diff --git a/EyeTrackApp/config.py b/EyeTrackApp/config.py index 5548981..e07faaf 100644 --- a/EyeTrackApp/config.py +++ b/EyeTrackApp/config.py @@ -45,6 +45,7 @@ class EyeTrackSettingsConfig(BaseModel): gui_RANSAC3DP: int = 2 gui_HSFP: int = 3 gui_BLOBP: int = 4 + gui_skip_autoradius: bool = True class EyeTrackConfig(BaseModel): version: int = 1 diff --git a/EyeTrackApp/eye_processor.py b/EyeTrackApp/eye_processor.py index ff9b832..f1b67e2 100644 --- a/EyeTrackApp/eye_processor.py +++ b/EyeTrackApp/eye_processor.py @@ -153,25 +153,8 @@ class EyeProcessor: self.failed = 0 - - self.response_list = [] #This might not be correct. - #HSF - - self.cv_mode = ["first_frame", "radius_adjust", "init", "normal"] - self.now_mode = self.cv_mode[0] - self.cvparam = CvParameters(default_radius, default_step) self.skip_blink_detect = False - self.default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact - # self.default_step==(x,y) - self.radius_cand_list = [] - self.blink_init_frames = 60 * 3 - prev_max_size = 60 * 3 # 60fps*3sec - # response_min=0 - self.response_max = None - - self.auto_radius_range = (self.settings.gui_HSF_radius - 10, self.settings.gui_HSF_radius + 10) - #blink self.max_ints = [] self.max_int = 0 @@ -179,6 +162,8 @@ class EyeProcessor: self.frames = 0 self.blinkvalue = False + self.prev_x = None + self.prev_y = None @@ -209,7 +194,7 @@ class EyeProcessor: self.previous_image = self.current_image self.previous_rotation = self.config.rotation_angle except: - print("E") + pass def capture_crop_rotate_image(self): # Get our current frame @@ -255,12 +240,22 @@ class EyeProcessor: def HSRACM(self): cx, cy, thresh = HSRAC(self) + if self.prev_x == None: + self.prev_x = cx + self.prev_y = cy + #print(self.prev_x, self.prev_y, cx, cy) + # if (cx - self.prev_x) <= 45 and (cy - self.prev_y) <= 45 : + # self.prev_x = cx + # self.prev_y = cy 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 + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, False)) #update app else: - self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue)) - + self.blinkvalue = False + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, False)) + # else: + # print("EYE MOVED TOO FAST") + # self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, 0, 0, 0, False)) def HSFM(self): cx, cy, frame = HSF(self) out_x, out_y = cal_osc(self, cx, cy) @@ -294,31 +289,24 @@ class EyeProcessor: def ALGOSELECT(self): - print(self.failed, self.firstalgo) - if self.failed == 0 and self.firstalgo != None: - print('first') - self.firstalgo() + if self.failed == 0 and self.firstalgo != None: + self.firstalgo() else: self.failed = self.failed + 1 - if self.failed == 1 and self.secondalgo != None: - print('2nd') #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1 + 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 self.secondalgo() else: self.failed = self.failed + 1 if self.failed == 2 and self.thirdalgo != None: - print('3rd') self.thirdalgo() - else: self.failed = self.failed + 1 if self.failed == 3 and self.fourthalgo != None: - print('4th') self.fourthalgo() - else: self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo @@ -352,7 +340,6 @@ class EyeProcessor: self.fourthalgo = self.RANSAC3DM if self.settings.gui_HSRAC == True and self.settings.gui_HSRACP == 1: - print("HERE") self.firstalgo = self.HSRACM elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2: self.secondalgo = self.HSRACM @@ -370,12 +357,6 @@ class EyeProcessor: elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4: self.fourthalgo = self.BLOBM - - if self.settings.gui_HSRACP == '1': - print("HERE") - - print(self.settings.gui_HSRACP, self.settings.gui_HSRAC, self.firstalgo) - f = True while True: # f = True @@ -391,7 +372,7 @@ class EyeProcessor: return continue - + # If our ROI configuration has changed, reset our model and detector if (self.camera_model is None diff --git a/EyeTrackApp/haar_surround_feature.py b/EyeTrackApp/haar_surround_feature.py index 6e2233e..57978ef 100644 --- a/EyeTrackApp/haar_surround_feature.py +++ b/EyeTrackApp/haar_surround_feature.py @@ -27,15 +27,22 @@ Copyright (c) 2022 EyeTrackVR <3 ''' -import cv2 -import numpy as np -import timeit -from functools import lru_cache -import os -import sys import functools import math -#HSF \/ +import os +import sys +import timeit +from functools import lru_cache + +import cv2 +import numpy as np + +# from line_profiler_pycharm import profile + +calc_print_enable = True +save_video = False +skip_autoradius = False +skip_blink_detect = False # cache param lru_maxsize_vvs = 16 @@ -46,7 +53,6 @@ auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30) 