mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
fix: left eye ransac thresh set to right
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@ -1,4 +1,4 @@
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'''
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"""
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------------------------------------------------------------------------------------------------------
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,@@@@@@
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@ -24,7 +24,7 @@ Algorithm App Implementations By: Prohurtz, qdot (Initial App Creator)
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Copyright (c) 2023 EyeTrackVR <3
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------------------------------------------------------------------------------------------------------
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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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from enum import IntEnum
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@ -35,7 +35,7 @@ import psutil
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import sys
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process = psutil.Process(os.getpid()) # set process priority to low
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try: # medium chance this does absolutely nothing but eh
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try: # medium chance this does absolutely nothing but eh
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sys.getwindowsversion()
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except AttributeError:
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process.nice(0) # UNIX: 0 low 10 high
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@ -44,11 +44,14 @@ else:
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process.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS) # Windows
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process.nice()
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class EyeId(IntEnum):
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RIGHT = 0
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LEFT = 1
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BOTH = 2
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SETTINGS = 3
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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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@ -61,60 +64,87 @@ def ellipse_model(data, y, f):
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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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def fit_rotated_ellipse_ransac(
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data: np.ndarray,
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rng: np.random.Generator,
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iter=100,
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sample_num=10,
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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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[
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data,
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data**2,
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(data[:, 0] * data[:, 1])[:, np.newaxis],
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np.ones((len_data, 1), dtype=ret_dtype),
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(-1 * data[:, 0] ** 2)[:, np.newaxis],
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],
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axis=1,
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dtype=ret_dtype,
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)
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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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datamod_rng_p5smp = np.matmul(
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np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans
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)
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datamod_rng_p = np.matmul(
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datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]
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).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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[
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datamod_rng_p[:, 2],
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datamod_rng_p[:, 3],
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np.ones(len(datamod_rng_p)),
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datamod_rng_p[:, 1],
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datamod_rng_p[:, 0],
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],
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dtype=ret_dtype,
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)
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ellipse_data_arr = ellipse_model(
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datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])
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).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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@ -130,30 +160,29 @@ def fit_rotated_ellipse(data, P):
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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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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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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 = 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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if cu > 1: #negatives can get thrown which cause errors, just ignore them
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if cu > 1: # negatives can get thrown which cause errors, just ignore them
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wh = np.sqrt(cu / cu_r)
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else:
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pass
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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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return (cx, cy, w, h, theta)
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def get_center_noclamp(center_xy, radius):
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@ -168,22 +197,52 @@ def get_center_noclamp(center_xy, radius):
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ransac_upper_y = center_y + max(20, radius)
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ransac_lower_y = center_y - max(20, radius)
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ransac_xy_offset = (ransac_lower_x, ransac_lower_y)
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return center_x, center_y, upper_x, lower_x, upper_y, lower_y, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_y, ransac_xy_offset
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return (
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center_x,
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center_y,
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upper_x,
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lower_x,
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upper_y,
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lower_y,
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ransac_lower_x,
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ransac_lower_y,
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ransac_upper_x,
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ransac_upper_y,
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ransac_xy_offset,
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)
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cct = 300
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def RANSAC3D(self, hsrac_en):
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f = False
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ranf = False
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blink = 0.7
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if hsrac_en:
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center_x, center_y, upper_x, lower_x, upper_y, lower_y, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_y, ransac_xy_offset = get_center_noclamp(
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(self.rawx, self.rawy), self.radius)
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(
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center_x,
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center_y,
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upper_x,
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lower_x,
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upper_y,
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lower_y,
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ransac_lower_x,
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ransac_lower_y,
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ransac_upper_x,
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ransac_upper_y,
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ransac_xy_offset,
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) = get_center_noclamp((self.rawx, self.rawy), self.radius)
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frame = safe_crop(self.current_image_gray_clean, ransac_lower_x, ransac_lower_y, ransac_upper_x, ransac_upper_y, 1)
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frame = safe_crop(
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self.current_image_gray_clean,
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ransac_lower_x,
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ransac_lower_y,
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ransac_upper_x,
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ransac_upper_y,
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1,
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)
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else:
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frame = self.current_image_gray_clean
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@ -201,29 +260,26 @@ def RANSAC3D(self, hsrac_en):
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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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# Crop first to reduce the amount of data to process.
