mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
new ransac + print tags and colors
This commit is contained in:
parent
0812ad4c0b
commit
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@ -33,7 +33,7 @@ class Camera:
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self.cancellation_event = cancellation_event
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self.cancellation_event = cancellation_event
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self.current_capture_source = config.capture_source
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self.current_capture_source = config.capture_source
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self.wired_camera: "cv2.VideoCapture" = None
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self.wired_camera: "cv2.VideoCapture" = None
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self.error_message = "Capture source {} not found, retrying"
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self.error_message = "\033[93m[WARN] Capture source {} not found, retrying...\033[0m"
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def set_output_queue(self, camera_output_outgoing: "queue.Queue"):
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def set_output_queue(self, camera_output_outgoing: "queue.Queue"):
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self.camera_output_outgoing = camera_output_outgoing
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self.camera_output_outgoing = camera_output_outgoing
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@ -41,7 +41,7 @@ class Camera:
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def run(self):
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def run(self):
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while True:
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while True:
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if self.cancellation_event.is_set():
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if self.cancellation_event.is_set():
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print("Exiting capture thread")
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print("\033[94m[INFO] Exiting capture thread\033[0m")
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return
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return
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should_push = True
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should_push = True
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# If things aren't open, retry until they are. Don't let read requests come in any earlier
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# If things aren't open, retry until they are. Don't let read requests come in any earlier
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@ -43,7 +43,7 @@ class CameraWidget:
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elif self.eye_id == EyeId.LEFT:
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elif self.eye_id == EyeId.LEFT:
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self.config = main_config.left_eye
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self.config = main_config.left_eye
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else:
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else:
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raise RuntimeError("Cannot have a camera widget represent both eyes!")
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raise RuntimeError("\033[91m[WARN] Cannot have a camera widget represent both eyes!\033[0m")
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self.roi_layout = [
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self.roi_layout = [
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[
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[
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@ -192,7 +192,7 @@ class CameraWidget:
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event == self.gui_save_tracking_button
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event == self.gui_save_tracking_button
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and values[self.gui_camera_addr] != self.config.capture_source
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and values[self.gui_camera_addr] != self.config.capture_source
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):
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):
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print("New value: {}".format(values[self.gui_camera_addr]))
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print("[INFO] New value: {}".format(values[self.gui_camera_addr]))
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try:
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try:
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# Try storing ints as ints, for those using wired cameras.
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# Try storing ints as ints, for those using wired cameras.
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self.config.capture_source = int(values[self.gui_camera_addr])
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self.config.capture_source = int(values[self.gui_camera_addr])
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@ -219,14 +219,14 @@ class CameraWidget:
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self.main_config.save()
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self.main_config.save()
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if event == self.gui_tracking_button:
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if event == self.gui_tracking_button:
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print("Moving to tracking mode")
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print("[INFO] Moving to tracking mode")
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self.in_roi_mode = False
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self.in_roi_mode = False
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self.camera.set_output_queue(self.capture_queue)
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self.camera.set_output_queue(self.capture_queue)
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window[self.gui_roi_layout].update(visible=False)
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window[self.gui_roi_layout].update(visible=False)
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window[self.gui_tracking_layout].update(visible=True)
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window[self.gui_tracking_layout].update(visible=True)
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if event == self.gui_roi_button:
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if event == self.gui_roi_button:
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print("Move to roi mode")
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print("[INFO] Move to roi mode")
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self.in_roi_mode = True
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self.in_roi_mode = True
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self.camera.set_output_queue(self.roi_queue)
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self.camera.set_output_queue(self.roi_queue)
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window[self.gui_roi_layout].update(visible=True)
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window[self.gui_roi_layout].update(visible=True)
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@ -592,13 +592,6 @@ def conv_int(frame_int, kernel, xy_step, padding, xy_steps_list):
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def ellipse_model(data, y, f):
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def ellipse_model(data, y, f):
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"""
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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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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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@ -612,12 +605,11 @@ def ellipse_model(data, y, f):
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return data.dot(y) + f
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return data.dot(y) + f
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def fit_rotated_ellipse_ransac(data: np.ndarray, iter=100, sample_num=10, offset=80 # 80.0, 10, 80
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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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): # 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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# However if you want to change any value just know that higher iterations will make processing frames slower
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count_max = 0
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effective_sample = None
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effective_sample = None
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rng = np.random.default_rng()
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# The array contents do not change during the loop, so only one call is needed.
