From cda06b830b38c4c7ac153bcf9f5045428fad37e1 Mon Sep 17 00:00:00 2001 From: Prohurtz <48768484+RedHawk989@users.noreply.github.com> Date: Thu, 4 Jul 2024 19:39:17 -0500 Subject: [PATCH] fix: revert LEAP bug fix --- EyeTrackApp/AHSF.py | 6 +- EyeTrackApp/leap.py | 224 ++++++++++++++++++++++++++++---------------- 2 files changed, 147 insertions(+), 83 deletions(-) diff --git a/EyeTrackApp/AHSF.py b/EyeTrackApp/AHSF.py index fec5c3a..b035cf6 100644 --- a/EyeTrackApp/AHSF.py +++ b/EyeTrackApp/AHSF.py @@ -907,6 +907,8 @@ if __name__ == "__main__": def External_Run_AHSF(frame_gray): average_color = np.mean(frame_gray) orig_height, orig_width = frame_gray.shape + + frame_clear_resize = frame_gray.copy() org_frame_gray = frame_gray.copy() frame_gray = cv2.resize(frame_gray, (130, 130)) # TODO TEST FIXED RESIZE @@ -928,7 +930,7 @@ def External_Run_AHSF(frame_gray): square_background[y_offset : y_offset + height, x_offset : x_offset + width] = frame_gray frame_gray = square_background - frame_clear_resize = frame_gray.copy() + wh_step = max((int(max_dimension / 80)),1) # TODO: FINETUNE VALUES xy_step = max(int(max_dimension / 24), 1) # TODO: FINETUNE VALUES @@ -1025,7 +1027,7 @@ def External_Run_AHSF(frame_gray): minor_diameter = min(width, height) average_diameter = (major_diameter + minor_diameter) / 2 - + # print(orig_width, orig_height) return org_frame_gray, frame_clear_resize, x_center, y_center, abs(width - height) diff --git a/EyeTrackApp/leap.py b/EyeTrackApp/leap.py index 5fa335c..f8244f7 100644 --- a/EyeTrackApp/leap.py +++ b/EyeTrackApp/leap.py @@ -50,9 +50,37 @@ DATASET CONTRIBUTIONS: """ +""" +------------------------------------------------------------------------------------------------------ + ,@@@@@@ + @@@@@@@@@@@ @@@ + @@@@@@@@@@@@ @@@@@@@@@@@ + @@@@@@@@@@@@@ @@@@@@@@@@@@@@ + @@@@@@@/ ,@@@@@@@@@@@@@ + /@@@@@@@@@@@@@@@ @@@@@@@@ + @@@@@@@@@@@@@@@@@@@@@@@@ @@@@@ + @@@@@@@@ @@@@@ + ,@@@ @@@@& + @@@@@@. @@@@ + @@@ @@@@@@@@@/ @@@@@ + ,@@@. @@@@@@((@ @@@@( + //@@@ ,, @@@@ @@@@@ + @@@( @@@@@@@ + @@@ @ @@@@@@@@# + @@@@@@@@@@@@@@@@@ + @@@@@@@@@@@@@( + +LEAP by: Prohurtz +Algorithm App Implementation By: Prohurtz + +Copyright (c) 2023 EyeTrackVR <3 +LICENSE: GNU GPLv3 +------------------------------------------------------------------------------------------------------ +""" # LEAP = Lightweight Eyelid And Pupil import os + os.environ["OMP_NUM_THREADS"] = "1" import onnxruntime import numpy as np @@ -67,14 +95,40 @@ import sys from utils.misc_utils import resource_path from pathlib import Path + frames = 0 models = Path("Models") + +def run_model(input_queue, output_queue, session): + while True: + frame = input_queue.get() + if frame is None: + break + + img_np = np.array(frame) + img_np = img_np.astype(np.float32) / 255.0 + gray_img = 0.299 * img_np[:, :, 0] + 0.587 * img_np[:, :, 1] + 0.114 * img_np[:, :, 2] + + # Add the channel and batch dimensions + gray_img = np.expand_dims(gray_img, axis=0) # Add channel dimension + img_np = np.expand_dims(gray_img, axis=0) # Add batch dimension + # img_np = np.transpose(img_np, (2, 0, 1)) + # img_np = np.expand_dims(img_np, axis=0) + ort_inputs = {session.get_inputs()[0].name: img_np} + pre_landmark = session.run(None, ort_inputs) + + # pre_landmark = pre_landmark[1] + # pre_landmark = np.reshape(pre_landmark, (12, 2)) + pre_landmark = np.reshape(pre_landmark, (-1, 2)) + output_queue.put((frame, pre_landmark)) + + class LEAP_C(object): def __init__(self): onnxruntime.disable_telemetry_events() # Config variables - self.num_threads = 1 # Number of python threads to use (using ~1 more than needed to achieve wanted fps yields lower cpu usage) + self.num_threads = 4 # Number of python threads to use (using ~1 more than needed to achieve wanted fps yields lower cpu usage) self.queue_max_size = 1 # Optimize for best