From bda504775247faba113a2f915e65ff2f7b61afbe Mon Sep 17 00:00:00 2001 From: Prohurtz <48768484+RedHawk989@users.noreply.github.com> Date: Wed, 28 Dec 2022 10:23:01 -0800 Subject: [PATCH] small update, flow POC --- EyeTrackApp/eye_processor.py | 256 ++++++++++------------------------- EyeTrackApp/hsrac.py | 121 ++++++++--------- 2 files changed, 132 insertions(+), 245 deletions(-) diff --git a/EyeTrackApp/eye_processor.py b/EyeTrackApp/eye_processor.py index 3706fdb..62ba555 100644 --- a/EyeTrackApp/eye_processor.py +++ b/EyeTrackApp/eye_processor.py @@ -197,17 +197,19 @@ class EyeProcessor: ) def output_images_and_update(self, threshold_image, output_information: EyeInformation): - image_stack = np.concatenate( - ( - cv2.cvtColor(self.current_image_gray, cv2.COLOR_GRAY2BGR), - cv2.cvtColor(threshold_image, cv2.COLOR_GRAY2BGR), - ), - axis=1, - ) - self.image_queue_outgoing.put((image_stack, output_information)) - self.previous_image = self.current_image - self.previous_rotation = self.config.rotation_angle - + try: + image_stack = np.concatenate( + ( + cv2.cvtColor(self.current_image_gray, cv2.COLOR_GRAY2BGR), + cv2.cvtColor(threshold_image, cv2.COLOR_GRAY2BGR), + ), + axis=1, + ) + self.image_queue_outgoing.put((image_stack, output_information)) + self.previous_image = self.current_image + self.previous_rotation = self.config.rotation_angle + except: + print("E") def capture_crop_rotate_image(self): # Get our current frame @@ -249,151 +251,44 @@ class EyeProcessor: except: pass - + - def HSF(self): - - frame = self.current_image_gray - if self.now_mode == self.cv_mode[1]: - - - prev_res_len = len(self.response_list) - # adjustment of radius - if prev_res_len == 1: - # len==1==self.response_list==[self.settings.gui_HSF_radius] - self.cvparam.radius = self.auto_radius_range[0] - elif prev_res_len == 2: - # len==2==self.response_list==[self.settings.gui_HSF_radius, self.auto_radius_range[0]] - self.cvparam.radius = self.auto_radius_range[1] - elif prev_res_len == 3: - # len==3==self.response_list==[self.settings.gui_HSF_radius,self.auto_radius_range[0],self.auto_radius_range[1]] - sort_res = sorted(self.response_list, key=lambda x: x[1])[0] - # Extract the radius with the lowest response value - if sort_res[0] == self.settings.gui_HSF_radius: - # If the default value is best, change self.now_mode to init after setting radius to the default value. - self.cvparam.radius = self.settings.gui_HSF_radius - self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3] - self.response_list = [] - elif sort_res[0] == self.auto_radius_range[0]: - self.radius_cand_list = [i for i in range(self.auto_radius_range[0], self.settings.gui_HSF_radius, self.default_step[0])][1:] - # self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy - # It should be no problem to set it to anything other than self.default_step - self.cvparam.radius = self.radius_cand_list.pop() - else: - self.radius_cand_list = [i for i in range(self.settings.gui_HSF_radius, self.auto_radius_range[1], self.default_step[0])][1:] - # self.default_step is defined separately for xy, but radius is shared by xy, so it may be buggy - # It should be no problem to set it to anything other than self.default_step - self.cvparam.radius = self.radius_cand_list.pop() - else: - # Try the contents of the self.radius_cand_list in order until the self.radius_cand_list runs out - # Better make it a binary search. - if len(self.radius_cand_list) == 0: - sort_res = sorted(self.response_list, key=lambda x: x[1])[0] - self.cvparam.radius = sort_res[0] - self.now_mode = self.cv_mode[2] if not self.skip_blink_detect else self.cv_mode[3] - self.response_list = [] - else: - self.cvparam.radius = self.radius_cand_list.pop() - - radius, pad, step, hsf = self.cvparam.get_rpsh() - - # For measuring processing time of image processing - cv_start_time = timeit.default_timer() - - gray_frame = frame - - # Calculate the integral image of the frame - int_start_time = timeit.default_timer() - # BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used. - 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() - 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 - center_x, center_y = center_xy - upper_x = center_x + 25 #TODO make this a setting - lower_x = center_x - 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? - - 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 - self.response_list.append((radius, response)) - elif self.now_mode == self.cv_mode[2]: - # Statistics for blink detection - if len(self.response_list) < self.blink_init_frames: - # Record the average value of cropped_image - self.response_list.append(cv2.mean(cropped_image)[0]) - else: - # 