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# frame = self.current_image_gray
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# frame = self.current_image_gray
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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, blur.
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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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# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
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if self.settings.gui_legacy_ransac:
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if self.eye_id in [EyeId.LEFT]:
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threshold_value = self.settings.gui_legacy_ransac_thresh_left
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threshold_value = self.settings.gui_legacy_ransac_thresh_left
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else:
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threshold_value = self.settings.gui_legacy_ransac_thresh_right
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else:
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@ -248,7 +304,7 @@ def RANSAC3D(self, hsrac_en):
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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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@ -260,31 +316,31 @@ def RANSAC3D(self, hsrac_en):
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cx, cy, w, h, theta = ransac_data
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# print(cx, cy)
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#cxi, cyi, wi, hi = int(cx), int(cy), int(w), int(h)
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#cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
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# cv2.circle(self.current_image_gray, (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(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
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# cxi, cyi, wi, hi = int(cx), int(cy), int(w), int(h)
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#img = newImage2[y1:y2, x1:x2]
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# cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
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# cv2.circle(self.current_image_gray, (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(self.current_image_gray, (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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ranf = True
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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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cv2.circle(
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self.current_image_gray, min_loc, 2, (0, 0, 255), -1
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) # the point of the darkest area in the image
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# However eyes are annoyingly three dimensional, so we need to take this ellipse and turn it
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# into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
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# ophthalmologist about possible issues with astigmatism.
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try:
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# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
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# from somewhat to completely magic, as most of it happens in native libraries (hence passing
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# via dicts).
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# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
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# from somewhat to completely magic, as most of it happens in native libraries (hence passing
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# via dicts).
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result_2d = {}
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result_2d_final = {}
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result_2d["center"] = (cx, cy)
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@ -297,11 +353,11 @@ def RANSAC3D(self, hsrac_en):
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result_2d_final["timestamp"] = self.current_frame_number / self.current_fps
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# Black magic happens here, but after this we have our reprojected pupil/eye, and all we had
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# to do was sell our soul to satan and/or C++.
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result_3d = self.detector_3d.update_and_detect(
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result_2d_final, self.current_image_gray
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)
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# Now we have our pupil
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ellipse_3d = result_3d["ellipse"]
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# And our eyeball that the pupil is on the surface of
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@ -323,88 +379,86 @@ def RANSAC3D(self, hsrac_en):
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csx = newFrame2.shape[1]
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if hsrac_en:
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if ranf:
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cx = self.rawx
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cy = self.rawy
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else:
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# print(int(cx), int(clamp(cx + ransac_lower_x, 0, csx)), ransac_lower_x, csx, "y", int(cy), int(clamp(cy + ransac_lower_y, 0, csy)), ransac_lower_y, csy)
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cx = int(clamp(cx + ransac_lower_x, 0, csx)) #dunno why this is being weird
