mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-09-26 23:09:28 +08:00

* initial changes * Mostly clean up, refactor registering listeners to make sense, backport tests * Add initial implementation of VRCFTModuleSender * Add basic GUI for the modules settings * Fix tooltip descriptions # TODO: # - there's ghosts in the machine - vrc osc is not working properly # - min/maxing will require field combinators in the modules lmao * Fix type validation bugs, fix typos # TODO: # - there's ghosts in the machine - vrc osc is not working properly # - min/maxing will require field combinators in the modules lmao * Add checkbox to switch to ETVR Module # TODO: # - there's ghosts in the machine - vrc osc is not working properly # - min/maxing will require field combinators in the modules lmao * Black stuff # TODO: # - there's ghosts in the machine - vrc osc is not working properly # - min/maxing will require field combinators in the modules lmao * Remove coverage by default # TODO: # - there's ghosts in the machine - vrc osc is not working properly # - min/maxing will require field combinators in the modules lmao * Fix timeout in tests # TODO: # - there's ghosts in the machine - vrc osc is not working properly # - min/maxing will require field combinators in the modules lmao * HEAVY WIP: Refactor native output, NOTE: I brought back the entire old OSC implementation as a live reference, this will be removed once I'm done. This also lays ground for other modes as they're pretty similar # TODO: # - there's ghosts in the machine - vrc osc is not working properly # - min/maxing will require field combinators in the modules lmao * HEAVY WIP: Refactor v1 params output, # TODO: # - min/maxing will require field combinators in the modules lmao * HEAVY WIP: Refactor v2 params output # TODO: # - min/maxing will require field combinators in the modules lmao * Finish refactoring v2 and v1, fixup tests, refactor native # TODO: # - min/maxing will require field combinators in the modules lmao * Add tests for v1 params # TODO: # - min/maxing will require field combinators in the modules lmao * Add tests for native params # TODO: # - min/maxing will require field combinators in the modules lmao * Fix OSC not getting up after config reset. Remove reset command, config sends everything changed anyway, sunset the idea of using single client and thus simplify the code a bit # TODO: # - min/maxing will require field combinators in the modules lmao * Rename gui_PortNumber to gui_VRCFTModulePort for readability # TODO: # - min/maxing will require field combinators in the modules lmao * Cleanup EyeID usage # TODO: # - min/maxing will require field combinators in the modules lmao * Cleanup osc after rebase # TODO: # - min/maxing will require field combinators in the modules lmao * Make VRChatOSCSender a bit more readable # TODO: # - min/maxing will require field combinators in the modules lmao * Remove unsued VRChatOSCReceiver, this is taken care of by generic OSCReceiver # TODO: # - min/maxing will require field combinators in the modules lmao * Commit crimes with try_convert_to_float to make osc, pysimplegui and pydantic happy * Cleanup after merge * Disable emulation by default * Fix OSCReceiver crashing on unknown addresses * Adjust VRCFT Module settings to look better in game * Fix recalibrate and recenter for OSC only working for the right eye * Fix save and restart button not restarting the tracking * Fix broken tracking on v1 params for eye_x, clean up implementation * Fix regular value being passed to OSC listeners instead of OSCMessage * Add a TODO, probably to be ignored * Add support for custom ETVR VRCFT Module listening address
1354 lines
52 KiB
Python
1354 lines
52 KiB
Python
import sys
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import math
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import os
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import timeit
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from functools import lru_cache
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from logging import Formatter, INFO, StreamHandler, FileHandler, getLogger
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import cv2
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import numpy as np
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from numpy.linalg import _umath_linalg
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if os.environ.get("PYCHARM_HOSTED", None) is None:
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sys.path.append("../")
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from utils.img_utils import safe_crop # noqa
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from utils.misc_utils import clamp # noqa
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from utils.time_utils import FPSResult, TimeitResult, format_time # noqa
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else:
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from utils.img_utils import safe_crop
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from utils.misc_utils import clamp
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from utils.time_utils import FPSResult, TimeitResult, format_time
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# from line_profiler_pycharm import profile
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this_file_basename = os.path.basename(__file__)
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this_file_name = this_file_basename.replace(".py", "")
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alg_ver = "230318-1" # Do not change it.
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##############################
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# These can be changed
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old_mode = False
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save_logfile = False # This setting is disabled when imshow_enable or save_img or save_video is true
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imshow_enable = False
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save_img = False
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save_video = False
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loop_num = 1 if imshow_enable or save_img or save_video else 100
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input_video_path = "Pro_demo2.mp4"
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output_img_path = f"./{this_file_name}_{alg_ver}_new.png" if not old_mode else f"./{this_file_name}_{alg_ver}_old.png"
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output_video_path = f"./{this_file_name}_{alg_ver}_new.mp4" if not old_mode else f"./{this_file_name}_{alg_ver}_old.mp4"
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logfilename = f"./{this_file_name}_{alg_ver}_new.log" if not old_mode else f"./{this_file_name}_{alg_ver}_old.log"
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print_enable = False # I don't recommend changing to True.
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# RANSAC
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thresh_add = 10
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skip_autoradius = False
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skip_blink_detect = False
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##############################
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##############################
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# Do not change these.
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imsave_flg = imshow_enable or save_img or save_video
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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 = 128
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# CV param
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default_radius = 20
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auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
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auto_radius_step = 1
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blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
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# step==(x,y)
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default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
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logger = getLogger(__name__)
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logger.setLevel(INFO)
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formatter = Formatter("%(message)s")
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handler = StreamHandler()
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handler.setLevel(INFO)
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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if save_logfile and not imsave_flg:
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handler = FileHandler(logfilename, encoding="utf8", mode="w")
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handler.setLevel(INFO)
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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else:
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save_logfile = False
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all_point_img = None
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video_wr = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"x264"), 60.0, (200, 150)) if save_video else None
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##############################
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class CvParameters:
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# It may be a little slower because a dict named "self" is read for each function call.
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def __init__(self, radius, step):
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# self.prev_radius=radius
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self._radius = radius
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self.pad = 2 * radius
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# self.prev_step=step
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self._step = step
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self._hsf = HaarSurroundFeature(radius)
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def get_rpsh(self):
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return self._radius, self.pad, self._step, self._hsf
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# Essentially, the following would be preferable, but it would take twice as long to call.
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# return self.radius, self.pad, self.step, self.hsf
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@property
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def radius(self):
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return self._radius
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@radius.setter
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def radius(self, now_radius):
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# self.prev_radius=self._radius
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self._radius = now_radius
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self.pad = 2 * now_radius
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self.hsf = now_radius
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@property
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def step(self):
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return self._step
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@step.setter
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def step(self, now_step):
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# self.prev_step=self.step
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self._step = now_step
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@property
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def hsf(self):
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return self._hsf
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@hsf.setter
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def hsf(self, now_radius):
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self._hsf = HaarSurroundFeature(now_radius)
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class HaarSurroundFeature:
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def __init__(self, r_inner, r_outer=None, val=None):
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if r_outer is None:
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r_outer = r_inner * 3
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r_inner2 = r_inner * r_inner
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count_inner = r_inner2
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count_outer = r_outer * r_outer - r_inner2
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if val is None:
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val_inner = 1.0 / r_inner2
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val_outer = -val_inner * count_inner / count_outer
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else:
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val_inner = val[0]
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val_outer = val[1]
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self.val_in = float(val_inner) # np.array(val_inner, dtype=np.float64)
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self.val_out = float(val_outer) # np.array(val_outer, dtype=np.float64)
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self.r_in = r_inner
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self.r_out = r_outer
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def get_kernel(self):
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# Defined here, but not yet used?