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] + def HSRACM(self): + cx, cy, thresh = HSRAC(self) + out_x, out_y = cal_osc(self, cx, cy) + if cx == 0: + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app else: - if 0 in cropped_image.shape: - # If shape contains 0, it is not detected well. - print("[WARN] HSF: Something's wrong.") - else: - # 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 + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue)) + def HSFM(self): + cx, cy, frame = HSF(self) + out_x, out_y = cal_osc(self, cx, cy) + if cx == 0: + self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, True)) #update app + else: + self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue)) + + + def RANSAC3DM(self): + cx, cy, thresh = RANSAC3D(self) + out_x, out_y = cal_osc(self, cx, cy) + if cx == 0: + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, True)) #update app + else: + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.RANSAC, out_x, out_y, 0, self.blinkvalue)) + + def BLOBM(self): + cx, cy, thresh = BLOB(self) + out_x, out_y = cal_osc(self, cx, cy) + if cx == 0: + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app + else: + self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue)) - out_x, out_y = cal_osc(self, center_x, center_y) - - cv2.circle(frame, (center_x, center_y), 10, (0, 0, 255), -1) - # print(center_x, center_y) - try: - if self.settings.gui_BLINK: #tbh this is redundant, the algo already has blink detection built in - self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, self.blinkvalue)) - else: - self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False)) - self.failed = 0 - - except: - if self.settings.gui_BLINK: #tbh this is redundant, the algo already has blink detection built in - self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, self.blinkvalue)) - else: - self.output_images_and_update(frame, EyeInformation(InformationOrigin.HSF, 0, 0, 0, False)) - self.failed = self.failed + 1 - - - if self.now_mode != self.cv_mode[0] and self.now_mode != self.cv_mode[1]: - if cropped_image.size < 400: - pass - - if self.now_mode == self.cv_mode[0]: - self.now_mode = self.cv_mode[1] - - return - #self.output_images_and_update(thresh, EyeInformation(InformationOrigin.FAILURE, 0, 0, 0, False)) - # return - - #self.output_images_and_update(larger_threshold,EyeInformation(InformationOrigin.HSF, out_x, out_y, 0, False),) - # return - #self.output_images_and_update(larger_threshold, EyeInformation(InformationOrigin.HSF, 0, 0, 0, True)) @@ -403,14 +298,14 @@ class EyeProcessor: if self.failed == 0 and self.firstalgo != None: print('first') self.firstalgo() + else: self.failed = self.failed + 1 if self.failed == 1 and self.secondalgo != None: - print('2nd') - self.secondalgo() #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1 - + print('2nd') #send the tracking algos previous fail number, in algo if we pass set to 0, if fail, + 1 + self.secondalgo() else: self.failed = self.failed + 1 @@ -427,7 +322,7 @@ class EyeProcessor: else: self.failed = 0 # we have reached last possible algo and it is disabled, move to first algo - print(self.failed) + @@ -438,47 +333,42 @@ class EyeProcessor: self.thirdalgo = None self.fourthalgo = None #set algo priorities - """" + 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 - self.firstalgo = self.HSF + self.firstalgo = self.HSFM elif self.settings.gui_HSF and self.settings.gui_HSFP == 2: - self.secondalgo = self.HSF + self.secondalgo = self.HSFM elif self.settings.gui_HSF and self.settings.gui_HSFP == 3: - self.thirdalgo = self.HSF + self.thirdalgo = self.HSFM elif self.settings.gui_HSF and self.settings.gui_HSFP == 4: - self.fourthalgo = self.HSF + self.fourthalgo = self.HSFM if self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 1: - self.firstalgo = self.RANSAC3D + self.firstalgo = self.RANSAC3DM elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 2: - self.secondalgo = self.RANSAC3D + self.secondalgo = self.RANSAC3DM elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 3: - self.thirdalgo = self.RANSAC3D + self.thirdalgo = self.RANSAC3DM elif self.settings.gui_RANSAC3D and self.settings.gui_RANSAC3DP == 4: - self.fourthalgo = self.RANSAC3D + self.fourthalgo = self.RANSAC3DM if self.settings.gui_HSRAC and self.settings.gui_HSRACP == 1: - self.firstalgo = self.HSRAC + self.firstalgo = self.HSRACM elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 2: - self.secondalgo = self.HSRAC + self.secondalgo = self.HSRACM elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 3: - self.thirdalgo = self.HSRAC + self.thirdalgo = self.HSRACM elif self.settings.gui_HSRAC