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https://github.com/EyeTrackVR/EyeTrackVR.git
synced 2025-11-04 14:39:42 +08:00
fix: latency with LEAP
This commit is contained in:
parent
ba42576498
commit
7dae037dd5
@ -218,7 +218,7 @@ class Camera:
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self.fl.pop(0)
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self.fl.append(self.fps)
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self.fps = sum(self.fl) / len(self.fl)
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# self.bps = image.nbytes
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# self.bps = image.nbytes
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if should_push:
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self.push_image_to_queue(image, frame_number, self.fps)
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except:
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@ -28,7 +28,6 @@ LICENSE: GNU GPLv3
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"""
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# LEAP = Lightweight Eyelid And Pupil
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import os
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os.environ["OMP_NUM_THREADS"] = "1"
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import onnxruntime
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import numpy as np
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@ -43,40 +42,14 @@ import sys
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from utils.misc_utils import resource_path
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from pathlib import Path
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frames = 0
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models = Path("Models")
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def run_model(input_queue, output_queue, session):
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while True:
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frame = input_queue.get()
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if frame is None:
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break
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img_np = np.array(frame)
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img_np = img_np.astype(np.float32) / 255.0
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gray_img = 0.299 * img_np[:, :, 0] + 0.587 * img_np[:, :, 1] + 0.114 * img_np[:, :, 2]
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# Add the channel and batch dimensions
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gray_img = np.expand_dims(gray_img, axis=0) # Add channel dimension
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img_np = np.expand_dims(gray_img, axis=0) # Add batch dimension
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# img_np = np.transpose(img_np, (2, 0, 1))
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# img_np = np.expand_dims(img_np, axis=0)
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ort_inputs = {session.get_inputs()[0].name: img_np}
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pre_landmark = session.run(None, ort_inputs)
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# pre_landmark = pre_landmark[1]
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# pre_landmark = np.reshape(pre_landmark, (12, 2))
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pre_landmark = np.reshape(pre_landmark, (-1, 2))
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output_queue.put((frame, pre_landmark))
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class LEAP_C(object):
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def __init__(self):
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onnxruntime.disable_telemetry_events()
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# Config variables
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self.num_threads = 4 # Number of python threads to use (using ~1 more than needed to achieve wanted fps yields lower cpu usage)
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self.num_threads = 1 # Number of python threads to use (using ~1 more than needed to achieve wanted fps yields lower cpu usage)
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self.queue_max_size = 1 # Optimize for best CPU usage, Memory, and Latency. A maxsize is needed to not create a potential memory leak.
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self.model_path = resource_path(models / 'LEAP053024.onnx')
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@ -100,7 +73,7 @@ class LEAP_C(object):
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self.queues.append(self.queue)
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opts = onnxruntime.SessionOptions()
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opts.inter_op_num_threads = 1
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opts.inter_op_num_threads = 4
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opts.intra_op_num_threads = 1
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opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
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opts.optimized_model_filepath = ""
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@ -144,25 +117,28 @@ class LEAP_C(object):
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self.ort_session1 = onnxruntime.InferenceSession(self.model_path, opts, providers=["CPUExecutionProvider"])
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threads = []
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for i in range(self.num_threads):
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thread = threading.Thread(
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target=run_model,
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args=(self.queues[i], self.output_queue, self.ort_session1),
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name=f"Thread {i}",
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)
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threads.append(thread)
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thread.start()
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def run_model(output_queue, session, frame):
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img_np = np.array(frame)
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img_np = img_np.astype(np.float32) / 255.0
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gray_img = 0.299 * img_np[:, :, 0] + 0.587 * img_np[:, :, 1] + 0.114 * img_np[:, :, 2]
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# Add the channel and batch dimensions
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gray_img = np.expand_dims(gray_img, axis=0) # Add channel dimension
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img_np = np.expand_dims(gray_img, axis=0) # Add batch dimension
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# img_np = np.transpose(img_np, (2, 0, 1))
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# img_np = np.expand_dims(img_np, axis=0)
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ort_inputs = {session.get_inputs()[0].name: img_np}
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pre_landmark = session.run(None, ort_inputs)
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# pre_landmark = pre_landmark[1]
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# pre_landmark = np.reshape(pre_landmark, (12, 2))
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pre_landmark = np.reshape(pre_landmark, (-1, 2))
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# output_queue.put((frame, pre_landmark))
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return frame, pre_landmark
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def to_numpy(self, tensor):
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return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
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def run_onnx_model(self, queues, session, frame):
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for i in range(len(queues)):
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if not queues[i].full():
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queues[i].put(frame)
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break
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def leap_run(self):
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img = self.current_image_gray_clean.copy()
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@ -172,103 +148,90 @@ class LEAP_C(object):
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frame = cv2.resize(img, (112, 112))
