mirror of
https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
small update, flow POC
This commit is contained in:
parent
77b2389a03
commit
bda5047752
@ -197,17 +197,19 @@ class EyeProcessor:
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)
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)
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def output_images_and_update(self, threshold_image, output_information: EyeInformation):
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def output_images_and_update(self, threshold_image, output_information: EyeInformation):
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image_stack = np.concatenate(
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try:
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(
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image_stack = np.concatenate(
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cv2.cvtColor(self.current_image_gray, cv2.COLOR_GRAY2BGR),
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(
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cv2.cvtColor(threshold_image, cv2.COLOR_GRAY2BGR),
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cv2.cvtColor(self.current_image_gray, cv2.COLOR_GRAY2BGR),
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),
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cv2.cvtColor(threshold_image, cv2.COLOR_GRAY2BGR),
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axis=1,
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),
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)
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axis=1,
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self.image_queue_outgoing.put((image_stack, output_information))
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)
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self.previous_image = self.current_image
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self.image_queue_outgoing.put((image_stack, output_information))
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self.previous_rotation = self.config.rotation_angle
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self.previous_image = self.current_image
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self.previous_rotation = self.config.rotation_angle
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except:
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print("E")
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def capture_crop_rotate_image(self):
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def capture_crop_rotate_image(self):
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# Get our current frame
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# Get our current frame
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@ -249,151 +251,44 @@ class EyeProcessor:
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except:
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except:
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pass
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pass
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def HSF(self):
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def HSRACM(self):
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cx, cy, thresh = HSRAC(self)
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frame = self.current_image_gray
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out_x, out_y = cal_osc(self, cx, cy)
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if self.now_mode == self.cv_mode[1]:
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if cx == 0:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app
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prev_res_len = len(self.response_list)
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# adjustment of radius
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if prev_res_len == 1:
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# len==1==self.response_list==[self.settings.gui_HSF_radius]
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self.cvparam.radius = self.auto_radius_range[0]
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elif prev_res_len == 2:
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# len==2==self.response_list==[self.settings.gui_HSF_radius, self.auto_radius_range[0]]
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self.cvparam.radius = self.auto_radius_range[1]
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elif prev_res_len == 3:
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# len==3==self.response_list==[self.settings.gui_HSF_radius,self.auto_radius_range[0],self.auto_radius_range[1]]
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sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
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# Extract the radius with the lowest response value
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if sort_res[0] == self.settings.gui_HSF_radius:
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# If the default value is best, change self.now_mode to init after setting radius to the default value.
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self.cvparam.radius = self.settings.gui_HSF_radius
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self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
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self.response_list = []
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elif sort_res[0] == self.auto_radius_range[0]:
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self.radius_cand_list = [i for i in range(self.auto_radius_range[0], self.settings.gui_HSF_radius, self.default_step[0])][1:]
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# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
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# It should be no problem to set it to anything other than self.default_step
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self.cvparam.radius = self.radius_cand_list.pop()
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else:
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self.radius_cand_list = [i for i in range(self.settings.gui_HSF_radius, self.auto_radius_range[1], self.default_step[0])][1:]
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# self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy
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# It should be no problem to set it to anything other than self.default_step
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self.cvparam.radius = self.radius_cand_list.pop()
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else:
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# Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out
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# Better make it a binary search.
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if len(self.radius_cand_list) == 0:
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sort_res = sorted(self.response_list, key=lambda x: x[1])[0]
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self.cvparam.radius = sort_res[0]
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self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3]
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self.response_list = []
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else:
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self.cvparam.radius = self.radius_cand_list.pop()
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radius, pad, step, hsf = self.cvparam.get_rpsh()
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# For measuring processing time of image processing
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cv_start_time = timeit.default_timer()
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gray_frame = frame
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# Calculate the integral image of the frame
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int_start_time = timeit.default_timer()
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# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
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frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
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frame_int = cv2.integral(frame_pad)
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# Convolve the feature with the integral image
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conv_int_start_time = timeit.default_timer()
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xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
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frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
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crop_start_time = timeit.default_timer()
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# Define the center point and radius
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center_x, center_y = center_xy
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upper_x = center_x + 25 #TODO make this a setting
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lower_x = center_x - 25
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upper_y = center_y + 25
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lower_y = center_y - 25
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# Crop the image using the calculated bounds
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cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why?
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if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
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# If mode is first_frame or radius_adjust, record current radius and response
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self.response_list.append((radius, response))
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elif self.now_mode == self.cv_mode[2]:
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# Statistics for blink detection
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if len(self.response_list) < self.blink_init_frames:
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# Record the average value of cropped_image
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self.response_list.append(cv2.mean(cropped_image)[0])
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else:
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# Calculate self.response_max by computing interquartile range, IQR
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# Change self.cv_mode to normal
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self.response_list = np.array(self.response_list)
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# 25%,75%
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# This value may need to be adjusted depending on the environment.
