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hsrac sep
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@ -1,3 +1,31 @@
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'''
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------------------------------------------------------------------------------------------------------
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,@@@@@@
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@@@@@@@@@@@ @@@
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@@@@@@@@@@@@ @@@@@@@@@@@
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@@@@@@@@@@@@@ @@@@@@@@@@@@@@
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@@@@@@@/ ,@@@@@@@@@@@@@
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/@@@@@@@@@@@@@@@ @@@@@@@@
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@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@
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@@@@@@@@ @@@@@
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,@@@ @@@@&
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@@@@@@. @@@@
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@@@ @@@@@@@@@/ @@@@@
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,@@@. @@@@@@((@ @@@@(
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//@@@ ,, @@@@ @@@@@
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@@@( @@@@@@@
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@@@ @ @@@@@@@@#
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@@@@@@@@@@@@@@@@@
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@@@@@@@@@@@@@(
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BLOB By: Prohurtz#0001 (Main App Developer)
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Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
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Copyright (c) 2022 EyeTrackVR <3
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------------------------------------------------------------------------------------------------------
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'''
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import cv2
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import numpy as np
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@ -53,12 +53,15 @@ if sys.platform.startswith("win"):
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from osc_calibrate_filter import *
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from haar_surround_feature import *
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from blob import *
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from ransac import *
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from hsrac import *
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class InformationOrigin(Enum):
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RANSAC = 1
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BLOB = 2
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FAILURE = 3
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HSF = 4
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HSRAC = 5
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bbb = 0
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@dataclass
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@ -91,111 +94,6 @@ async def delayed_setting_change(setting, value):
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def ellipse_model(data, y, f):
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"""
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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.
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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.
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a = 1.0,b = P[0],c = P[1],d = P[2],e = P[3],f = P[4]
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:param data:
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:param y: np.c_[d, e, a, c, b]
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:param f: f == P[4, 0]
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:return: this_return == np.array([ellipse_model(x, y) for (x, y) in data ])
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"""
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return data.dot(y) + f
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# @profile
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def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=100, sample_num=10, offset=80 # 80.0, 10, 80
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): # before changing these values, please read up on the ransac algorithm
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# However if you want to change any value just know that higher iterations will make processing frames slower
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effective_sample = None
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# The array contents do not change during the loop, so only one call is needed.
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# They say len is faster than shape.
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# Reference url: https://stackoverflow.com/questions/35547853/what-is-faster-python3s-len-or-numpys-shape
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len_data = len(data)
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if len_data < sample_num:
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return None
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# Type of calculation result
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ret_dtype = np.float64
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# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting.
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# If the array size is less than about 100, this is faster than rng.choice.
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rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
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# or
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# I don't see any advantage to doing this.
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# rng_sample = np.asarray(rng.random((iter, len_data)).argsort()[:, :sample_num], dtype=np.int32)
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# I don't think it looks beautiful.
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# x,y,x**2,y**2,x*y,1,-1*x**2
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datamod = np.concatenate(
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[data, data ** 2, (data[:, 0] * data[:, 1])[:, np.newaxis], np.ones((len_data, 1), dtype=ret_dtype),
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(-1 * data[:, 0] ** 2)[:, np.newaxis]], axis=1,
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dtype=ret_dtype)
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datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
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datamod_rng = datamod[rng_sample]
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datamod_rng6 = datamod_rng[:, :, 6]
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datamod_rng_swap = datamod_rng[:, :, [4, 3, 0, 1, 5]]
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datamod_rng_swap_trans = datamod_rng_swap.transpose((0, 2, 1))
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# These two lines are one of the bottlenecks
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datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
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datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
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datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
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# I don't think it looks beautiful.
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ellipse_y_arr = np.asarray(
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[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)
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ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
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ellipse_data_abs = np.abs(ellipse_data_arr)
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ellipse_data_index = np.argmax(np.sum(ellipse_data_abs < offset, axis=1), axis=0)
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effective_data_arr = ellipse_data_arr[ellipse_data_index]
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effective_sample_p_arr = datamod_rng_p[ellipse_data_index]
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return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr)
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# @profile
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def fit_rotated_ellipse(data, P):
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a = 1.0
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b = P[0]
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c = P[1]
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d = P[2]
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e = P[3]
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f = P[4]
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# The cost of trigonometric functions is high.
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theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
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theta_sin = np.sin(theta, dtype=np.float64)
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theta_cos = np.cos(theta, dtype=np.float64)
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tc2 = theta_cos ** 2
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ts2 = theta_sin ** 2
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b_tcs = b * theta_cos * theta_sin
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# Do the calculation only once
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cxy = b ** 2 - 4 * a * c
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cx = (2 * c * d - b * e) / cxy
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cy = (2 * a * e - b * d) / cxy
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# I just want to clear things up around here.