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# print(int(cx), int(clamp(cx + ransac_lower_x, 0, csx)), ransac_lower_x, csx, "y", int(cy), int(clamp(cy + ransac_lower_y, 0, csy)), ransac_lower_y, csy)
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cx = int(
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clamp(cx + ransac_lower_x, 0, csx)
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) # dunno why this is being weird
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cy = int(clamp(cy + ransac_lower_y, 0, csy))
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#print(contours)
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# print(contours)
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for cnt in contours:
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(x, y, w, h) = cv2.boundingRect(cnt)
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perscalarw = w / csx
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perscalarh = h / csy
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# print(abs(perscalarw-perscalarh))
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# if abs(perscalarw-perscalarh) >= 0.2: # TODO setting
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# print(abs(perscalarw-perscalarh))
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# if abs(perscalarw-perscalarh) >= 0.2: # TODO setting
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# blink = 0.0
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if self.settings.gui_RANSACBLINK:
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if self.ran_blink_check_for_file:
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if self.eye_id in [EyeId.LEFT]:
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file_path = 'RANSAC_blink_LEFT.cfg'
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file_path = "RANSAC_blink_LEFT.cfg"
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if self.eye_id in [EyeId.RIGHT]:
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file_path = 'RANSAC_blink_RIGHT.cfg'
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file_path = "RANSAC_blink_RIGHT.cfg"
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else:
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file_path = 'RANSAC_blink_RIGHT.cfg'
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file_path = "RANSAC_blink_RIGHT.cfg"
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if os.path.exists(file_path):
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with open(file_path, 'r') as file:
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with open(file_path, "r") as file:
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self.blink_list = [float(line.strip()) for line in file]
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else:
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print(f"\033[93m[INFO] RANSAC Blink Config '{file_path}' not found. Waiting for calibration.\033[0m")
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print(
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f"\033[93m[INFO] RANSAC Blink Config '{file_path}' not found. Waiting for calibration.\033[0m"
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)
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self.ran_blink_check_for_file = False
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|
||||
|
||||
if len(self.blink_list) == 10000: # self calibrate ransac blink IN TESTING
|
||||
if len(self.blink_list) == 10000: # self calibrate ransac blink IN TESTING
|
||||
if self.eye_id in [EyeId.LEFT]:
|
||||
with open("RANSAC_BLINK_LEFT.cfg", 'w') as file:
|
||||
with open("RANSAC_BLINK_LEFT.cfg", "w") as file:
|
||||
for item in self.blink_list:
|
||||
file.write(str(item) + '\n')
|
||||
file.write(str(item) + "\n")
|
||||
|
||||
if self.eye_id in [EyeId.RIGHT]:
|
||||
with open("RANSAC_BLINK_RIGHT.cfg", 'w') as file:
|
||||
with open("RANSAC_BLINK_RIGHT.cfg", "w") as file:
|
||||
for item in self.blink_list:
|
||||
file.write(str(item) + '\n')
|
||||
print('SAVE')
|
||||
file.write(str(item) + "\n")
|
||||
print("SAVE")
|
||||
|
||||
# self.blink_list.pop(0)
|
||||
self.blink_list.append(abs(perscalarw-perscalarh))
|
||||
# self.blink_list.pop(0)
|
||||
self.blink_list.append(abs(perscalarw - perscalarh))
|
||||
|
||||
elif len(self.blink_list) < 10000:
|
||||
self.blink_list.append(abs(perscalarw-perscalarh))
|
||||
self.blink_list.append(abs(perscalarw - perscalarh))
|
||||
|
||||
|
||||
if abs(perscalarw-perscalarh) >= np.percentile(
|
||||
self.blink_list, 94
|
||||
):
|
||||
if abs(perscalarw - perscalarh) >= np.percentile(self.blink_list, 94):
|
||||
blink = 0.0
|
||||
|
||||
|
||||
|
||||
try:
|
||||
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1) # TODO: fix visualizations with HSRAC
|
||||
cv2.drawContours(
|
||||
self.current_image_gray, contours, -1, (255, 0, 0), 1
|
||||
) # TODO: fix visualizations with HSRAC
|
||||
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
|
||||
# )
|
||||
# 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.
|
||||
# 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:
|
||||
@ -421,23 +475,21 @@ def RANSAC3D(self, hsrac_en):
|
||||
|
||||
# draw line from center of eyeball to center of pupil
|
||||
# cv2.line(
|
||||
# self.current_image_gray,
|
||||
# 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
|
||||
# tuple(int(v) for v in ellipse_3d["center"]),
|
||||
# (0, 255, 0), # color (BGR): red
|
||||
# )
|
||||
|
||||
except:
|
||||
pass
|
||||
|
||||
|
||||
|
||||
self.current_image_gray = newFrame2
|
||||
y, x = self.current_image_gray.shape
|
||||
thresh = cv2.resize(thresh, (x,y))
|
||||
try:
|
||||
self.failed = 0 # we have succeded, continue with this
|
||||
thresh = cv2.resize(thresh, (x, y))
|
||||
try:
|
||||
self.failed = 0 # we have succeded, continue with this
|
||||
return cx, cy, thresh, blink
|
||||
except:
|
||||
self.failed = self.failed + 1 #we have failed, move onto next algo
|
||||
return 0, 0, thresh, blink
|
||||
self.failed = self.failed + 1 # we have failed, move onto next algo
|
||||
return 0, 0, thresh, blink
|
||||
|
||||
Loading…
Reference in New Issue
Block a user