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run_grasping_detection_threading.py
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run_grasping_detection_threading.py
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#!/usr/bin/env python3
import argparse
import multiprocessing
import threading
import torch
import gc
import os
import cv2
from i_grip import RgbdCameras as rgbd
from i_grip import Hands3DDetectors as hd
from i_grip import Object2DDetectors as o2d
from i_grip import ObjectPoseEstimators as ope
from i_grip import Scene_threading as sc
# from i_grip import Scene_nocopy as sc
from i_grip import Plotters3 as pl
from i_grip.utils import kill_gpu_processes
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def report_gpu():
print(torch.cuda.list_gpu_processes())
gc.collect()
print(torch.cuda.memory_snapshot())
torch.cuda.empty_cache()
class GraspingDetector:
def __init__(self, ) -> None:
dataset = "ycbv"
self.rgbd_cam = rgbd.RgbdCamera()
cam_data = self.rgbd_cam.get_device_data()
self.hand_detector = hd.Hands3DDetector(cam_data,
hd.Hands3DDetector.LIVE_STREAM_MODE)
plotter = pl.NBPlot()
# plotter = None
self.object_detector = o2d.get_object_detector(dataset,
cam_data)
self.object_pose_estimator = ope.get_pose_estimator(dataset,
cam_data,
use_tracking = True,
fuse_detections=False)
self.scene = sc.LiveScene(cam_data,
name = 'Full tracking', plotter = plotter)
self.object_detections = None
self.is_hands= False
self.img_for_objects = None
def estimate_objects_task(self, start_event, estimate_event):
while self.rgbd_cam.is_on():
start_flag = start_event.wait(1)
if start_flag:
if estimate_event.wait(1):
self.objects_pose = self.object_pose_estimator.estimate(self.img_for_objects, detections = self.object_detections)
self.scene.update_objects(self.objects_pose)
estimate_event.clear()
def detect_objects_task(self, start_event, detect_event, estimate_event):
while self.rgbd_cam.is_on():
start_flag = start_event.wait(1)
if start_flag:
detect_flag = detect_event.wait(1)
if detect_flag:
# self.object_detections = self.object_detector.detect(cv2.flip(self.img,1))
self.object_detections = self.object_detector.detect(self.img_for_objects)
if self.object_detections is not None:
detect_event.clear()
estimate_event.set()
else:
self.object_detections = None
def run(self):
print(multiprocessing.get_start_method())
multiprocessing.set_start_method('spawn')
print(self.__dict__)
print('start')
self.rgbd_cam.start()
start_event = threading.Event()
detect_event = threading.Event()
estimate_event = threading.Event()
self.t_obj_d = threading.Thread(target=self.detect_objects_task, args=(start_event, detect_event,estimate_event,))
self.t_obj_e = threading.Thread(target=self.estimate_objects_task, args=(start_event, estimate_event,))
# self.t_plot = threading.Thread(target=self.plot_task)
# self.t_plot.start()
self.t_obj_d.start()
self.t_obj_e.start()
started = True
obj_path = './YCBV_test_pictures/javel.png'
# obj_path = './YCBV_test_pictures/mustard_front.png'
# obj_path = './YCBV_test_pictures/YCBV.png'
obj_img = cv2.imread(obj_path)
obj_img = cv2.resize(obj_img, (int(obj_img.shape[1]/2), int(obj_img.shape[0]/2)))
while self.rgbd_cam.is_on():
# pl.plot()
k = cv2.waitKey(2)
success, img, depth_map = self.rgbd_cam.next_frame()
if not success:
self.img_for_objects = None
continue
else:
img_for_hands = img.copy()
# img_for_hands = cv2.resize(img_for_hands, (int(self.hand_detector.resolution[0]/2), int(self.hand_detector.resolution[1]/2)))
img_for_hands = cv2.cvtColor(img_for_hands, cv2.COLOR_RGB2BGR)
img.flags.writeable = False
if estimate_event.is_set() or detect_event.is_set():
self.img_for_objects = img.copy()
self.img_for_objects[0:obj_img.shape[0], 0:obj_img.shape[1]] = obj_img
# incorporate obj_img in img_for_objects
self.img_for_objects = cv2.cvtColor(self.img_for_objects, cv2.COLOR_RGB2BGR)
self.img_for_objects.flags.writeable = False
if started:
start_event.set()
detect_event.set()
started = False
if not estimate_event.is_set():
estimate_event.set()
hands = self.hand_detector.get_hands(img_for_hands, depth_map)
# print(f'img_for_hands.shape: {img_for_hands.shape}')
if hands is not None and len(hands)>0:
self.scene.update_hands(hands)
# Avant de commencer à utiliser la mémoire GPU
torch.cuda.empty_cache() # Pour libérer toute mémoire inutilisée
# Utilisez cette ligne pour obtenir la mémoire GPU utilisée en octets
gpu_memory_used = torch.cuda.memory_allocated()
# Utilisez cette ligne pour obtenir la mémoire GPU réservée en octets (y compris la mémoire non allouée)
gpu_memory_reserved = torch.cuda.memory_reserved()
# Convertissez les valeurs en méga-octets (Mo) pour une meilleure lisibilité
gpu_memory_used_mb = gpu_memory_used / 1024 / 1024
gpu_memory_reserved_mb = gpu_memory_reserved / 1024 / 1024
# print(f"GPU Memory Used: {gpu_memory_used_mb:.2f} MB")
# print(f"GPU Memory Reserved: {gpu_memory_reserved_mb:.2f} MB")
if k == 32:
print('DETEEEEEEEEEEEEEEEEEECT')
detect_event.set()
self.scene.render(img)
cv2.imshow('render_img', img)
if k == 27:
print('end')
self.stop()
break
exit()
def stop(self):
self.rgbd_cam.stop()
self.t_obj_d.join()
self.t_obj_e.join()
self.scene.stop()
cv2.destroyAllWindows()
self.hand_detector.stop()
self.object_detector.stop()
self.object_pose_estimator.stop()
exit()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-hd', '--hand_detection', choices=['mediapipe', 'depthai', 'hybridOAKMediapipe'],
default = 'hybridOAKMediapipe', help="Hand pose reconstruction solution")
parser.add_argument('-od', '--object_detection', choices=['cosypose, megapose'],
default = 'cosypose', help="Object pose reconstruction detection")
args = vars(parser.parse_args())
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
print('start')
report_gpu()
kill_gpu_processes()
i_grip = GraspingDetector()
i_grip.run()