# adjustment of radius
|
||||
@ -299,35 +451,21 @@ class AutoRadiusCalc(object):
|
||||
else:
|
||||
self.left_item = self.response_list[0]
|
||||
self.right_item = self.response_list[2]
|
||||
self.radius_cand_list = [
|
||||
i
|
||||
for i in range(
|
||||
self.left_item[0],
|
||||
self.right_item[0] + auto_radius_step,
|
||||
auto_radius_step,
|
||||
)
|
||||
]
|
||||
self.radius_cand_list = [i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)]
|
||||
self.left_index = 0
|
||||
self.right_index = len(self.radius_cand_list) - 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
else:
|
||||
if (
|
||||
self.left_index <= self.right_index
|
||||
and self.left_index != self.radius_middle_index
|
||||
):
|
||||
if (self.left_item[1] + self.response_list[-1][1]) < (
|
||||
self.right_item[1] + self.response_list[-1][1]
|
||||
):
|
||||
if self.left_index <= self.right_index and self.left_index != self.radius_middle_index:
|
||||
if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]):
|
||||
self.right_item = self.response_list[-1]
|
||||
self.right_index = self.radius_middle_index - 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
if (self.left_item[1] + self.response_list[-1][1]) > (
|
||||
self.right_item[1] + self.response_list[-1][1]
|
||||
):
|
||||
if (self.left_item[1] + self.response_list[-1][1]) > (self.right_item[1] + self.response_list[-1][1]):
|
||||
self.left_item = self.response_list[-1]
|
||||
self.left_index = self.radius_middle_index + 1
|
||||
self.radius_middle_index = (self.left_index + self.right_index) // 2
|
||||
@ -335,13 +473,13 @@ class AutoRadiusCalc(object):
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
self.adj_comp_flag = True
|
||||
return self.radius_cand_list[self.radius_middle_index]
|
||||
|
||||
|
||||
def get_radius_base(self):
|
||||
"""
|
||||
Use it when the new version doesn't work well.
|
||||
:return:
|
||||
"""
|
||||
|
||||
|
||||
prev_res_len = len(self.response_list)
|
||||
# adjustment of radius
|
||||
if prev_res_len == 1:
|
||||
@ -361,21 +499,11 @@ class AutoRadiusCalc(object):
|
||||
self.adj_comp_flag = True
|
||||
return default_radius
|
||||
elif sort_res[0] == auto_radius_range[0]:
|
||||
self.radius_cand_list = [
|
||||
i
|
||||
for i in range(
|
||||
auto_radius_range[0], default_radius, auto_radius_step
|
||||
)
|
||||
][1:]
|
||||
self.radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, auto_radius_step)][1:]
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
else:
|
||||
self.radius_cand_list = [
|
||||
i
|
||||
for i in range(
|
||||
default_radius, auto_radius_range[1], auto_radius_step
|
||||
)
|
||||
][1:]
|
||||
self.radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], auto_radius_step)][1:]
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
else:
|
||||
@ -388,19 +516,19 @@ class AutoRadiusCalc(object):
|
||||
else:
|
||||
self.adj_comp_flag = False
|
||||
return self.radius_cand_list.pop()
|
||||
|
||||
|
||||
def add_response(self, radius, response):
|
||||
self.response_list.append((radius, response))
|
||||
return None
|
||||
|
||||
|
||||
class BlinkDetector(object):
|
||||
class Blink_Detector(object):
|
||||
def __init__(self):
|
||||
self.response_list = []
|
||||
self.response_max = None
|
||||
self.enable_detect_flg = False
|
||||
self.quartile_1 = None
|
||||
|
||||
|
||||
def calc_thresh(self):
|
||||
# Calculate response_max by computing interquartile range, IQR
|
||||
# self.response_listo = np.array(self.response_listo)
|
||||
@ -409,28 +537,28 @@ class BlinkDetector(object):
|
||||
# quartile_1, quartile_3 = np.percentile(self.response_listo, [25, 75])
|
||||
# iqr = quartile_3 - quartile_1
|
||||
# self.response_maxo = quartile_3 + (iqr * 1.5)
|
||||