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quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
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iqr = quartile_3 - quartile_1
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# response_min = quartile_1 - (iqr * 1.5)
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self.response_max = quartile_3 + (iqr * 1.5)
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self.now_mode = self.cv_mode[3]
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else:
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else:
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if 0 in cropped_image.shape:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
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# If shape contains 0, it is not detected well.
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print("[WARN] HSF: Something's wrong.")
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else:
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# If the average value of cropped_image is greater than self.response_max
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# (i.e., if the cropimage is whitish
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if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max:
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# blink
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cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
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# If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way
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# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
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def HSFM(self):
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cx, cy, frame = HSF(self)
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out_x, out_y = cal_osc(self, cx, cy)
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if cx == 0:
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, True)) #update app
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else:
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
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def RANSAC3DM(self):
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cx, cy, thresh = RANSAC3D(self)
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out_x, out_y = cal_osc(self, cx, cy)
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if cx == 0:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, True)) #update app
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else:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue))
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def BLOBM(self):
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cx, cy, thresh = BLOB(self)
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out_x, out_y = cal_osc(self, cx, cy)
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if cx == 0:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app
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else:
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self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
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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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# print(center_x, center_y)
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try:
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if self.settings.gui_BLINK: #tbh this is redundant, the algo already has blink detection built in
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue))
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else:
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False))
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self.failed = 0
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except:
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if self.settings.gui_BLINK: #tbh this is redundant, the algo already has blink detection built in
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, self.blinkvalue))
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else:
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self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, False))
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self.failed = self.failed + 1
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if self.now_mode != self.cv_mode[0] and self.now_mode != self.cv_mode[1]:
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if cropped_image.size < 400:
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pass
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if self.now_mode == self.cv_mode[0]:
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self.now_mode = self.cv_mode[1]
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return
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#self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False))
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# return
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#self.output_images_and_update(larger_threshold,EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False),)
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# return
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#self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True))
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@ -403,14 +298,14 @@ class EyeProcessor:
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if self.failed == 0 and self.firstalgo != None:
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if self.failed == 0 and self.firstalgo != None:
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print('first')
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print('first')
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self.firstalgo()
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self.firstalgo()
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else:
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else:
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self.failed = self.failed + 1
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self.failed = self.failed + 1
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if self.failed == 1 and self.secondalgo != None:
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if self.failed == 1 and self.secondalgo != None:
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print('2nd')
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print('2nd') #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
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self.secondalgo() #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1
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self.secondalgo()
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else:
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else:
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self.failed = self.failed + 1
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self.failed = self.failed + 1
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@ -427,7 +322,7 @@ class EyeProcessor:
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else:
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else:
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self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
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self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo
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print(self.failed)
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@ -438,47 +333,42 @@ class EyeProcessor:
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self.thirdalgo = None
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self.thirdalgo = None
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self.fourthalgo = None
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self.fourthalgo = None
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#set algo priorities
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#set algo priorities
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""""
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if self.settings.gui_HSF and self.settings.gui_HSFP == 1: #I feel like this is super innefficient though it only runs at startup and no solution is coming to me atm
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if self.settings.gui_HSF and self.settings.gui_HSFP == 1: #I feel like this is super innefficient though it only runs at startup and no solution is coming to me atm
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self.firstalgo = self.HSF
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self.firstalgo = self.HSFM
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 2:
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 2:
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self.secondalgo = self.HSF
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self.secondalgo = self.HSFM
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 3:
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 3:
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self.thirdalgo = self.HSF
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self.thirdalgo = self.HSFM
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 4:
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elif self.settings.gui_HSF and self.settings.gui_HSFP == 4:
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self.fourthalgo = self.HSF