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 -response_list = [] """ Attention. @@ -303,12 +309,6 @@ class HaarSurroundFeature: return kernel -def to_gray(frame): - # Faster by quitting checking if the input image is already grayscale - # Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function - return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - - @lru_cache(maxsize=lru_maxsize_vs) def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None): """ @@ -430,154 +430,179 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list): 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(self.response_list) + # 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)) frame_conv_stride[:, :] = response_list # or - # frame_conv_stride[:, :] = self.response_list.astype(np.uint8) + # frame_conv_stride[:, :] = response_list.astype(np.uint8) return frame_conv, min_response, center +# @profile + + + +timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []} +# 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 +main_start_time = timeit.default_timer() + +rng = np.random.default_rng() +cvparam = CvParameters(default_radius, default_step) + +cv_mode = ["first_frame", "radius_adjust", "init", "normal"] +now_mode = cv_mode[0] + +radius_cand_list = [] + +# response_min=0 +response_max = None +response_list = [] + def HSF(self): - frame = self.current_image_gray - if self.now_mode == self.cv_mode[1]: - - prev_res_len = len(self.response_list) - # adjustment of radius - if prev_res_len == 1: - # len==1==self.response_list==[self.settings.gui_HSF_radius] - self.cvparam.radius = self.auto_radius_range[0] - elif prev_res_len == 2: - # len==2==self.response_list==[self.settings.gui_HSF_radius, self.auto_radius_range[0]] - self.cvparam.radius = self.auto_radius_range[1] - elif prev_res_len == 3: - # len==3==self.response_list==[self.settings.gui_HSF_radius,self.auto_radius_range[0],self.auto_radius_range[1]] - sort_res = sorted(self.response_list, key=lambda x: x[1])[0] - # Extract the radius with the lowest response value - if sort_res[0] == self.settings.gui_HSF_radius: - # If the default value is best, change self.now_mode to init after setting radius to the default value. - self.cvparam.radius = self.settings.gui_HSF_radius - self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3] - self.response_list = [] - elif sort_res[0] == self.auto_radius_range[0]: - self.radius_cand_list = [i for i in range(self.auto_radius_range[0], self.settings.gui_HSF_radius, self.default_step[0])][1:] - # self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy - # It should be no problem to set it to anything other than self.default_step - self.cvparam.radius = self.radius_cand_list.pop() - else: - self.radius_cand_list = [i for i in range(self.settings.gui_HSF_radius, self.auto_radius_range[1], self.default_step[0])][1:] - # self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy - # It should be no problem to set it to anything other than self.default_step - self.cvparam.radius = self.radius_cand_list.pop() + global now_mode + global response_list + global radius_cand_list + global response_max + # default_radius = 15 + frame = self.current_image_gray + if now_mode == cv_mode[1]: + prev_res_len = len(response_list) + # adjustment of radius + if prev_res_len == 1: + # len==1==response_list==[default_radius] + cvparam.radius = auto_radius_range[0] + elif prev_res_len == 2: + # len==2==response_list==[default_radius, auto_radius_range[0]] + cvparam.radius = auto_radius_range[1] + elif prev_res_len == 3: + # len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]] + sort_res = sorted(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. + cvparam.radius = default_radius + now_mode = cv_mode[2] if not skip_blink_detect else cv_mode[3] + response_list = [] + elif sort_res[0] == auto_radius_range[0]: + radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, default_step[0])][1:] + # default_step is defined separately for xy, but radius is shared by xy, so it may be buggy + # It should be no problem to set it to anything other than default_step + cvparam.radius = radius_cand_list.pop() else: - # Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out - # Better make it a binary search. - if len(self.radius_cand_list) == 0: - sort_res = sorted(self.response_list, key=lambda x: x[1])[0] - self.cvparam.radius = sort_res[0] - self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3] - self.response_list = [] - else: - self.cvparam.radius = self.radius_cand_list.pop() - - radius, pad, step, hsf = self.cvparam.get_rpsh() - - # For measuring processing time of image processing - cv_start_time = timeit.default_timer() - - gray_frame = frame - - # Calculate the integral image of the frame - int_start_time = timeit.default_timer() - # BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used. - frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT) - frame_int = cv2.integral(frame_pad) - - # Convolve the feature with the integral image - conv_int_start_time = timeit.default_timer() - xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None) - frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step) - - crop_start_time = timeit.default_timer() - # Define the center point and radius - center_x, center_y = center_xy - upper_x = center_x + 25 #TODO make this a setting - lower_x = center_x - 25 - upper_y = center_y + 25 - lower_y = center_y - 25 - - # Crop the image using the calculated bounds - cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why? - - if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]: - # If mode is first_frame or radius_adjust, record current radius and response - self.response_list.append((radius, response)) - elif self.now_mode == self.cv_mode[2]: - # Statistics for blink detection - if len(self.response_list) < self.blink_init_frames: - # Record the average value of cropped_image - self.response_list.append(cv2.mean(cropped_image)[0]) - else: - # Calculate self.response_max by computing interquartile range, IQR - # Change self.cv_mode to normal - self.response_list = np.array(self.response_list) - # 25%,75% - # This value may need to be adjusted depending on the environment. - quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75]) - iqr = quartile_3 - quartile_1 - # response_min = quartile_1 - (iqr * 1.5) - self.response_max = quartile_3 + (iqr * 1.5) - self.now_mode = self.cv_mode[3] + radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], default_step[0])][1:] + # default_step is defined separately for xy, but radius is shared by xy, so it may be buggy + # It should be no problem to set it to anything other than default_step + cvparam.radius = radius_cand_list.pop() else: - if 0 in cropped_image.shape: - # If shape contains 0, it is not detected well. - print("[WARN] HSF: Something's wrong.") + # 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(radius_cand_list) == 0: + sort_res = sorted(response_list, key=lambda x: x[1])[0] + cvparam.radius = sort_res[0] + now_mode = cv_mode[2] if not skip_blink_detect else cv_mode[3] + response_list = [] else: - # If the average value of cropped_image is greater than self.response_max - # (i.e., if the cropimage is whitish - if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max: - # blink - - cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1) - # If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way - # https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue - - - - - - - cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1) - # print(center_x, center_y) - - if self.now_mode != self.cv_mode[0] and self.now_mode != self.cv_mode[1]: - if cropped_image.size < 400: - pass + cvparam.radius = radius_cand_list.pop() - if self.now_mode == self.cv_mode[0]: - self.now_mode = self.cv_mode[1] + radius, pad, step, hsf = cvparam.get_rpsh() + + # For measuring processing time of image processing + cv_start_time = timeit.default_timer() + + gray_frame = frame + 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_int = cv2.integral(frame_pad) + 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) + frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step) + 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 + upper_x = center_x + radius + lower_x = center_x - radius + upper_y = center_y + radius + lower_y = center_y - radius + + # Crop the image using the calculated bounds + cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] + + if now_mode == cv_mode[0] or now_mode == cv_mode[1]: + # If mode is first_frame or radius_adjust, record current radius and response + response_list.append((radius, response)) + elif now_mode == cv_mode[2]: + # Statistics for blink detection + if len(response_list) < blink_init_frames: + # Record the average value of cropped_image + response_list.append(cv2.mean(cropped_image)[0]) + else: + # Calculate response_max by computing interquartile range, IQR + # Change cv_mode to normal + response_list = np.array(response_list) + # 25%,75% + # This value may need to be adjusted depending on the environment. + quartile_1, quartile_3 = np.percentile(response_list, [25, 75]) + iqr = quartile_3 - quartile_1 + # response_min = quartile_1 - (iqr * 1.5) + response_max = quartile_3 + (iqr * 1.5) + now_mode = cv_mode[3] + else: + if 0 in cropped_image.shape: + # If shape contains 0, it is not detected well. + print("Something's wrong.") + else: + # If the average value of cropped_image is greater than response_max + # (i.e., if the cropimage is whitish + if response_max is not None and cv2.mean(cropped_image)[0] > response_max: + # blink + cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -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() + timedict["crop"].append(cv_end_time - crop_start_time) + timedict["total_cv"].append(cv_end_time - cv_start_time) + + # 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 now_mode == cv_mode[0]: + # Moving from first_frame to the next mode + if skip_autoradius and skip_blink_detect: + now_mode = cv_mode[3] + response_list = [] + elif skip_autoradius: + now_mode = cv_mode[2] + response_list = [] + else: + now_mode = cv_mode[1] + + try: + self.failed = 0 + return center_x, center_y, frame + + except: + self.failed = self.failed + 1 + return 0, 0, frame - - try: - self.failed = 0 - return center_x, center_y, frame - - except: - self.failed = self.failed + 1 - return 0, 0, frame - - - - #self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False)) - # return - - #self.output_images_and_update(larger_threshold,EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False),) - # return - #self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True)) diff --git a/EyeTrackApp/hsrac.py b/EyeTrackApp/hsrac.py index d36012f..9cf0c1c 100644 --- a/EyeTrackApp/hsrac.py +++ b/EyeTrackApp/hsrac.py @@ -1,25 +1,298 @@ -import cv2 -import numpy as np -import timeit -from functools import lru_cache -import os -import sys import functools import math +import os +import sys +import timeit +from functools import lru_cache -#HSF \/ +import cv2 +import numpy as np + +# from line_profiler_pycharm import profile + +#RANSACAHA + + + + +thresh_add = 20 + + +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'') + + +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'') + + +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 ยต, 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]) + + +def ellipse_model(data, y, f): + """ + There is no need to make this process a function, since making the process a function will slow it down a little by calling it. + The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same. + a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4] + :param data: + :param y: np.c_[d, e, a, c, b] + :param f: f == P[4, 0] + :return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ]) + """ + return data.dot(y) + f + + +# @profile +def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80 + ): # before changing these values, please read up on the ransac algorithm + # However if you want to change any value just know that higher iterations will make processing frames slower + effective_sample = None + + # The array contents do not change during the loop, so only one call is needed. + # They say len is faster than shape. + # Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape + len_data = len(data) + + if len_data < sample_num: + return None + + # Type of calculation result + ret_dtype = np.float64 + + # Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting. + # If the array size is less than about 100, this is faster than rng.choice. + rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num] + # or + # I don't see any advantage to doing this. + # rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32) + + # I don't think it looks beautiful. + # x,y,x**2,y**2,x*y,1,-1*x**2 + datamod = np.concatenate( + [data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype), + (-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1, + dtype=ret_dtype) + + datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype) + + datamod_rng = datamod[rng_sample] + datamod_rng6 = datamod_rng[:, :, 6] + datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]] + datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1)) + + # These two lines are one of the bottlenecks + datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap) + datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans) + + datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5)) + + # I don't think it looks beautiful. + ellipse_y_arr = np.asarray( + [datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(len(datamod_rng_p)), datamod_rng_p[:, 1], datamod_rng_p[:, 0]], dtype=ret_dtype) + + ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0)) + ellipse_data_abs = np.abs(ellipse_data_arr) + ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0) + effective_data_arr = ellipse_data_arr[ellipse_data_index] + effective_sample_p_arr = datamod_rng_p[ellipse_data_index] + + return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr) + + +# @profile +def fit_rotated_ellipse(data, P): + a = 1.0 + b = P[0] + c = P[1] + d = P[2] + e = P[3] + f = P[4] + # The cost of trigonometric functions is high. + theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64) + theta_sin = np.sin(theta, dtype=np.float64) + theta_cos = np.cos(theta, dtype=np.float64) + tc2 = theta_cos ** 2 + ts2 = theta_sin ** 2 + b_tcs = b * theta_cos * theta_sin + + # Do the calculation only once + cxy = b ** 2 - 4 * a * c + cx = (2 * c * d - b * e) / cxy + cy = (2 * a * e - b * d) / cxy + + # I just want to clear things up around here. + cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f + cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)]) + wh = np.sqrt(cu / cu_r) + + w, h = wh[0], wh[1] + + error_sum = np.sum(data) + # print("fitting error = %.3f" % (error_sum)) + + return (cx, cy, w, h, theta) + + + + + + + + + + + + +# HSF +calc_print_enable = True +save_video = False +skip_autoradius = True +skip_blink_detect = False # cache param lru_maxsize_vvs = 16 lru_maxsize_vs = 64 # CV param - -#default_radius = 15 -#auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30) +default_radius = 10 +auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30) 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 -response_list = [] """ Attention. @@ -205,7 +478,6 @@ 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 @@ -277,12 +549,6 @@ class HaarSurroundFeature: return kernel -def to_gray(frame): - # Faster by quitting checking if the input image is already grayscale - # Perhaps it would be faster with less overhead to call cv2.cvtColor directly instead of using this function - return cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) - - @lru_cache(maxsize=lru_maxsize_vs) def frameint_get_xy_step(imageshape, xysteps, pad, start_offset=None, end_offset=None): """ @@ -404,170 +670,388 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list): 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(self.response_list) + # 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)) frame_conv_stride[:, :] = response_list # or - # frame_conv_stride[:, :] = self.response_list.astype(np.uint8) + # frame_conv_stride[:, :] = response_list.astype(np.uint8) return frame_conv, min_response, center - -#RANSAC \/ -def ellipse_model(data, y, f): - """ - There is no need to make this process a function, since making the process a function will slow it down a little by calling it. - The results may be slightly different from the lambda version due to calculation errors derived from float types, but the calculation results are virtually the same. - a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4] - :param data: - :param y: np.c_[d, e, a, c, b] - :param f: f == P[4, 0] - :return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ]) - """ - return data.dot(y) + f - # @profile -def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80 - ): # before changing these values, please read up on the ransac algorithm - # However if you want to change any value just know that higher iterations will make processing frames slower - effective_sample = None - - # The array contents do not change during the loop, so only one call is needed. - # They say len is faster than shape. - # Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape - len_data = len(data) - - if len_data < sample_num: - return None - - # Type of calculation result - ret_dtype = np.float64 - - # Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting. - # If the array size is less than about 100, this is faster than rng.choice. - rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num] - # or - # I don't see any advantage to doing this. - # rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32) - - # I don't think it looks beautiful. - # x,y,x**2,y**2,x*y,1,-1*x**2 - datamod = np.concatenate( - [data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype), - (-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1, - dtype=ret_dtype) - - datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype) - - datamod_rng = datamod[rng_sample] - datamod_rng6 = datamod_rng[:, :, 6] - datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]] - datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1)) - - # These two lines are one of the bottlenecks - datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap) - datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans) - - datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5)) - - # I