CPU usage, Memory, and Latency. A maxsize is needed to not create a potential memory leak. self.model_path = resource_path(models / 'LEAP062120246epoch.onnx') @@ -142,28 +196,25 @@ class LEAP_C(object): self.ort_session1 = onnxruntime.InferenceSession(self.model_path, opts, providers=["CPUExecutionProvider"]) - def run_model(output_queue, session, frame): + threads = [] + for i in range(self.num_threads): + thread = threading.Thread( + target=run_model, + args=(self.queues[i], self.output_queue, self.ort_session1), + name=f"Thread {i}", + ) + threads.append(thread) + thread.start() - img_np = np.array(frame) - img_np = img_np.astype(np.float32) / 255.0 - gray_img = 0.299 * img_np[:, :, 0] + 0.587 * img_np[:, :, 1] + 0.114 * img_np[:, :, 2] - - # Add the channel and batch dimensions - gray_img = np.expand_dims(gray_img, axis=0) # Add channel dimension - img_np = np.expand_dims(gray_img, axis=0) # Add batch dimension - # img_np = np.transpose(img_np, (2, 0, 1)) - # img_np = np.expand_dims(img_np, axis=0) - ort_inputs = {session.get_inputs()[0].name: img_np} - pre_landmark = session.run(None, ort_inputs) - - # pre_landmark = pre_landmark[1] - # pre_landmark = np.reshape(pre_landmark, (12, 2)) - pre_landmark = np.reshape(pre_landmark, (-1, 2)) - # output_queue.put((frame, pre_landmark)) - return frame, pre_landmark def to_numpy(self, tensor): return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy() + def run_onnx_model(self, queues, session, frame): + for i in range(len(queues)): + if not queues[i].full(): + queues[i].put(frame) + break + def leap_run(self): img = self.current_image_gray_clean.copy() @@ -173,90 +224,103 @@ class LEAP_C(object): frame = cv2.resize(img, (112, 112)) imgvis = self.current_image_gray.copy() + self.run_onnx_model(self.queues, self.ort_session1, frame) - frame, pre_landmark = self.run_model(self.ort_session1, frame) + if not self.output_queue.empty(): - for point in pre_landmark: - # x, y = (point*112).astype(int) + frame, pre_landmark = self.output_queue.get() + # pre_landmark = np.reshape(pre_landmark, (-1, 2)) - x, y = point # Assuming point is a tuple (x, y) + # pre_landmark = self.one_euro_filter(pre_landmark) - # Scale the coordinates to image width and height - x = int(x * img_width) - y = int(y * img_height) - # x, y = int(x), int(y) # Ensure x and y are integers + for point in pre_landmark: + # x, y = (point*112).astype(int) - cv2.circle(imgvis, (int(x), int(y)), 2, (255, 255, 0), -1) + x, y = point # Assuming point is a tuple (x, y) + + # Scale the coordinates to image width and height + x = int(x * img_width) + y = int(y * img_height) + # x, y = int(x), int(y) # Ensure x and y are integers + + cv2.circle(imgvis, (int(x), int(y)), 2, (255, 255, 0), -1) + x1, y1 = pre_landmark[1] + x2, y2 = pre_landmark[3] - d1 = math.dist(pre_landmark[1], pre_landmark[3]) - # a more fancy method could be used taking into acount the relative size of the landmarks so that weirdness can be acounted for better - d2 = math.dist(pre_landmark[2], pre_landmark[4]) - d = (d1 + d2) / 2 - # by averaging both sets we can get less error? i think part of why 1 eye was better than the other is because we only considered one offset points. - # considering both should smooth things out between eyes + x3, y3 = pre_landmark[4] + x4, y4 = pre_landmark[2] - try: - if d >= np.percentile( - self.openlist, 80 # do not go above 85, but this value can be tuned - ): # an aditional approach could be using the place where on average it is most stable, denoting what distance is the most stable "open" - self.maxlist.append(d) + d1 = math.dist(pre_landmark[1], pre_landmark[3]) + # a more fancy method could be used taking into acount the relative size of the landmarks so that weirdness can be acounted for better + d2 = math.dist(pre_landmark[2], pre_landmark[4]) + d = (d1 + d2) / 2 + # by averaging both sets we can get less error? i think part of why 1 eye was better than the other is because we only considered one offset points. + # considering both should smooth things out between eyes - if len(self.maxlist) > 2000: # i feel that this is very cpu intensive. think of a better method - self.maxlist.pop(0) + try: + if d >= np.percentile( + self.openlist, 80 # do not go above 85, but this value can be tuned + ): # an aditional approach could be using the place where on average it is most stable, denoting what distance is the most stable "open" + self.maxlist.append(d) - # this should be the average most open value, the average of top 2000 values in rolling calibration - # with this we can use it as the "openstate" (0.7, for expanded squeeze) + if len(self.maxlist) > 2000: # i feel that this is very cpu intensive. think of a better method + self.maxlist.pop(0) - # weighted values to shift slightly to max value - normal_open = ((sum(self.maxlist) / len(self.maxlist)) * 0.90 + max(self.openlist) * 0.10) / ( - 0.95 + 0.15 - ) + # this should be the average most open value, the average of top 2000 values in rolling calibration + # with this we can use it as the "openstate" (0.7, for expanded squeeze) - except: - normal_open = 0.8 + # weighted values to shift slightly to max value + normal_open = ((sum(self.maxlist) / len(self.maxlist)) * 0.90 + max(self.openlist) * 0.10) / ( + 0.95 + 0.15 + ) - if len(self.openlist) < 5000: # TODO expose as setting? - self.openlist.append(d) - else: - self.openlist.pop(0) - self.openlist.append(d) + except: + normal_open = 0.8 - try: - per = (d - normal_open) / (min(self.openlist) - normal_open) + if len(self.openlist) < 5000: # TODO expose as setting? + self.openlist.append(d) + else: + self.openlist.pop(0) + self.openlist.append(d) - oldper = (d - max(self.openlist)) / ( - min(self.openlist) - max(self.openlist) - ) # TODO: remove when testing is done + try: + per = (d - normal_open) / (min(self.openlist) - normal_open) - per = 1 - per - per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense. - per = min(per, 1.0) # clamp to 1.0 max - per = max(per, 0.0) # clamp to 1.0 min + oldper = (d - max(self.openlist)) / ( + min(self.openlist) - max(self.openlist) + ) # TODO: remove when testing is done - # print("new: ", per, "vs old: ", oldper) + per = 1 - per + per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense. + per = min(per, 1.0) # clamp to 1.0 max + per = max(per, 0.0) # clamp to 1.0 min - except: - per = 0.8 - pass + # print("new: ", per, "vs old: ", oldper) - x = pre_landmark[6][0] - y = pre_landmark[6][1] + except: + per = 0.8 + pass - self.last_lid = per - calib_array = np.array([per, per]).reshape(1, 2) + x = pre_landmark[6][0] + y = pre_landmark[6][1] - per = self.one_euro_filter_float(calib_array) + self.last_lid = per + calib_array = np.array([per, per]).reshape(1, 2) - per = per[0][0] - # print(per) - if per <= 0.2: # TODO: EXPOSE AS SETTING - per == 0.0 - # this should be tuned, i could make this auto calib based on min from a list of per values. + per = self.one_euro_filter_float(calib_array) - return imgvis, float(x), float(y), per + per = per[0][0] + # print(per) + if per <= 0.2: # TODO: EXPOSE AS SETTING + per == 0.0 + # this should be tuned, i could make this auto calib based on min from a list of per values. + return imgvis, float(x), float(y), per + + imgvis = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) + return imgvis, 0, 0, 0 class External_Run_LEAP(object): @@ -267,6 +331,4 @@ class External_Run_LEAP(object): self.algo.current_image_gray = current_image_gray self.algo.current_image_gray_clean = current_image_gray_clean img, x, y, per = self.algo.leap_run() - return img, x, y, per - - + return img, x, y, per \ No newline at end of file