diff --git a/EyeTrackApp/eye_processor.py b/EyeTrackApp/eye_processor.py index 1434e67..2fff820 100644 --- a/EyeTrackApp/eye_processor.py +++ b/EyeTrackApp/eye_processor.py @@ -19,8 +19,8 @@ @@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@( -HSR By: Sean.Denka (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer) -RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization) +HSR By: PallasNeko (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer) +RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization) BLOB By: Prohurtz#0001 (Main App Developer) Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator) @@ -51,10 +51,10 @@ if sys.platform.startswith("win"): import importlib from osc_calibrate_filter import * -from haar_surround_feature import * +from haar_surround_feature import External_Run_HSF from blob import * from ransac import * -from hsrac import * +from hsrac import External_Run_HSRACS from blink import * @@ -147,7 +147,6 @@ class EyeProcessor: self.cccs = False self.ts = 10 self.previous_rotation = self.config.rotation_angle - self.calibration_frame_counter self.camera_model = None self.detector_3d = None @@ -246,9 +245,9 @@ class EyeProcessor: def HSRACM(self): - cx, cy, thresh, gray_frame, uncropframe = External_Run_HSRACS.HSRACS(self) + cx, cy, thresh, gray_frame, uncropframe = External_Run_HSRACS().run(self.current_image_gray) self.current_image_gray = gray_frame - if self.prev_x == None: + if self.prev_x is None: self.prev_x = cx self.prev_y = cy #print(self.prev_x, self.prev_y, cx, cy) @@ -268,7 +267,7 @@ class EyeProcessor: # print("EYE MOVED TOO FAST") # self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, 0, 0, 0, False)) def HSFM(self): - cx, cy, frame = External_Run_HSF.HSFS(self) + cx, cy, frame = External_Run_HSF().run(self.current_image_gray) self.eyeopen = intense(cx, cy, self.current_image_gray) out_x, out_y = cal_osc(self, cx, cy) if cx == 0: @@ -347,7 +346,7 @@ class EyeProcessor: elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4: self.fourthalgo = self.RANSAC3DM - if self.settings.gui_HSRAC == True and self.settings.gui_HSRACP == 1: + if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1: self.firstalgo = self.HSRACM elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2: self.secondalgo = self.HSRACM diff --git a/EyeTrackApp/haar_surround_feature.py b/EyeTrackApp/haar_surround_feature.py index bbbe36b..2e66daf 100644 --- a/EyeTrackApp/haar_surround_feature.py +++ b/EyeTrackApp/haar_surround_feature.py @@ -1,16 +1,18 @@ -import functools -import math -import sys import timeit from functools import lru_cache import cv2 import numpy as np + +from utils.misc_utils import clamp +from utils.img_utils import safe_crop + + # from line_profiler_pycharm import profile video_path = "ezgif.com-gif-maker.avi" -imshow_enable = True +imshow_enable = False calc_print_enable = True save_video = False skip_autoradius = False @@ -27,181 +29,6 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames # step==(x,y) default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact -""" -Attention. -If using cv2.filter2D in this code, be careful with the kernel -https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python -""" - - -def TimeitWrapper(*args, **kwargs): - """ - This decorator @TimeitWrapper() prints the function name and execution time in seconds. - :param args: - :param kwargs: - :return: - """ - - def decorator(function): - @functools.wraps(function) - def wrapper(*args, **kwargs): - start = timeit.default_timer() - results = function(*args, **kwargs) - end = timeit.default_timer() - print('{} execution time: {:.10f} s'.format(function.__name__, end - start)) - return results - - return wrapper - - return decorator - - -class TimeitResult(object): - """ - from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55 - - Object returned by the timeit magic with info about the run. - Contains the following attributes : - loops: (int) number of loops done per measurement - repeat: (int) number of times the measurement has been repeated - best: (float) best execution time / number - all_runs: (list of float) execution time of each run (in s) - """ - - def __init__(self, loops, repeat, best, worst, all_runs, precision): - self.loops = loops - self.repeat = repeat - self.best = best - self.worst = worst - self.all_runs = all_runs - self._precision = precision - self.timings = [dt / self.loops for dt in all_runs] - - @property - def average(self): - return math.fsum(self.timings) / len(self.timings) - - @property - def stdev(self): - mean = self.average - return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5 - - def __str__(self): - pm = '+-' - if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: - try: - u'\xb1'.encode(sys.stdout.encoding) - pm = u'\xb1' - except: - pass - return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format( - pm=pm, - runs=self.repeat, - loops=self.loops, - loop_plural="" if self.loops == 1 else "s", - run_plural="" if self.repeat == 1 else "s", - mean=format_time(self.average, self._precision), - std=format_time(self.stdev, self._precision), - best=format_time(self.best, self._precision), - worst=format_time(self.worst, self._precision), - ) - - def _repr_pretty_(self, p, cycle): - unic = self.