and self.settings.gui_HSRACP == 4: - self.fourthalgo = self.HSRAC + self.fourthalgo = self.HSRACM if self.settings.gui_BLOB and self.settings.gui_BLOBP == 1: - self.firstalgo = self.BLOB + self.firstalgo = self.BLOBM elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 2: - self.secondalgo = self.BLOB + self.secondalgo = self.BLOBM elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 3: - self.thirdalgo = self.BLOB + self.thirdalgo = self.BLOBM elif self.settings.gui_BLOB and self.settings.gui_BLOBP == 4: - self.fourthalgo = self.BLOB - - """ - # if self.settings.gui_BLOBP - # if self.settings.gui_HSFP - # if self.settings.gui_RANSAC3DP + self.fourthalgo = self.BLOBM f = True while True: @@ -536,14 +426,14 @@ class EyeProcessor: self.current_image_gray_clean = self.current_image_gray.copy() #copy this frame to have a clean image for blink algo # print(self.settings.gui_RANSAC3D) - BLINK(self) + # BLINK(self) - cx, cy, thresh = HSRAC(self) - out_x, out_y = cal_osc(self, cx, cy) - if cx == 0: - self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app - else: - self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue)) + # cx, cy, thresh = HSRAC(self) + # out_x, out_y = cal_osc(self, cx, cy) + # if cx == 0: + # self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, True)) #update app + # else: + # self.output_images_and_update(thresh, EyeInformation(InformationOrigin.HSRAC, out_x, out_y, 0, self.blinkvalue)) # 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 #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 diff --git a/EyeTrackApp/hsrac.py b/EyeTrackApp/hsrac.py index 6c42085..d36012f 100644 --- a/EyeTrackApp/hsrac.py +++ b/EyeTrackApp/hsrac.py @@ -14,8 +14,8 @@ lru_maxsize_vvs = 16 lru_maxsize_vs = 64 # CV param -default_radius = 15 -auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30) +#default_radius = 15 +#auto_radius_range = (default_radius - 10, default_radius + 10) # (10,30) blink_init_frames = 60 * 3 # 60fps*3sec,Number of blink statistical frames # step==(x,y) 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._step = step self._hsf = HaarSurroundFeature(radius) + def get_rpsh(self): return self._radius, self.pad, self._step, self._hsf @@ -570,67 +571,63 @@ def HSRAC(self): radius, pad, step, hsf = self.cvparam.get_rpsh() - # For measuring processing time of image processing - cv_start_time = timeit.default_timer() - gray_frame = frame - - # Calculate the integral image of the frame - int_start_time = timeit.default_timer() - # BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used. - 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() - 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 - center_x, center_y = center_xy - upper_x = center_x + 25 #TODO make this a setting - lower_x = center_x - 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? - - 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 - self.response_list.append((radius, response)) - elif self.now_mode == self.cv_mode[2]: - # Statistics for blink detection - if len(self.response_list) < self.blink_init_frames: - # Record the average value of cropped_image - self.response_list.append(cv2.mean(cropped_image)[0]) + try: + # BORDER_CONSTANT is faster than BORDER_REPLICATE There seems to be almost no negative impact when BORDER_CONSTANT is used. + 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() + 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 + center_x, center_y = center_xy + upper_x = center_x + 25 #TODO make this a setting + lower_x = center_x - 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? + + 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 + self.response_list.append((radius, response)) + elif self.now_mode == self.cv_mode[2]: + # Statistics for blink detection + if len(self.response_list) < self.blink_init_frames: + # Record the average value of cropped_image + self.response_list.append(cv2.mean(cropped_image)[0]) + else: + # 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: - # 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: - if 0 in cropped_image.shape: - # If shape contains 0, it is not detected well. - print("Something's wrong.") - else: - # 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 - - + if 0 in cropped_image.shape: + # If shape contains 0, it is not detected well. + print("Something's wrong.") + else: + # 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 + + except: + return 0, 0, frame #run ransac on the HSF crop\ try: 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), -1) # the point of the darkest area in the image try: - print(radius) + # print(radius) return out_x, out_y, thresh except: