some what working now

This commit is contained in:
Prohurtz 2022-12-20 15:24:31 -08:00
parent d87ab06415
commit a2377c722c
2 changed files with 199 additions and 245 deletions

View File

@ -47,7 +47,7 @@ class CameraWidget:
self.roi_layout = [
[
sg.Graph(
sg.Graph(
(640, 480),
(0, 480),
(640, 0),
@ -241,6 +241,7 @@ class CameraWidget:
self.config.roi_window_w = abs(self.x0 - self.x1)
self.config.roi_window_h = abs(self.y0 - self.y1)
self.main_config.save()
if event == self.gui_roi_selection:
# Event for mouse button down or mouse drag in ROI mode

View File

@ -30,16 +30,6 @@ Copyright (c) 2022 EyeTrackVR <3
------------------------------------------------------------------------------------------------------
'''
from operator import truth
from dataclasses import dataclass
import sys
@ -910,6 +900,8 @@ class EyeProcessor:
self.ts = 10
self.previous_rotation = self.config.rotation_angle
self.calibration_frame_counter
self.camera_model = None
self.detector_3d = None
#HSF
# CV param
@ -945,6 +937,7 @@ class EyeProcessor:
def capture_crop_rotate_image(self):
# Get our current frame
try:
# Get frame from capture source, crop to ROI
self.current_image = self.current_image[
@ -955,6 +948,7 @@ class EyeProcessor:
self.config.roi_window_x + self.config.roi_window_w
),
]
except:
# Failure to process frame, reuse previous frame.
self.current_image = self.previous_image
@ -1251,7 +1245,9 @@ class EyeProcessor:
print(center_x, center_y)
out_x, out_y = cal_osc(self, center_x, center_y)
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
self.output_images_and_update(frame,EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
if now_mode != cv_mode[0] and now_mode != cv_mode[1]:
@ -1269,29 +1265,186 @@ class EyeProcessor:
#self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True))
def RANSAC3D(self):
camera_model = None
detector_3d = None
f = 0
# If our ROI configuration has changed, reset our model and detector
if (camera_model is None
or detector_3d is None
or camera_model.resolution != (
self.config.roi_window_w,
self.config.roi_window_h,
)
):
camera_model = CameraModel(
focal_length=self.config.focal_length,
resolution=(self.config.roi_window_w, self.config.roi_window_h),
)
detector_3d = Detector3D(
camera=camera_model, long_term_mode=DetectorMode.blocking
f = False
self.capture_crop_rotate_image()
# 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.
if self.config.gui_circular_crop == True:
if self.cct == 0:
try:
ht, wd = self.current_image_gray.shape[:2]
radius = int(float(self.lkg_projected_sphere["axes"][0]))
self.xc = int(float(self.lkg_projected_sphere["center"][0]))
self.yc = int(float(self.lkg_projected_sphere["center"][1]))
# draw filled circle in white on black background as mask
mask = np.zeros((ht, wd), dtype=np.uint8)
mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
# create white colored background
color = np.full_like(self.current_image_gray, (255))
# apply mask to image
masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
# apply inverse mask to colored image
masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
# combine the two masked images
self.current_image_gray = cv2.add(masked_img, masked_color)
except:
pass
else:
self.cct = self.cct - 1
else:
self.cct = 300
_, thresh = cv2.threshold(
self.current_image_gray,
int(self.config.threshold),
255,
cv2.THRESH_BINARY,
)
# Set up morphological transforms, for smoothing and clearing the image we get out of the
# thresholding operation. After this, we'd really like to just have a black blob in the middle
# of a bunch of white area.
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
image = 255 - closing
# Now that the image is relatively clean, run contour finding in order to get us our pupil
# boundaries in the 2D context. Ideally, we just get one border.
contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# Find the convex shape based on each contour, and sort the list of them from smallest to
# largest area.
convex_hulls = []
for i in range(len(contours)):
convex_hulls.append(cv2.convexHull(contours[i], False))
# If we have no convex maidens, we have no pupil, and can't progress from here. Dump back to
# using blob tracking.
if len(convex_hulls) == 0:
pass
# Find our largest hull, which we expect will probably be the ellipse that represents the 2d
# area for the pupil, which we can use as the search area for the eye in general.
largest_hull = sorted(convex_hulls, key=cv2.contourArea)[-1]
# However eyes are annoyingly three dimensional, so we need to take this ellipse and turn it
# into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
# ophthalmologist about possible issues with astigmatism.
try:
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(
largest_hull.reshape(-1, 2)
)
# Check to make sure we haven't been requested to close
# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
# from somewhat to completely magic, as most of it happens in native libraries (hence passing
# via dicts).
result_2d = {}
result_2d_final = {}
result_2d["center"] = (cx, cy)
result_2d["axes"] = (w, h)
result_2d["angle"] = theta * 180.0 / np.pi
result_2d_final["ellipse"] = result_2d
result_2d_final["diameter"] = w
result_2d_final["location"] = (cx, cy)
result_2d_final["confidence"] = 0.99
result_2d_final["timestamp"] = self.current_frame_number / self.current_fps
# Black magic happens here, but after this we have our reprojected pupil/eye, and all we had
# to do was sell our soul to satan and/or C++.
result_3d = self.detector_3d.update_and_detect(
result_2d_final, self.current_image_gray
)
# Now we have our pupil
ellipse_3d = result_3d["ellipse"]
# And our eyeball that the pupil is on the surface of
self.lkg_projected_sphere = result_3d["projected_sphere"]
# Record our pupil center
exm = ellipse_3d["center"][0]
eym = ellipse_3d["center"][1]
d = result_3d["diameter_3d"]
out_x, out_y = cal_osc(self, cx, cy) #filter and calibrate values
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False))
except:
f = True
# Draw our image and stack it for visual output
try:
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
except:
pass
try:
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(
self.current_image_gray,
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
# Shove a concatenated image out to the main GUI thread for rendering
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0 ,0, 0, False))
#self.output_images_and_update(thresh, output_info)
#except:
# f = True
return f
def run(self):
self.camera_model = None
self.detector_3d = None
f = False
while True:
f = True
# Check to make sure we haven't been requested to close
if self.cancellation_event.is_set():
print("Exiting RANSAC thread")
print("Exiting Tracking thread")
return
if self.config.roi_window_w <= 0 or self.config.roi_window_h <= 0:
@ -1299,22 +1452,24 @@ class EyeProcessor:
# Sleep a bit while we wait.
if self.cancellation_event.wait(0.1):
return
pass
continue
# If our ROI configuration has changed, reset our model and detector
if (camera_model is None
or detector_3d is None
or camera_model.resolution != (
if (self.camera_model is None
or self.detector_3d is None
or self.camera_model.resolution != (
self.config.roi_window_w,
self.config.roi_window_h,
)
):
camera_model = CameraModel(
self.camera_model = CameraModel(
focal_length=self.config.focal_length,
resolution=(self.config.roi_window_w, self.config.roi_window_h),
)
detector_3d = Detector3D(
camera=camera_model, long_term_mode=DetectorMode.blocking
self.detector_3d = Detector3D(
camera=self.camera_model, long_term_mode=DetectorMode.blocking
)
try:
@ -1328,222 +1483,20 @@ class EyeProcessor:
) = self.capture_queue_incoming.get(block=True, timeout=0.2)
except queue.Empty:
# print("No image available")
pass
continue
if not self.capture_crop_rotate_image():
pass
# 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.
self.current_image_gray = cv2.cvtColor(
self.current_image, cv2.COLOR_BGR2GRAY
)
if self.config.gui_circular_crop == True:
if self.cct == 0:
try:
ht, wd = self.current_image_gray.shape[:2]
radius = int(float(self.lkg_projected_sphere["axes"][0]))
self.xc = int(float(self.lkg_projected_sphere["center"][0]))
self.yc = int(float(self.lkg_projected_sphere["center"][1]))
# draw filled circle in white on black background as mask
mask = np.zeros((ht, wd), dtype=np.uint8)
mask = cv2.circle(mask, (self.xc, self.yc), radius, 255, -1)
# create white colored background
color = np.full_like(self.current_image_gray, (255))
# apply mask to image
masked_img = cv2.bitwise_and(self.current_image_gray, self.current_image_gray, mask=mask)
# apply inverse mask to colored image
masked_color = cv2.bitwise_and(color, color, mask=255 - mask)
# combine the two masked images
self.current_image_gray = cv2.add(masked_img, masked_color)
except:
pass
else:
self.cct = self.cct - 1
else:
self.cct = 300
_, thresh = cv2.threshold(
self.current_image_gray,
int(self.config.threshold),
255,
cv2.THRESH_BINARY,
)
# Set up morphological transforms, for smoothing and clearing the image we get out of the
# thresholding operation. After this, we'd really like to just have a black blob in the middle
# of a bunch of white area.
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