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cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
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cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
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wh = np.sqrt(cu / cu_r)
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w, h = wh[0], wh[1]
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error_sum = np.sum(data)
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# print("fitting error = %.3f" % (error_sum))
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return (cx, cy, w, h, theta)
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class EyeProcessor:
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def __init__(
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self,
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@ -500,227 +398,6 @@ class EyeProcessor:
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def HSRAC(self):
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frame = self.current_image_gray
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if self.now_mode == self.cv_mode[1]:
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prev_res_len = len(self.response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==self.response_list==[self.settings.gui_HSF_radius]
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self.cvparam.radius = self.auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==self.response_list==[self.settings.gui_HSF_radius, self.auto_radius_range[0]]
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self.cvparam.radius = self.auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==self.response_list==[self.settings.gui_HSF_radius,self.auto_radius_range[0],self.auto_radius_range[1]]
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sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
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# Extract the radius with the lowest response value
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if sort_res[0] == self.settings.gui_HSF_radius:
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# If the default value is best, change self.now_mode to init after setting radius to the default value.
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self.cvparam.radius = self.settings.gui_HSF_radius
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self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
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self.response_list = []
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elif sort_res[0] == self.auto_radius_range[0]:
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self.radius_cand_list = [i for i in range(self.auto_radius_range[0], self.settings.gui_HSF_radius, self.default_step[0])][1:]
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# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
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# It should be no problem to set it to anything other than self.default_step
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self.cvparam.radius = self.radius_cand_list.pop()
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else:
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self.radius_cand_list = [i for i in range(self.settings.gui_HSF_radius, self.auto_radius_range[1], self.default_step[0])][1:]
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# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
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# It should be no problem to set it to anything other than self.default_step
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self.cvparam.radius = self.radius_cand_list.pop()
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else:
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# Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out
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# Better make it a binary search.
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if len(self.radius_cand_list) == 0:
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sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
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self.cvparam.radius = sort_res[0]
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self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
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self.response_list = []
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else:
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self.cvparam.radius = self.radius_cand_list.pop()
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radius, pad, step, hsf = self.cvparam.get_rpsh()
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# For measuring processing time of image processing
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cv_start_time = timeit.default_timer()
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gray_frame = frame
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# Calculate the integral image of the frame
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int_start_time = timeit.default_timer()
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# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
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frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
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frame_int = cv2.integral(frame_pad)
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# Convolve the feature with the integral image
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conv_int_start_time = timeit.default_timer()
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xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
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frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
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crop_start_time = timeit.default_timer()
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# Define the center point and radius
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center_x, center_y = center_xy
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upper_x = center_x + 25 #TODO make this a setting
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lower_x = center_x - 25
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upper_y = center_y + 25
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lower_y = center_y - 25
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# Crop the image using the calculated bounds
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cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why?
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if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
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# If mode is first_frame or radius_adjust, record current radius and response
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self.response_list.append((radius, response))
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elif self.now_mode == self.cv_mode[2]:
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# Statistics for blink detection
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if len(self.response_list) < self.blink_init_frames:
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# Record the average value of cropped_image
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self.response_list.append(cv2.mean(cropped_image)[0])
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else:
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# Calculate self.response_max by computing interquartile range, IQR
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# Change self.cv_mode to normal
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self.response_list = np.array(self.response_list)
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# 25%,75%
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# This value may need to be adjusted depending on the environment.
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quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
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iqr = quartile_3 - quartile_1
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# response_min = quartile_1 - (iqr * 1.5)
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self.response_max = quartile_3 + (iqr * 1.5)
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self.now_mode = self.cv_mode[3]
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else:
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if 0 in cropped_image.shape:
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# If shape contains 0, it is not detected well.
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print("Something's wrong.")
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else:
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# If the average value of cropped_image is greater than self.response_max
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# (i.e., if the cropimage is whitish
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if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max:
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# blink
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cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
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# If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way
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# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
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#run ransac on the HSF crop\
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try:
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
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thresh_add = 10
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rng = np.random.default_rng()
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f = False
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# Convert the image to grayscale, and set up thresholding. Thresholds here are basically a
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# low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user
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# configurable in this utility as we're dealing with variable lighting amounts/placement, as
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# well as camera positioning and lensing. Therefore everyone's cutoff may be different.
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#
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# The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we
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# crop the image earlier; it gives us less possible dark area to get confused about in the
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# next step.
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frame = cropped_image
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# For measuring processing time of image processing
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# Crop first to reduce the amount of data to process.
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frame = frame[0:len(frame) - 5, :]
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# To reduce the processing data, first convert to 1-channel and then blur.
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# The processing results were the same when I swapped the order of blurring and 1-channelization.
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frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
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# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(frame_gray)
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maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
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# crop 15% sqare around min_loc
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# frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf,
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# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
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threshold_value = min_val + thresh_add
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_, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY)
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try:
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opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
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closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
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th_frame = 255 - closing
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except:
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# I want to eliminate try here because try tends to be slow in execution.
