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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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@ -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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@ -61,8 +64,14 @@ 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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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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@ -88,9 +97,16 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=
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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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@ -101,15 +117,29 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, rng: np.random.Generator, iter=
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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_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(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
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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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[
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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(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
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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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@ -155,7 +185,6 @@ def fit_rotated_ellipse(data, P):
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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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center_x, center_y = center_xy
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upper_x = center_x + 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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@ -201,8 +260,6 @@ 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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@ -212,7 +269,6 @@ def RANSAC3D(self, hsrac_en):
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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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@ -273,9 +329,9 @@ def RANSAC3D(self, hsrac_en):
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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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@ -323,16 +379,16 @@ 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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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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for cnt in contours:
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(x, y, w, h) = cv2.boundingRect(cnt)
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@ -346,31 +402,32 @@ def RANSAC3D(self, hsrac_en):
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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
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if self.eye_id in [EyeId.LEFT]:
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with open("RANSAC_BLINK_LEFT.cfg", 'w') as file:
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with open("RANSAC_BLINK_LEFT.cfg", "w") as file:
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for item in self.blink_list:
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file.write(str(item) + '\n')
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file.write(str(item) + "\n")
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if self.eye_id in [EyeId.RIGHT]:
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with open("RANSAC_BLINK_RIGHT.cfg", 'w') as file:
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with open("RANSAC_BLINK_RIGHT.cfg", "w") as file:
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for item in self.blink_list:
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file.write(str(item) + '\n')
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print('SAVE')
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file.write(str(item) + "\n")
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print("SAVE")
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# self.blink_list.pop(0)
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self.blink_list.append(abs(perscalarw - perscalarh))
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@ -378,16 +435,13 @@ def RANSAC3D(self, hsrac_en):
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elif len(self.blink_list) < 10000:
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self.blink_list.append(abs(perscalarw - perscalarh))
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if abs(perscalarw-perscalarh) >= np.percentile(
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self.blink_list, 94
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):
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if abs(perscalarw - perscalarh) >= np.percentile(self.blink_list, 94):
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blink = 0.0
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try:
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cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1) # TODO: fix visualizations with HSRAC
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cv2.drawContours(
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self.current_image_gray, contours, -1, (255, 0, 0), 1
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) # TODO: fix visualizations with HSRAC
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cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
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except:
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pass
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@ -430,8 +484,6 @@ def RANSAC3D(self, hsrac_en):
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except:
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pass
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self.current_image_gray = newFrame2
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y, x = self.current_image_gray.shape
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thresh = cv2.resize(thresh, (x, y))
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