image = 255 - closing
# Now that the image is relatively clean, run contour finding in order to get us our pupil
# boundaries in the 2D context. Ideally, we just get one border.
contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# Find the convex shape based on each contour, and sort the list of them from smallest to
# largest area.
convex_hulls = []
for i in range(len(contours)):
convex_hulls.append(cv2.convexHull(contours[i], False))
# If we have no convex maidens, we have no pupil, and can't progress from here. Dump back to
# using blob tracking.
if len(convex_hulls) == 0:
if self.settings.gui_BLOB:
self.BLOB()
else:
print("[INFO] Blob fallback disabled. Assuming blink.")
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, True))
pass
# Find our largest hull, which we expect will probably be the ellipse that represents the 2d
# area for the pupil, which we can use as the search area for the eye in general.
largest_hull = sorted(convex_hulls, key=cv2.contourArea)[-1]
# However eyes are annoyingly three dimensional, so we need to take this ellipse and turn it
# into a curve patch on the surface of a sphere (the eye itself). If it's not a sphere, see your
# ophthalmologist about possible issues with astigmatism.
try:
cx, cy, w, h, theta = fit_rotated_ellipse_ransac(
largest_hull.reshape(-1, 2)
)
except:
f = True
# Get axis and angle of the ellipse, using pupil labs 2d algos. The next bit of code ranges
# from somewhat to completely magic, as most of it happens in native libraries (hence passing
# via dicts).
result_2d = {}
result_2d_final = {}
result_2d["center"] = (cx, cy)
result_2d["axes"] = (w, h)
result_2d["angle"] = theta * 180.0 / np.pi
result_2d_final["ellipse"] = result_2d
result_2d_final["diameter"] = w
result_2d_final["location"] = (cx, cy)
result_2d_final["confidence"] = 0.99
result_2d_final["timestamp"] = self.current_frame_number / self.current_fps
# Black magic happens here, but after this we have our reprojected pupil/eye, and all we had
# to do was sell our soul to satan and/or C++.
result_3d = detector_3d.update_and_detect(
result_2d_final, self.current_image_gray
)
# Now we have our pupil
ellipse_3d = result_3d["ellipse"]
# And our eyeball that the pupil is on the surface of
self.lkg_projected_sphere = result_3d["projected_sphere"]
# Record our pupil center
exm = ellipse_3d["center"][0]
eym = ellipse_3d["center"][1]
d = result_3d["diameter_3d"]
out_x, out_y = cal_osc(self, cx, cy) #filter and calibrate values
output_info = EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 1, False)
# Draw our image and stack it for visual output
try:
cv2.drawContours(self.current_image_gray, contours, -1, (255, 0, 0), 1)
cv2.circle(self.current_image_gray, (int(cx), int(cy)), 2, (0, 0, 255), -1)
except:
pass
try:
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(
self.current_image_gray,
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
)
except:
pass
# 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
)
# Shove a concatenated image out to the main GUI thread for rendering
self.output_images_and_update(thresh, output_info)
except:
f = True
return f
def run(self):
while True:
f = False
# Check to make sure we haven't been requested to close
if self.cancellation_event.is_set():
print("Exiting Tracking thread")
return
if self.config.roi_window_w <= 0 or self.config.roi_window_h <= 0:
# At this point, we're waiting for the user to set up the ROI window in the GUI.
# Sleep a bit while we wait.
if self.cancellation_event.wait(0.1):
return
continue
try:
if self.capture_queue_incoming.empty():
self.capture_event.set()
# Wait a bit for images here. If we don't get one, just try again.
(
self.current_image,
self.current_frame_number,
self.current_fps,
) = self.capture_queue_incoming.get(block=True, timeout=0.2)
except queue.Empty:
# print("No image available")
continue
self.current_image_gray = cv2.cvtColor(
self.current_image, cv2.COLOR_BGR2GRAY
)
if not self.capture_crop_rotate_image():
continue
try:
if self.settings.gui_RANSAC3D: #for now ransac goes first
f == self.RANSAC3D()
if f and self.settings.gui_HSF: #if a fail has been reported and other algo is enabled, use it.
f == self.RANSAC3D
if f and self.settings.gui_HSF: #if a fail has been reported and other algo is enabled, use it.
f == self.HSF()
if f and self.settings.gui_blob_fallback:
if f and self.settings.gui_BLOB:
f == self.BLOB()
except:
print("[WARN] ALL ALGORITHIMS HAVE FAILED OR ARE DISABLED.")