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th_frame = 255 - frame_gray
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detect_start_time = timeit.default_timer()
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contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
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hull = []
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# This way is faster than contours[i]
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# But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
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for cnt in contours:
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hull.append(cv2.convexHull(cnt, False))
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if not hull:
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# If empty, go to next loop
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pass
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try:
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cnt = sorted(hull, key=cv2.contourArea)
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maxcnt = cnt[-1]
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# ellipse = cv2.fitEllipse(maxcnt)
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ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), rng)
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if ransac_data is None:
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# ransac_data is None==maxcnt.shape[0]<sample_num
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# go to next loop
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pass
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cx, cy, w, h, theta = ransac_data
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csx = frame.shape[0]
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csy = frame.shape[1]
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cx = center_x - (csx - cx) # we find the difference between the crop size and ransac point, and subtract from the center point from HSF
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cy = center_y - (csy - cy)
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out_x, out_y = cal_osc(self, cx, cy)
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cx, cy, w, h = int(cx), int(cy), int(w), int(h)
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cv2.drawContours(frame, contours, -1, (255, 0, 0), 1)
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cv2.circle(frame, (cx, cy), 2, (0, 0, 255), -1)
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# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
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cv2.ellipse(frame, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
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#img = newImage2[y1:y2, x1:x2]
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except:
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pass
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self.current_image_gray = frame
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cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
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-1) # the point of the darkest area in the image
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try:
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if self.settings.gui_BLINK:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
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else:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
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f = False
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except:
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pass
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except:
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try:
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if abs(self.settings.gui_HSFP - self.settings.gui_HSRACP) < 2: #at this point we have successfully tan HSF, if ransac fails and HSF is the next algo, just send HSF values and continue
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if self.settings.gui_BLINK:
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
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else:
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
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f = False
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else: #HSF must not be next algo, so fail and move to the next one.
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self.failed = self.failed + 1
|
||||
#if self.settings.gui_BLINK:
|
||||
# self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
|
||||
#else:
|
||||
# self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
|
||||
@ -731,234 +408,6 @@ class EyeProcessor:
|
||||
|
||||
|
||||
|
||||
def RANSAC3D(self):
|
||||
f = False
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||
thresh_add = 10
|
||||
rng = np.random.default_rng()
|
||||
|
||||
f = False
|
||||
self.capture_crop_rotate_image()
|
||||
|
||||
# Convert the image to grayscale, and set up thresholding. Thresholds here are basically a
|
||||
# low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user
|
||||
# configurable in this utility as we're dealing with variable lighting amounts/placement, as
|
||||
# well as camera positioning and lensing. Therefore everyone's cutoff may be different.
|
||||
#
|
||||
# The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we
|
||||
# crop the image earlier; it gives us less possible dark area to get confused about in the
|
||||
# next step.
|
||||
|
||||
if self.config.gui_circular_crop == True:
|
||||
if self.cct == 0:
|
||||
try:
|
||||
ht, wd = self.current_image_gray.shape[:2]
|
||||
radius = int(float(self.lkg_projected_sphere["axes"][0]))
|
||||
self.xc = int(float(self.lkg_projected_sphere["center"][0]))
|
||||
self.yc = int(float(self.lkg_projected_sphere["center"][1]))
|
||||
# draw filled circle in white on black background as mask
|
||||
mask = np.zeros((ht, wd), dtype=np.uint8)
|
||||
mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
|
||||
# create white colored background
|
||||
color = np.full_like(self.current_image_gray, (255))
|
||||
# apply mask to image
|
||||
masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
|
||||
# apply inverse mask to colored image
|
||||
masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
|
||||
# combine the two masked images
|
||||
self.current_image_gray = cv2.add(masked_img, masked_color)
|
||||
except:
|
||||
pass
|
||||
else:
|
||||
self.cct = self.cct - 1
|
||||
else:
|
||||
self.cct = 300
|
||||
|
||||
|
||||
# Crop first to reduce the amount of data to process.
|
||||
newFrame2 = self.current_image_gray.copy()
|
||||
frame = self.current_image_gray
|
||||
# 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.
|
||||
frame_gray = 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)
|
||||
|
||||
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]
|
||||
|
||||
threshold_value = min_val + thresh_add
|
||||
_, thresh = cv2.threshold(frame_gray, 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
|
||||
|
||||
|
||||
detect_start_time = timeit.default_timer()
|
||||
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]<sample_num
|
||||
# go to next loop
|
||||
pass
|
||||
|
||||
crop_start_time = timeit.default_timer()
|
||||
cx, cy, w, h, theta = ransac_data
|
||||
out_x, out_y = cal_osc(self, cx, cy)
|
||||
# print(cx, cy)
|
||||
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
||||
# once a pupil is found, crop 100x100 around it
|
||||
x1 = cx - 50
|
||||
x2 = cx + 50
|
||||
y1 = cy - 50
|
||||
y2 = cy + 50
|
||||
cropped_image = newFrame2[y1:y2, x1:x2]
|
||||
|
||||
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
||||
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
|
||||
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
|
||||
|
||||
#img = newImage2[y1:y2, x1:x2]
|
||||
except:
|
||||
pass
|
||||
|
||||
self.current_image_gray = frame
|
||||
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
|
||||
-1) # the point of the darkest area in the image
|
||||
|
||||
|
||||
# However eyes are annoyingly three dimensional, so we need to take this ellipse and turn it
|
||||
# into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
|
||||
# ophthalmologist about possible issues with astigmatism.
|
||||
try:
|
||||
|
||||
|
||||
# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
|
||||
# from somewhat to completely magic, as most of it happens in native libraries (hence passing
|
||||
# via dicts).
|
||||
result_2d = {}
|
||||
result_2d_final = {}
|
||||
|
||||
result_2d["center"] = (cx, cy)
|
||||
|
||||
result_2d["axes"] = (w, h)
|
||||
result_2d["angle"] = theta * 180.0 / np.pi
|
||||
result_2d_final["ellipse"] = result_2d
|
||||
result_2d_final["diameter"] = w
|
||||
result_2d_final["location"] = (cx, cy)
|
||||
result_2d_final["confidence"] = 0.99
|
||||
result_2d_final["timestamp"] = self.current_frame_number / self.current_fps
|
||||
# Black magic happens here, but after this we have our reprojected pupil/eye, and all we had
|
||||
# to do was sell our soul to satan and/or C++.
|
||||
|
||||
result_3d = self.detector_3d.update_and_detect(
|
||||
result_2d_final, self.current_image_gray
|
||||
)
|
||||
|
||||
# Now we have our pupil
|
||||
ellipse_3d = result_3d["ellipse"]
|
||||
# And our eyeball that the pupil is on the surface of
|
||||
self.lkg_projected_sphere = result_3d["projected_sphere"]
|
||||
|
||||
# Record our pupil center
|
||||
exm = ellipse_3d["center"][0]
|
||||
eym = ellipse_3d["center"][1]
|
||||
|
||||
d = result_3d["diameter_3d"]
|
||||
|
||||
except:
|
||||
f = True
|
||||
# Draw our image and stack it for visual output
|
||||
try:
|
||||
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
||||
cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||
except:
|
||||
pass
|
||||
|
||||
# try: #for some reason the pye3d visualizations are wack, im going to just not visualize it for now..
|
||||
# cv2.ellipse(
|
||||
# self.current_image_gray,
|
||||
# tuple(int(v) for v in ellipse_3d["center"]),
|
||||
# tuple(int(v) for v in ellipse_3d["axes"]),
|
||||
# ellipse_3d["angle"],
|
||||
# 0,
|
||||
# 360, # start/end angle for drawing
|
||||
# (0, 255, 0), # color (BGR): red
|
||||
# )
|
||||
# except Exception:
|
||||
# Sometimes we get bogus axes and trying to draw this throws. Ideally we should check for
|
||||
# validity beforehand, but for now just pass. It usually fixes itself on the next frame.
|
||||
# pass
|
||||
|
||||
try:
|
||||
# print(self.lkg_projected_sphere["angle"], self.lkg_projected_sphere["axes"], self.lkg_projected_sphere["center"])
|
||||
cv2.ellipse(
|
||||
self.current_image_gray,
|
||||
tuple(int(v) for v in self.lkg_projected_sphere["center"]),
|
||||
tuple(int(v) for v in self.lkg_projected_sphere["axes"]),
|
||||
self.lkg_projected_sphere["angle"],
|
||||
0,
|
||||
360, # start/end angle for drawing
|
||||
(0, 255, 0), # color (BGR): red
|
||||
)
|
||||
|
||||
# draw line from center of eyeball to center of pupil
|
||||
# cv2.line(
|
||||
# self.current_image_gray,
|
||||
# tuple(int(v) for v in self.lkg_projected_sphere["center"]),
|
||||
# tuple(int(v) for v in ellipse_3d["center"]),
|
||||
# (0, 255, 0), # color (BGR): red
|
||||
# )
|
||||
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
if self.settings.gui_BLINK:
|
||||
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
|
||||
else:
|
||||
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False))
|
||||
self.failed = 0 # we have succeded, continue with this
|
||||
except:
|
||||
if self.settings.gui_BLINK:
|
||||
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, self.blinkvalue))
|
||||
else:
|
||||
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, True))
|
||||
self.failed = self.failed + 1 #we have failed, move onto next algo
|
||||
pass
|
||||
# Shove a concatenated image out to the main GUI thread for rendering
|
||||
#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0 ,0, 0, False))
|
||||
