mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
some what working now
This commit is contained in:
parent
d87ab06415
commit
a2377c722c
@ -47,7 +47,7 @@ class CameraWidget:
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self.roi_layout = [
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[
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sg.Graph(
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sg.Graph(
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(640, 480),
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(0, 480),
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(640, 0),
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@ -241,6 +241,7 @@ class CameraWidget:
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self.config.roi_window_w = abs(self.x0 - self.x1)
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self.config.roi_window_h = abs(self.y0 - self.y1)
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self.main_config.save()
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if event == self.gui_roi_selection:
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# Event for mouse button down or mouse drag in ROI mode
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@ -30,16 +30,6 @@ Copyright (c) 2022 EyeTrackVR <3
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------------------------------------------------------------------------------------------------------
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'''
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from operator import truth
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from dataclasses import dataclass
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import sys
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@ -910,6 +900,8 @@ class EyeProcessor:
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self.ts = 10
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self.previous_rotation = self.config.rotation_angle
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self.calibration_frame_counter
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self.camera_model = None
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self.detector_3d = None
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#HSF
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# CV param
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@ -945,6 +937,7 @@ class EyeProcessor:
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def capture_crop_rotate_image(self):
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# Get our current frame
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try:
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# Get frame from capture source, crop to ROI
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self.current_image = self.current_image[
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@ -955,6 +948,7 @@ class EyeProcessor:
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self.config.roi_window_x + self.config.roi_window_w
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),
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]
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except:
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# Failure to process frame, reuse previous frame.
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self.current_image = self.previous_image
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@ -1251,7 +1245,9 @@ class EyeProcessor:
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print(center_x, center_y)
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out_x, out_y = cal_osc(self, center_x, center_y)
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cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
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self.output_images_and_update(frame,EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
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if now_mode != cv_mode[0] and now_mode != cv_mode[1]:
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@ -1269,29 +1265,186 @@ class EyeProcessor:
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#self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True))
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def RANSAC3D(self):
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camera_model = None
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detector_3d = None
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f = 0
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# If our ROI configuration has changed, reset our model and detector
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if (camera_model is None
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or detector_3d is None
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or camera_model.resolution != (
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self.config.roi_window_w,
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self.config.roi_window_h,
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)
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):
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camera_model = CameraModel(
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focal_length=self.config.focal_length,
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resolution=(self.config.roi_window_w, self.config.roi_window_h),
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)
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detector_3d = Detector3D(
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camera=camera_model, long_term_mode=DetectorMode.blocking
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f = False
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self.capture_crop_rotate_image()
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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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_, thresh = cv2.threshold(
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self.current_image_gray,
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int(self.config.threshold),
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255,
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cv2.THRESH_BINARY,
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)
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# Set up morphological transforms, for smoothing and clearing the image we get out of the
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# thresholding operation. After this, we'd really like to just have a black blob in the middle
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# of a bunch of white area.
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
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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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image = 255 - closing
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# Now that the image is relatively clean, run contour finding in order to get us our pupil
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# boundaries in the 2D context. Ideally, we just get one border.
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contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
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# Find the convex shape based on each contour, and sort the list of them from smallest to
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# largest area.
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convex_hulls = []
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for i in range(len(contours)):
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convex_hulls.append(cv2.convexHull(contours[i], False))
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# If we have no convex maidens, we have no pupil, and can't progress from here. Dump back to
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# using blob tracking.
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if len(convex_hulls) == 0:
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pass
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# Find our largest hull, which we expect will probably be the ellipse that represents the 2d
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# area for the pupil, which we can use as the search area for the eye in general.
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largest_hull = sorted(convex_hulls, key=cv2.contourArea)[-1]
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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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cx, cy, w, h, theta = fit_rotated_ellipse_ransac(
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largest_hull.reshape(-1, 2)
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)
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# Check to make sure we haven't been requested to close
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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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out_x, out_y = cal_osc(self, cx, cy) #filter and calibrate values
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, False))
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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:
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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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# Shove a concatenated image out to the main GUI thread for rendering
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.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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# f = True
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return f
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def run(self):
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self.camera_model = None
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self.detector_3d = None
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f = False
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while True:
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f = True
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# Check to make sure we haven't been requested to close
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if self.cancellation_event.is_set():
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print("Exiting RANSAC thread")
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print("Exiting Tracking thread")
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return
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if self.config.roi_window_w <= 0 or self.config.roi_window_h <= 0:
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@ -1299,22 +1452,24 @@ class EyeProcessor:
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# Sleep a bit while we wait.
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if self.cancellation_event.wait(0.1):
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return
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pass
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continue
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# If our ROI configuration has changed, reset our model and detector
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if (camera_model is None
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or detector_3d is None
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or camera_model.resolution != (
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if (self.camera_model is None
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or self.detector_3d is None
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or self.camera_model.resolution != (
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self.config.roi_window_w,
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self.config.roi_window_h,
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)
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):
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camera_model = CameraModel(
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self.camera_model = CameraModel(
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focal_length=self.config.focal_length,
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resolution=(self.config.roi_window_w, self.config.roi_window_h),
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)
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detector_3d = Detector3D(
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camera=camera_model, long_term_mode=DetectorMode.blocking
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self.detector_3d = Detector3D(
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camera=self.camera_model, long_term_mode=DetectorMode.blocking
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)
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try:
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@ -1328,222 +1483,20 @@ class EyeProcessor:
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) = self.capture_queue_incoming.get(block=True, timeout=0.2)
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except queue.Empty:
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# print("No image available")
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pass
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continue
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if not self.capture_crop_rotate_image():
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pass
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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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self.current_image_gray = cv2.cvtColor(
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self.current_image, cv2.COLOR_BGR2GRAY
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)
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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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_, thresh = cv2.threshold(
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self.current_image_gray,
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int(self.config.threshold),
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255,
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cv2.THRESH_BINARY,
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)
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# Set up morphological transforms, for smoothing and clearing the image we get out of the
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# thresholding operation. After this, we'd really like to just have a black blob in the middle
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# of a bunch of white area.
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
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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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image = 255 - closing
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# Now that the image is relatively clean, run contour finding in order to get us our pupil
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# boundaries in the 2D context. Ideally, we just get one border.
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contours, _ = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
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# Find the convex shape based on each contour, and sort the list of them from smallest to
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# largest area.
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convex_hulls = []
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for i in range(len(contours)):
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convex_hulls.append(cv2.convexHull(contours[i], False))
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# If we have no convex maidens, we have no pupil, and can't progress from here. Dump back to
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# using blob tracking.
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if len(convex_hulls) == 0:
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if self.settings.gui_BLOB:
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self.BLOB()
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else:
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print("[INFO] Blob fallback disabled. Assuming blink.")
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, 0, 0, 0, True))
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pass
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# Find our largest hull, which we expect will probably be the ellipse that represents the 2d
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# area for the pupil, which we can use as the search area for the eye in general.
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largest_hull = sorted(convex_hulls, key=cv2.contourArea)[-1]
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|
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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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cx, cy, w, h, theta = fit_rotated_ellipse_ransac(
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largest_hull.reshape(-1, 2)
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)
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except:
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f = True
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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 = 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"]
|
||||
|
||||
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.")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user