__str__() - p.text(u'') - - -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 µ, but has no sys.stdout.encoding set. - units = [u"s", u"ms", u'us', "ns"] # the save value - if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: - try: - u'\xb5'.encode(sys.stdout.encoding) - units = [u"s", u"ms", u'\xb5s', "ns"] - except: - pass - scaling = [1, 1e3, 1e6, 1e9] - - if timespan > 0.0: - order = min(-int(math.floor(math.log10(timespan)) // 3), 3) - else: - order = 3 - return u"%.*g %s" % (precision, timespan * scaling[order], units[order]) - - class CvParameters: # It may be a little slower because a dict named "self" is read for each function call. def __init__(self, radius, step): @@ -419,7 +246,7 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list): return frame_conv, min_response, center -class Auto_Radius_Calc(object): +class AutoRadiusCalc(object): def __init__(self): self.response_list = [] self.radius_cand_list = [] @@ -522,7 +349,7 @@ class Auto_Radius_Calc(object): return None -class Blink_Detector(object): +class BlinkDetector(object): def __init__(self): self.response_list = [] self.response_max = None @@ -665,7 +492,7 @@ class CenterCorrection(object): return out_x, out_y -class HSRAC_cls(object): +class HSF_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. @@ -679,9 +506,9 @@ class HSRAC_cls(object): self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"] self.now_modeo = self.cv_modeo[0] - self.auto_radius_calc = Auto_Radius_Calc() - self.blink_detector = Blink_Detector() - self.center_q1 = Blink_Detector() + self.auto_radius_calc = AutoRadiusCalc() + self.blink_detector = BlinkDetector() + self.center_q1 = BlinkDetector() self.center_correct = CenterCorrection() self.cap = None @@ -709,9 +536,13 @@ class HSRAC_cls(object): def single_run(self): # Temporary implementation to run - + + ## default_radius = 14 + # cropbox=[] # debug code + + frame = self.current_image_gray if self.now_modeo == self.cv_modeo[1]: # adjustment of radius @@ -756,7 +587,12 @@ class HSRAC_cls(object): lower_y = center_y - radius # Crop the image using the calculated bounds - cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] + + cropped_image = safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y) + + # cropbox = [clamp(val, 0, gray_frame.shape[i]) for i, val in + # zip([1, 0, 1, 0], [lower_x, lower_y, upper_x, upper_y])] # debug code + 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 @@ -770,8 +606,14 @@ class HSRAC_cls(object): lower_x = center_x - self.center_correct.center_q1_radius upper_y = center_y + self.center_correct.center_q1_radius lower_y = center_y - self.center_correct.center_q1_radius - self.center_q1.add_response(cv2.mean(gray_frame[lower_y:upper_y, lower_x:upper_x])[0]) - + + self.center_q1.add_response( + cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y,keepsize=False))[ + 0 + ] + ) + + else: self.blink_detector.calc_thresh() @@ -792,9 +634,19 @@ class HSRAC_cls(object): else: # pass if not self.center_correct.setup_comp: - self.center_correct.init_array(gray_frame.shape, self.center_q1.quartile_1, radius) - - center_x, center_y = self.center_correct.correction(gray_frame, center_x, center_y) + self.center_correct.init_array( + gray_frame.shape, self.center_q1.quartile_1, radius + ) + elif self.center_correct.frame_shape != gray_frame.shape: + """The resolution should have changed and the statistics should have changed, so essentially the statistics + need to be reworked, but implementation will be postponed as viability is the highest priority. """ + self.center_correct.init_array( + gray_frame.shape, self.center_q1.quartile_1, radius + ) + + center_x, center_y = self.center_correct.correction( + gray_frame, center_x, center_y + ) # Define the center point and radius center_xy = (center_x, center_y) upper_x = center_x + radius @@ -802,10 +654,16 @@ class HSRAC_cls(object): 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 imshow_enable or save_video: - # cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1) - # cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1) + cropped_image = safe_crop( + gray_frame, lower_x, lower_y, upper_x, upper_y + ) + # cropbox = [clamp(val, 0, gray_frame.shape[i]) for i, val in + # zip([1, 0, 1, 0], [lower_x, lower_y, upper_x, upper_y])] # debug code + + # if imshow_enable or save_video: + # cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1) + # cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1) + # If you want to update response_max. it may be more cost-effective to rewrite response_list in the following way # https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue @@ -837,20 +695,28 @@ class HSRAC_cls(object): self.now_modeo = self.cv_modeo[2] else: self.now_modeo = self.cv_modeo[1] - + + + # debug code + # return center_x,center_y,cropbox,frame return center_x, center_y, frame -class External_Run_HSF: +class External_Run_HSF(object): + def __init__(self): + self.algo = HSF_cls() - hsrac = HSRAC_cls() - - def HSFS(self): - External_Run_HSF.hsrac.current_image_gray = self.current_image_gray - center_x, center_y, frame = External_Run_HSF.hsrac.single_run() + def run(self, current_image_gray): + self.algo.current_image_gray = current_image_gray + # debug code + # center_x, center_y,cropbox, frame = self.algo.single_run() + # return center_x, center_y,cropbox, frame + center_x, center_y, frame = self.algo.single_run() return center_x, center_y, frame -if __name__ == '__main__': - hsrac = HSRAC_cls() - hsrac.open_video(video_path) - while hsrac.read_frame(): - _ = hsrac.single_run() \ No newline at end of file + + +if __name__ == "__main__": + hsf = HSF_cls() + hsf.open_video(video_path) + while hsf.read_frame(): + _ = hsf.single_run() diff --git a/EyeTrackApp/hsrac.py b/EyeTrackApp/hsrac.py index 671a706..cc4265b 100644 --- a/EyeTrackApp/hsrac.py +++ b/EyeTrackApp/hsrac.py @@ -1,15 +1,24 @@ -import functools -import math -import sys import timeit -from functools import lru_cache import cv2 import numpy as np +from haar_surround_feature import ( + AutoRadiusCalc, + BlinkDetector, + CvParameters, conv_int, + frameint_get_xy_step, +) +from utils.img_utils import safe_crop +from utils.misc_utils import clamp + # from line_profiler_pycharm import profile +#RANSAC + +thresh_add = 10 + video_path = "ezgif.com-gif-maker.avi" imshow_enable = True calc_print_enable = True @@ -28,799 +37,6 @@ blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames # step==(x,y) default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact -""" -Attention. -If using cv2.filter2D in this code, be careful with the kernel -https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python -""" - - -def TimeitWrapper(*args, **kwargs): - """ - This decorator @TimeitWrapper() prints the function name and execution time in seconds. - :param args: - :param kwargs: - :return: - """ - - def decorator(function): - @functools.wraps(function) - def wrapper(*args, **kwargs): - start = timeit.default_timer() - results = function(*args, **kwargs) - end = timeit.default_timer() - print('{} execution time: {:.10f} s'.format(function.__name__, end - start)) - return results - - return wrapper - - return decorator - - -class TimeitResult(object): - """ - from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55 - - Object returned by the timeit magic with info about the run. - Contains the following attributes : - loops: (int) number of loops done per measurement - repeat: (int) number of times the measurement has been repeated - best: (float) best execution time / number - all_runs: (list of float) execution time of each run (in s) - """ - - def __init__(self, loops, repeat, best, worst, all_runs, precision): - self.loops = loops - self.repeat = repeat - self.best = best - self.worst = worst - self.all_runs = all_runs - self._precision = precision - self.timings = [dt / self.loops for dt in all_runs] - - @property - def average(self): - return math.fsum(self.timings) / len(self.timings) - - @property - def stdev(self): - mean = self.average - return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5 - - def __str__(self): - pm = '+-' - if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: - try: - u'\xb1'.encode(sys.stdout.encoding) - pm = u'\xb1' - except: - pass - return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format( - pm=pm, - runs=self.repeat, - loops=self.loops, - loop_plural="" if self.loops == 1 else "s", - run_plural="" if self.repeat == 1 else "s", - mean=format_time(self.average, self._precision), - std=format_time(self.stdev, self._precision), - best=format_time(self.best, self._precision), - worst=format_time(self.worst, self._precision), - ) - - def _repr_pretty_(self, p, cycle): - unic = self.__str__() - p.text(u'') - - -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 µ, but has no sys.stdout.encoding set. - units = [u"s", u"ms", u'us', "ns"] # the save value - if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: - try: - u'\xb5'.encode(sys.stdout.encoding) - units = [u"s", u"ms", u'\xb5s', "ns"] - except: - pass - scaling = [1, 1e3, 1e6, 1e9] - - if timespan > 0.0: - order = min(-int(math.floor(math.log10(timespan)) // 3), 3) - else: - order = 3 - return u"%.*g %s" % (precision, timespan * scaling[order], units[order]) - - -class CvParameters: - # It may be a little slower because a dict named "self" is read for each function call. - def __init__(self, radius, step): - # self.prev_radius=radius - self._radius = radius - self.pad = 2 * radius - # self.prev_step=step - self._step = step - self._hsf = HaarSurroundFeature(radius) - - def get_rpsh(self): - return self._radius, self.pad, self._step, self._hsf - # Essentially, the following would be preferable, but it would take twice as long to call. - # return self.radius, self.pad, self.step, self.hsf - - @property - def radius(self): - return self._radius - - @radius.setter - def radius(self, now_radius): - # self.prev_radius=self._radius - self._radius = now_radius - self.pad = 2 * now_radius - self.hsf = now_radius - - @property - def step(self): - return self._step - - @step.setter - def step(self, now_step): - # self.prev_step=self.step - self._step = now_step - - @property - def hsf(self): - return self._hsf - - @hsf.setter - def hsf(self, now_radius): - self._hsf = HaarSurroundFeature(now_radius) - - -class HaarSurroundFeature: - - def __init__(self, r_inner, r_outer=None, val=None): - if r_outer is None: - r_outer = r_inner * 3 - # print(r_outer) - r_inner2 = r_inner * r_inner - count_inner = r_inner2 - count_outer = r_outer * r_outer - r_inner2 - - if val is None: - val_inner = 1.0 / r_inner2 - val_outer = -val_inner * count_inner / count_outer - - else: - val_inner = val[0] - val_outer = val[1] - - self.val_in = np.array(val_inner, dtype=np.float64) - self.val_out = np.array(val_outer, dtype=np.float64) - self.r_in = r_inner - self.r_out = r_outer - - def get_kernel(self): - # Defined here, but not yet used? - # Create a kernel filled with the value of self.val_out - kernel = np.ones(shape=(2 * self.r_out - 1, 2 * self.r_out - 1), dtype=np.float64) * self.val_out - - # Set