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self.fourthalgo = self.HSFM
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if self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 1:
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if self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 1:
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self.firstalgo = self.RANSAC3D
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self.firstalgo = self.RANSAC3DM
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 2:
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 2:
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self.secondalgo = self.RANSAC3D
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self.secondalgo = self.RANSAC3DM
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 3:
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 3:
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self.thirdalgo = self.RANSAC3D
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self.thirdalgo = self.RANSAC3DM
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4:
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elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4:
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self.fourthalgo = self.RANSAC3D
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self.fourthalgo = self.RANSAC3DM
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if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1:
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if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1:
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self.firstalgo = self.HSRAC
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self.firstalgo = self.HSRACM
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2:
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2:
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self.secondalgo = self.HSRAC
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self.secondalgo = self.HSRACM
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 3:
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 3:
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self.thirdalgo = self.HSRAC
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self.thirdalgo = self.HSRACM
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 4:
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elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 4:
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self.fourthalgo = self.HSRAC
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self.fourthalgo = self.HSRACM
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if self.settings.gui_BLOB and self.settings.gui_BLOBP == 1:
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if self.settings.gui_BLOB and self.settings.gui_BLOBP == 1:
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self.firstalgo = self.BLOB
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self.firstalgo = self.BLOBM
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 2:
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 2:
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self.secondalgo = self.BLOB
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self.secondalgo = self.BLOBM
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 3:
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 3:
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self.thirdalgo = self.BLOB
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self.thirdalgo = self.BLOBM
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4:
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elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4:
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self.fourthalgo = self.BLOB
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self.fourthalgo = self.BLOBM
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"""
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# if self.settings.gui_BLOBP
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# if self.settings.gui_HSFP
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# if self.settings.gui_RANSAC3DP
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f = True
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f = True
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while True:
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while True:
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@ -536,14 +426,14 @@ class EyeProcessor:
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self.current_image_gray_clean = self.current_image_gray.copy() #copy this frame to have a clean image for blink algo
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self.current_image_gray_clean = self.current_image_gray.copy() #copy this frame to have a clean image for blink algo
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# print(self.settings.gui_RANSAC3D)
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# print(self.settings.gui_RANSAC3D)
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BLINK(self)
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# BLINK(self)
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cx, cy, thresh = HSRAC(self)
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# cx, cy, thresh = HSRAC(self)
|
||||||
out_x, out_y = cal_osc(self, cx, cy)
|
# out_x, out_y = cal_osc(self, cx, cy)
|
||||||
if cx == 0:
|
# if cx == 0:
|
||||||
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app
|
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app
|
||||||
else:
|
# else:
|
||||||
self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
|
# self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue))
|
||||||
|
|
||||||
|
|
||||||
# cx, cy, thresh = RANSAC3D(self)
|
# cx, cy, thresh = RANSAC3D(self)
|
||||||
@ -559,7 +449,7 @@ class EyeProcessor:
|
|||||||
#out_x, out_y = cal_osc(self, center_x, center_y) #filter and calibrate
|
#out_x, out_y = cal_osc(self, center_x, center_y) #filter and calibrate
|
||||||
#self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False)) #update app
|
#self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False)) #update app
|
||||||
|
|
||||||
# self.ALGOSELECT() #run our algos in priority order set in settings
|
self.ALGOSELECT() #run our algos in priority order set in settings
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -14,8 +14,8 @@ lru_maxsize_vvs = 16
|
|||||||
lru_maxsize_vs = 64
|
lru_maxsize_vs = 64
|
||||||
# CV param
|
# CV param
|
||||||
|
|
||||||
default_radius = 15
|
#default_radius = 15
|
||||||
auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
|
#auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30)
|
||||||
blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
|
blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames
|
||||||
# step==(x,y)
|
# step==(x,y)
|
||||||
default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
|
default_step = (5, 5) # bigger the steps,lower the processing time! ofc acc also takes an impact
|
||||||
@ -205,6 +205,7 @@ class CvParameters:
|
|||||||
# self.prev_step=step
|
# self.prev_step=step
|
||||||
self._step = step
|
self._step = step
|
||||||
self._hsf = HaarSurroundFeature(radius)
|
self._hsf = HaarSurroundFeature(radius)
|
||||||
|
|
||||||
|
|
||||||
def get_rpsh(self):
|
def get_rpsh(self):
|
||||||
return self._radius, self.pad, self._step, self._hsf
|
return self._radius, self.pad, self._step, self._hsf
|
||||||
@ -570,67 +571,63 @@ def HSRAC(self):
|
|||||||
|
|
||||||
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
radius, pad, step, hsf = self.cvparam.get_rpsh()
|
||||||
|
|
||||||
# For measuring processing time of image processing
|
|
||||||
cv_start_time = timeit.default_timer()
|
|
||||||
|
|
||||||
gray_frame = frame
|
gray_frame = frame
|
||||||
|
try:
|
||||||
# Calculate the integral image of the frame
|
# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
|
||||||
int_start_time = timeit.default_timer()
|
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
|
||||||
# BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used.
|
frame_int = cv2.integral(frame_pad)
|
||||||
frame_pad = cv2.copyMakeBorder(gray_frame, pad, pad, pad, pad, cv2.BORDER_CONSTANT)
|
|
||||||
frame_int = cv2.integral(frame_pad)
|
# Convolve the feature with the integral image
|
||||||
|
conv_int_start_time = timeit.default_timer()
|
||||||
# Convolve the feature with the integral image
|
xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
||||||
conv_int_start_time = timeit.default_timer()
|
frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
|
||||||
xy_step = frameint_get_xy_step(frame_int.shape, step, pad, start_offset=None, end_offset=None)
|
|
||||||
frame_conv, response, center_xy = conv_int(frame_int, hsf, step, pad, xy_step)
|
crop_start_time = timeit.default_timer()
|
||||||
|
# Define the center point and radius
|
||||||
crop_start_time = timeit.default_timer()
|
center_x, center_y = center_xy
|
||||||
# Define the center point and radius
|
upper_x = center_x + 25 #TODO make this a setting
|
||||||
center_x, center_y = center_xy
|
lower_x = center_x - 25
|
||||||
upper_x = center_x + 25 #TODO make this a setting
|
upper_y = center_y + 25
|
||||||
lower_x = center_x - 25
|
lower_y = center_y - 25
|
||||||
upper_y = center_y + 25
|
|
||||||
lower_y = center_y - 25
|
# Crop the image using the calculated bounds
|
||||||
|
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why?