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imgvis = self.current_image_gray.copy()
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self.run_onnx_model(self.queues, self.ort_session1, frame)
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if not self.output_queue.empty():
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frame, pre_landmark = self.run_model(self.ort_session1, frame)
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frame, pre_landmark = self.output_queue.get()
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# pre_landmark = np.reshape(pre_landmark, (-1, 2))
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for point in pre_landmark:
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# x, y = (point*112).astype(int)
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# pre_landmark = self.one_euro_filter(pre_landmark)
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x, y = point # Assuming point is a tuple (x, y)
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for point in pre_landmark:
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# x, y = (point*112).astype(int)
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# Scale the coordinates to image width and height
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x = int(x * img_width)
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y = int(y * img_height)
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# x, y = int(x), int(y) # Ensure x and y are integers
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x, y = point # Assuming point is a tuple (x, y)
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# Scale the coordinates to image width and height
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x = int(x * img_width)
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y = int(y * img_height)
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# x, y = int(x), int(y) # Ensure x and y are integers
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cv2.circle(imgvis, (int(x), int(y)), 2, (255, 255, 0), -1)
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cv2.circle(imgvis, (int(x), int(y)), 2, (255, 255, 0), -1)
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x1, y1 = pre_landmark[1]
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x2, y2 = pre_landmark[3]
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x3, y3 = pre_landmark[4]
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x4, y4 = pre_landmark[2]
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d1 = math.dist(pre_landmark[1], pre_landmark[3])
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# a more fancy method could be used taking into acount the relative size of the landmarks so that weirdness can be acounted for better
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d2 = math.dist(pre_landmark[2], pre_landmark[4])
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d = (d1 + d2) / 2
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# 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.
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# considering both should smooth things out between eyes
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d1 = math.dist(pre_landmark[1], pre_landmark[3])
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# a more fancy method could be used taking into acount the relative size of the landmarks so that weirdness can be acounted for better
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d2 = math.dist(pre_landmark[2], pre_landmark[4])
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d = (d1 + d2) / 2
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# 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.
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# considering both should smooth things out between eyes
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try:
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if d >= np.percentile(
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self.openlist, 80 # do not go above 85, but this value can be tuned
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): # an aditional approach could be using the place where on average it is most stable, denoting what distance is the most stable "open"
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self.maxlist.append(d)
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try:
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if d >= np.percentile(
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self.openlist, 80 # do not go above 85, but this value can be tuned
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): # an aditional approach could be using the place where on average it is most stable, denoting what distance is the most stable "open"
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self.maxlist.append(d)
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if len(self.maxlist) > 2000: # i feel that this is very cpu intensive. think of a better method
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self.maxlist.pop(0)
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if len(self.maxlist) > 2000: # i feel that this is very cpu intensive. think of a better method
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self.maxlist.pop(0)
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# this should be the average most open value, the average of top 2000 values in rolling calibration
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# with this we can use it as the "openstate" (0.7, for expanded squeeze)
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# this should be the average most open value, the average of top 2000 values in rolling calibration
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# with this we can use it as the "openstate" (0.7, for expanded squeeze)
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# weighted values to shift slightly to max value
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normal_open = ((sum(self.maxlist) / len(self.maxlist)) * 0.90 + max(self.openlist) * 0.10) / (
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0.95 + 0.15
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)
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# weighted values to shift slightly to max value
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normal_open = ((sum(self.maxlist) / len(self.maxlist)) * 0.90 + max(self.openlist) * 0.10) / (
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0.95 + 0.15
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)
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except:
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normal_open = 0.8
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except:
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normal_open = 0.8
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if len(self.openlist) < 5000: # TODO expose as setting?
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self.openlist.append(d)
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else:
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self.openlist.pop(0)
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self.openlist.append(d)
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if len(self.openlist) < 5000: # TODO expose as setting?
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self.openlist.append(d)
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else:
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self.openlist.pop(0)
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self.openlist.append(d)
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try:
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per = (d - normal_open) / (min(self.openlist) - normal_open)
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try:
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per = (d - normal_open) / (min(self.openlist) - normal_open)
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oldper = (d - max(self.openlist)) / (
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min(self.openlist) - max(self.openlist)
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) # TODO: remove when testing is done
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oldper = (d - max(self.openlist)) / (
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min(self.openlist) - max(self.openlist)
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) # TODO: remove when testing is done
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per = 1 - per
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per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense.