|
||||
|
||||
# quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
||||
# or
|
||||
quartile_1, quartile_3 = np.percentile(np.array(self.response_list), [25, 75])
|
||||
self.quartile_1 = quartile_1
|
||||
iqr = quartile_3 - quartile_1
|
||||
# response_min = quartile_1 - (iqr * 1.5)
|
||||
|
||||
|
||||
self.response_max = float(quartile_3 + (iqr * 1.5))
|
||||
# or
|
||||
# self.response_max = quartile_3 + (iqr * 1.5)
|
||||
|
||||
|
||||
self.enable_detect_flg = True
|
||||
return None
|
||||
|
||||
|
||||
def detect(self, now_response):
|
||||
return now_response > self.response_max
|
||||
|
||||
|
||||
def add_response(self, response):
|
||||
self.response_list.append(response)
|
||||
return None
|
||||
|
||||
|
||||
def response_len(self):
|
||||
return len(self.response_list)
|
||||
|
||||
@ -441,7 +569,7 @@ class CenterCorrection(object):
|
||||
kernel_size = 7 # 3 or 5 or 7
|
||||
self.hist_thr = float(4) # 4%
|
||||
self.center_q1_radius = 20
|
||||
|
||||
|
||||
self.setup_comp = False
|
||||
self.quartile_1 = None
|
||||
self.radius = None
|
||||
@ -449,14 +577,12 @@ class CenterCorrection(object):
|
||||
self.frame_mask = None
|
||||
self.frame_bin = None
|
||||
self.frame_final = None
|
||||
self.morph_kernel = cv2.getStructuringElement(
|
||||
cv2.MORPH_RECT, (kernel_size, kernel_size)
|
||||
)
|
||||
self.morph_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
|
||||
self.morph_kernel2 = np.ones((3, 3))
|
||||
self.hist_index = np.arange(256)
|
||||
self.hist = np.empty((256, 1))
|
||||
self.hist_norm = np.empty((256, 1))
|
||||
|
||||
|
||||
def init_array(self, gray_shape, quartile_1, radius):
|
||||
self.frame_shape = gray_shape
|
||||
self.frame_mask = np.empty(gray_shape, dtype=np.uint8)
|
||||
@ -465,44 +591,34 @@ class CenterCorrection(object):
|
||||
self.quartile_1 = quartile_1
|
||||
self.radius = radius
|
||||
self.setup_comp = True
|
||||
|
||||
|
||||
# def reset_array(self):
|
||||
# self.frame_mask.fill(0)
|
||||
|
||||
|
||||
def correction(self, gray_frame, orig_x, orig_y):
|
||||
center_x, center_y = orig_x, orig_y
|
||||
self.frame_mask.fill(0)
|
||||
|
||||
# cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1)
|
||||
|
||||
|
||||
# cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1)
|
||||
|
||||
# bottleneck
|
||||
cv2.calcHist([gray_frame], [0], None, [256], [0, 256], hist=self.hist)
|
||||
|
||||
|
||||
cv2.normalize(self.hist, self.hist_norm, alpha=100.0, norm_type=cv2.NORM_L1)
|
||||
hist_per = self.hist_norm.cumsum()
|
||||
hist_index_list = self.hist_index[hist_per >= self.hist_thr]
|
||||
frame_thr = (
|
||||
hist_index_list[0]
|
||||
if len(hist_index_list)
|
||||
else np.percentile(cv2.bitwise_or(255 - self.frame_mask, gray_frame), 4)
|
||||
)
|
||||
|
||||
frame_thr = hist_index_list[0] if len(hist_index_list) else np.percentile(cv2.bitwise_or(255 - self.frame_mask, gray_frame), 4)
|
||||
|
||||
# bottleneck
|
||||
self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[
|
||||
1
|
||||
]
|
||||
self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[1]
|
||||
cropped_x, cropped_y, cropped_w, cropped_h = cv2.boundingRect(self.frame_bin)
|
||||
|
||||
|
||||
self.frame_final = cv2.bitwise_and(self.frame_bin, self.frame_mask)
|
||||
|
||||
|
||||
# bottleneck
|
||||
self.frame_final = cv2.morphologyEx(
|
||||
self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel
|
||||
)
|
||||
self.frame_final = cv2.morphologyEx(
|
||||
self.frame_final, cv2.MORPH_OPEN, self.morph_kernel
|
||||
)
|
||||
|
||||
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel)
|
||||
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_OPEN, self.morph_kernel)
|
||||
|
||||
if (cropped_h, cropped_w) == self.frame_shape:
|
||||
# Not detected.
|
||||
base_x, base_y = center_x, center_y
|
||||
@ -511,54 +627,36 @@ class CenterCorrection(object):
|
||||
base_y = cropped_y + cropped_h // 2
|
||||
if self.frame_final[base_y, base_x] != 1:
|
||||
if self.frame_final[center_y, center_x] != 1:
|
||||
self.frame_final = cv2.morphologyEx(
|
||||
self.frame_final,
|
||||
cv2.MORPH_DILATE,
|
||||
self.morph_kernel2,
|
||||
iterations=3,
|
||||
)
|
||||
self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_DILATE, self.morph_kernel2, iterations=3)
|
||||
else:
|
||||
base_x, base_y = center_x, center_y
|
||||
|
||||
contours, _ = cv2.findContours(
|
||||
self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
|
||||
)
|
||||
|
||||
contours, _ = cv2.findContours(self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
||||
contours_box = [cv2.boundingRect(cnt) for cnt in contours]
|
||||
contours_dist = np.array(
|
||||
[
|
||||
abs(base_x - (cnt_x + cnt_w / 2)) + abs(base_y - (cnt_y + cnt_h / 2))
|
||||
for cnt_x, cnt_y, cnt_w, cnt_h in contours_box
|
||||
]
|
||||
)
|
||||
|
||||
[abs(base_x - (cnt_x + cnt_w / 2)) + abs(base_y - (cnt_y + cnt_h / 2)) for cnt_x, cnt_y, cnt_w, cnt_h in contours_box])
|
||||
|
||||
if len(contours_box):
|
||||
cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[
|
||||
contours_dist.argmin()
|
||||
]
|
||||
cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[contours_dist.argmin()]
|
||||
x = cropped_x2 + cropped_w2 // 2
|
||||
y = cropped_y2 + cropped_h2 // 2
|
||||
else:
|
||||
x = center_x
|
||||
y = center_y
|
||||
|
||||
|
||||
# if imshow_enable:
|
||||
# cv2.circle(frame, (orig_x, orig_y), 10, (255, 0, 0), -1)
|
||||
# cv2.circle(frame, (x, y), 7, (0, 0, 255), -1)
|
||||
|
||||
|
||||
#
|
||||
# out_x = center_x if abs(x - center_x) > radius else x
|
||||
# out_y = center_y if abs(y - center_y) > radius else y
|
||||
out_x, out_y = orig_x, orig_y
|
||||
if (
|
||||
gray_frame[
|
||||
int(max(y - 5, 0)) : int(min(y + 5, self.frame_shape[0])),
|
||||
int(max(x - 5, 0)) : int(min(x + 5, self.frame_shape[1])),
|
||||
].min()
|
||||
< self.quartile_1
|
||||
):
|
||||
if gray_frame[int(max(y - 5, 0)):int(min(y + 5, self.frame_shape[0])),
|
||||
int(max(x - 5, 0)):int(min(x + 5, self.frame_shape[1]))].min() < self.quartile_1:
|
||||
out_x = x
|
||||
out_y = y
|
||||
|
||||
|
||||
# if imshow_enable:
|
||||
# cv2.circle(frame, (out_x, out_y), 5, (0, 255, 0), -1)
|
||||
#
|
||||
@ -567,36 +665,29 @@ class CenterCorrection(object):
|
||||
return out_x, out_y
|
||||
|
||||
|
||||
# temporary name
|
||||
class HSF_cls(object):
|
||||
class HSRAC_cls(object):
|
||||
def __init__(self):
|
||||
# I'd like to take into account things like print, end_time - start_time processing time, etc., but it's too much trouble.