the values of the inner area of the kernel using array slicing - start = (self.r_out - self.r_in) - end = (self.r_out + self.r_in - 1) - kernel[start:end, start:end] = self.val_in - - return kernel - - -def to_gray(frame): - # 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): - """ - :param imageshape: (height(row),width(col)). row==y,cal==x - :param xysteps: (x,y) - :param pad: int - :param start_offset: (x,y) or None - :param end_offset: (x,y) or None - :return: xy_np:tuple(x,y) - """ - row, col = imageshape - row -= 1 - col -= 1 - x_step, y_step = xysteps - - # This is not beautiful. - start_pad_x = start_pad_y = end_pad_x = end_pad_y = pad - - if start_offset is not None: - start_pad_x += start_offset[0] - start_pad_y += start_offset[1] - if end_offset is not None: - end_pad_x += end_offset[0] - end_pad_y += end_offset[1] - y_np = np.arange(start_pad_y, row - end_pad_y, y_step) - x_np = np.arange(start_pad_x, col - end_pad_x, x_step) - - xy_np = (x_np, y_np) - - return xy_np - - -@lru_cache(maxsize=lru_maxsize_vvs) -def get_hsf_empty_array(len_syx, frameint_x, frame_int_dtype, fcshape): - # Function to reduce array allocation by providing an empty array first and recycling it with lru - inner_sum = np.empty(len_syx, dtype=frame_int_dtype) - outer_sum = np.empty(len_syx, dtype=frame_int_dtype) - p_temp = np.empty((len_syx[0], frameint_x), dtype=frame_int_dtype) - p00 = np.empty(len_syx, dtype=frame_int_dtype) - p11 = np.empty(len_syx, dtype=frame_int_dtype) - p01 = np.empty(len_syx, dtype=frame_int_dtype) - p10 = np.empty(len_syx, dtype=frame_int_dtype) - response_list = np.empty(len_syx, dtype=np.float64) - frame_conv = np.zeros(shape=fcshape[0], dtype=np.uint8) - frame_conv_stride = frame_conv[::fcshape[1], ::fcshape[2]] - return (inner_sum, outer_sum), p_temp, (p00, p11, p01, p10), response_list, (frame_conv, frame_conv_stride) - - -# @profile -def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list): - """ - :param frame_int: - :param kernel: hsf - :param step: (x,y) - :param padding: int - :return: - """ - row, col = frame_int.shape - row -= 1 - col -= 1 - x_step, y_step = xy_step - # padding2 = 2 * padding - f_shape = row - 2 * padding, col - 2 * padding - r_in = kernel.r_in - - len_sx, len_sy = len(xy_steps_list[0]), len(xy_steps_list[1]) - inout_sum, p_temp, p_list, response_list, frameconvlist = get_hsf_empty_array((len_sy, len_sx), col + 1, - frame_int.dtype, (f_shape, y_step, x_step)) - inner_sum, outer_sum = inout_sum - p00, p11, p01, p10 = p_list - frame_conv, frame_conv_stride = frameconvlist - - y_rin_m = xy_steps_list[1] - r_in - x_rin_m = xy_steps_list[0] - r_in - y_rin_p = xy_steps_list[1] + r_in - x_rin_p = xy_steps_list[0] + r_in - # xx==(y,x),m==MINUS,p==PLUS, ex: mm==(y-,x-) - inarr_mm = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step] - inarr_mp = frame_int[y_rin_m[0]:y_rin_m[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step] - inarr_pm = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_m[0]:x_rin_m[-1] + 1:x_step] - inarr_pp = frame_int[y_rin_p[0]:y_rin_p[-1] + 1:y_step, x_rin_p[0]:x_rin_p[-1] + 1:x_step] - - # == inarr_mm + inarr_pp - inarr_mp - inarr_pm - inner_sum[:, :] = inarr_mm - inner_sum += inarr_pp - inner_sum -= inarr_mp - inner_sum -= inarr_pm - - # Bottleneck here, I want to make it smarter. Someone do it. - # (y,x) - # p00=max(y_ro_m,0),max(x_ro_m,0) - # p11=min(y_ro_p,ylim),min(x_ro_p,xlim) - # p01=max(y_ro_m,0),min(x_ro_p,xlim) - # p10=min(y_ro_p,ylim),max(x_ro_m,0) - y_ro_m = xy_steps_list[1] - kernel.r_out - x_ro_m = xy_steps_list[0] - kernel.r_out - y_ro_p = xy_steps_list[1] + kernel.r_out - x_ro_p = xy_steps_list[0] + kernel.r_out - # p00 calc - np.take(frame_int, y_ro_m, axis=0, mode="clip", out=p_temp) - np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p00) - # p01 calc - np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p01) - # p11 calc - np.take(frame_int, y_ro_p, axis=0, mode="clip", out=p_temp) - np.take(p_temp, x_ro_p, axis=1, mode="clip", out=p11) - # p10 calc - np.take(p_temp, x_ro_m, axis=1, mode="clip", out=p10) - # the point is this - # p00=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip") - # p11=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip") - # p01=np.take(np.take(frame_int, y_ro_m, axis=0, mode="clip"), x_ro_p, axis=1, mode="clip") - # p10=np.take(np.take(frame_int, y_ro_p, axis=0, mode="clip"), x_ro_m, axis=1, mode="clip") - - outer_sum[:, :] = p00 + p11 - p01 - p10 - inner_sum - - np.multiply(kernel.val_in, inner_sum, dtype=np.float64, out=response_list) - response_list += kernel.val_out * outer_sum - - # min_response, max_val, min_loc, max_loc = cv2.minMaxLoc(response_list) - min_response, _, min_loc, _ = cv2.minMaxLoc(response_list) - - center = ((xy_steps_list[0][min_loc[0]] - padding), (xy_steps_list[1][min_loc[1]] - padding)) - - frame_conv_stride[:, :] = response_list - # or - # frame_conv_stride[:, :] = response_list.astype(np.uint8) - - return frame_conv, min_response, center - - -class Auto_Radius_Calc(object): - def __init__(self): - self.response_list = [] - self.radius_cand_list = [] - self.adj_comp_flag = False - - self.radius_middle_index = None - - self.left_item = None - self.right_item = None - self.left_index = None - self.right_index = None - - def get_radius(self): - prev_res_len = len(self.response_list) - # adjustment of radius - if prev_res_len == 1: - # len==1==response_list==[default_radius] - self.adj_comp_flag = False - return auto_radius_range[0] - elif prev_res_len == 2: - # len==2==response_list==[default_radius, auto_radius_range[0]] - self.adj_comp_flag = False - return auto_radius_range[1] - elif prev_res_len == 3: - # len==3==response_list==[default_radius,auto_radius_range[0],auto_radius_range[1]] - if self.response_list[1][1] < self.response_list[2][1]: - self.left_item = self.response_list[1] - self.right_item = self.response_list[0] - else: - self.left_item = self.response_list[0] - self.right_item = self.response_list[2] - self.radius_cand_list = [i for i in range(self.left_item[0], self.right_item[0] + auto_radius_step, auto_radius_step)] - self.left_index = 0 - self.right_index = len(self.radius_cand_list) - 1 - self.radius_middle_index = (self.left_index + self.right_index) // 2 - self.adj_comp_flag = False - return self.radius_cand_list[self.radius_middle_index] - else: - if self.left_index <= self.right_index and self.left_index != self.radius_middle_index: - if (self.left_item[1] + self.response_list[-1][1]) < (self.right_item[1] + self.response_list[-1][1]): - self.right_item = self.response_list[-1] - self.right_index = self.radius_middle_index - 1 - self.radius_middle_index = (self.left_index + self.right_index) // 2 - self.adj_comp_flag = False - return self.radius_cand_list[self.radius_middle_index] - if (self.left_item[1] + self.response_list[-1][1]) > (self.right_item[1] + self.response_list[-1][1]): - self.left_item = self.response_list[-1] - self.left_index = self.radius_middle_index + 1 - self.radius_middle_index = (self.left_index + self.right_index) // 2 - self.adj_comp_flag = False - return self.radius_cand_list[self.radius_middle_index] - self.adj_comp_flag = True - return self.radius_cand_list[self.radius_middle_index] - - def get_radius_base(self): - """ - Use it when the new version doesn't work well. - :return: - """ - - prev_res_len = len(self.response_list) - # adjustment of radius - if prev_res_len == 1: - # len==1==response_list==[default_radius] - self.adj_comp_flag = False - return auto_radius_range[0] - elif prev_res_len == 2: - # len==2==response_list==[default_radius, auto_radius_range[0]] - self.adj_comp_flag = False - return 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(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 Blink_Detector(object): - def __init__(self): - self.response_list = [] - self.response_max = None - self.enable_detect_flg = False - self.quartile_1 = None - - def calc_thresh(self): - # Calculate response_max by computing interquartile range, IQR - # self.response_listo = np.array(self.response_listo) - # 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) - - -class CenterCorrection(object): - def __init__(self): - # Tunable parameters - kernel_size = 7 # 3 or 5 or 7 - self.hist_thr = float(4) # 4% - self.center_q1_radius = 20 - - self.setup_comp = False - self.quartile_1 = None - self.radius = None - self.frame_shape = None - self.frame_mask = None - self.frame_bin = None - self.frame_final = None - self.morph_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size)) - self.morph_kernel2 = np.ones((3, 3)) - self.hist_index = np.arange(256) - self.hist = np.empty((256, 1)) - self.hist_norm = np.empty((256, 1)) - - def init_array(self, gray_shape, quartile_1, radius): - self.frame_shape = gray_shape - self.frame_mask = np.empty(gray_shape, dtype=np.uint8) - self.frame_bin = np.empty(gray_shape, dtype=np.uint8) - self.frame_final = np.empty(gray_shape, dtype=np.uint8) - self.quartile_1 = quartile_1 - self.radius = radius - self.setup_comp = True - - # def reset_array(self): - # self.frame_mask.fill(0) - - def correction(self, gray_frame, orig_x, orig_y): - center_x, center_y = orig_x, orig_y - self.frame_mask.fill(0) - - # cv2.circle(self.frame_mask, center=(center_x, center_y), radius=int(self.radius * 2), color=255, thickness=-1) - - # bottleneck - cv2.calcHist([gray_frame], [0], None, [256], [0, 256], hist=self.hist) - - cv2.normalize(self.hist, self.hist_norm, alpha=100.0, norm_type=cv2.NORM_L1) - hist_per = self.hist_norm.cumsum() - hist_index_list = self.hist_index[hist_per >= self.hist_thr] - frame_thr = hist_index_list[0] if len(hist_index_list) else np.percentile(cv2.bitwise_or(255 - self.frame_mask, gray_frame), 4) - - # bottleneck - self.frame_bin = cv2.threshold(gray_frame, frame_thr, 1, cv2.THRESH_BINARY_INV)[1] - cropped_x, cropped_y, cropped_w, cropped_h = cv2.boundingRect(self.frame_bin) - - self.frame_final = cv2.bitwise_and(self.frame_bin, self.frame_mask) - - # bottleneck - self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_CLOSE, self.morph_kernel) - self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_OPEN, self.morph_kernel) - - if (cropped_h, cropped_w) == self.frame_shape: - # Not detected. - base_x, base_y = center_x, center_y - else: - base_x = cropped_x + cropped_w // 2 - base_y = cropped_y + cropped_h // 2 - if self.frame_final[base_y, base_x] != 1: - if self.frame_final[center_y, center_x] != 1: - self.frame_final = cv2.morphologyEx(self.frame_final, cv2.MORPH_DILATE, self.morph_kernel2, iterations=3) - else: - base_x, base_y = center_x, center_y - - contours, _ = cv2.findContours(self.frame_final, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) - contours_box = [cv2.boundingRect(cnt) for cnt in contours] - contours_dist = np.array( - [abs(base_x - (cnt_x + cnt_w / 2)) + abs(base_y - (cnt_y + cnt_h / 2)) for cnt_x, cnt_y, cnt_w, cnt_h in contours_box]) - - if