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# Create a kernel filled with the value of self.val_out
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kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out
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# Set the values of the inner area of the kernel using array slicing
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start = self.r_out - self.r_in
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end = self.r_out + self.r_in - 1
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kernel[start:end, start:end] = self.val_in
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return kernel
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def to_gray(frame):
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# Faster by quitting checking if the input image is already grayscale
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# Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function
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return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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@lru_cache(maxsize=lru_maxsize_vvs)
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def get_frameint_empty_array(frame_shape, pad, x_step, y_step, r_in, r_out):
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frame_int_dtype = np.intc
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frame_pad = np.empty((frame_shape[0] + (pad * 2), frame_shape[1] + (pad * 2)), dtype=np.uint8)
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row, col = frame_pad.shape
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frame_int = np.empty((row + 1, col + 1), dtype=frame_int_dtype)
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y_steps_arr = np.arange(pad, row - pad, y_step, dtype=np.int16)
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x_steps_arr = np.arange(pad, col - pad, x_step, dtype=np.int16)
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len_sx, len_sy = len(x_steps_arr), len(y_steps_arr)
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len_syx = (len_sy, len_sx)
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y_end = pad + (y_step * (len_sy - 1))
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x_end = pad + (x_step * (len_sx - 1))
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y_rin_m = slice(pad - r_in, y_end - r_in + 1, y_step)
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y_rin_p = slice(pad + r_in, y_end + r_in + 1, y_step)
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x_rin_m = slice(pad - r_in, x_end - r_in + 1, x_step)
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x_rin_p = slice(pad + r_in, x_end + r_in + 1, x_step)
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in_p00 = frame_int[y_rin_m, x_rin_m]
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in_p11 = frame_int[y_rin_p, x_rin_p]
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in_p01 = frame_int[y_rin_m, x_rin_p]
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in_p10 = frame_int[y_rin_p, x_rin_m]
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y_ro_m = np.maximum(y_steps_arr - r_out, 0) # [:,np.newaxis]
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x_ro_m = np.maximum(x_steps_arr - r_out, 0) # [np.newaxis,:]
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y_ro_p = np.minimum(row, y_steps_arr + r_out) # [:,np.newaxis]
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x_ro_p = np.minimum(col, x_steps_arr + r_out) # [np.newaxis,:]
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inner_sum = np.empty(len_syx, dtype=frame_int_dtype)
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outer_sum = np.empty(len_syx, dtype=frame_int_dtype)
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out_p_temp = np.empty((len_sy, col + 1), dtype=frame_int_dtype)
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out_p00 = np.empty(len_syx, dtype=frame_int_dtype)
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out_p11 = np.empty(len_syx, dtype=frame_int_dtype)
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out_p01 = np.empty(len_syx, dtype=frame_int_dtype)
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out_p10 = np.empty(len_syx, dtype=frame_int_dtype)
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response_list = np.empty(len_syx, dtype=np.float64) # or np.int32
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frame_conv = np.zeros(shape=(row - 2 * pad, col - 2 * pad), dtype=np.uint8) # or np.float64
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frame_conv_stride = frame_conv[::y_step, ::x_step]
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return (
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frame_pad,
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frame_int,
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inner_sum,
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in_p00,
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in_p11,
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in_p01,
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in_p10,
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y_ro_m,
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x_ro_m,
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y_ro_p,
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x_ro_p,
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outer_sum,
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out_p_temp,
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out_p00,
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out_p11,
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out_p01,
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out_p10,
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response_list,
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frame_conv,
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frame_conv_stride,
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)
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def conv_int(
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frame_int,
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kernel,
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inner_sum,
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in_p00,
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in_p11,
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in_p01,
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in_p10,
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y_ro_m,
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x_ro_m,
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y_ro_p,
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x_ro_p,
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outer_sum,
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out_p_temp,
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out_p00,
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out_p11,
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out_p01,
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out_p10,
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response_list,
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frame_conv_stride,
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):
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# inner_sum[:, :] = in_p00 + in_p11 - in_p01 - in_p10
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cv2.add(in_p00, in_p11, dst=inner_sum)
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cv2.subtract(inner_sum, in_p01, dst=inner_sum)
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cv2.subtract(inner_sum, in_p10, dst=inner_sum)
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# p00 calc
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frame_int.take(y_ro_m, axis=0, mode="clip", out=out_p_temp)
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out_p_temp.take(x_ro_m, axis=1, mode="clip", out=out_p00)
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# p01 calc
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out_p_temp.take(x_ro_p, axis=1, mode="clip", out=out_p01)
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# p11 calc
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frame_int.take(y_ro_p, axis=0, mode="clip", out=out_p_temp)
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out_p_temp.take(x_ro_p, axis=1, mode="clip", out=out_p11)
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# p10 calc
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out_p_temp.take(x_ro_m, axis=1, mode="clip", out=out_p10)
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# outer_sum[:, :] = out_p00 + out_p11 - out_p01 - out_p10 - inner_sum
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cv2.add(out_p00, out_p11, dst=outer_sum)
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cv2.subtract(outer_sum, out_p01, dst=outer_sum)
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cv2.subtract(outer_sum, out_p10, dst=outer_sum)
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cv2.subtract(outer_sum, inner_sum, dst=outer_sum)
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# cv2.transform(np.asarray([p00, p11, -p01, -p10, -inner_sum]).transpose((1, 2, 0)), np.ones((1, 5)),
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# dst=outer_sum) # https://answers.opencv.org/question/3120/how-to-sum-a-3-channel-matrix-to-a-one-channel-matrix/
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# np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list)
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# response_list += kernel.val_out * outer_sum
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cv2.addWeighted(
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inner_sum,
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kernel.val_in,
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outer_sum, # or p00 + p11 - p01 - p10 - inner_sum
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kernel.val_out,
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0.0,
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dtype=cv2.CV_64F, # or cv2.CV_32S
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dst=response_list,
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)
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min_response, _, min_loc, _ = cv2.minMaxLoc(response_list)
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frame_conv_stride[:, :] = response_list
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# or
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# frame_conv_stride[:, :] = response_list.astype(np.uint8)
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return min_response, min_loc
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@lru_cache(maxsize=lru_maxsize_s)
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def get_hsf_center(padding, x_step, y_step, min_loc): # min_x,min_y):
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return padding + (x_step * min_loc[0]) - padding, padding + (y_step * min_loc[1]) - padding
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class AutoRadiusCalc(object):
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def __init__(self):
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self.response_list = []
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self.radius_cand_list = []
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self.adj_comp_flag = False
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self.radius_middle_index = None
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self.left_item = None
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self.right_item = None
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self.left_index = None
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self.right_index = None
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def get_radius(self):
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prev_res_len = len(self.response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==response_list==[default_radius]
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self.adj_comp_flag = False
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return auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==response_list==[default_radius, auto_radius_range[0]]
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self.adj_comp_flag = False
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return auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
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if self.response_list[1][1] < self.response_list[2][1]:
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self.left_item = self.response_list[1]
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self.right_item = self.response_list[0]
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else:
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self.left_item = self.response_list[0]
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self.right_item = self.response_list[2]
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self.radius_cand_list = [
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i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)
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]
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self.left_index = 0
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self.right_index = len(self.radius_cand_list) - 1
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self.radius_middle_index = (self.left_index + self.right_index) // 2
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self.adj_comp_flag = False
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return self.radius_cand_list[self.radius_middle_index]
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else:
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if self.left_index <= self.right_index and self.left_index != self.radius_middle_index:
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if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]):
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self.right_item = self.response_list[-1]
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self.right_index = self.radius_middle_index - 1
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self.radius_middle_index = (self.left_index + self.right_index) // 2
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self.adj_comp_flag = False
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return self.radius_cand_list[self.radius_middle_index]
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if (self.left_item[1] + self.response_list[-1][1]) > (self.right_item[1] + self.response_list[-1][1]):
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self.left_item = self.response_list[-1]
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self.left_index = self.radius_middle_index + 1
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self.radius_middle_index = (self.left_index + self.right_index) // 2
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self.adj_comp_flag = False
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return self.radius_cand_list[self.radius_middle_index]
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self.adj_comp_flag = True
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return self.radius_cand_list[self.radius_middle_index]
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def get_radius_base(self):
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"""
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Use it when the new version doesn't work well.