|
||||
|
||||
|
||||
# For measuring total processing time
|
||||
|
||||
|
||||
self.main_start_time = timeit.default_timer()
|
||||
|
||||
|
||||
self.rng = np.random.default_rng()
|
||||
self.cvparam = CvParameters(default_radius, default_step)
|
||||
|
||||
|
||||
self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"]
|
||||
self.now_modeo = self.cv_modeo[0]
|
||||
|
||||
self.auto_radius_calc = AutoRadiusCalc()
|
||||
self.blink_detector = BlinkDetector()
|
||||
self.center_q1 = BlinkDetector()
|
||||
|
||||
self.auto_radius_calc = Auto_Radius_Calc()
|
||||
self.blink_detector = Blink_Detector()
|
||||
self.center_q1 = Blink_Detector()
|
||||
self.center_correct = CenterCorrection()
|
||||
|
||||
|
||||
self.cap = None
|
||||
|
||||
self.timedict = {
|
||||
"to_gray": [],
|
||||
"int_img": [],
|
||||
"conv_int": [],
|
||||
"crop": [],
|
||||
"total_cv": [],
|
||||
}
|
||||
|
||||
|
||||
self.timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []}
|
||||
|
||||
def open_video(self, video_path):
|
||||
# Temporary implementation to run
|
||||
cap = cv2.VideoCapture(video_path)
|
||||
@ -604,7 +695,7 @@ class HSF_cls(object):
|
||||
raise IOError("Error opening video stream or file")
|
||||
self.cap = cap
|
||||
return True
|
||||
|
||||
|
||||
def read_frame(self):
|
||||
# Temporary implementation to run
|
||||
if not self.cap.isOpened():
|
||||
@ -615,53 +706,47 @@ class HSF_cls(object):
|
||||
self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def single_run(self):
|
||||
# Temporary implementation to run
|
||||
|
||||
|
||||
## default_radius = 14
|
||||
|
||||
|
||||
frame = self.current_image_gray
|
||||
if self.now_modeo == self.cv_modeo[1]:
|
||||
# adjustment of radius
|
||||
|
||||
|
||||
# debug print
|
||||
# if calc_print_enable:
|
||||
# temp_radius = self.auto_radius_calc.get_radius()
|
||||
# print('Now radius:', temp_radius)
|
||||
# self.cvparam.radius = temp_radius
|
||||
|
||||
|
||||
self.cvparam.radius = self.auto_radius_calc.get_radius()
|
||||
if self.auto_radius_calc.adj_comp_flag:
|
||||
self.now_modeo = (
|
||||
self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
||||
)
|
||||
|
||||
self.now_modeo = self.cv_modeo[2] if not skip_blink_detect else self.cv_modeo[3]
|
||||
|
||||
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
||||
|
||||
|
||||
# For measuring processing time of image processing
|
||||
cv_start_time = timeit.default_timer()
|
||||
|
||||
|
||||
gray_frame = frame
|
||||
self.timedict["to_gray"].append(timeit.default_timer() - cv_start_time)
|
||||
|
||||
|
||||
# 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_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
|
||||
frame_int = cv2.integral(frame_pad)
|
||||
self.timedict["int_img"].append(timeit.default_timer() - int_start_time)
|
||||
|
||||
|
||||
# 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
|
||||
)
|
||||
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)
|
||||
self.timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time)
|
||||
|
||||
|
||||
crop_start_time = timeit.default_timer()
|
||||
# Define the center point and radius
|
||||
center_x, center_y = center_xy
|
||||
@ -669,10 +754,10 @@ class HSF_cls(object):
|
||||
lower_x = center_x - radius
|
||||
upper_y = center_y + radius
|
||||
lower_y = center_y - radius
|
||||
|
||||
|
||||
# Crop the image using the calculated bounds
|
||||
cropped_image = safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y)
|
||||
|
||||
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
|
||||
|
||||
if self.now_modeo == self.cv_modeo[0] or self.now_modeo == self.cv_modeo[1]:
|
||||
# If mode is first_frame or radius_adjust, record current radius and response
|
||||
self.auto_radius_calc.add_response(radius, response)
|
||||
@ -680,19 +765,15 @@ class HSF_cls(object):
|
||||
# Statistics for blink detection
|
||||
if self.blink_detector.response_len() < blink_init_frames:
|
||||
self.blink_detector.add_response(cv2.mean(cropped_image)[0])
|
||||
|
||||
|
||||
upper_x = center_x + self.center_correct.center_q1_radius
|
||||
lower_x = center_x - self.center_correct.center_q1_radius
|
||||
upper_y = center_y + self.center_correct.center_q1_radius
|
||||
lower_y = center_y - self.center_correct.center_q1_radius
|
||||