don't think it looks beautiful. - ellipse_y_arr = np.asarray( - [datamod_rng_p[:, 2], datamod_rng_p[:, 3], np.ones(len(datamod_rng_p)), datamod_rng_p[:, 1], datamod_rng_p[:, 0]], dtype=ret_dtype) - - ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0)) - ellipse_data_abs = np.abs(ellipse_data_arr) - ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0) - effective_data_arr = ellipse_data_arr[ellipse_data_index] - effective_sample_p_arr = datamod_rng_p[ellipse_data_index] - - return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr) + -# @profile -def fit_rotated_ellipse(data, P): - a = 1.0 - b = P[0] - c = P[1] - d = P[2] - e = P[3] - f = P[4] - # The cost of trigonometric functions is high. - theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64) - theta_sin = np.sin(theta, dtype=np.float64) - theta_cos = np.cos(theta, dtype=np.float64) - tc2 = theta_cos ** 2 - ts2 = theta_sin ** 2 - b_tcs = b * theta_cos * theta_sin - - # Do the calculation only once - cxy = b ** 2 - 4 * a * c - cx = (2 * c * d - b * e) / cxy - cy = (2 * a * e - b * d) / cxy - - # I just want to clear things up around here. - cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f - cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)]) - wh = np.sqrt(cu / cu_r) - - w, h = wh[0], wh[1] - - error_sum = np.sum(data) - # print("fitting error = %.3f" % (error_sum)) - - return (cx, cy, w, h, theta) +timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []} +# 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 +main_start_time = timeit.default_timer() +rng = np.random.default_rng() +cvparam = CvParameters(default_radius, default_step) +cv_mode = ["first_frame", "radius_adjust", "init", "normal"] +now_mode = cv_mode[0] + +radius_cand_list = [] + +# response_min=0 +response_max = None +response_list = [] + +prev_hsfx = 0 +prev_hsfy = 0 +prev_ranx = 0 +prev_rany = 0 def HSRAC(self): - default_radius = self.settings.gui_HSF_radius - auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30) - frame = self.current_image_gray - if self.now_mode == self.cv_mode[1]: + global now_mode + global response_list + global radius_cand_list + global response_max + global skip_autoradius + global default_radius + global prev_rany + global prev_ranx + global prev_hsfy + global prev_hsfx + skip_autoradius = self.settings.gui_skip_autoradius + default_radius = self.settings.gui_HSF_radius + frame = self.current_image_gray - - prev_res_len = len(self.response_list) + if now_mode == cv_mode[1]: + prev_res_len = len(response_list) # adjustment of radius if prev_res_len == 1: - # len==1==self.response_list==[default_radius] - self.cvparam.radius = self.auto_radius_range[0] + # len==1==response_list==[default_radius] + cvparam.radius = auto_radius_range[0] elif prev_res_len == 2: - # len==2==self.response_list==[default_radius, self.auto_radius_range[0]] - self.cvparam.radius = self.auto_radius_range[1] + # len==2==response_list==[default_radius, auto_radius_range[0]] + cvparam.radius = auto_radius_range[1] elif prev_res_len == 3: - # len==3==self.response_list==[default_radius,self.auto_radius_range[0],self.auto_radius_range[1]] - sort_res = sorted(self.response_list, key=lambda x: x[1])[0] + # len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]] + sort_res = sorted(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 self.now_mode to init after setting radius to the default value. - self.cvparam.radius = default_radius - self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3] - self.response_list = [] - elif sort_res[0] == self.auto_radius_range[0]: - self.radius_cand_list = [i for i in range(self.auto_radius_range[0], default_radius, self.default_step[0])][1:] - # self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy - # It should be no problem to set it to anything other than self.default_step - self.cvparam.radius = self.radius_cand_list.pop() + # If the default value is best, change now_mode to init after setting radius to the default value. + cvparam.radius = default_radius + now_mode = cv_mode[2] if not skip_blink_detect else cv_mode[3] + response_list = [] + elif sort_res[0] == auto_radius_range[0]: + radius_cand_list = [i for i in range(auto_radius_range[0], default_radius, default_step[0])][1:] + # default_step is defined separately for xy, but radius is shared by xy, so it may be buggy + # It should be no problem to set it to anything other than default_step + cvparam.radius = radius_cand_list.pop() else: - self.radius_cand_list = [i for i in range(default_radius, self.auto_radius_range[1], self.default_step[0])][1:] - # self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy - # It should be no problem to set it to anything other than self.default_step - self.cvparam.radius = self.radius_cand_list.pop() + radius_cand_list = [i for i in range(default_radius, auto_radius_range[1], default_step[0])][1:] + # default_step is defined separately for xy, but radius is shared by xy, so it may be buggy + # It should be no problem to set it to anything other than default_step + cvparam.radius = radius_cand_list.pop() else: - # Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out + # 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.cvparam.radius = sort_res[0] - self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3] - self.response_list = [] + if len(radius_cand_list) == 0: + sort_res = sorted(response_list, key=lambda x: x[1])[0] + cvparam.radius = sort_res[0] + now_mode = cv_mode[2] if not skip_blink_detect else cv_mode[3] + response_list = [] else: - self.cvparam.radius = self.radius_cand_list.pop() + cvparam.radius = radius_cand_list.pop() + + radius, pad, step, hsf = cvparam.get_rpsh() + + # For measuring processing time of image processing + cv_start_time = timeit.default_timer() + + gray_frame = frame + 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_int = cv2.integral(frame_pad) + 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) + frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step) + timedict["conv_int"].append(timeit.default_timer() - conv_int_start_time) + + # Define the center point and radius + center_x, center_y = center_xy + upper_x = center_x + 20 + lower_x = center_x - 20 + upper_y = center_y + 20 + lower_y = center_y - 20 + + # Crop the image using the calculated bounds + cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] + frame = cropped_image + if now_mode == cv_mode[0] or now_mode == cv_mode[1]: + # If mode is first_frame or radius_adjust, record current radius and response + response_list.append((radius, response)) + elif now_mode == cv_mode[2]: + # Statistics for blink detection + if len(response_list) < blink_init_frames: + # Record the average value of cropped_image + response_list.append(cv2.mean(cropped_image)[0]) + else: + # Calculate response_max by computing interquartile range, IQR + # Change cv_mode to normal + response_list = np.array(response_list) + # 25%,75% + # This value may need to be adjusted depending on the environment. + quartile_1, quartile_3 = np.percentile(response_list, [25, 75]) + iqr = quartile_3 - quartile_1 + # response_min = quartile_1 - (iqr * 1.5) + response_max = quartile_3 + (iqr * 1.5) + now_mode = cv_mode[3] + else: + if 0 in cropped_image.shape: + # If shape contains 0, it is not detected well. + print("Something's wrong.") + else: + # If the average value of cropped_image is greater than response_max + # (i.e., if the cropimage is whitish + if response_max is not None and cv2.mean(cropped_image)[0] > response_max: + # blink + self.blinkvalue = True + print("HSF BLINK") + + # 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 + + + # 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 now_mode == cv_mode[0]: + # Moving from first_frame to the next mode + if skip_autoradius and skip_blink_detect: + now_mode = cv_mode[3] + response_list = [] + elif skip_autoradius: + now_mode = cv_mode[2] + response_list = [] + else: + now_mode = cv_mode[1] + + #run ransac on the HSF crop\ + frame = cropped_image + + # try: + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) + thresh_add = 10 + rng = np.random.default_rng() + + f = False + + # Convert the image to grayscale, and set up thresholding. Thresholds here are basically a + # low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user + # configurable in this utility as we're dealing with variable lighting amounts/placement, as + # well as camera positioning and lensing. Therefore everyone's cutoff may be different. + # + # The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we + # crop the image earlier; it gives us less possible dark area to get confused about in the + # next step. + + # For measuring processing time of image processing + # Crop first to reduce the amount of data to process. + + #frame = frame[0:len(frame) - 5, :] + + # To reduce the processing data, first convert to 1-channel and then blur. + # The processing results were the same when I swapped the order of blurring and 1-channelization. + try: + frame = cv2.GaussianBlur(frame, (5, 5), 0) + except: + pass + + # 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) + + maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1]) + + # crop 15% sqare around min_loc +# frame = frame[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf, + # max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf] + + threshold_value = min_val + thresh_add + _, thresh = cv2.threshold(frame, threshold_value, 255, cv2.THRESH_BINARY) + try: + opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel) + closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel) + th_frame = 255 - closing + except: + # I want to eliminate try here because try tends to be slow in execution. + th_frame = 255 - frame + + + contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) + hull = [] + # This way is faster than contours[i] + # But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours] + for cnt in contours: + hull.append(cv2.convexHull(cnt, False)) + if not hull: + # If empty, go to next loop + pass + try: + + cnt = sorted(hull, key=cv2.contourArea) + maxcnt = cnt[-1] + # ellipse = cv2.fitEllipse(maxcnt) + ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), rng) + if ransac_data is None: + # ransac_data is None==maxcnt.shape[0] self.response_max: + if response_max is not None and cv2.mean(cropped_image)[0] > response_max: # blink cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1) - # If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way + # 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 except: @@ -652,27 +1136,27 @@ def HSRAC(self): # To reduce the processing data, first convert to 1-channel and then blur. # The processing results were the same when I swapped the order of blurring and 1-channelization. - frame_gray = cv2.GaussianBlur(frame, (5, 5), 0) + frame = cv2.GaussianBlur(frame, (5, 5), 0) # this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m - min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray) + min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame) maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1]) # crop 15% sqare around min_loc - # frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf, + # frame = frame[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf, # max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf] threshold_value = min_val + thresh_add - _, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY) + _, thresh = cv2.threshold(frame, threshold_value, 255, cv2.THRESH_BINARY) try: opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel) closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel) th_frame = 255 - closing except: # I want to eliminate try here because try tends to be slow in execution. - th_frame = 255 - frame_gray + th_frame = 255 - frame detect_start_time = timeit.default_timer() @@ -737,3 +1221,4 @@ def HSRAC(self): +''' \ No newline at end of file diff --git a/EyeTrackApp/osc_calibrate_filter.py b/EyeTrackApp/osc_calibrate_filter.py index 1b00e94..de5675c 100644 --- a/EyeTrackApp/osc_calibrate_filter.py +++ b/EyeTrackApp/osc_calibrate_filter.py @@ -12,9 +12,6 @@ def cal_osc(self, cx, cy): self.calibration_frame_counter = None self.xoff = cx self.yoff = cy - self.now_mode = self.cv_mode[0] - self.response_list = [] - self.response_max = 0 if sys.platform.startswith("win"): PlaySound('Audio/compleated.wav', SND_FILENAME | SND_ASYNC) elif self.calibration_frame_counter != None: diff --git a/EyeTrackApp/settings_widget.py b/EyeTrackApp/settings_widget.py index f1140a4..b6d7b22 100644 --- a/EyeTrackApp/settings_widget.py +++ b/EyeTrackApp/settings_widget.py @@ -32,7 +32,7 @@ class SettingsWidget: self.gui_min_cutoff = f"-MINCUTOFF{widget_id}-" self.gui_eye_falloff = f"-EYEFALLOFF{widget_id}-" self.gui_blink_sync = f"-BLINKSYNC{widget_id}-" - + self.gui_skip_autoradius = f"-SKIPAUTORADIUS{widget_id}-" self.gui_HSRACP = f"-HSRACP{widget_id}-" self.gui_RANSAC3DP = f"-RANSAC3DP{widget_id}-" self.gui_HSFP = f"-HSFP{widget_id}-" @@ -177,7 +177,15 @@ class SettingsWidget: ), ], - [sg.Text("HSF Radius:", background_color='#424042'), + [sg.Checkbox( + "HSF: Skip Auto Radius", + default=self.config.gui_skip_autoradius, + key=self.gui_skip_autoradius, + background_color='#424042', + tooltip = "To gain more control and possibly better tracking quality of HSF, please disable auto radius to enable manual adjustment.", + ), + + sg.Text("HSF Radius:", background_color='#424042'), sg.Slider( range=(1, 50), default_value=self.config.gui_HSF_radius, @@ -398,6 +406,11 @@ class SettingsWidget: self.config.gui_HSRAC = values[self.gui_HSRAC] changed = True + if self.config.gui_skip_autoradius != values[self.gui_skip_autoradius]: + self.config.gui_skip_autoradius = values[self.gui_skip_autoradius] + changed = True + + if self.config.gui_BLINK != values[self.gui_BLINK]: self.config.gui_BLINK = values[self.gui_BLINK] changed = True