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:return:
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"""
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prev_res_len = len(self.response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==response_list==[default_radius]
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self.adj_comp_flag = False
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return auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==response_list==[default_radius, auto_radius_range[0]]
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self.adj_comp_flag = False
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return auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]]
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|
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
|
# Extract the radius with the lowest response value
|
|
if sort_res[0] == default_radius:
|
|
# If the default value is best, change now_mode to init after setting radius to the default value.
|
|
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.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.adj_comp_flag = False
|
|
return self.radius_cand_list.pop()
|
|
else:
|
|
# Try the contents of the radius_cand_list in order until the radius_cand_list runs out
|
|
# Better make it a binary search.
|
|
if len(self.radius_cand_list) == 0:
|
|
sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
|
|
self.adj_comp_flag = True
|
|
return sort_res[0]
|
|
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):
|
|
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)
|
|
# 25%,75%
|
|
# This value may need to be adjusted depending on the environment.
|
|
# 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)
|
|
|
|
|
|
@lru_cache(maxsize=lru_maxsize_s)
|
|
def get_ransac_empty_array_old(iter_num, sample_num, len_data):
|
|
# Function to reduce array allocation by providing an empty array first and recycling it with lru
|
|
use_dtype = np.float64
|
|
dm_rng = np.empty((iter_num, sample_num, 7), dtype=use_dtype)
|
|
dm_rng_swap = np.empty((iter_num, sample_num, 5), dtype=use_dtype)
|
|
dm_rng_swap_trans = dm_rng_swap.transpose((0, 2, 1))
|
|
# dm_rng_swap_trans = np.empty((iter_num, 5,sample_num), dtype=use_dtype)
|
|
dm_rng_5x5 = np.empty((iter_num, 5, 5), dtype=use_dtype)
|
|
dm_rng_p5smp = np.empty((iter_num, 5, sample_num), dtype=use_dtype)
|
|
dm_rng_p = np.empty((iter_num, 5), dtype=use_dtype)
|
|
dm_rng_p_npaxis = dm_rng_p[:, :, np.newaxis]
|
|
ellipse_y_arr = np.empty((iter_num, 5), dtype=use_dtype)
|
|
ellipse_y_arr[:, 2] = 1
|
|
swap_index = np.array([4, 3, 0, 1, 5], dtype=np.uint8)
|
|
dm_brod = np.broadcast_to(dm_rng_p[:, 4, np.newaxis], (iter_num, len_data))
|
|
dm_rng_six = dm_rng[:, :, 6, np.newaxis]
|
|
dm_rng_p_24 = dm_rng_p[:, 2:4]
|
|
dm_rng_p_10 = dm_rng_p[:, 1::-1]
|
|
el_y_arr_2 = ellipse_y_arr[:, :2]
|
|
el_y_arr_3 = ellipse_y_arr[:, 3:]
|
|
datamod = np.empty((len_data, 7), dtype=use_dtype) # np.empty((len(data), 7), dtype=ret_dtype)
|
|
datamod[:, 5] = 1
|
|
datamod_b = datamod[:, :5] # .T
|
|
rdm_index_init_arr = np.empty((iter_num, len_data), dtype=np.uint16)
|
|
rdm_index_init_arr[:, :] = np.arange(len_data, dtype=np.uint16)
|
|
rdm_index = np.empty((iter_num, len_data), dtype=np.uint16)
|
|
rdm_index_smpnum = rdm_index[:, :sample_num]
|
|
ellipse_data_arr = np.empty((iter_num, len_data), dtype=use_dtype)
|
|
th_abs = np.empty((iter_num, len_data), dtype=use_dtype)
|
|
dm_data = datamod[:, :2] # = data
|
|
dm_p2 = datamod[:, 2:4] # = data * data
|
|
dm_mul = datamod[:, 4] # = data[:, 0] * data[:, 1]
|
|
dm_neg = datamod[:, 6] # = -datamod[:, 2]
|
|
inv_ext = np.linalg.linalg.get_linalg_error_extobj(np.linalg.linalg._raise_linalgerror_singular)
|
|
return (
|
|
dm_rng,
|
|
dm_rng_swap,
|
|
dm_rng_swap_trans,
|
|
dm_rng_5x5,
|
|
dm_rng_p5smp,
|
|
dm_rng_p,
|
|
dm_rng_p_npaxis,
|
|
ellipse_y_arr,
|
|
swap_index,
|
|
dm_brod,
|
|
dm_rng_six,
|
|
dm_rng_p_24,
|
|
dm_rng_p_10,
|
|
el_y_arr_2,
|
|
el_y_arr_3,
|
|
datamod,
|
|
datamod_b,
|
|
dm_data,
|
|
dm_p2,
|
|
dm_mul,
|
|
dm_neg,
|
|
rdm_index_init_arr,
|
|
rdm_index,
|
|
rdm_index_smpnum,
|
|
ellipse_data_arr,
|
|
th_abs,
|
|
inv_ext,
|
|
)
|
|
|
|
|
|
# @profile
|
|
def fit_rotated_ellipse_ransac_old(data: np.ndarray, sfc: np.random.Generator, iter_num=100, sample_num=10, offset=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
|
|
|
|
# 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
|
|
|
|
(
|
|
