new ransac + print tags and colors

This commit is contained in:
Prohurtz 2022-12-22 11:52:19 -08:00
parent 0812ad4c0b
commit d5f9fabf09
5 changed files with 80 additions and 111 deletions

View File

@ -33,7 +33,7 @@ class Camera:
self.cancellation_event = cancellation_event self.cancellation_event = cancellation_event
self.current_capture_source = config.capture_source self.current_capture_source = config.capture_source
self.wired_camera: "cv2.VideoCapture" = None self.wired_camera: "cv2.VideoCapture" = None
self.error_message = "Capture source {} not found, retrying" self.error_message = "\033[93m[WARN] Capture source {} not found, retrying...\033[0m"
def set_output_queue(self, camera_output_outgoing: "queue.Queue"): def set_output_queue(self, camera_output_outgoing: "queue.Queue"):
self.camera_output_outgoing = camera_output_outgoing self.camera_output_outgoing = camera_output_outgoing
@ -41,7 +41,7 @@ class Camera:
def run(self): def run(self):
while True: while True:
if self.cancellation_event.is_set(): if self.cancellation_event.is_set():
print("Exiting capture thread") print("\033[94m[INFO] Exiting capture thread\033[0m")
return return
should_push = True should_push = True
# If things aren't open, retry until they are. Don't let read requests come in any earlier # If things aren't open, retry until they are. Don't let read requests come in any earlier

View File

@ -43,7 +43,7 @@ class CameraWidget:
elif self.eye_id == EyeId.LEFT: elif self.eye_id == EyeId.LEFT:
self.config = main_config.left_eye self.config = main_config.left_eye
else: else:
raise RuntimeError("Cannot have a camera widget represent both eyes!") raise RuntimeError("\033[91m[WARN] Cannot have a camera widget represent both eyes!\033[0m")
self.roi_layout = [ self.roi_layout = [
[ [
@ -192,7 +192,7 @@ class CameraWidget:
event == self.gui_save_tracking_button event == self.gui_save_tracking_button
and values[self.gui_camera_addr] != self.config.capture_source and values[self.gui_camera_addr] != self.config.capture_source
): ):
print("New value: {}".format(values[self.gui_camera_addr])) print("[INFO] New value: {}".format(values[self.gui_camera_addr]))
try: try:
# Try storing ints as ints, for those using wired cameras. # Try storing ints as ints, for those using wired cameras.
self.config.capture_source = int(values[self.gui_camera_addr]) self.config.capture_source = int(values[self.gui_camera_addr])
@ -219,14 +219,14 @@ class CameraWidget:
self.main_config.save() self.main_config.save()
if event == self.gui_tracking_button: if event == self.gui_tracking_button:
print("Moving to tracking mode") print("[INFO] Moving to tracking mode")
self.in_roi_mode = False self.in_roi_mode = False
self.camera.set_output_queue(self.capture_queue) self.camera.set_output_queue(self.capture_queue)
window[self.gui_roi_layout].update(visible=False) window[self.gui_roi_layout].update(visible=False)
window[self.gui_tracking_layout].update(visible=True) window[self.gui_tracking_layout].update(visible=True)
if event == self.gui_roi_button: if event == self.gui_roi_button:
print("Move to roi mode") print("[INFO] Move to roi mode")
self.in_roi_mode = True self.in_roi_mode = True
self.camera.set_output_queue(self.roi_queue) self.camera.set_output_queue(self.roi_queue)
window[self.gui_roi_layout].update(visible=True) window[self.gui_roi_layout].update(visible=True)