self.center_q1.add_response(
|
||||
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y))[
|
||||
0
|
||||
]
|
||||
)
|
||||
|
||||
self.center_q1.add_response(cv2.mean(gray_frame[lower_y:upper_y, lower_x:upper_x])[0])
|
||||
|
||||
else:
|
||||
|
||||
|
||||
self.blink_detector.calc_thresh()
|
||||
self.center_q1.calc_thresh()
|
||||
self.now_modeo = self.cv_modeo[3]
|
||||
@ -709,15 +790,11 @@ class HSF_cls(object):
|
||||
# blink
|
||||
pass
|
||||
else:
|
||||
# pass
|
||||
# pass
|
||||
if not self.center_correct.setup_comp:
|
||||
self.center_correct.init_array(
|
||||
gray_frame.shape, self.center_q1.quartile_1, radius
|
||||
)
|
||||
|
||||
center_x, center_y = self.center_correct.correction(
|
||||
gray_frame, center_x, center_y
|
||||
)
|
||||
self.center_correct.init_array(gray_frame.shape, self.center_q1.quartile_1, radius)
|
||||
|
||||
center_x, center_y = self.center_correct.correction(gray_frame, center_x, center_y)
|
||||
# Define the center point and radius
|
||||
center_xy = (center_x, center_y)
|
||||
upper_x = center_x + radius
|
||||
@ -725,29 +802,24 @@ class HSF_cls(object):
|
||||
upper_y = center_y + radius
|
||||
lower_y = center_y - radius
|
||||
# Crop the image using the calculated bounds
|
||||
cropped_image = safe_crop(
|
||||
gray_frame, lower_x, lower_y, upper_x, upper_y
|
||||
)
|
||||
# if imshow_enable or save_video:
|
||||
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
||||
# cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1)
|
||||
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x]
|
||||
# if imshow_enable or save_video:
|
||||
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
||||
# cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1)
|
||||
# If you want to update 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
|
||||
|
||||
|
||||
cv_end_time = timeit.default_timer()
|
||||
self.timedict["crop"].append(cv_end_time - crop_start_time)
|
||||
self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
||||
|
||||
# if calc_print_enable:
|
||||
# the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly
|
||||
|
||||
# if calc_print_enable:
|
||||
# the lower the response the better the likelyhood of there being a pupil. you can adujst the radius and steps accordingly
|
||||
# print('Kernel response:', response)
|
||||
# print('Pixel position:', center_xy)
|
||||
|
||||
# print('Pixel position:', center_xy)
|
||||
|
||||
if imshow_enable:
|
||||
if (
|
||||
self.now_modeo != self.cv_modeo[0]
|
||||
and self.now_modeo != self.cv_modeo[1]
|
||||
):
|
||||
if self.now_modeo != self.cv_modeo[0] and self.now_modeo != self.cv_modeo[1]:
|
||||
if 0 in cropped_image.shape:
|
||||
# If shape contains 0, it is not detected well.
|
||||
pass
|
||||
@ -756,7 +828,7 @@ class HSF_cls(object):
|
||||
cv2.imshow("frame", frame)
|
||||
if cv2.waitKey(1) & 0xFF == ord("q"):
|
||||
pass
|
||||
|
||||
|
||||
if self.now_modeo == self.cv_modeo[0]:
|
||||
# Moving from first_frame to the next mode
|
||||
if skip_autoradius and skip_blink_detect:
|
||||
@ -765,22 +837,20 @@ class HSF_cls(object):
|
||||
self.now_modeo = self.cv_modeo[2]
|
||||
else:
|
||||
self.now_modeo = self.cv_modeo[1]
|
||||
|
||||
|
||||
return center_x, center_y, frame
|
||||
|
||||
class External_Run_HSF:
|
||||
|
||||
class External_Run_HSF(object):
|
||||
def __init__(self):
|
||||
self.algo = HSF_cls()
|
||||
hsrac = HSRAC_cls()
|
||||
|
||||
def run(self, current_image_gray):
|
||||
self.algo.current_image_gray = current_image_gray
|
||||
center_x, center_y, frame = self.algo.single_run()
|
||||
def HSFS(self):
|
||||
External_Run_HSF.hsrac.current_image_gray = self.current_image_gray
|
||||
center_x, center_y, frame = External_Run_HSF.hsrac.single_run()
|
||||
return center_x, center_y, frame
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
hsf = HSF_cls()
|
||||
hsf.open_video(video_path)
|
||||
while hsf.read_frame():
|
||||
_ = hsf.single_run()
|
||||
if __name__ == '__main__':
|
||||
hsrac = HSRAC_cls()
|
||||
hsrac.open_video(video_path)
|
||||
while hsrac.read_frame():
|
||||
_ = hsrac.single_run()
|
||||
1110
EyeTrackApp/hsrac.py
1110
EyeTrackApp/hsrac.py
File diff suppressed because it is too large
Load Diff
@ -1,12 +0,0 @@
|
||||
import cv2
|
||||
|
||||
|
||||
def safe_crop(img, x, y, x2, y2):