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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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# They say len is faster than shape.
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@ -628,13 +620,8 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, iter=100, sample_num=10, offset
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return None
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return None
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# Type of calculation result
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# Type of calculation result
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# Although the accuracy may be lower, I feel that float32 is better considering the memory used.
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# Whether float32 or float64 is faster depends on the execution environment.
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ret_dtype = np.float64
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ret_dtype = np.float64
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# Declare this number only once, since it is immutable.
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a = np.array(1.0, dtype=ret_dtype)
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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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# 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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# 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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rng_sample = rng.random((iter, len_data)).argsort()[:, :sample_num]
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@ -652,64 +639,40 @@ def fit_rotated_ellipse_ransac(data: np.ndarray, iter=100, sample_num=10, offset
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datamod_slim = np.array(datamod[:, :5], 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_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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P5x5 = np.empty((5, 5), dtype=ret_dtype)
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# These two lines are one of the bottlenecks
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P5xSmp = np.empty((5, sample_num), dtype=ret_dtype)
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datamod_rng_5x5 = np.matmul(datamod_rng_swap_trans, datamod_rng_swap)
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P = np.empty(5, dtype=ret_dtype)
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datamod_rng_p5smp = np.matmul(np.linalg.inv(datamod_rng_5x5), datamod_rng_swap_trans)
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for data_smp in datamod_rng:
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datamod_rng_p = np.matmul(datamod_rng_p5smp, datamod_rng6[:, :, np.newaxis]).reshape((-1, 5))
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# np.random.choice is slow
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# I don't think it looks beautiful.
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# data_smp = datamod[sample]
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ellipse_y_arr = np.asarray(
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# 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]
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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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J = data_smp[:, [4, 3, 0, 1, 5]]
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# Y = -1 * xs2
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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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Y = data_smp[:, 6]
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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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J_T = J.T
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return fit_rotated_ellipse(effective_data_arr, effective_sample_p_arr)
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# I don't know which is faster, this or np.dot.
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J_T.dot(J, out=P5x5)
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np.linalg.inv(P5x5).dot(J_T, out=P5xSmp)
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P5xSmp.dot(Y, out=P)
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# fitter a*x**2 + b*x*y + c*y**2 + d*x + e*y + f = 0
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# 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.
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ellipse_y = np.asarray([P[2], P[3], a, P[1], P[0]], dtype=ret_dtype)
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ellipse_data = np.abs(ellipse_model(datamod_slim, ellipse_y, P[4]))
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# threshold
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ran_sample = datamod[ellipse_data < offset]
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# Reduce one function call by using a variable.
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len_ran = len(ran_sample)
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if len_ran > count_max:
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count_max = len_ran
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effective_sample = ran_sample
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return fit_rotated_ellipse(effective_sample)
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def fit_rotated_ellipse(data):
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J = data[:, [4, 3, 0, 1, 5]]
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# Y = -1 * xs2
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Y = data[:, 6]
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J_T = J.T
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P = np.linalg.inv(J_T.dot(J)).dot(J_T).dot(Y)
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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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a = 1.0
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b = P[0]
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b = P[0]
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c = P[1]
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c = P[1]
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d = P[2]
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d = P[2]
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e = P[3]
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e = P[3]
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f = P[4]
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f = P[4]
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theta = 0.5 * np.arctan(b / (a - c), dtype=np.float64)
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# The cost of trigonometric functions is high.