View File

@ -592,13 +592,6 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
def ellipse_model(data, y, f): 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. 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.
@ -612,12 +605,11 @@ def ellipse_model(data, y, f):
return data.dot(y) + f return data.dot(y) + f
def fit_rotated_ellipse_ransac(data: np.ndarray, iter=100, sample_num=10, offset=80 # 80.0, 10, 80 # @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 ): # 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 # However if you want to change any value just know that higher iterations will make processing frames slower
count_max = 0
effective_sample = None effective_sample = None
rng = np.random.default_rng()
# The array contents do not change during the loop, so only one call is needed. # The array contents do not change during the loop, so only one call is needed.
# They say len is faster than shape. # They say len is faster than shape.
@ -628,13 +620,8 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, iter=100, sample_num=10, offset
return None return None
# Type of calculation result # Type of calculation result
# Although the accuracy may be lower, I feel that float32 is better considering the memory used.
# Whether float32 or float64 is faster depends on the execution environment.
ret_dtype = np.float64 ret_dtype = np.float64
# Declare this number only once, since it is immutable.
a = np.array(1.0, dtype=ret_dtype)
# Sorts a random number array of size (iter,len_data). After sorting, returns the index of sample_num random numbers before sorting. # 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. # 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] rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
@ -652,64 +639,40 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, iter=100, sample_num=10, offset
datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype) datamod_slim = np.array(datamod[:, :5], dtype=ret_dtype)
datamod_rng = datamod[rng_sample] 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))
P5x5 = np.empty((5, 5), dtype=ret_dtype) # These two lines are one of the bottlenecks
P5xSmp = np.empty((5, sample_num), dtype=ret_dtype) datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
P = np.empty(5, dtype=ret_dtype) datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
for data_smp in datamod_rng: datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
# np.random.choice is slow # I don't think it looks beautiful.
# data_smp = datamod[sample] ellipse_y_arr = np.asarray(
# xs, ys, xs2, ys2, xy, smp_ones = data_smp[:, 0], data_smp[:, 1], data_smp[:, 2], data_smp[:, 3], data_smp[:, 4], data_smp[:, 5] [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)
J = data_smp[:, [4, 3, 0, 1, 5]]
# Y = -1 * xs2 ellipse_data_arr = ellipse_model(datamod_slim, ellipse_y_arr, np.asarray(datamod_rng_p[:, 4])).transpose((1, 0))
Y = data_smp[:, 6] 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]
J_T = J.T return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr)
# I don't know which is faster, this or np.dot.
J_T.dot(J, out=P5x5)
np.linalg.inv(P5x5).dot(J_T, out=P5xSmp)
P5xSmp.dot(Y, out=P)
# fitter a*x**2 + b*x*y + c*y**2 + d*x + e*y + f = 0
# b,c,d,e,f = P[0],P[1],P[2],P[3],P[4] # It looks like they are making copies of these and I want to remove it.
ellipse_y = np.asarray([P[2], P[3], a, P[1], P[0]], dtype=ret_dtype)
ellipse_data = np.abs(ellipse_model(datamod_slim, ellipse_y, P[4]))
# threshold
ran_sample = datamod[ellipse_data < offset]
# Reduce one function call by using a variable.
len_ran = len(ran_sample)
if len_ran > count_max:
count_max = len_ran
effective_sample = ran_sample
return fit_rotated_ellipse(effective_sample)
def fit_rotated_ellipse(data):
J = data[:, [4, 3, 0, 1, 5]]
# Y = -1 * xs2
Y = data[:, 6]
J_T = J.T
P = np.linalg.inv(J_T.dot(J)).dot(J_T).dot(Y)
# @profile
def fit_rotated_ellipse(data, P):
a = 1.0 a = 1.0
b = P[0] b = P[0]
c = P[1] c = P[1]
d = P[2] d = P[2]
e = P[3] e = P[3]
f = P[4] f = P[4]
theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
# The cost of trigonometric functions is high. # 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_sin = np.sin(theta, dtype=np.float64)
theta_cos = np.cos(theta, dtype=np.float64) theta_cos = np.cos(theta, dtype=np.float64)
tc2 = theta_cos ** 2 tc2 = theta_cos ** 2
@ -725,17 +688,15 @@ def fit_rotated_ellipse(data):
cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f 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)]) cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
wh = np.sqrt(cu / cu_r) wh = np.sqrt(cu / cu_r)
w, h = wh[0], wh[1] w, h = wh[0], wh[1]
ellipse_y = np.asarray([d, e, a, c, b], dtype=np.float64) error_sum = np.sum(data)
error_sum = np.sum(ellipse_model(data[:, :5], ellipse_y, f))
# print("fitting error = %.3f" % (error_sum)) # print("fitting error = %.3f" % (error_sum))
return (cx, cy, w, h, theta) return (cx, cy, w, h, theta)
class EyeProcessor: class EyeProcessor:
def __init__( def __init__(
self, self,
@ -1265,37 +1226,39 @@ class EyeProcessor:
self.cct = 300 self.cct = 300
newImage2 = self.current_image_gray.copy()
# Crop first to reduce the amount of data to process. # Crop first to reduce the amount of data to process.
newFrame2 = self.current_image_gray.copy()
img = self.current_image_gray[0:len(self.current_image_gray) - 10, :] frame = self.current_image_gray
# For measuring processing time of image processing
# Crop first to reduce the amount of data to process.
frame = frame[0:len(frame) - 5, :]
# To reduce the processing data, first convert to 1-channel and then blur. # To reduce the processing data, first convert to 1-channel and then blur.
# The processing results were the same when I swapped the order of blurring and 1-channelization. # The processing results were the same when I swapped the order of blurring and 1-channelization.
# image_gray = self.current_image_gray frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
image_gray = cv2.GaussianBlur(self.current_image_gray, (5, 5), 0)
# this will need to be adjusted everytime hardware is changed (brightness of IR, Camera postion, etc)m # 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(image_gray) 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]) maxloc0_hf, maxloc1_hf = int(0.5 * max_loc[0]), int(0.5 * max_loc[1])
# crop 15% sqare around min_loc # crop 15% sqare around min_loc
# image_gray = image_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf, # frame_gray = frame_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf,
# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf] # max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
threshold_value = min_val + thresh_add threshold_value = min_val + thresh_add
th_ret, thresh = cv2.threshold(image_gray, threshold_value, 255, cv2.THRESH_BINARY) _, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY)
try: try:
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel) opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel) closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
image = 255 - closing th_frame = 255 - closing
except: except:
# I want to eliminate try here because try tends to be slow in execution. # I want to eliminate try here because try tends to be slow in execution.
image = 255 - image_gray th_frame = 255 - frame_gray
contours, hierarchy = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
detect_start_time = timeit.default_timer()
contours, _ = cv2.findContours(th_frame, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
hull = [] hull = []
# This way is faster than contours[i] # This way is faster than contours[i]
# But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours] # But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
@ -1305,34 +1268,38 @@ class EyeProcessor:
# If empty, go to next loop # If empty, go to next loop
pass pass
try: try:
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
cnt = sorted(hull, key=cv2.contourArea) cnt = sorted(hull, key=cv2.contourArea)
maxcnt = cnt[-1] maxcnt = cnt[-1]
ellipse = cv2.fitEllipse(maxcnt) # ellipse = cv2.fitEllipse(maxcnt)
ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2)) ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2), rng)
if ransac_data is None: if ransac_data is None:
# ransac_data is None==maxcnt.shape[0]<sample_num # ransac_data is None==maxcnt.shape[0]<sample_num
# go to next loop # go to next loop
pass pass
cx, cy, w, h, theta = ransac_data
print(cx, cy)
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
crop_start_time = timeit.default_timer()
cx, cy, w, h, theta = ransac_data
out_x, out_y = cal_osc(self, cx, cy)
# print(cx, cy)
cx, cy, w, h = int(cx), int(cy), int(w), int(h)
# once a pupil is found, crop 100x100 around it # once a pupil is found, crop 100x100 around it
x1 = cx - 50 x1 = cx - 50
x2 = cx + 50 x2 = cx + 50
y1 = cy - 50 y1 = cy - 50
y2 = cy + 50 y2 = cy + 50
out_x, out_y = cal_osc(self, cx, cy) cropped_image = newFrame2[y1:y2, x1:x2]
#img = newImage2[y1:y2, x1:x2] cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
cv2.circle(self.current_image_gray, (cx, cy), 2, (0, 0, 255), -1)
# cx1, cy1, w1, h1, theta1 = fit_rotated_ellipse(maxcnt.reshape(-1, 2))
cv2.ellipse(self.current_image_gray, (cx, cy), (w, h), theta * 180.0 / np.pi, 0.0, 360.0, (50, 250, 200), 1, )
#img = newImage2[y1:y2, x1:x2]
except: except:
pass pass
self.current_image_gray = frame
cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255), cv2.circle(self.current_image_gray, min_loc, 2, (0, 0, 255),
-1) # the point of the darkest area in the image -1) # the point of the darkest area in the image
@ -1419,8 +1386,10 @@ class EyeProcessor:
# tuple(int(v) for v in ellipse_3d["center"]), # tuple(int(v) for v in ellipse_3d["center"]),
# (0, 255, 0), # color (BGR): red # (0, 255, 0), # color (BGR): red
# ) # )
except: except:
pass pass
try: try:
if self.settings.gui_BLINK: if self.settings.gui_BLINK:
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue)) self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
@ -1476,7 +1445,7 @@ class EyeProcessor:
# f = True # f = True
# Check to make sure we haven't been requested to close # Check to make sure we haven't been requested to close
if self.cancellation_event.is_set(): if self.cancellation_event.is_set():
print("Exiting Tracking thread") print("\033[94m[INFO] Exiting Tracking thread\033[0m")
return return
if self.config.roi_window_w <= 0 or self.config.roi_window_h <= 0: if self.config.roi_window_w <= 0 or self.config.roi_window_h <= 0:
@ -1538,9 +1507,9 @@ class EyeProcessor:
pass pass
""" #print("[WARN] ALL ALGORITHIMS HAVE FAILED OR ARE DISABLED.") """ #print("[WARN] ALL ALGORITHIMS HAVE FAILED OR ARE DISABLED.")
#self.RANSAC3D() self.RANSAC3D()
#self.BLINK() #self.BLINK()
self.HSF() # self.HSF()
# f == self.RANSAC3D()''' # f == self.RANSAC3D()'''
#FLOW MOCK #FLOW MOCK