#self.output_images_and_update(thresh, output_info)
|
||||
#except:
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
|
||||
return f
|
||||
|
||||
|
||||
|
||||
def BLINK(self):
|
||||
@ -1017,7 +466,6 @@ class EyeProcessor:
|
||||
|
||||
def run(self):
|
||||
|
||||
print("running")
|
||||
self.firstalgo = None
|
||||
self.secondalgo = None
|
||||
self.thirdalgo = None
|
||||
@ -1114,15 +562,26 @@ class EyeProcessor:
|
||||
if not self.capture_crop_rotate_image():
|
||||
continue
|
||||
|
||||
|
||||
self.current_image_gray = cv2.cvtColor(
|
||||
self.current_image, cv2.COLOR_BGR2GRAY
|
||||
)
|
||||
self.current_image_gray_clean = self.current_image_gray.copy() #copy this frame to have a clean image for blink algo
|
||||
# print(self.settings.gui_RANSAC3D)
|
||||
|
||||
|
||||
cx, cy, larger_threshold = BLOB(self)
|
||||
cx, cy, thresh = HSRAC(self)
|
||||
out_x, out_y = cal_osc(self, cx, cy)
|
||||
self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, False)) #update app
|
||||
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, False)) #update app
|
||||
|
||||
|
||||
# cx, cy, thresh = RANSAC3D(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False)) #update app
|
||||
|
||||
|
||||
# cx, cy, larger_threshold = BLOB(self)
|
||||
# out_x, out_y = cal_osc(self, cx, cy)
|
||||
# self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.BLOB, out_x, out_y, 0, False)) #update app
|
||||
|
||||
#center_x, center_y, frame = HSF(self) #run algo
|
||||
#out_x, out_y = cal_osc(self, center_x, center_y) #filter and calibrate
|
||||
|
||||
@ -1,3 +1,32 @@
|
||||
'''
|
||||
------------------------------------------------------------------------------------------------------
|
||||
|
||||
,@@@@@@
|
||||
@@@@@@@@@@@ @@@
|
||||
@@@@@@@@@@@@ @@@@@@@@@@@
|
||||
@@@@@@@@@@@@@ @@@@@@@@@@@@@@
|
||||
@@@@@@@/ ,@@@@@@@@@@@@@
|
||||
/@@@@@@@@@@@@@@@ @@@@@@@@
|
||||
@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@
|
||||
@@@@@@@@ @@@@@
|
||||
,@@@ @@@@&
|
||||
@@@@@@. @@@@
|
||||
@@@ @@@@@@@@@/ @@@@@
|
||||
,@@@. @@@@@@((@ @@@@(
|
||||
//@@@ ,, @@@@ @@@@@
|
||||
@@@( @@@@@@@
|
||||
@@@ @ @@@@@@@@#
|
||||
@@@@@@@@@@@@@@@@@
|
||||
@@@@@@@@@@@@@(
|
||||
|
||||
HSR By: Sean.Denka (Optimization Wizard, Contributor), Summer#2406 (Main Algorithm Engineer)
|
||||
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
|
||||
|
||||
Copyright (c) 2022 EyeTrackVR <3
|
||||
------------------------------------------------------------------------------------------------------
|
||||
'''
|
||||
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import timeit
|
||||
@ -418,7 +447,6 @@ 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:
|
||||
|
||||
737
EyeTrackApp/hsrac.py
Normal file
737
EyeTrackApp/hsrac.py
Normal file
@ -0,0 +1,737 @@
|
||||
import cv2
|
||||
import numpy as np
|
||||
import timeit
|
||||
from functools import lru_cache
|
||||
import os
|
||||
import sys
|
||||
import functools
|
||||
import math
|
||||
|
||||
#HSF \/
|
||||
|
||||
# cache param
|
||||
lru_maxsize_vvs = 16
|
||||
lru_maxsize_vs = 64
|
||||
# CV param
|
||||
default_radius = 20
|
||||
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.
|
||||
If using cv2.filter2D in this code, be careful with the kernel
|
||||
https://stackoverflow.com/questions/39457468/convolution-without-any-padding-opencv-python
|
||||
"""
|
||||
|
||||
|
||||
def TimeitWrapper(*args, **kwargs):
|
||||
"""
|
||||
This decorator @TimeitWrapper() prints the function name and execution time in seconds.
|
||||
:param args:
|
||||
:param kwargs:
|
||||
:return:
|
||||
"""
|
||||
|
||||
def decorator(function):
|
||||
@functools.wraps(function)
|
||||
def wrapper(*args, **kwargs):
|
||||
start = timeit.default_timer()
|
||||
results = function(*args, **kwargs)
|
||||
end = timeit.default_timer()
|
||||
print('{} execution time: {:.10f} s'.format(function.__name__, end - start))
|
||||
return results
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
class TimeitResult(object):
|
||||
"""
|
||||
from https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
|
||||
Object returned by the timeit magic with info about the run.