dm_rng,
|
|
dm_rng_swap,
|
|
dm_rng_swap_trans,
|
|
dm_rng_5x5,
|
|
dm_rng_p5smp,
|
|
dm_rng_p,
|
|
dm_rng_p_npaxis,
|
|
ellipse_y_arr,
|
|
swap_index,
|
|
dm_brod,
|
|
dm_rng_six,
|
|
dm_rng_p_24,
|
|
dm_rng_p_10,
|
|
el_y_arr_2,
|
|
el_y_arr_3,
|
|
datamod,
|
|
datamod_b,
|
|
dm_data,
|
|
dm_p2,
|
|
dm_mul,
|
|
dm_neg,
|
|
rdm_index_init_arr,
|
|
rdm_index,
|
|
rdm_index_smpnum,
|
|
ellipse_data_arr,
|
|
th_abs,
|
|
inv_ext,
|
|
) = get_ransac_empty_array_old(iter_num, sample_num, len_data)
|
|
|
|
dm_data[:, :] = data # [:]
|
|
dm_p2[:, :] = data * data
|
|
dm_mul[:] = data[:, 0] * data[:, 1]
|
|
dm_neg[:] = -dm_p2[:, 0] # -1 * data[:, 0] ** 2#
|
|
|
|
sfc.permuted(rdm_index_init_arr, axis=1, out=rdm_index)
|
|
|
|
# np.take replaces a[ind,:] and is 3-4 times faster, https://gist.github.com/rossant/4645217
|
|
# a.take() is faster than np.take(a)
|
|
datamod.take(rdm_index_smpnum, axis=0, mode="clip", out=dm_rng)
|
|
|
|
dm_rng_swap[:, :, :] = dm_rng[:, :, swap_index]
|
|
# or
|
|
# dm_rng.take(swap_index, axis=2, mode="clip", out=dm_rng_swap)
|
|
# or
|
|
# dm_rng_swap = np.take(dm_rng,[4, 3, 0, 1, 5],axis=2)
|
|
|
|
np.matmul(dm_rng_swap_trans, dm_rng_swap, out=dm_rng_5x5)
|
|
# np.linalg.solve(np.matmul(dm_rng_swap_trans, dm_rng_swap), dm_rng_swap_trans) # solve is slow https://github.com/bogovicj/JaneliaMLCourse/issues/1
|
|
_umath_linalg.inv(dm_rng_5x5, signature="d->d", extobj=inv_ext, out=dm_rng_5x5)
|
|
np.matmul(dm_rng_5x5, dm_rng_swap_trans, out=dm_rng_p5smp)
|
|
|
|
np.matmul(dm_rng_p5smp, dm_rng_six, out=dm_rng_p_npaxis)
|
|
|
|
el_y_arr_2[:, :] = dm_rng_p_24
|
|
el_y_arr_3[:, :] = dm_rng_p_10
|
|
|
|
cv2.gemm(ellipse_y_arr, datamod_b, 1.0, dm_brod, 1.0, dst=ellipse_data_arr, flags=cv2.GEMM_2_T)
|
|
|
|
np.abs(ellipse_data_arr, out=th_abs)
|
|
cv2.threshold(th_abs, offset, 1.0, cv2.THRESH_BINARY_INV, dst=th_abs)
|
|
ellipse_data_index = cv2.minMaxLoc(cv2.reduce(th_abs, 1, cv2.REDUCE_SUM))[3][1]
|
|
|
|
# error_num = ellipse_data_arr[ellipse_data_index].sum()
|
|
error_num = cv2.sumElems(ellipse_data_arr[ellipse_data_index])[0]
|
|
effective_sample_p_arr = dm_rng_p[ellipse_data_index].tolist()
|
|
|
|
return fit_rotated_ellipse_old(error_num, effective_sample_p_arr)
|
|
|
|
|
|
# @profile
|
|
def fit_rotated_ellipse_old(data, P):
|
|
a = 1.0
|
|
# b, c, d, e, f = P[0], P[1], P[2], P[3], P[4]
|
|
b, c, d, e = P[0], P[1], P[2], P[3]
|
|
theta = 0.5 * math.atan(b / (a - c)) # math.atan2(b, a - c)
|
|
theta_sin, theta_cos = math.sin(theta), math.cos(theta)
|
|
tc2 = theta_cos * theta_cos
|
|
ts2 = theta_sin * theta_sin
|
|
b_tcs = b * theta_cos * theta_sin
|
|
cxy = b * b - 4 * a * c
|
|
cx = (2 * c * d - b * e) / cxy
|
|
cy = (2 * a * e - b * d) / cxy
|
|
# cu = a * cx * cx + b * cx * cy + c * cy * cy - P[4]
|
|
cu = c * cy * cy + cx * (a * cx + b * cy) - P[4]
|
|
# here: https://stackoverflow.com/questions/327002/which-is-faster-in-python-x-5-or-math-sqrtx
|
|
# and : https://gist.github.com/zed/783011
|
|
try:
|
|
# For some reason, a negative value may cause an error.
|
|
w = math.sqrt(cu / (a * tc2 + b_tcs + c * ts2))
|
|
h = math.sqrt(cu / (a * ts2 - b_tcs + c * tc2))
|
|
except ValueError:
|
|
return None
|
|
error_sum = data # sum(data)
|
|
# print("fitting error = %.3f" % (error_sum))
|
|
|
|
return cx, cy, w, h, theta
|
|
|
|
|
|
@lru_cache(maxsize=lru_maxsize_s)
|
|
def get_ransac_empty_array_new(iter_num, sample_num, len_data):
|
|
# Function to reduce array allocation by providing an empty array first and recycling it with lru
|
|
use_dtype = np.float64
|
|
dm_rng = np.empty((iter_num, sample_num, 7), dtype=use_dtype)
|
|
dm_rng_swap = np.empty((iter_num, sample_num, 5), dtype=use_dtype)
|
|
dm_rng_swap_trans = dm_rng_swap.transpose((0, 2, 1))
|
|
# dm_rng_swap_trans = np.empty((iter_num, 5,sample_num), dtype=use_dtype)
|
|
dm_rng_5x5 = np.empty((iter_num, 5, 5), dtype=use_dtype)
|
|
dm_rng_p5smp = np.empty((iter_num, 5, sample_num), dtype=use_dtype)
|
|
dm_rng_p = np.empty((iter_num, 5), dtype=use_dtype)
|
|
dm_rng_p_npaxis = dm_rng_p[:, :, np.newaxis]
|
|
ellipse_y_arr = np.empty((iter_num, 5), dtype=use_dtype)
|
|
ellipse_y_arr[:, 2] = 1
|
|
swap_index = np.array([4, 3, 0, 1, 5], dtype=np.uint8)
|
|
dm_brod = np.broadcast_to(dm_rng_p[:, 4, np.newaxis], (iter_num, len_data))
|
|
dm_rng_six = dm_rng[:, :, 6, np.newaxis]
|
|
dm_rng_p_24 = dm_rng_p[:, 2:4]
|
|
dm_rng_p_10 = dm_rng_p[:, 1::-1]
|
|
el_y_arr_2 = ellipse_y_arr[:, :2]
|
|
el_y_arr_3 = ellipse_y_arr[:, 3:]
|
|