len(contours_box): - cropped_x2, cropped_y2, cropped_w2, cropped_h2 = contours_box[contours_dist.argmin()] - x = cropped_x2 + cropped_w2 // 2 - y = cropped_y2 + cropped_h2 // 2 - else: - x = center_x - y = center_y - - # if imshow_enable: - # cv2.circle(frame, (orig_x, orig_y), 10, (255, 0, 0), -1) - # cv2.circle(frame, (x, y), 7, (0, 0, 255), -1) - - # - # out_x = center_x if abs(x - center_x) > radius else x - # out_y = center_y if abs(y - center_y) > radius else y - out_x, out_y = orig_x, orig_y - if gray_frame[int(max(y - 5, 0)):int(min(y + 5, self.frame_shape[0])), - int(max(x - 5, 0)):int(min(x + 5, self.frame_shape[1]))].min() < self.quartile_1: - out_x = x - out_y = y - - # if imshow_enable: - # cv2.circle(frame, (out_x, out_y), 5, (0, 255, 0), -1) - # - # cv2.imshow("frame_bin", self.frame_bin * 255) - # cv2.imshow("frame_final", self.frame_final * 255) - return out_x, out_y - - -#RANSAC - - -video_path = "demo2.mp4" -imshow_enable = True -save_video = False - -thresh_add = 10 - - -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 µ, but has no sys.stdout.encoding set. - units = [u"s", u"ms", u'us', "ns"] # the save value - if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: - try: - u'\xb5'.encode(sys.stdout.encoding) - units = [u"s", u"ms", u'\xb5s', "ns"] - except: - pass - scaling = [1, 1e3, 1e6, 1e9] - - if timespan > 0.0: - order = min(-int(math.floor(math.log10(timespan)) // 3), 3) - else: - order = 3 - return u"%.*g %s" % (precision, timespan * scaling[order], units[order]) - def ellipse_model(data, y, f): """ @@ -926,11 +142,6 @@ def fit_rotated_ellipse(data, P): return (cx, cy, w, h, theta) - - - - - 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. @@ -944,20 +155,23 @@ class HSRAC_cls(object): self.cv_modeo = ["first_frame", "radius_adjust", "blink_adjust", "normal"] self.now_modeo = self.cv_modeo[0] - - self.auto_radius_calc = Auto_Radius_Calc() - self.blink_detector = Blink_Detector() - self.center_q1 = Blink_Detector() - self.center_correct = CenterCorrection() - + + self.auto_radius_calc = AutoRadiusCalc() + self.blink_detector = BlinkDetector() + self.center_q1 = BlinkDetector() + + self.cap = None self.timedict = {"to_gray": [], "int_img": [], "conv_int": [], "crop": [], "total_cv": []} # ransac self.rng = np.random.default_rng() - self.kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) - + + # self.kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) + # or + self.kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3)) + def open_video(self, video_path): # Temporary implementation to run cap = cv2.VideoCapture(video_path) @@ -973,17 +187,20 @@ class HSRAC_cls(object): 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. + # self.current_image=frame # debug code self.current_image_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) return True return False def single_run(self): # Temporary implementation to run - ## default_radius = 14 + # ori_frame = self.current_image.copy()# debug code + # cropbox=[] # debug code + + blink_bd = False frame = self.current_image_gray - # frame = cv2.copyMakeBorder(frame, 21, 21, 21, 21, cv2.BORDER_CONSTANT, value=[255, 255, 255]) # add a border to prevent overcropping the image. if self.now_modeo == self.cv_modeo[1]: # adjustment of radius @@ -1028,7 +245,9 @@ class HSRAC_cls(object): lower_y = center_y - radius # Crop the image using the calculated bounds - cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] + cropped_image = safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y) + + # cropbox=[clamp(val, 0, gray_frame.shape[i]) for i,val in zip([1,0,1,0],[lower_x,lower_y,upper_x,upper_y])] # debug code 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 @@ -1037,20 +256,24 @@ class HSRAC_cls(object): # Statistics for blink detection if self.blink_detector.response_len() < blink_init_frames: self.blink_detector.add_response(cv2.mean(cropped_image)[0]) - - upper_x = center_x + self.center_correct.center_q1_radius - lower_x = center_x - self.center_correct.center_q1_radius - upper_y = center_y + self.center_correct.center_q1_radius - lower_y = center_y - self.center_correct.center_q1_radius - self.center_q1.add_response(cv2.mean(gray_frame[lower_y:upper_y, lower_x:upper_x])[0]) - + + upper_x = center_x + 20 + lower_x = center_x - 20 + upper_y = center_y + 20 + lower_y = center_y - 20 + self.center_q1.add_response( + cv2.mean(safe_crop(gray_frame, lower_x, lower_y, upper_x, upper_y,keepsize=False))[ + 0 + ] + ) + else: self.blink_detector.calc_thresh() self.center_q1.calc_thresh() self.now_modeo = self.cv_modeo[3] else: - if 0 in cropped_image.shape: + if 0 in cropped_image.shape: # This line may not be needed. The image will be cropped using safecrop. # If shape contains 0, it is not detected well. print("Something's wrong.") else: @@ -1060,24 +283,11 @@ class HSRAC_cls(object): # (i.e., if the cropimage is whitish if self.blink_detector.detect(cv2.mean(cropped_image)[0]): # blink - pass - else: - # pass - if not self.center_correct.setup_comp: - self.center_correct.init_array(gray_frame.shape, self.center_q1.quartile_1, radius) - - center_x, center_y = self.center_correct.correction(gray_frame, center_x, center_y) - # Define the center point and radius - center_xy = (center_x, center_y) - 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 imshow_enable or save_video: - # cv2.circle(frame, (orig_x, orig_y), 6, (0, 0, 255), -1) - # cv2.circle(frame, (center_x, center_y), 3, (255, 0, 