|
||||
Contains the following attributes :
|
||||
loops: (int) number of loops done per measurement
|
||||
repeat: (int) number of times the measurement has been repeated
|
||||
best: (float) best execution time / number
|
||||
all_runs: (list of float) execution time of each run (in s)
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = best
|
||||
self.worst = worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.timings = [dt / self.loops for dt in all_runs]
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.timings) / len(self.timings)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean=format_time(self.average, self._precision),
|
||||
std=format_time(self.stdev, self._precision),
|
||||
best=format_time(self.best, self._precision),
|
||||
worst=format_time(self.worst, self._precision),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<TimeitResult : ' + unic + u'>')
|
||||
|
||||
|
||||
class FPSResult(object):
|
||||
"""
|
||||
base https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L55
|
||||
"""
|
||||
|
||||
def __init__(self, loops, repeat, best, worst, all_runs, precision):
|
||||
self.loops = loops
|
||||
self.repeat = repeat
|
||||
self.best = 1 / best
|
||||
self.worst = 1 / worst
|
||||
self.all_runs = all_runs
|
||||
self._precision = precision
|
||||
self.fps = [1 / dt for dt in all_runs]
|
||||
self.unit = "fps"
|
||||
|
||||
@property
|
||||
def average(self):
|
||||
return math.fsum(self.fps) / len(self.fps)
|
||||
|
||||
@property
|
||||
def stdev(self):
|
||||
mean = self.average
|
||||
return (math.fsum([(x - mean) ** 2 for x in self.fps]) / len(self.fps)) ** 0.5
|
||||
|
||||
def __str__(self):
|
||||
pm = '+-'
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb1'.encode(sys.stdout.encoding)
|
||||
pm = u'\xb1'
|
||||
except:
|
||||
pass
|
||||
return "min:{best} max:{worst} mean:{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops:,} loop{loop_plural} each)".format(
|
||||
pm=pm,
|
||||
runs=self.repeat,
|
||||
loops=self.loops,
|
||||
loop_plural="" if self.loops == 1 else "s",
|
||||
run_plural="" if self.repeat == 1 else "s",
|
||||
mean="%.*g%s" % (self._precision, self.average, self.unit),
|
||||
std="%.*g%s" % (self._precision, self.stdev, self.unit),
|
||||
best="%.*g%s" % (self._precision, self.best, self.unit),
|
||||
worst="%.*g%s" % (self._precision, self.worst, self.unit),
|
||||
)
|
||||
|
||||
def _repr_pretty_(self, p, cycle):
|
||||
unic = self.__str__()
|
||||
p.text(u'<FPSResult : ' + unic + u'>')
|
||||
|
||||
|
||||
def format_time(timespan, precision=3):
|
||||
"""
|
||||
https://github.com/ipython/ipython/blob/339c0d510a1f3cb2158dd8c6e7f4ac89aa4c89d8/IPython/core/magics/execution.py#L1473
|
||||
Formats the timespan in a human readable form
|
||||
"""
|
||||
|
||||
if timespan >= 60.0:
|
||||
# we have more than a minute, format that in a human readable form
|
||||
# Idea from http://snipplr.com/view/5713/
|
||||
parts = [("d", 60 * 60 * 24), ("h", 60 * 60), ("min", 60), ("s", 1)]
|
||||
time = []
|
||||
leftover = timespan
|
||||
for suffix, length in parts:
|
||||
value = int(leftover / length)
|
||||
if value > 0:
|
||||
leftover = leftover % length
|
||||
time.append(u'%s%s' % (str(value), suffix))
|
||||
if leftover < 1:
|
||||
break
|
||||
return " ".join(time)
|
||||
|
||||
# Unfortunately the unicode 'micro' symbol can cause problems in
|
||||
# certain terminals.
|
||||
# See bug: https://bugs.launchpad.net/ipython/+bug/348466
|
||||
# Try to prevent crashes by being more secure than it needs to
|
||||
# E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.
|
||||
units = [u"s", u"ms", u'us', "ns"] # the save value
|
||||
if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:
|
||||
try:
|
||||
u'\xb5'.encode(sys.stdout.encoding)
|
||||
units = [u"s", u"ms", u'\xb5s', "ns"]
|
||||
except:
|
||||
pass
|
||||
scaling = [1, 1e3, 1e6, 1e9]
|
||||
|
||||
if timespan > 0.0:
|
||||
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
|
||||
else:
|
||||
order = 3
|
||||
return u"%.*g %s" % (precision, timespan * scaling[order], units[order])
|
||||
|
||||
|
||||
class CvParameters:
|
||||
# It may be a little slower because a dict named "self" is read for each function call.
|
||||
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
|
||||
|
||||
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(self.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)
|
||||
|
||||
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)
|
||||
|
||||
|
||||
|
||||
|
||||
def HSRAC(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.default_radius]
|
||||
self.cvparam.radius = self.auto_radius_range[0]
|
||||
elif prev_res_len == 2:
|
||||
# len==2==self.response_list==[self.default_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.default_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.default_radius:
|
||||
# If the default value is best, change self.now_mode to init after setting radius to the default value.
|
||||
self.cvparam.radius = self.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], self.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()
|
||||
else:
|
||||
self.radius_cand_list = [i for i in range(self.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()
|
||||
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]
|
||||
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 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
|
||||
|
||||
|
||||
#run ransac on the HSF crop\
|
||||
try:
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||
thresh_add = 10
|
||||
rng = np.random.default_rng()
