mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
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342 lines
15 KiB
Python
342 lines
15 KiB
Python
'''
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//@@@ ,, @@@@ @@@@@
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@@@ @ @@@@@@@@#
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RANSAC 3D By: Summer#2406 (Main Algorithm Engineer), Pupil Labs (pye3d), PallasNeko (Optimization)
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Algorithm App Implementations By: Prohurtz#0001, 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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import cv2
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import numpy as np
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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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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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def RANSAC3D(self):
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f = False
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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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# 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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if self.config.gui_circular_crop == True:
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if self.cct == 0:
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try:
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ht, wd = self.current_image_gray.shape[:2]
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radius = int(float(self.lkg_projected_sphere["axes"][0]))
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self.xc = int(float(self.lkg_projected_sphere["center"][0]))
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self.yc = int(float(self.lkg_projected_sphere["center"][1]))
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# draw filled circle in white on black background as mask
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mask = np.zeros((ht, wd), dtype=np.uint8)
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mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
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# create white colored background
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color = np.full_like(self.current_image_gray, (255))
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# apply mask to image
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masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
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# apply inverse mask to colored image
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masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
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# combine the two masked images
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self.current_image_gray = cv2.add(masked_img, masked_color)
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except:
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pass
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else:
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self.cct = self.cct - 1
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else:
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self.cct = 300
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# Crop first to reduce the amount of data to process.
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newFrame2 = self.current_image_gray.copy()
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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, 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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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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# 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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#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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# 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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result_2d = {}
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result_2d_final = {}
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result_2d["center"] = (cx, cy)
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result_2d["axes"] = (w, h)
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result_2d["angle"] = theta * 180.0 / np.pi
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result_2d_final["ellipse"] = result_2d
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result_2d_final["diameter"] = w
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result_2d_final["location"] = (cx, cy)
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result_2d_final["confidence"] = 0.99
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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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self.lkg_projected_sphere = result_3d["projected_sphere"]
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# Record our pupil center
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exm = ellipse_3d["center"][0]
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eym = ellipse_3d["center"][1]
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d = result_3d["diameter_3d"]
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except:
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f = True
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# Draw our image and stack it for visual output
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try:
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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, (int(cx), int(cy)), 2, (0, 0, 255), -1)
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except:
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pass
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# try: #for some reason the pye3d visualizations are wack, im going to just not visualize it for now..
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# cv2.ellipse(
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# self.current_image_gray,
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# tuple(int(v) for v in ellipse_3d["center"]),
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# tuple(int(v) for v in ellipse_3d["axes"]),
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# ellipse_3d["angle"],
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# 0,
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# 360, # start/end angle for drawing
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# (0, 255, 0), # color (BGR): red
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# )
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# except Exception:
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# Sometimes we get bogus axes and trying to draw this throws. Ideally we should check for
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# validity beforehand, but for now just pass. It usually fixes itself on the next frame.
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# pass
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try:
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# print(self.lkg_projected_sphere["angle"], self.lkg_projected_sphere["axes"], self.lkg_projected_sphere["center"])
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cv2.ellipse(
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self.current_image_gray,
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tuple(int(v) for v in self.lkg_projected_sphere["center"]),
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tuple(int(v) for v in self.lkg_projected_sphere["axes"]),
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self.lkg_projected_sphere["angle"],
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0,
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360, # start/end angle for drawing
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(0, 255, 0), # color (BGR): red
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)
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# draw line from center of eyeball to center of pupil
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# cv2.line(
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# self.current_image_gray,
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# tuple(int(v) for v in self.lkg_projected_sphere["center"]),
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# tuple(int(v) for v in ellipse_3d["center"]),
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# (0, 255, 0), # color (BGR): red
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# )
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except:
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pass
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try:
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self.failed = 0 # we have succeded, continue with this
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return cx, cy, thresh
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except:
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self.failed = self.failed + 1 #we have failed, move onto next algo
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return 0, 0, thresh
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# Shove a concatenated image out to the main GUI thread for rendering
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#self.output_images_and_update(thresh, EyeInfo(EyeInfoOrigin.FAILURE, 0 ,0, 0, False))
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#self.output_images_and_update(thresh, output_info)
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#except:
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# self.output_images_and_update(thresh, EyeInfo(EyeInfoOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
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