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@ -51,7 +51,7 @@ def main():
# Check to see if we can connect to our video source first. If not, bring up camera finding # Check to see if we can connect to our video source first. If not, bring up camera finding
# dialog. # dialog.
print("[INFO] Checking for updates...")
url = "https://raw.githubusercontent.com/RedHawk989/EyeTrackVR-Installer/master/Version-Data/Version_Num.txt" url = "https://raw.githubusercontent.com/RedHawk989/EyeTrackVR-Installer/master/Version-Data/Version_Num.txt"
html = urlopen(url).read() html = urlopen(url).read()
soup = BeautifulSoup(html, features="html.parser") soup = BeautifulSoup(html, features="html.parser")
@ -67,9 +67,9 @@ def main():
latestversion = '\n'.join(chunk for chunk in chunks if chunk) latestversion = '\n'.join(chunk for chunk in chunks if chunk)
if appversion == latestversion: # If what we scraped and hardcoded versions are same, assume we are up to date. if appversion == latestversion: # If what we scraped and hardcoded versions are same, assume we are up to date.
print(f"[INFO] App is up to date! {latestversion}") print(f"\033[92m[INFO] App is up to date! {latestversion}\033[0m")
else: else:
print(f"[INFO] You have app version {appversion} installed. Please update to {latestversion} for the newest fixes.") print(f"\033[93m[INFO] You have app version {appversion} installed. Please update to {latestversion} for the newest fixes.\033[0m")
if sys.platform.startswith("win"): if sys.platform.startswith("win"):
toaster = ToastNotifier() toaster = ToastNotifier()
toaster.show_toast( #show windows toast toaster.show_toast( #show windows toast
@ -190,7 +190,7 @@ def main():
# and then call the pythonosc shutdown function # and then call the pythonosc shutdown function
osc_receiver.shutdown() osc_receiver.shutdown()
osc_receiver_thread.join() osc_receiver_thread.join()
print("Exiting EyeTrackApp") print("\033[94m[INFO] Exiting EyeTrackApp\033[0m")
return return
if values[RIGHT_EYE_RADIO_NAME] and config.eye_display_id != EyeId.RIGHT: if values[RIGHT_EYE_RADIO_NAME] and config.eye_display_id != EyeId.RIGHT:

View File

@ -38,7 +38,7 @@ class VRChatOSC:
lb = False lb = False
while True: while True:
if self.cancellation_event.is_set(): if self.cancellation_event.is_set():
print("Exiting OSC Queue") print("\033[94m[INFO] Exiting OSC Queue\033[0m")
return return
try: try:
(eye_id, eye_info) = self.msg_queue.get(block=True, timeout=0.1) (eye_id, eye_info) = self.msg_queue.get(block=True, timeout=0.1)
@ -160,10 +160,10 @@ class VRChatOSCReceiver:
try: try:
self.server = osc_server.OSCUDPServer((self.config.gui_osc_address, int(self.config.gui_osc_receiver_port)), self.dispatcher) self.server = osc_server.OSCUDPServer((self.config.gui_osc_address, int(self.config.gui_osc_receiver_port)), self.dispatcher)
except: except:
print(f"[ERROR] OSC Recieve port: {self.config.gui_osc_receiver_port} occupied. ") print(f"\033[91m[ERROR] OSC Recieve port: {self.config.gui_osc_receiver_port} occupied.\033[0m")
def shutdown(self): def shutdown(self):
print("Shutting down OSC receiver") print("\033[94m[INFO] Shutting down OSC receiver")
try: try:
self.server.shutdown() self.server.shutdown()
except: except:
@ -189,8 +189,8 @@ class VRChatOSCReceiver:
self.dispatcher.map(self.config.gui_osc_recalibrate_address, self.recalibrate_eyes) self.dispatcher.map(self.config.gui_osc_recalibrate_address, self.recalibrate_eyes)
self.dispatcher.map(self.config.gui_osc_recenter_address, self.recenter_eyes) self.dispatcher.map(self.config.gui_osc_recenter_address, self.recenter_eyes)
# start the server # start the server
print("VRChatOSCReceiver serving on {}".format(self.server.server_address)) print("\033[92m[INFO] VRChatOSCReceiver serving on {}\033[0m".format(self.server.server_address))
self.server.serve_forever() self.server.serve_forever()
except: except:
print(f"[ERROR] OSC Recieve port: {self.config.gui_osc_receiver_port} occupied. ") print(f"\033[91m[ERROR] OSC Recieve port: {self.config.gui_osc_receiver_port} occupied.\033[0m")