datamod = np.empty((len_data, 7), dtype=use_dtype) # np.empty((len(data), 7), dtype=ret_dtype)
|
|
datamod[:, 5] = 1
|
|
datamod_b = datamod[:, :5] # .T
|
|
rdm_index_init_arr = np.empty((iter_num, len_data), dtype=np.uint16)
|
|
rdm_index_init_arr[:, :] = np.arange(len_data, dtype=np.uint16)
|
|
rdm_index = np.empty((iter_num, len_data), dtype=np.uint16)
|
|
rdm_index_smpnum = rdm_index[:, :sample_num]
|
|
ellipse_data_arr = np.empty((iter_num, len_data), dtype=use_dtype)
|
|
th_abs = np.empty((iter_num, len_data), dtype=use_dtype)
|
|
dm_data = datamod[:, :2] # = data
|
|
dm_p2 = datamod[:, 2:4] # = data * data
|
|
dm_mul = datamod[:, 4] # = data[:, 0] * data[:, 1]
|
|
dm_neg = datamod[:, 6] # = -datamod[:, 2]
|
|
inv_ext = np.linalg.linalg.get_linalg_error_extobj(np.linalg.linalg._raise_linalgerror_singular)
|
|
return (
|
|
dm_rng,
|
|
dm_rng_swap,
|
|
dm_rng_swap_trans,
|
|
dm_rng_5x5,
|
|
dm_rng_p5smp,
|
|
dm_rng_p,
|
|
dm_rng_p_npaxis,
|
|
ellipse_y_arr,
|
|
swap_index,
|
|
dm_brod,
|
|
dm_rng_six,
|
|
dm_rng_p_24,
|
|
dm_rng_p_10,
|
|
el_y_arr_2,
|
|
el_y_arr_3,
|
|
datamod,
|
|
datamod_b,
|
|
dm_data,
|
|
dm_p2,
|
|
dm_mul,
|
|
dm_neg,
|
|
rdm_index_init_arr,
|
|
rdm_index,
|
|
rdm_index_smpnum,
|
|
ellipse_data_arr,
|
|
th_abs,
|
|
inv_ext,
|
|
)
|
|
|
|
|
|
# @profile
|
|
def fit_rotated_ellipse_ransac_new(data: np.ndarray, sfc: np.random.Generator, iter_num=100, sample_num=10, offset=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
|
|
|
|
# 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
|
|
|
|
(
|
|
dm_rng,
|
|
dm_rng_swap,
|
|
dm_rng_swap_trans,
|
|
dm_rng_5x5,
|
|
dm_rng_p5smp,
|
|
dm_rng_p,
|
|
dm_rng_p_npaxis,
|
|
ellipse_y_arr,
|
|
swap_index,
|
|
dm_brod,
|
|
dm_rng_six,
|
|
dm_rng_p_24,
|
|
dm_rng_p_10,
|
|
el_y_arr_2,
|
|
el_y_arr_3,
|
|
datamod,
|
|
datamod_b,
|
|
dm_data,
|
|
dm_p2,
|
|
dm_mul,
|
|
dm_neg,
|
|
rdm_index_init_arr,
|
|
rdm_index,
|
|
rdm_index_smpnum,
|
|
ellipse_data_arr,
|
|
th_abs,
|
|
inv_ext,
|
|
) = get_ransac_empty_array_new(iter_num, sample_num, len_data)
|
|
|
|
dm_data[:, :] = data # [:]
|
|
dm_p2[:, :] = data * data
|
|
dm_mul[:] = data[:, 0] * data[:, 1]
|
|
dm_neg[:] = -dm_p2[:, 0] # -1 * data[:, 0] ** 2#
|
|
|
|
sfc.permuted(rdm_index_init_arr, axis=1, out=rdm_index)
|
|
|
|
# np.take replaces a[ind,:] and is 3-4 times faster, https://gist.github.com/rossant/4645217
|
|
# a.take() is faster than np.take(a)
|
|
datamod.take(rdm_index_smpnum, axis=0, mode="clip", out=dm_rng)
|
|
|
|
dm_rng_swap[:, :, :] = dm_rng[:, :, swap_index]
|
|
# or
|
|
# dm_rng.take(swap_index, axis=2, mode="clip", out=dm_rng_swap)
|
|
# or
|
|
# dm_rng_swap = np.take(dm_rng,[4, 3, 0, 1, 5],axis=2)
|
|
|
|
np.matmul(dm_rng_swap_trans, dm_rng_swap, out=dm_rng_5x5)
|
|
# np.linalg.solve(np.matmul(dm_rng_swap_trans, dm_rng_swap), dm_rng_swap_trans) # solve is slow https://github.com/bogovicj/JaneliaMLCourse/issues/1
|
|
_umath_linalg.inv(dm_rng_5x5, signature="d->d", extobj=inv_ext, out=dm_rng_5x5)
|
|
np.matmul(dm_rng_5x5, dm_rng_swap_trans, out=dm_rng_p5smp)
|
|
|
|
np.matmul(dm_rng_p5smp, dm_rng_six, out=dm_rng_p_npaxis)
|
|
|
|
el_y_arr_2[:, :] = dm_rng_p_24
|
|
el_y_arr_3[:, :] = dm_rng_p_10
|
|
|
|
cv2.gemm(ellipse_y_arr, datamod_b, 1.0, dm_brod, 1.0, dst=ellipse_data_arr, flags=cv2.GEMM_2_T)
|
|
|
|
np.abs(ellipse_data_arr, out=th_abs)
|
|
cv2.threshold(th_abs, offset, 1.0, cv2.THRESH_BINARY_INV, dst=th_abs)
|
|
ellipse_data_index = cv2.minMaxLoc(cv2.reduce(th_abs, 1, cv2.REDUCE_SUM))[3][1]
|
|
|
|
# error_num = ellipse_data_arr[ellipse_data_index].sum()
|
|
error_num = cv2.sumElems(ellipse_data_arr[ellipse_data_index])[0]
|
|
effective_sample_p_arr = dm_rng_p[ellipse_data_index].tolist()
|
|
|
|
return fit_rotated_ellipse_new(error_num, effective_sample_p_arr)
|
|
|
|
|
|
# @profile
|
|
def fit_rotated_ellipse_new(data, P):
|
|
a = 1.0
|
|
# b, c, d, e, f = P[0], P[1], P[2], P[3], P[4]
|
|
b, c, d, e = P[0], P[1], P[2], P[3]
|
|
theta = 0.5 * math.atan(b / (a - c)) # math.atan2(b, a - c)
|
|
theta_sin, theta_cos = math.sin(theta), math.cos(theta)
|
|
tc2 = theta_cos * theta_cos
|
|
ts2 = theta_sin * theta_sin
|
|
b_tcs = b * theta_cos * theta_sin
|
|
cxy = b * b - 4 * a * c
|
|
cx = (2 * c * d - b * e) / cxy
|
|
cy = (2 * a * e - b * d) / cxy
|
|
|
|
cu = a * cx * cx + b * cx * cy + c * cy * cy - P[4]
|
|
# cu = c * cy * cy + cx * (a * cx + b * cy) - P[4]
|
|
# here: https://stackoverflow.com/questions/327002/which-is-faster-in-python-x-5-or-math-sqrtx
|
|
# and : https://gist.github.com/zed/783011
|
|
try:
