EyeTrackVR/EyeTrackApp/mommy.py
Prohurtz eb6a48df91 fix opencv hang when no cameras
Add MOMMY v1 (early small model)
2023-06-23 12:29:44 -05:00

188 lines
6.8 KiB
Python

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MOMMY by: Prohurtz
Algorithm App Implementation By: Prohurtz
Copyright (c) 2023 EyeTrackVR <3
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"""
# MOMMY = Model for Observing Mindful Movement of Your Eyes
import os
os.environ["OMP_NUM_THREADS"] = "1"
import onnxruntime
import numpy as np
from PIL import Image
import torchvision.transforms as transforms
import cv2
import time
import math
from queue import Queue
import threading
from one_euro_filter import OneEuroFilter
frame_count = 0
start_time = time.time()
frames = 0
def run_model(input_queue, output_queue, session):
while True:
frame = input_queue.get()
if frame is None:
break
to_tensor = transforms.ToTensor()
img_tensor = to_tensor(frame)
img_tensor.unsqueeze_(0)
img_np = img_tensor.numpy()
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, (22, 2))
output_queue.put((frame, pre_landmark))
class MOMMY_C(object):
def __init__(self):
onnxruntime.disable_telemetry_events()
opts = onnxruntime.SessionOptions()
opts.inter_op_num_threads = 4
opts.intra_op_num_threads = 1 # 1 = 30fps 2 =60 fps #TODO: add to settings page
# ort_session = onnxruntime.InferenceSession("pfld.onnx")
opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
self.ort_session = onnxruntime.InferenceSession("Models/mommy062023.onnx", opts, providers=['CPUExecutionProvider'])
min_cutoff = 0.04
beta = 0.9
# print(np.random.rand(22, 2))
# noisy_point = np.array([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1])
one_euro_filter = OneEuroFilter(
np.random.rand(22, 2),
min_cutoff=min_cutoff,
beta=beta
)
self.dmax = 0
self.dmin = 0
self.x = 0
self.y = 0
self.num_threads = 2
self.output_queue = Queue(maxsize=self.num_threads + 4) # can be adjusted
self.queues = []
self.num_threads = 2
self.output_queue = Queue(maxsize=self.num_threads + 4) # can be adjusted
for _ in range(self.num_threads):
self.queue = Queue(maxsize=self.num_threads + 4)
self.queues.append(self.queue)
opts = onnxruntime.SessionOptions()
opts.inter_op_num_threads = 1
opts.intra_op_num_threads = 1
opts.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
opts.optimized_model_filepath = ''
self.ort_session1 = onnxruntime.InferenceSession(
"Models/mommy062023.onnx", opts,
providers=['CPUExecutionProvider'])
# ort_session1 = onnxruntime.InferenceSession("C:/Users/beaul/PycharmProjects/EyeTrackVR/EyeTrackApp/Models/mommy062023.onnx", opts, providers=['DmlExecutionProvider'])
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()
cap = cv2.VideoCapture('DikablisSA_2_1.mp4')
frames = 0
start_time = time.time()
interval = 1 # Time interval in seconds
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 mommy_run(self):
img = self.current_image_gray.copy()
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
# img = imutils.rotate(img, angle=320)
img_height, img_width = img.shape[:2] # Move outside the loop
frame = cv2.resize(img, (112, 112))
self.run_onnx_model(self.queues, self.ort_session1, frame)
if not self.output_queue.empty():
frame, pre_landmark = self.output_queue.get()
# frame = cv2.resize(frame, (112, 112))
for point in pre_landmark:
x, y = point
cv2.circle(img, (int(x * img_width), int(y * img_height)), 2, (0, 0, 50), -1)
cv2.circle(img, tuple(int(x*112) for x in pre_landmark[4]), 1, (255, 255, 0), -1)
cv2.circle(img, tuple(int(x*112) for x in pre_landmark[12]), 1, (255, 255, 0), -1)
cv2.circle(img, tuple(int(x*112) for x in pre_landmark[17]), 1, (255, 255, 255), -1)
# print(pre_landmark)
d = math.dist(pre_landmark[4], pre_landmark[12])
if d > self.dmax:
self.dmax = d
if d < self.dmin:
self.dmin = d
try:
per = (((d - self.dmax)) / (self.dmin - self.dmax))
per = 1 - per
except:
pass
x = pre_landmark[17][0]
y = pre_landmark[17][1]
frame = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
return frame, x, y, per
frame = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
return frame, 0, 0, 0
class External_Run_MOMMY(object):
def __init__(self):
self.algo = MOMMY_C()
def run(self, current_image_gray):
self.algo.current_image_gray = current_image_gray
img, x, y, per = self.algo.mommy_run()
return img, x, y, per