""" ------------------------------------------------------------------------------------------------------ ,@@@@@@ @@@@@@@@@@@ @@@ @@@@@@@@@@@@ @@@@@@@@@@@ @@@@@@@@@@@@@ @@@@@@@@@@@@@@ @@@@@@@/ ,@@@@@@@@@@@@@ /@@@@@@@@@@@@@@@ @@@@@@@@ @@@@@@@@@@@@@@@@@@@@@@@@ @@@@@ @@@@@@@@ @@@@@ ,@@@ @@@@& @@@@@@. @@@@ @@@ @@@@@@@@@/ @@@@@ ,@@@. @@@@@@((@ @@@@( //@@@ ,, @@@@ @@@@@ @@@( @@@@@@@ @@@ @ @@@@@@@@# @@@@@@@@@@@@@@@@@ @@@@@@@@@@@@@( RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization) Algorithm App Implementations By: Prohurtz, qdot (Initial App Creator) Copyright (c) 2023 EyeTrackVR <3 ------------------------------------------------------------------------------------------------------ """ import cv2 import numpy as np from enum import IntEnum from utils.img_utils import safe_crop from utils.misc_utils import clamp import os import psutil import sys process = psutil.Process(os.getpid()) # set process priority to low try: # medium chance this does absolutely nothing but eh sys.getwindowsversion() except AttributeError: process.nice(0) # UNIX: 0 low 10 high process.nice() else: process.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS) # Windows process.nice() class EyeId(IntEnum): RIGHT = 0 LEFT = 1 BOTH = 2 SETTINGS = 3 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)]) if cu > 1: # negatives can get thrown which cause errors, just ignore them wh = np.sqrt(cu / cu_r) else: pass w, h = wh[0], wh[1] error_sum = np.sum(data) # print("fitting error = %.3f" % (error_sum)) return (cx, cy, w, h, theta) def get_center_noclamp(center_xy, radius): center_x, center_y = center_xy upper_x = center_x + radius lower_x = center_x - radius upper_y = center_y + radius lower_y = center_y - radius ransac_upper_x = center_x + max(20, radius) ransac_lower_x = center_x - max(20, radius) ransac_upper_y = center_y + max(20, radius) ransac_lower_y = center_y - max(20, radius) ransac_xy_offset = (ransac_lower_x, ransac_lower_y) 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, ) cct = 300 def RANSAC3D(self, hsrac_en): f = False ranf = False blink = 0.7 if hsrac_en: ( 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((self.rawx, self.rawy), self.radius) frame = safe_crop( self.current_image_gray_clean, int(ransac_lower_x), int(ransac_lower_y), int(ransac_upper_x), int(ransac_upper_y), 1, ) else: frame = self.current_image_gray_clean kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)) rng = np.random.default_rng() newFrame2 = self.current_image_gray.copy() # 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. # Crop first to reduce the amount of data to process. # 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, blur. 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] if self.settings.gui_legacy_ransac: if self.eye_id in [EyeId.LEFT]: threshold_value = self.settings.gui_legacy_ransac_thresh_left else: threshold_value = self.settings.gui_legacy_ransac_thresh_right else: threshold_value = min_val + self.settings.gui_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]= 0.2: # TODO setting # blink = 0.0 if self.settings.gui_RANSACBLINK: if self.ran_blink_check_for_file: if self.eye_id in [EyeId.LEFT]: file_path = "RANSAC_blink_LEFT.cfg" if self.eye_id in [EyeId.RIGHT]: file_path = "RANSAC_blink_RIGHT.cfg" else: file_path = "RANSAC_blink_RIGHT.cfg" if os.path.exists(file_path): with open(file_path, "r") as file: self.blink_list = [float(line.strip()) for line in file] else: print( f"\033[93m[INFO] RANSAC Blink Config '{file_path}' not found. Waiting for calibration.\033[0m" ) self.ran_blink_check_for_file = False 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: for item in self.blink_list: file.write(str(item) + "\n") if self.eye_id in [EyeId.RIGHT]: with open("RANSAC_BLINK_RIGHT.cfg", "w") as file: for item in self.blink_list: file.write(str(item) + "\n") # print("SAVE") # 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)) if abs(perscalarw - perscalarh) >= np.percentile(self.blink_list, 92): blink = 0.0 try: 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 # ) # 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( newFrame2, 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 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 return cx, cy, thresh, blink, w, h except: self.failed = self.failed + 1 # we have failed, move onto next algo return 0, 0, thresh, blink, 0, 0