|
|
# For some reason, a negative value may cause an error.
|
|
w = math.sqrt(cu / (a * tc2 + b_tcs + c * ts2))
|
|
h = math.sqrt(cu / (a * ts2 - b_tcs + c * tc2))
|
|
except ValueError:
|
|
return None
|
|
error_sum = data # sum(data)
|
|
# print("fitting error = %.3f" % (error_sum))
|
|
|
|
return cx, cy, w, h, theta
|
|
|
|
|
|
@lru_cache(lru_maxsize_vvs)
|
|
def get_ransac_frame(frame_shape):
|
|
return np.empty(frame_shape, dtype=np.uint8), np.empty(frame_shape, dtype=np.uint8) # np.float64)
|
|
|
|
|
|
@lru_cache(lru_maxsize_s)
|
|
def get_center_noclamp(center_xy, radius):
|
|
center_x, center_y = center_xy
|
|
upper_x = center_x + radius
|
|
lower_x = center_x - radius
|
|
upper_y = center_y + radius
|
|
lower_y = center_y - radius
|
|
|
|
ransac_upper_x = center_x + max(20, radius)
|
|
ransac_lower_x = center_x - max(20, radius)
|
|
ransac_upper_y = center_y + max(20, radius)
|
|
ransac_lower_y = center_y - max(20, radius)
|
|
ransac_xy_offset = (ransac_lower_x, ransac_lower_y)
|
|
return (
|
|
center_x,
|
|
center_y,
|
|
upper_x,
|
|
lower_x,
|
|
upper_y,
|
|
lower_y,
|
|
ransac_lower_x,
|
|
ransac_lower_y,
|
|
ransac_upper_x,
|
|
ransac_upper_y,
|
|
ransac_xy_offset,
|
|
)
|
|
|
|
|
|
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()
|
|
# if old_mode:
|
|
# self.cvparam = CvParameters_old(default_radius, default_step)
|
|
# else:
|
|
# # os.environ["OPENBLAS_NUM_THREADS"]="1" # https://github.com/numpy/numpy/issues/22928
|
|
# self.cvparam = CvParameters_new(default_radius, default_step)
|
|
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.cap = None
|
|
|
|
self.timedict = {"to_gray": [], "int_img": [], "hsf": [], "crop": [], "ransac": [], "total_cv": []}
|
|
|
|
# ransac
|
|
# self.rng = np.random.default_rng()
|
|
self.sfc = np.random.default_rng(np.random.SFC64())
|
|
|
|
# self.kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
|
# or
|
|
# https://stackoverflow.com/questions/31025368/erode-is-too-slow-opencv
|
|
self.kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
|
|
if old_mode:
|
|
self.gauss_k = cv2.getGaussianKernel(5, 2)
|
|
else:
|
|
self.gauss_k = cv2.getGaussianKernel(5, 1)
|
|
# cv2.getGaussianKernel(kernel size, sigma)
|
|
# Increasing the kernel size improves accuracy but slows down performance.
|
|
# Increasing sigma improves accuracy a little, but has less effect than kernel size.
|
|
|
|
def open_video(self, video_path):
|
|
# Temporary implementation to run
|
|
cap = cv2.VideoCapture(video_path)
|
|
if not cap.isOpened():
|
|
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():
|
|
return False
|
|
ret, frame = self.cap.read()
|
|
if ret:
|
|
# I have set it to grayscale (1ch) just in case, but if the frame is 1ch, this line can be commented out.
|
|
if imsave_flg:
|
|
self.current_image = frame # debug code
|
|
self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
return True
|
|
return False
|
|
|
|
# @profile
|
|
def single_run(self):
|
|
# Temporary implementation to run
|
|
if imsave_flg:
|
|
ori_frame = self.current_image_gray.copy() # debug code
|
|
|
|
blink_bd = False
|
|
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]
|
|
|
|
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
|
|
|
# For measuring processing time of image processing
|
|
cv_start_time = timeit.default_timer()
|
|
frame = self.current_image_gray
|
|
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()
|
|
(
|
|
frame_pad,
|
|
frame_int,
|
|
inner_sum,
|
|
in_p00,
|
|
in_p11,
|
|
in_p01,
|
|
in_p10,
|
|
y_ro_m,
|
|
x_ro_m,
|
|
y_ro_p,
|
|
x_ro_p,
|
|
outer_sum,
|
|
out_p_temp,
|
|
out_p00,
|
|
out_p11,
|
|
out_p01,
|
|
out_p10,
|
|
response_list,
|
|
frame_conv,
|
|
frame_conv_stride,
|
|
) = get_frameint_empty_array(gray_frame.shape, pad, step[0], step[1], hsf.r_in, hsf.r_out)
|
|
cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT, dst=frame_pad)
|
|
cv2.integral(frame_pad, sum=frame_int, sdepth=cv2.CV_32S)
|
|
|
|
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()
|
|
# if old_mode:
|
|
# response, hsf_min_loc = conv_int_old(frame_int, hsf, inner_sum, in_p00, in_p11, in_p01, in_p10, y_ro_m, x_ro_m, y_ro_p, x_ro_p,
|
|
# outer_sum, out_p_temp, out_p00, out_p11, out_p01, out_p10, response_list,
|
|
# frame_conv_stride)
|
|
# else:
|
|
# response, hsf_min_loc = conv_int_new(frame_int, hsf, inner_sum, in_p00, in_p11, in_p01, in_p10, y_ro_m, x_ro_m, y_ro_p, x_ro_p,
|
|
# outer_sum, out_p_temp, out_p00, out_p11, out_p01, out_p10, response_list,
|
|
# frame_conv_stride)
|
|
response, hsf_min_loc = conv_int(
|
|
frame_int,
|
|
hsf,
|
|
inner_sum,
|
|
in_p00,
|
|
in_p11,
|
|
in_p01,
|
|
in_p10,
|
|
y_ro_m,
|
|
x_ro_m,
|
|
y_ro_p,
|
|
x_ro_p,
|
|
outer_sum,
|
|
out_p_temp,
|
|
out_p00,
|
|
out_p11,
|
|
out_p01,
|
|
out_p10,
|
|
response_list,
|
|
frame_conv_stride,
|
|
)
|
|
center_xy = get_hsf_center(pad, step[0], step[1], hsf_min_loc)
|
|
# visualization of HSF
|
|
# cv2.normalize(cv2.filter2D(cv2.filter2D(frame_pad, cv2.CV_64F, hsf.get_kernel()[hsf.get_kernel().shape[0]//2,:].reshape(1,-1), borderType=cv2.BORDER_CONSTANT), cv2.CV_64F, hsf.get_kernel()[:,hsf.get_kernel().shape[1]//2].reshape(-1,1), borderType=cv2.BORDER_CONSTANT),None,0,255,cv2.NORM_MINMAX,dtype=cv2.CV_8U))
|
|
|
|
self.timedict["hsf"].append(timeit.default_timer() - conv_int_start_time)
|
|
|
|
crop_start_time = timeit.default_timer()
|
|
# Define the center point and radius
|
|
|
|
(
|
|
center_x,
|
|
center_y,
|
|
upper_x,
|
|
lower_x,
|
|
upper_y,
|
|
lower_y,
|
|
ransac_lower_x,
|
|
ransac_lower_y,
|
|
ransac_upper_x,
|
|
ransac_upper_y,
|
|
ransac_xy_offset,
|
|
) = get_center_noclamp(center_xy, radius)
|
|
|
|
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)
|
|
elif self.now_modeo == self.cv_modeo[2]:
|
|
# Statistics for blink detection
|
|
if self.blink_detector.response_len() < blink_init_frames:
|
|
self.blink_detector.add_response(
|
|
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y, 1))[0]
|
|
)
|
|
self.center_q1.add_response(
|
|
cv2.mean(
|
|
safe_crop(
|
|
gray_frame,
|
|
center_x - max(20, radius),
|
|
center_y - max(20, radius),
|
|
center_x + max(20, radius),
|
|
center_y + max(20, radius),
|
|
keepsize=False,
|
|
)
|
|
)[0]
|
|
)
|
|
|
|
else:
|
|
|
|
self.blink_detector.calc_thresh()
|
|
self.center_q1.calc_thresh()
|
|
self.now_modeo = self.cv_modeo[3]
|
|
else:
|
|
if self.blink_detector.enable_detect_flg and self.blink_detector.detect(
|
|
cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y, 1))[0]
|
|
):
|
|
# If the average value of cropped_image is greater than response_max
|
|
# (i.e., if the cropimage is whitish blink
|
|
blink_bd = True
|
|
|
|
# if imshow_enable or save_video:
|
|
# cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1)
|
|
# cv2.circle(ori_frame, (center_x, center_y), 7, (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(timeit.default_timer() - 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