|
||||
|
||||
f = False
|
||||
|
||||
# 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.
|
||||
frame = cropped_image
|
||||
# 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.
|
||||
frame_gray = 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)
|
||||
|
||||
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]
|
||||
|
||||
threshold_value = min_val + thresh_add
|
||||
_, thresh = cv2.threshold(frame_gray, 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
|
||||
|
||||
|
||||
detect_start_time = timeit.default_timer()
|
||||
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]<sample_num
|
||||
# go to next loop
|
||||
pass
|
||||
|
||||
crop_start_time = timeit.default_timer()
|
||||
cx, cy, w, h, theta = ransac_data
|
||||
|
||||
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)
|
||||
out_x, out_y = cx, cy
|
||||
|
||||
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
|
||||
|
||||
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
||||
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
|
||||
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
|
||||
|
||||
#img = newImage2[y1:y2, x1:x2]
|
||||
except:
|
||||
pass
|
||||
|
||||
self.current_image_gray = frame
|
||||
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
|
||||
-1) # the point of the darkest area in the image
|
||||
try:
|
||||
return out_x, out_y, thresh
|
||||
except:
|
||||
return 0, 0, thresh
|
||||
|
||||
|
||||
except:
|
||||
return center_x, center_y, self.current_image_gray
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
338
EyeTrackApp/ransac.py
Normal file
338
EyeTrackApp/ransac.py
Normal file
@ -0,0 +1,338 @@
|
||||
'''
|
||||
------------------------------------------------------------------------------------------------------
|
||||
|
||||
,@@@@@@
|
||||
@@@@@@@@@@@ @@@
|
||||
@@@@@@@@@@@@ @@@@@@@@@@@
|
||||
@@@@@@@@@@@@@ @@@@@@@@@@@@@@
|
||||
@@@@@@@/ ,@@@@@@@@@@@@@
|
||||
/@@@@@@@@@@@@@@@ @@@@@@@@
|
||||
@@@@@@@@@@@@@@@@@@@@@@@@ @@@@@
|
||||
@@@@@@@@ @@@@@
|
||||
,@@@ @@@@&
|
||||
@@@@@@. @@@@
|
||||
@@@ @@@@@@@@@/ @@@@@
|
||||
,@@@. @@@@@@((@ @@@@(
|
||||
//@@@ ,, @@@@ @@@@@
|
||||
@@@( @@@@@@@
|
||||
@@@ @ @@@@@@@@#
|
||||
@@@@@@@@@@@@@@@@@
|
||||
@@@@@@@@@@@@@(
|
||||
|
||||
RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), Sean.Denka (Optimization)
|
||||
Algorithm App Implimentations By: Prohurtz#0001, qdot (Inital App Creator)
|
||||
|
||||
Copyright (c) 2022 EyeTrackVR <3
|
||||
------------------------------------------------------------------------------------------------------
|
||||
'''
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
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)
|
||||
|
||||
|
||||
def RANSAC3D(self):
|
||||
f = False
|
||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||
thresh_add = 10
|
||||
rng = np.random.default_rng()
|
||||
|
||||
# Convert the image to grayscale, and set up thresholding. Thresholds here are basically a
|
||||
# low-pass filter that will set any pixel < the threshold value to 0. Thresholding is user
|
||||
# configurable in this utility as we're dealing with variable lighting amounts/placement, as
|
||||
# well as camera positioning and lensing. Therefore everyone's cutoff may be different.
|
||||
#
|
||||
# The goal of thresholding settings is to make sure we can ONLY see the pupil. This is why we
|
||||
# crop the image earlier; it gives us less possible dark area to get confused about in the
|
||||
# next step.
|
||||
|
||||
if self.config.gui_circular_crop == True:
|
||||
if self.cct == 0:
|
||||
try:
|
||||
ht, wd = self.current_image_gray.shape[:2]
|
||||
radius = int(float(self.lkg_projected_sphere["axes"][0]))
|
||||
self.xc = int(float(self.lkg_projected_sphere["center"][0]))
|
||||
self.yc = int(float(self.lkg_projected_sphere["center"][1]))
|
||||
# draw filled circle in white on black background as mask
|
||||
mask = np.zeros((ht, wd), dtype=np.uint8)
|
||||
mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
|
||||
# create white colored background
|
||||
color = np.full_like(self.current_image_gray, (255))
|
||||
# apply mask to image
|
||||
masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
|
||||
# apply inverse mask to colored image
|
||||
masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
|
||||
# combine the two masked images
|
||||
self.current_image_gray = cv2.add(masked_img, masked_color)
|
||||
except:
|
||||
pass
|
||||
else:
|
||||
self.cct = self.cct - 1
|
||||
else:
|
||||
self.cct = 300
|
||||
|
||||
|
||||
# Crop first to reduce the amount of data to process.
|
||||
newFrame2 = self.current_image_gray.copy()