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per = min(per, 1.0) # clamp to 1.0 max
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per = max(per, 0.0) # clamp to 1.0 min
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per = 1 - per
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per = per - 0.2 # allow for eye widen? might require a more legit math way but this makes sense.
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per = min(per, 1.0) # clamp to 1.0 max
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per = max(per, 0.0) # clamp to 1.0 min
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# print("new: ", per, "vs old: ", oldper)
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# print("new: ", per, "vs old: ", oldper)
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except:
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per = 0.8
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pass
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except:
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per = 0.8
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pass
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x = pre_landmark[6][0]
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y = pre_landmark[6][1]
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x = pre_landmark[6][0]
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y = pre_landmark[6][1]
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self.last_lid = per
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calib_array = np.array([per, per]).reshape(1, 2)
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self.last_lid = per
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calib_array = np.array([per, per]).reshape(1, 2)
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per = self.one_euro_filter_float(calib_array)
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per = self.one_euro_filter_float(calib_array)
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per = per[0][0]
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# print(per)
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if per <= 0.2: # TODO: EXPOSE AS SETTING
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per == 0.0
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# this should be tuned, i could make this auto calib based on min from a list of per values.
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per = per[0][0]
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# print(per)
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if per <= 0.2: # TODO: EXPOSE AS SETTING
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per == 0.0
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# this should be tuned, i could make this auto calib based on min from a list of per values.
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return imgvis, float(x), float(y), per
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return imgvis, float(x), float(y), per
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imgvis = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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return imgvis, 0, 0, 0
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class External_Run_LEAP(object):
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@ -280,3 +243,6 @@ class External_Run_LEAP(object):
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self.algo.current_image_gray_clean = current_image_gray_clean
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img, x, y, per = self.algo.leap_run()
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return img, x, y, per
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@ -161,75 +161,11 @@ def overlay_calibrate_3d(self):
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var.overlay_active = False
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def calculate_real_angle(angle, ipd):
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return math.degrees(math.atan(math.tan(math.radians(angle)) * (ipd / 2)))
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def calibrate_tracked_data(tracked_data, calibrated_data, ipd):
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for point in tracked_data:
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x, y, angle = point
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# Find the nearest calibration point
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min_distance_point = min(calibration_points, key=lambda p: math.dist((x, y), (p[0], p[1])))
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cal_x, cal_y, _ = min_distance_point
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# Calculate the real angle for each eye
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left_eye_angle = calculate_real_angle(angle, ipd / 2)
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right_eye_angle = calculate_real_angle(angle, -ipd / 2)
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# Adjust the tracked data using calibration information
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calibrated_x = x + (cal_x - x)
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calibrated_y = y + (cal_y - y)
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calibrated_angle = angle + (cal_x - x) * math.tan(math.radians(left_eye_angle))
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calibrated_data.append((calibrated_x, calibrated_y, calibrated_angle))
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return calibrated_data
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def rotate_around_y(point, angle):
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"""
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Rotate a 3D point around the y-axis by a given angle.
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"""
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rotation_matrix = np.array(
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[[math.cos(angle), 0, -math.sin(angle)], [0, 1, 0], [math.sin(angle), 0, math.cos(angle)]]
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)
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rotated_point = np.dot(rotation_matrix, point)
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return rotated_point
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def calculate_rotation_angles(target_point, ipd, eye="left"):
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"""
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Calculate yaw and pitch angles to converge left or right eye at the target point.
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"""
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if eye == "left":
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x = target_point[0] - ipd
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else:
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x = target_point[0] + ipd
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y = target_point[1]
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z = target_point[2]
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if x == 0:
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yaw = 90.0 # Assign a specific value when b is zero
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else:
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yaw = math.degrees(math.atan2(z, x))
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if y == 0:
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pitch = 0
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else:
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pitch = math.degrees(math.atan2(x, y))
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# print(yaw, pitch)
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return yaw, pitch
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class cal:
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def cal_osc(self, cx, cy, angle):
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# Example usage for the left eye
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# Example usage for the center point
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target_point_center = [0.8, 0.8, 1] # x y z
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ipd = 0.058 # Interpupillary Distance in meters
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calculate_rotation_angles(target_point_center, ipd, eye="left")
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#print(self.eye_id)
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if cx == None or cy == None:
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return 0, 0
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@ -249,8 +185,8 @@ class cal:
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if self.grab_3d_point:
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self.grab_3d_point = False
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self.config.calibration_points.append((cx, cy, angle))
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print(self.config.calibration_points)
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self.config.calibration_points.append((cx, cy))
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print(self.config.calibration_points, self.eye_id)
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# print("calib")
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