|
|
# print('Kernel response:', response)
|
|
# 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 0 in cropped_image.shape:
|
|
# If shape contains 0, it is not detected well.
|
|
# pass
|
|
# else:
|
|
# cv2.imshow("crop", cropped_image)
|
|
# 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:
|
|
self.now_modeo = self.cv_modeo[3]
|
|
elif skip_autoradius:
|
|
self.now_modeo = self.cv_modeo[2]
|
|
else:
|
|
self.now_modeo = self.cv_modeo[1]
|
|
|
|
# For measuring processing time of image processing
|
|
ransac_start_time = timeit.default_timer()
|
|
|
|
# frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
|
|
# cv2.GaussianBlur is slow (uses 10% of the time of all this script)
|
|
# use cv2.blur()
|
|
# or
|
|
# frame_gray =cv2.boxFilter(frame, -1,(5, 5))# https://github.com/bfraboni/FastGaussianBlur
|
|
# cv2.boxFilter(frame_gray, -1,(5, 5),dst=frame_gray)
|
|
# cv2.boxFilter(frame_gray, -1,(5, 5),dst=frame_gray)
|
|
# or
|
|
if old_mode:
|
|
frame_gray = cv2.sepFilter2D(frame, -1, self.gauss_k, self.gauss_k)
|
|
else:
|
|
frame_gray = cv2.sepFilter2D(frame, -1, self.gauss_k, self.gauss_k)
|
|
|
|
# Crop the image using the calculated bounds
|
|
# todo:safecrop tune
|
|
frame_gray_crop = safe_crop(frame_gray, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_y, 1)
|
|
th_frame, fic_frame = get_ransac_frame(frame_gray_crop.shape)
|
|
frame = frame_gray_crop # todo: It can cause bugs.
|
|
|
|
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m
|
|
# min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray_crop)
|
|
min_val = cv2.minMaxLoc(frame_gray_crop)[0]
|
|
# threshold_value = min_val + thresh_add
|
|
|
|
if old_mode:
|
|
cv2.threshold(frame_gray_crop, min_val + thresh_add, 255, cv2.THRESH_BINARY_INV, dst=th_frame)
|
|
# print(thresh.shape, frame_gray.shape)
|
|
|
|
# cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame)
|
|
# cv2.morphologyEx(fic_frame, cv2.MORPH_CLOSE, self.kernel, dst=fic_frame)
|
|
# cv2.bitwise_not(fic_frame, fic_frame)
|
|
# https://stackoverflow.com/questions/23062572/why-multiple-openings-closing-with-a-same-kernel-does-not-have-effect
|
|
# try (cv2.absdiff(cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel),cv2.morphologyEx( cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel), cv2.MORPH_CLOSE, self.kernel))>1).sum()
|
|
cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame) # or cv2.MORPH_CLOSE
|
|
else:
|
|
if not blink_bd and self.blink_detector.enable_detect_flg:
|
|
cv2.threshold(
|
|
frame_gray_crop,
|
|
(min_val + thresh_add + self.center_q1.quartile_1) / 2,
|
|
255,
|
|
cv2.THRESH_BINARY_INV,
|
|
dst=th_frame,
|
|
)
|
|
cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame)
|
|
# cv2.morphologyEx(fic_frame, cv2.MORPH_CLOSE, self.kernel, dst=fic_frame)
|
|
# cv2.erode(fic_frame,self.kernel,dst=fic_frame)
|
|
# cv2.bitwise_not(fic_frame, fic_frame)
|
|
# cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame) # or cv2.MORPH_CLOSE
|
|
else:
|
|
cv2.threshold(frame_gray_crop, min_val + thresh_add, 255, cv2.THRESH_BINARY, dst=th_frame)
|
|
# print(thresh.shape, frame_gray.shape)
|
|
cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame) # or cv2.MORPH_CLOSE
|
|
cv2.morphologyEx(fic_frame, cv2.MORPH_CLOSE, self.kernel, dst=fic_frame)
|
|
cv2.bitwise_not(fic_frame, fic_frame)
|
|
|
|
contours = cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
|
|
# or
|
|
# contours = cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0]
|
|
# if not blink_bd and self.blink_detector.enable_detect_flg:
|
|
# threshold_value = self.center_q1.quartile_1
|
|
# if threshold_value < min_val + thresh_add:
|
|
# # In most of these cases, the pupil is at the edge of the eye.
|
|
# cv2.threshold(frame_gray_crop, (min_val + thresh_add * 4 + threshold_value) / 2, 255, cv2.THRESH_BINARY, dst=th_frame)
|
|
# else:
|
|
# threshold_value = self.center_q1.quartile_1
|
|
# cv2.threshold(frame_gray_crop, threshold_value, 255, cv2.THRESH_BINARY_INV, dst=th_frame)
|
|
# # cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame)
|
|
# # cv2.morphologyEx(fic_frame, cv2.MORPH_CLOSE, self.kernel, dst=fic_frame)
|
|
# # cv2.bitwise_not(fic_frame, fic_frame)
|
|
# # https://stackoverflow.com/questions/23062572/why-multiple-openings-closing-with-a-same-kernel-does-not-have-effect
|
|
# # try (cv2.absdiff(cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel),cv2.morphologyEx( cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel), cv2.MORPH_CLOSE, self.kernel))>1).sum()
|
|
# cv2.morphologyEx(th_frame, cv2.MORPH_OPEN, self.kernel, dst=fic_frame) # or cv2.MORPH_CLOSE
|
|
# contours = (*contours, *cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0])
|
|
# # or
|
|
# # contours = (*contours, *cv2.findContours(fic_frame, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)[0])
|
|
|
|
if not contours:
|
|
# If empty, go to next loop
|
|
return int(center_x), int(center_y), th_frame, frame, gray_frame
|
|
cnt_ind = None
|
|
max_area = -1
|
|
for i, cnt in enumerate(contours):
|
|
now_area = cv2.contourArea(cnt)
|
|
if max_area < now_area:
|
|
max_area = now_area
|
|
cnt_ind = i
|
|
hull = cv2.convexHull(contours[cnt_ind], False)
|
|
if old_mode:
|
|
ransac_data = fit_rotated_ellipse_ransac_old(hull.reshape(-1, 2).astype(np.float64), self.sfc)
|
|
else:
|
|
ransac_data = fit_rotated_ellipse_ransac_new(hull.reshape(-1, 2).astype(np.float64), self.sfc)
|
|
if ransac_data is None:
|
|
# ransac_data is None==maxcnt.shape[0]<sample_num
|
|
# go to next loop
|
|
# pass
|
|