|
||||
# The order of the arguments can be reconsidered.
|
||||
img_h, img_w = img.shape[1::-1]
|
||||
outimg = img[max(0, y) : min(img_h, y2), max(0, x) : min(img_w, x2)].copy()
|
||||
reqsize_x, reqsize_y = abs(x2 - x), abs(y2 - y)
|
||||
if outimg.shape[1::-1] != (reqsize_y, reqsize_x):
|
||||
# If the size is different from the expected size (smaller by the amount that is out of range)
|
||||
outimg = cv2.resize(outimg, (reqsize_x, reqsize_y))
|
||||
return outimg
|
||||
@ -19,7 +19,7 @@
|
||||
@@@@@@@@@@@@@@@@@
|
||||
@@@@@@@@@@@@@(
|
||||
|
||||
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization)
|
||||
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization)
|
||||
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
|
||||
|
||||
Copyright (c) 2022 EyeTrackVR <3
|
||||
|
||||
@ -1,171 +0,0 @@
|
||||
import functools
|
||||
import math
|
||||
import sys
|
||||
import timeit
|
||||
|
||||
def TimeitWrapper(*args, **kwargs):
|
||||
"""
|
||||
This decorator @TimeitWrapper() prints the function name and execution time in seconds.
|
||||
:param args:
|
||||
:param kwargs:
|
||||
:return:
|
||||
"""
|
||||
|
||||
def decorator(function):
|
||||
@functools.wraps(function)
|
||||
def wrapper(*args, **kwargs):
|
||||
start = timeit.default_timer()
|
||||
results = function(*args, **kwargs)
|
||||
end = timeit.default_timer()
|
||||
print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
|
||||
return results
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
class TimeitResult(object):
|
||||
"""
|
||||
from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
|
||||
Object returned by the timeit magic with info about the run.
|
||||
Contains the following attributes :
|
||||
loops: (int) number of loops done per measurement
|
||||
repeat: (int) number of times the measurement has been repeated
|
||||
best: (float) best execution time / number
|
||||
all_runs: (list of float) execution time of each run (in s)
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = best
|
||||
self.worst = worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.timings = [dt / self.loops for dt in all_runs]
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.timings) / len(self.timings)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean=format_time(self.average, self._precision),
|
||||
std=format_time(self.stdev, self._precision),
|
||||
best=format_time(self.best, self._precision),
|
||||
worst=format_time(self.worst, self._precision),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<TimeitResult : ' + unic + u'>')
|
||||
|
||||
|
||||
class FPSResult(object):
|
||||
"""
|
||||
base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = 1 / best
|
||||
self.worst = 1 / worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.fps = [1 / dt for dt in all_runs]
|
||||
self.unit = "fps"
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.fps) / len(self.fps)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean="%.*g%s" % (self._precision, self.average, self.unit),
|
||||
std="%.*g%s" % (self._precision, self.stdev, self.unit),
|
||||
best="%.*g%s" % (self._precision, self.best, self.unit),
|
||||
worst="%.*g%s" % (self._precision, self.worst, self.unit),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<FPSResult : ' + unic + u'>')
|
||||
|
||||
|
||||
def format_time(timespan, precision=3):
|
||||
"""
|
||||
https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
|
||||
Formats the timespan in a human readable form
|
||||
"""
|
||||
|
||||
if timespan >= 60.0:
|
||||
# we have more than a minute, format that in a human readable form
|
||||
# Idea from http://snipplr.com/view/5713/
|
||||
parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
|
||||
time = []
|
||||
leftover = timespan
|
||||
for suffix, length in parts:
|
||||
value = int(leftover / length)
|
||||
if value > 0:
|
||||
leftover = leftover % length
|
||||
time.append(u'%s%s' % (str(value), suffix))
|
||||
if leftover < 1:
|
||||
break
|
||||
return " ".join(time)
|
||||
|
||||
# Unfortunately the unicode 'micro' symbol can cause problems in
|
||||
# certain terminals.
|
||||
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
|
||||
# Try to prevent crashes by being more secure than it needs to
|
||||
# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
|
||||
units = [u"s", u"ms", u'us', "ns"] # the save value
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb5'.encode(sys.stdout.encoding)
|
||||
units = [u"s", u"ms", u'\xb5s', "ns"]
|
||||
except:
|
||||
pass
|
||||
scaling = [1, 1e3, 1e6, 1e9]
|
||||
|
||||
if timespan > 0.0:
|
||||
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
|
||||
else:
|
||||
order = 3
|
||||
return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
|
||||
@ -1,2 +0,0 @@
|
||||
def clamp(x, low, high):
|
||||
return max(low, min(x, high))
|
||||
Loading…
Reference in New Issue
Block a user