0), -1) + print("BLINK BD") + 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 @@ -1086,23 +296,22 @@ class HSRAC_cls(object): 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 + # 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: @@ -1113,45 +322,63 @@ class HSRAC_cls(object): self.now_modeo = self.cv_modeo[1] - newFrame2 = frame.copy() - #frame = cropped_image # For measuring processing time of image processing cv_start_time = timeit.default_timer() # Crop first to reduce the amount of data to process. - # frame = cropped_image[0:len(cropped_image) - 10, :] + # frame = cropped_image[0:len(cropped_image) - 10, :] # To reduce the processing data, first convert to 1-channel and then blur. # The processing results were the same when I swapped the order of blurring and 1-channelization. frame_gray = cv2.GaussianBlur(frame, (5, 5), 0) - - upper_x = center_x + 20 - lower_x = center_x - 20 - upper_y = center_y + 20 - lower_y = center_y - 20 + hsf_center_x, hsf_center_y = center_x.copy(), center_y.copy() + ransac_xy_offset = (hsf_center_x-20, hsf_center_y-20) + upper_x = hsf_center_x + 20 + lower_x = hsf_center_x - 20 + upper_y = hsf_center_y + 20 + lower_y = hsf_center_y - 20 # Crop the image using the calculated bounds - frame_gray = frame_gray[lower_y:upper_y, lower_x:upper_x] - frame = frame_gray + + frame_gray_crop = safe_crop(frame_gray, lower_x, lower_y, upper_x, upper_y) + frame = frame_gray_crop # this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m - min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray) - - maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1]) - - # crop 15% sqare around min_loc - # frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf, - # max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf] - + min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray_crop) + threshold_value = min_val + thresh_add - _, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY) - # print(thresh.shape, frame_gray.shape) + _, thresh = cv2.threshold(frame_gray_crop, threshold_value, 255, cv2.THRESH_BINARY) + # print(thresh.shape, frame_gray.shape) try: + opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, self.kernel) closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, self.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_gray_crop contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) + # or + # contours, _=cv2.findContours(th_frame, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) + + if not blink_bd and self.blink_detector.enable_detect_flg: + threshold_value = self.center_q1.quartile_1 + if threshold_value= 2.1 * h: #new blink detection algo lmao this works pretty good actually print("RAN BLINK") #return center_x, center_y, frame, frame, True - csx = frame.shape[0] - csy = frame.shape[1] + # cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF + # cy = center_y - (csy - cy) - #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) - - cx = (cx - 20) + center_x - cy = (cy - 20) + center_y + # 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 imshow_enable or save_video: - cv2.drawContours(frame_gray, contours, -1, (255, 0, 0), 1) - cv2.circle(frame_gray, (cx, cy), 2, (0, 0, 255), -1) - # cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2)) - cv2.ellipse(frame_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, ) - - except: + # if imshow_enable or save_video: + # + # cv2.circle(ori_frame, (orig_x, orig_y), 3, (0, 255, 0), -1) + # cv2.drawContours(ori_frame, contours, -1, (255, 0, 0), 1) + # cv2.circle(ori_frame, (cx, cy), 2, (0, 0, 255), -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) + # cv2.imshow("ori_frame",ori_frame) + # if cv2.waitKey(1) & 0xFF == ord("q"): + # pass + + except Exception as e: + # print(e) pass + + # debug code + # try: + # if any([isinstance(val, float) for val in [cx, cy]]): + # print() + # return int(cx), int(cy),cropbox, ori_frame,thresh, frame, gray_frame + # except: + # if any([isinstance(val, float) for val in [center_x, center_y]]): + # print() + # return center_x, center_y,cropbox, ori_frame,thresh, frame, gray_frame + # print(frame_gray.shape, thresh.shape) - - # print(frame_gray.shape, thresh.shape) try: - return cx, cy, thresh, frame, gray_frame + return int(cx), int(cy), thresh, frame, gray_frame except: - return center_x, center_y, thresh, frame, gray_frame + return int(center_x), int(center_y), thresh, frame, gray_frame -class External_Run_HSRACS: - hsrac = HSRAC_cls() - def HSRACS(self): - External_Run_HSRACS.hsrac.current_image_gray = self.current_image_gray - center_x, center_y, thresh, frame, gray_frame = External_Run_HSRACS.hsrac.single_run() +class External_Run_HSRACS(object): + def __init__(self): + self.algo = HSRAC_cls() + + def run(self, current_image_gray): + self.algo.current_image_gray = current_image_gray + #debug code + # center_x, center_y,cropbox,ori_frame, thresh, frame, gray_frame = self.algo.single_run() + # return center_x, center_y,cropbox,ori_frame, thresh, frame, gray_frame + center_x, center_y, thresh, frame, gray_frame = self.algo.single_run() return center_x, center_y, thresh, frame, gray_frame -if __name__ == '__main__': + +if __name__ == "__main__": hsrac = HSRAC_cls() hsrac.open_video(video_path) while hsrac.read_frame(): _ = hsrac.single_run() - + + # hsrac = HSRAC_cls() + # hsrac.open_video(video_path) + # hsf = HSF_cls() + # while hsrac.read_frame(): + # hsf.current_image_gray = hsrac.current_image_gray.copy() + # _ = hsrac.single_run() + # + # _ = hsf.single_run() + + # w_video=True + # + # er_hsracs=External_Run_HSRACS() + # er_hsracs.algo.open_video(video_path) + # er_hsf=External_Run_HSF() + # + # if w_video: + # filepath = 'test.mp4' + # codec = cv2.VideoWriter_fourcc(*"x264") + # video = cv2.VideoWriter(filepath, codec, 60.0, (200,150))#(60, 60)) # (150, 200)) + # while er_hsracs.algo.read_frame(): + # base_gray = er_hsracs.algo.current_image_gray.copy() + # base_img=er_hsracs.algo.current_image.copy() + # cv2.imshow("frame",base_gray) + # hsf_x, hsf_y, hsf_cropbox,*_ = er_hsf.run(base_gray) + # + # # 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), 6, (0, 0, 255), -1) + # try: + # cv2.circle(base_img, (hsrac_x, hsrac_y), 3, (255, 0, 0), -1) + # except: + # print() + # cv2.imshow("hsf_hsrac",base_img) + # if cv2.waitKey(1) & 0xFF == ord("q"): + # pass + # if w_video: + # video.write(ori_frame) + # if w_video: + # video.release() + # # cv2.imwrite("b.png",er_hsracs.algo.result2) + # er_hsracs.algo.cap.release() + # cv2.destroyAllWindows() + \ No newline at end of file diff --git a/EyeTrackApp/ransac.py b/EyeTrackApp/ransac.py index 355600a..06d7c40 100644 --- a/EyeTrackApp/ransac.py +++ b/EyeTrackApp/ransac.py @@ -19,7 +19,7 @@ @@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@( -RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization) +RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization) Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator) Copyright (c) 2022 EyeTrackVR <3 diff --git a/EyeTrackApp/utils/__init__.py b/EyeTrackApp/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/EyeTrackApp/utils/img_utils.py b/EyeTrackApp/utils/img_utils.py new file mode 100644 index 0000000..b93d4b1 --- /dev/null +++ b/EyeTrackApp/utils/img_utils.py @@ -0,0 +1,12 @@ +import cv2 + + +def safe_crop(img, x, y, x2, y2, keepsize=True): + # The order of the arguments can be reconsidered. + img_h, img_w = img.shape[:2] + outimg = img[max(0, y) : min(img_h, y2), max(0, x) : min(img_w, x2)].copy() + reqsize_x, reqsize_y = abs(x2 - x), abs(y2 - y) + if keepsize and outimg.shape[:2] != (reqsize_y, reqsize_x): + # If the size is different from the expected size (smaller by the amount that is out of range) + outimg = cv2.resize(outimg, (reqsize_x, reqsize_y)) + return outimg diff --git a/EyeTrackApp/utils/misc_utils.py b/EyeTrackApp/utils/misc_utils.py new file mode 100644 index 0000000..13931a7 --- /dev/null +++ b/EyeTrackApp/utils/misc_utils.py @@ -0,0 +1,2 @@ +def clamp(x, low, high): + return max(low, min(x, high)) \ No newline at end of file diff --git a/EyeTrackApp/utils/time_utils.py b/EyeTrackApp/utils/time_utils.py new file mode 100644 index 0000000..ec46710 --- /dev/null +++ b/EyeTrackApp/utils/time_utils.py @@ -0,0 +1,171 @@ +import functools +import math +import sys +import timeit + +def TimeitWrapper(*args, **kwargs): + """ + This decorator @TimeitWrapper() prints the function name and execution time in seconds. + :param args: + :param kwargs: + :return: + """ + + def decorator(function): + @functools.wraps(function) + def wrapper(*args, **kwargs): + start = timeit.default_timer() + results = function(*args, **kwargs) + end = timeit.default_timer() + print('{} execution time: {:.10f} s'.format(function.__name__, end - start)) + return results + + return wrapper + + return decorator + + +class TimeitResult(object): + """ + from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55 + + Object returned by the timeit magic with info about the run. + Contains the following attributes : + loops: (int) number of loops done per measurement + repeat: (int) number of times the measurement has been repeated + best: (float) best execution time / number + all_runs: (list of float) execution time of each run (in s) + """ + + def __init__(self, loops, repeat, best, worst, all_runs, precision): + self.loops = loops + self.repeat = repeat + self.best = best + self.worst = worst + self.all_runs = all_runs + self._precision = precision + self.timings = [dt / self.loops for dt in all_runs] + + @property + def average(self): + return math.fsum(self.timings) / len(self.timings) + + @property + def stdev(self): + mean = self.average + return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5 + + def __str__(self): + pm = '+-' + if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: + try: + u'\xb1'.encode(sys.stdout.encoding) + pm = u'\xb1' + except: + pass + return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format( + pm=pm, + runs=self.repeat, + loops=self.loops, + loop_plural="" if self.loops == 1 else "s", + run_plural="" if self.repeat == 1 else "s", + mean=format_time(self.average, self._precision), + std=format_time(self.stdev, self._precision), + best=format_time(self.best, self._precision), + worst=format_time(self.worst, self._precision), + ) + + def _repr_pretty_(self, p, cycle): + unic = self.__str__() + p.text(u'') + + +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 µ, but has no sys.stdout.encoding set. + units = [u"s", u"ms", u'us', "ns"] # the save value + if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding: + try: + u'\xb5'.encode(sys.stdout.encoding) + units = [u"s", u"ms", u'\xb5s', "ns"] + except: + pass + scaling = [1, 1e3, 1e6, 1e9] + + if timespan > 0.0: + order = min(-int(math.floor(math.log10(timespan)) // 3), 3) + else: + order = 3 + return u"%.*g %s" % (precision, timespan * scaling[order], units[order]) \ No newline at end of file