return int(center_x), int(center_y), th_frame, frame, gray_frame
|
|
|
|
# crop_start_time = timeit.default_timer()
|
|
cx, cy, w, h, theta = ransac_data
|
|
# print(cx, cy)
|
|
# if w >= 2.1 * h: # new blink detection algo lmao this works pretty good actually
|
|
# pass
|
|
# return center_x, center_y, frame, frame, True
|
|
|
|
# cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF
|
|
# cy = center_y - (csy - cy)
|
|
|
|
# csy = frame.shape[0]
|
|
# csx = frame.shape[1]
|
|
csy = gray_frame.shape[0]
|
|
csx = gray_frame.shape[1]
|
|
|
|
# cx = clamp((cx - 20) + center_x, 0, csx)
|
|
# cy = clamp((cy - 20) + center_y, 0, csy)
|
|
cx = int(clamp(cx + ransac_xy_offset[0], 0, csx))
|
|
cy = int(clamp(cy + ransac_xy_offset[1], 0, csy))
|
|
|
|
# cv_end_time = timeit.default_timer()
|
|
if imsave_flg:
|
|
|
|
cv2.circle(ori_frame, (int(center_x), int(center_y)), 3, (128, 0, 0), -1)
|
|
cv2.drawContours(ori_frame, contours, -1, (255, 0, 0), 1)
|
|
cv2.circle(ori_frame, (int(cx), int(cy)), 2, (255, 0, 0), -1)
|
|
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
|
# cv2.ellipse(
|
|
# ori_frame,
|
|
# (cx, cy),
|
|
# (int(w), int(h)),
|
|
# theta * 180.0 / np.pi,
|
|
# 0.0,
|
|
# 360.0,
|
|
# (50, 250, 200),
|
|
# 1,
|
|
# )
|
|
# cv2.imshow("crop", cropped_image)
|
|
# cv2.imshow("frame", frame)
|
|
if imshow_enable:
|
|
cv2.imshow("ori_frame", ori_frame)
|
|
cv2.imshow("fic", fic_frame)
|
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
|
pass
|
|
|
|
cv_end_time = timeit.default_timer()
|
|
self.timedict["ransac"].append(cv_end_time - ransac_start_time)
|
|
self.timedict["total_cv"].append(cv_end_time - cv_start_time)
|
|
|
|
try:
|
|
return int(cx), int(cy), th_frame, frame, gray_frame
|
|
except:
|
|
return int(center_x), int(center_y), th_frame, frame, gray_frame
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# print(np.show_config())
|
|
logger.info(this_file_basename)
|
|
if save_logfile:
|
|
logger.info("log path: {}".format(logfilename))
|
|
logger.info("alg ver: {}".format(alg_ver))
|
|
logger.info("alg mode: {}".format("old" if old_mode else "new"))
|
|
logger.info("loops: {}".format(loop_num))
|
|
if not os.path.exists(input_video_path) or not os.path.isfile(input_video_path):
|
|
raise FileNotFoundError(input_video_path)
|
|
logger.info("video name: {}".format(os.path.basename(input_video_path)))
|
|
cap = cv2.VideoCapture(input_video_path)
|
|
logger.info(
|
|
"video info: size:{}x{} fps:{} frames:{} total:{:.3f} sec".format(
|
|
int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
|
|
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
|
|
cap.get(cv2.CAP_PROP_FPS),
|
|
int(cap.get(cv2.CAP_PROP_FRAME_COUNT)),
|
|
cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS),
|
|
)
|
|
)
|
|
if save_img:
|
|
all_point_img = np.zeros(
|
|
(int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), 3), dtype=np.uint8
|
|
)
|
|
cap.release()
|
|
|
|
if not print_enable:
|
|
|
|
def print(*args, **kwargs):
|
|
pass
|
|
|
|
hsrac = HSRAC_cls()
|
|
# For measuring total processing time
|
|
main_start_time = timeit.default_timer()
|
|
|
|
for i in range(loop_num):
|
|
hsrac.open_video(input_video_path)
|
|
|
|
while hsrac.read_frame():
|
|
if imsave_flg:
|
|
base_gray = hsrac.current_image_gray.copy()
|
|
base_img = hsrac.current_image.copy()
|
|
|
|
hsf_x, hsf_y, hsf_cropbox, *_ = hsrac.single_run()
|
|
|
|
# # hsrac_x, hsrac_y, hsrac_cropbox, *_ = er_hsracs.run(base_gray)
|
|
# if 0:#random.random()<0.1:
|
|
# hsrac_x, hsrac_y, hsrac_cropbox, *_ = er_hsracs.run(cv2.resize(base_gray,None,fx=0.75,fy=0.75).copy())
|
|
# hsrac_x=int(hsrac_x*1.25)
|
|
# hsrac_y=int(hsrac_y*1.25)
|
|
# hsrac_cropbox=[int(val*1.25) for val in hsrac_cropbox]
|
|
# else:
|
|
# hsrac_x, hsrac_y, hsrac_cropbox,ori_frame, *_ = er_hsracs.run(base_gray)
|
|
# cv2.rectangle(base_img, hsf_cropbox[:2], hsf_cropbox[2:], (0, 0, 255), 3)
|
|
# cv2.rectangle(base_img, hsrac_cropbox[:2], hsrac_cropbox[2:], (255, 0, 0), 1)
|
|
cv2.circle(base_img, (hsf_x, hsf_y), 3, (0, 0, 255), -1)
|
|
if save_img:
|
|
cv2.circle(all_point_img, (hsf_x, hsf_y), 2, (0, 0, 255), -1)
|
|
# try:
|
|
# cv2.circle(base_img, (hsrac_x, hsrac_y), 3, (255, 0, 0), -1)
|
|
# except:
|
|
# print()
|
|
if imshow_enable:
|
|
cv2.imshow("frame", base_gray)
|
|
cv2.imshow("hsf_hsrac", base_img)
|
|
if cv2.waitKey(1) & 0xFF == ord("q"):
|
|
pass
|
|
# if save_video:
|
|
# video_wr.write(cv2.resize(base_img, (200, 150)))
|
|
else:
|
|
_ = hsrac.single_run()
|
|
|
|
if save_video:
|
|
video_wr.release()
|
|
logger.info("video output: {}".format(output_video_path))
|
|
hsrac.cap.release()
|
|
cv2.destroyAllWindows()
|
|
main_end_time = timeit.default_timer()
|
|
main_total_time = main_end_time - main_start_time
|
|
if save_img:
|
|
# cv2.imwrite(output_img_path, all_point_img)
|
|
logger.info("image output: {}".format(output_img_path))
|
|
if imshow_enable:
|
|
cv2.imshow("allpoint", all_point_img)
|
|
if cv2.waitKey(10000): # wait 10sec
|
|
cv2.destroyAllWindows()
|
|
if not print_enable:
|
|
# del print
|
|
# or
|
|
print = __builtins__.print
|
|
logger.info("")
|
|
for k, v in hsrac.timedict.items():
|
|
# number=1, precision=5
|
|
len_v = len(v)
|
|
best = min(v) # / number
|
|
worst = max(v) # / number
|
|
logger.info(k + ":")
|
|
logger.info(TimeitResult(loop_num, len_v, best, worst, v, 5))
|
|
logger.info(FPSResult(loop_num, len_v, worst, best, v, 5))
|
|
# print("")
|
|
logger.info("")
|
|
logger.info(f"{this_file_basename}: ALL Finish {format_time(main_total_time)}")
|
|
|