|
||||
frame = self.current_image_gray
|
||||
# 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.
|
||||
frame_gray = 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)
|
||||
|
||||
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]
|
||||
|
||||
threshold_value = min_val + thresh_add
|
||||
_, thresh = cv2.threshold(frame_gray, 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
|
||||
|
||||
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]<sample_num
|
||||
# go to next loop
|
||||
pass
|
||||
|
||||
cx, cy, w, h, theta = ransac_data
|
||||
# print(cx, cy)
|
||||
#cxi, cyi, wi, hi = int(cx), int(cy), int(w), int(h)
|
||||
|
||||
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
||||
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
|
||||
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
|
||||
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
|
||||
|
||||
#img = newImage2[y1:y2, x1:x2]
|
||||
except:
|
||||
pass
|
||||
|
||||
self.current_image_gray = frame
|
||||
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
|
||||
-1) # the point of the darkest area in the image
|
||||
|
||||
|
||||
# However eyes are annoyingly three dimensional, so we need to take this ellipse and turn it
|
||||
# into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
|
||||
# ophthalmologist about possible issues with astigmatism.
|
||||
try:
|
||||
|
||||
# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
|
||||
# from somewhat to completely magic, as most of it happens in native libraries (hence passing
|
||||
# via dicts).
|
||||
result_2d = {}
|
||||
result_2d_final = {}
|
||||
|
||||
result_2d["center"] = (cx, cy)
|
||||
|
||||
result_2d["axes"] = (w, h)
|
||||
result_2d["angle"] = theta * 180.0 / np.pi
|
||||
result_2d_final["ellipse"] = result_2d
|
||||
result_2d_final["diameter"] = w
|
||||
result_2d_final["location"] = (cx, cy)
|
||||
result_2d_final["confidence"] = 0.99
|
||||
result_2d_final["timestamp"] = self.current_frame_number / self.current_fps
|
||||
# Black magic happens here, but after this we have our reprojected pupil/eye, and all we had
|
||||
# to do was sell our soul to satan and/or C++.
|
||||
|
||||
result_3d = self.detector_3d.update_and_detect(
|
||||
result_2d_final, self.current_image_gray
|
||||
)
|
||||
|
||||
# Now we have our pupil
|
||||
ellipse_3d = result_3d["ellipse"]
|
||||
# And our eyeball that the pupil is on the surface of
|
||||
self.lkg_projected_sphere = result_3d["projected_sphere"]
|
||||
|
||||
# Record our pupil center
|
||||
exm = ellipse_3d["center"][0]
|
||||
eym = ellipse_3d["center"][1]
|
||||
|
||||
d = result_3d["diameter_3d"]
|
||||
|
||||
except:
|
||||
f = True
|
||||
# Draw our image and stack it for visual output
|
||||
try:
|
||||
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
|
||||
cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
|
||||
except:
|
||||
pass
|
||||
|
||||
# try: #for some reason the pye3d visualizations are wack, im going to just not visualize it for now..
|
||||
# cv2.ellipse(
|
||||
# self.current_image_gray,
|
||||
# tuple(int(v) for v in ellipse_3d["center"]),
|
||||
# tuple(int(v) for v in ellipse_3d["axes"]),
|
||||
# ellipse_3d["angle"],
|
||||
# 0,
|
||||
# 360, # start/end angle for drawing
|
||||
# (0, 255, 0), # color (BGR): red
|
||||
# )
|
||||
# except Exception:
|
||||
# Sometimes we get bogus axes and trying to draw this throws. Ideally we should check for
|
||||
# validity beforehand, but for now just pass. It usually fixes itself on the next frame.
|
||||
# pass
|
||||
|
||||
try:
|
||||
# print(self.lkg_projected_sphere["angle"], self.lkg_projected_sphere["axes"], self.lkg_projected_sphere["center"])
|
||||
cv2.ellipse(
|
||||
self.current_image_gray,
|
||||
tuple(int(v) for v in self.lkg_projected_sphere["center"]),
|
||||
tuple(int(v) for v in self.lkg_projected_sphere["axes"]),
|
||||
self.lkg_projected_sphere["angle"],
|
||||
0,
|
||||
360, # start/end angle for drawing
|
||||
(0, 255, 0), # color (BGR): red
|
||||
)
|
||||
|
||||
# draw line from center of eyeball to center of pupil
|
||||
# cv2.line(
|
||||
# self.current_image_gray,
|
||||
# tuple(int(v) for v in self.lkg_projected_sphere["center"]),
|
||||
# tuple(int(v) for v in ellipse_3d["center"]),
|
||||
# (0, 255, 0), # color (BGR): red
|
||||
# )
|
||||
|
||||
except:
|
||||
pass
|
||||
|
||||
try:
|
||||
self.failed = 0 # we have succeded, continue with this
|
||||
return cx, cy, thresh
|
||||
except:
|
||||
self.failed = self.failed + 1 #we have failed, move onto next algo
|
||||
return 0, 0, thresh
|
||||
# Shove a concatenated image out to the main GUI thread for rendering
|
||||
#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0 ,0, 0, False))
|
||||
#self.output_images_and_update(thresh, output_info)
|
||||
#except:
|
||||
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
|
||||
Loading…
Reference in New Issue
Block a user