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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_sin = np.sin(theta, dtype=np.float64)
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theta_cos = np.cos(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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tc2 = theta_cos ** 2
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@ -725,17 +688,15 @@ def fit_rotated_ellipse(data):
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cu = a * cx ** 2 + b * cx * cy + c * cy ** 2 - f
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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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cu_r = np.array([(a * tc2 + b_tcs + c * ts2), (a * ts2 - b_tcs + c * tc2)])
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wh = np.sqrt(cu / cu_r)
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wh = np.sqrt(cu / cu_r)
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w, h = wh[0], wh[1]
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w, h = wh[0], wh[1]
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ellipse_y = np.asarray([d, e, a, c, b], dtype=np.float64)
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error_sum = np.sum(data)
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error_sum = np.sum(ellipse_model(data[:, :5], ellipse_y, f))
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# print("fitting error = %.3f" % (error_sum))
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# print("fitting error = %.3f" % (error_sum))
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return (cx, cy, w, h, theta)
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return (cx, cy, w, h, theta)
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class EyeProcessor:
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class EyeProcessor:
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def __init__(
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def __init__(
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self,
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self,
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@ -1265,37 +1226,39 @@ class EyeProcessor:
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self.cct = 300
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self.cct = 300
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newImage2 = self.current_image_gray.copy()
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# Crop first to reduce the amount of data to process.
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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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img = self.current_image_gray[0:len(self.current_image_gray) - 10, :]
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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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# 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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# The processing results were the same when I swapped the order of blurring and 1-channelization.
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# image_gray = self.current_image_gray
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frame_gray = cv2.GaussianBlur(frame, (5, 5), 0)
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image_gray = cv2.GaussianBlur(self.current_image_gray, (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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# 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(image_gray)
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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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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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# crop 15% sqare around min_loc
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# image_gray = image_gray[max_loc[1] - maxloc1_hf:max_loc[1] + maxloc1_hf,
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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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# max_loc[0] - maxloc0_hf:max_loc[0] + maxloc0_hf]
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threshold_value = min_val + thresh_add
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threshold_value = min_val + thresh_add
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th_ret, thresh = cv2.threshold(image_gray, threshold_value, 255, cv2.THRESH_BINARY)
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_, thresh = cv2.threshold(frame_gray, threshold_value, 255, cv2.THRESH_BINARY)
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try:
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try:
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opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
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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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closing = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel)
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image = 255 - closing
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th_frame = 255 - closing
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except:
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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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# I want to eliminate try here because try tends to be slow in execution.
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image = 255 - image_gray
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th_frame = 255 - frame_gray
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contours, hierarchy = cv2.findContours(image, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
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detect_start_time = timeit.default_timer()
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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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hull = []
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# This way is faster than contours[i]
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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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# But maybe this one is faster. hull = [cv2.convexHull(cnt, False) for cnt in contours]
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@ -1305,34 +1268,38 @@ class EyeProcessor:
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# If empty, go to next loop
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# If empty, go to next loop
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pass
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pass
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try:
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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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cnt = sorted(hull, key=cv2.contourArea)
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cnt = sorted(hull, key=cv2.contourArea)
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maxcnt = cnt[-1]
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maxcnt = cnt[-1]
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ellipse = cv2.fitEllipse(maxcnt)
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# ellipse = cv2.fitEllipse(maxcnt)
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ransac_data = fit_rotated_ellipse_ransac(maxcnt.reshape(-1, 2))
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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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if ransac_data is None:
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# ransac_data is None==maxcnt.shape[0]<sample_num
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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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# go to next loop
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pass
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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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cx, cy, w, h = int(cx), int(cy), int(w), int(h)
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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))
|
|
||||||
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
|
||||||
|
|||||||
@ -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:
|
||||||
|
|||||||
@ -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")
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user