|
||||||
# Crop the image using the calculated bounds
|
|
||||||
cropped_image = gray_frame[lower_y:upper_y, lower_x:upper_x] # y is 50px, x is 45? why?
|
if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
|
||||||
|
# If mode is first_frame or radius_adjust, record current radius and response
|
||||||
if self.now_mode == self.cv_mode[0] or self.now_mode == self.cv_mode[1]:
|
self.response_list.append((radius, response))
|
||||||
# If mode is first_frame or radius_adjust, record current radius and response
|
elif self.now_mode == self.cv_mode[2]:
|
||||||
self.response_list.append((radius, response))
|
# Statistics for blink detection
|
||||||
elif self.now_mode == self.cv_mode[2]:
|
if len(self.response_list) < self.blink_init_frames:
|
||||||
# Statistics for blink detection
|
# Record the average value of cropped_image
|
||||||
if len(self.response_list) < self.blink_init_frames:
|
self.response_list.append(cv2.mean(cropped_image)[0])
|
||||||
# Record the average value of cropped_image
|
else:
|
||||||
self.response_list.append(cv2.mean(cropped_image)[0])
|
# Calculate self.response_max by computing interquartile range, IQR
|
||||||
|
# Change self.cv_mode to normal
|
||||||
|
self.response_list = np.array(self.response_list)
|
||||||
|
# 25%,75%
|
||||||
|
# This value may need to be adjusted depending on the environment.
|
||||||
|
quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
||||||
|
iqr = quartile_3 - quartile_1
|
||||||
|
# response_min = quartile_1 - (iqr * 1.5)
|
||||||
|
self.response_max = quartile_3 + (iqr * 1.5)
|
||||||
|
self.now_mode = self.cv_mode[3]
|
||||||
else:
|
else:
|
||||||
# Calculate self.response_max by computing interquartile range, IQR
|
if 0 in cropped_image.shape:
|
||||||
# Change self.cv_mode to normal
|
# If shape contains 0, it is not detected well.
|
||||||
self.response_list = np.array(self.response_list)
|
print("Something's wrong.")
|
||||||
# 25%,75%
|
else:
|
||||||
# This value may need to be adjusted depending on the environment.
|
# If the average value of cropped_image is greater than self.response_max
|
||||||
quartile_1, quartile_3 = np.percentile(self.response_list, [25, 75])
|
# (i.e., if the cropimage is whitish
|
||||||
iqr = quartile_3 - quartile_1
|
if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max:
|
||||||
# response_min = quartile_1 - (iqr * 1.5)
|
# blink
|
||||||
self.response_max = quartile_3 + (iqr * 1.5)
|
|
||||||
self.now_mode = self.cv_mode[3]
|
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
|
||||||
else:
|
# If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way
|
||||||
if 0 in cropped_image.shape:
|
# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
|
||||||
# If shape contains 0, it is not detected well.
|
|
||||||
print("Something's wrong.")
|
except:
|
||||||
else:
|
return 0, 0, frame
|
||||||
# If the average value of cropped_image is greater than self.response_max
|
|
||||||
# (i.e., if the cropimage is whitish
|
|
||||||
if self.response_max is not None and cv2.mean(cropped_image)[0] > self.response_max:
|
|
||||||
# blink
|
|
||||||
|
|
||||||
cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1)
|
|
||||||
# If you want to update self.response_max. it may be more cost-effective to rewrite self.response_list in the following way
|
|
||||||
# https://stackoverflow.com/questions/42771110/fastest-way-to-left-cycle-a-numpy-array-like-pop-push-for-a-queue
|
|
||||||
|
|
||||||
|
|
||||||
#run ransac on the HSF crop\
|
#run ransac on the HSF crop\
|
||||||
try:
|
try:
|
||||||
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
|
||||||
@ -724,7 +721,7 @@ def HSRAC(self):
|
|||||||
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
|
||||||
try:
|
try:
|
||||||
print(radius)
|
# print(radius)
|
||||||
return out_x, out_y, thresh
|
return out_x, out_y, thresh
|
||||||
|
|
||||||
except:
|
except:
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user