def main():
# load config
cfg.merge_from_file(args.config)
# create model
model = ModelBuilder()
# load model
checkpoint = torch.load(args.snapshot, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint)
for param in model.parameters():
param.requires_grad = False
def main():
# Initialize ecci sdk and connect to the broker in edge-cloud
ecci_client = Client()
mqtt_thread = threading.Thread(target=ecci_client.initialize)
mqtt_thread.start()
ecci_client.wait_for_ready()
print('edge start --------')
# load config
cfg.merge_from_file(args.config)
cfg.CUDA = torch.cuda.is_available() and cfg.CUDA
device = torch.device('cuda' if cfg.CUDA else 'cpu')
# create model
model = ModelBuilder()
# load model
checkpoint = torch.load(args.snapshot)
model.load_state_dict(checkpoint)
for param in model.parameters():
param.requires_grad = False
model.eval().to(device)
#multiprocessing
manager = mp.Manager()
resQueue = manager.Queue()
multiProcess = []
label = []
probs = []
for i in range(10):
multiProcess.append(build_multitracker(model, label, probs, resQueue))
multiProcess[i].start()
first_frame = True
image_files = sorted(glob.glob('./test/image/*.JPEG'))
for f, image_file in enumerate(image_files):
frame = cv2.imread(image_file)
if first_frame:
# keyframe need to be uploaded to cloud
print('first frame')
key_frame = frame
payload = {"type": "data", "contents": {"frame": frame}}
print("####################", payload)
ecci_client.publish(payload, "cloud")
cloud_data = ecci_client.get_sub_data_payload_queue().get()
print("###########recieve data from cloud", cloud_data)
bbox = cloud_data["bbox"]
label = cloud_data["label"]
probs = cloud_data["probs"]
num_process = len(bbox)
t_detect_start = time.time()
for i in range(num_process):
init_rect = [
bbox[i][0], bbox[i][1], bbox[i][2] - bbox[i][0],
bbox[i][3] - bbox[i][1]
]
multiProcess[i].init(frame, init_rect, label[i], probs[i])
cv2.rectangle(frame, (int(bbox[i][0]), int(bbox[i][1])),
(int(bbox[i][2]), int(bbox[i][3])), (0, 0, 255),
3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[i][0], bbox[i][1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255),
thickness=1)
t_detect_end = time.time()
print("detect fps : ", 1 / (t_detect_end - t_detect_start))
first_frame = False
index = 1
elif index % 10 == 0:
if is_key(key_frame, frame) or index % 20 == 0:
# keyframe need to be uploaded to cloud ##### outputs, time ######
print('key frame')
key_frame = frame
payload = {"type": "data", "contents": {"frame": frame}}
print("####################", payload)
ecci_client.publish(payload, "cloud")
cloud_data = ecci_client.get_sub_data_payload_queue().get()
print("###########recieve data from cloud", cloud_data)
bbox = cloud_data["bbox"]
label = cloud_data["label"]
probs = cloud_data["probs"]
num_process = len(bbox)
t_detect_start = time.time()
for i in range(num_process):
init_rect = [
bbox[i][0], bbox[i][1], bbox[i][2] - bbox[i][0],
bbox[i][3] - bbox[i][1]
]
multiProcess[i].init(frame, init_rect, label[i], probs[i])
cv2.rectangle(frame, (int(bbox[i][0]), int(bbox[i][1])),
(int(bbox[i][2]), int(bbox[i][3])),
(0, 0, 255), 3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[i][0], bbox[i][1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255),
thickness=1)
t_detect_end = time.time()
print("detect fps : ", 1 / (t_detect_end - t_detect_start))
index = 1
else:
print('non-key frame')
t_track_start = time.time()
for i in range(num_process):
multiProcess[i].track(frame)
t_track_end = time.time()
print("track fps : ", 1 / (t_track_end - t_track_start))
for i in range(num_process):
resDict = resQueue.get()
print(resDict)
bbox = list(map(int, resDict['bbox']))
cv2.rectangle(frame, (bbox[0], bbox[1]),
(bbox[0] + bbox[2], bbox[1] + bbox[3]),
(0, 255, 0), 3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[0], bbox[1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 255, 0),
thickness=1)
index += 1
else:
print('non-key frame')
t_track_start = time.time()
for i in range(num_process):
multiProcess[i].track(frame)
t_track_end = time.time()
print("track fps : ", 1 / (t_track_end - t_track_start))
for i in range(num_process):
resDict = resQueue.get()
print(resDict)
bbox = list(map(int, resDict['bbox']))
cv2.rectangle(frame, (bbox[0], bbox[1]),
(bbox[0] + bbox[2], bbox[1] + bbox[3]),
(0, 255, 0), 3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[0], bbox[1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 255, 0),
thickness=1)
index += 1
cv2.imwrite('./test/output/%s.jpg' % f, frame)
for i in range(10):
multiProcess.append(build_multitracker(model, label, probs, resQueue))
multiProcess[i].join()
def main():
# Initialize ecci sdk and connect to the broker in edge-cloud
ecci_client = Client()
mqtt_thread = threading.Thread(target=ecci_client.initialize)
mqtt_thread.start()
ecci_client.wait_for_ready()
print('edge start --------')
# load config
cfg.merge_from_file(args.config)
cfg.CUDA = torch.cuda.is_available() and cfg.CUDA
device = torch.device('cuda' if cfg.CUDA else 'cpu')
# create model
model = ModelBuilder()
# load model
checkpoint = torch.load(args.snapshot)
model.load_state_dict(checkpoint)
for param in model.parameters():
param.requires_grad = False
model.eval().to(device)
#multiprocessing
manager = mp.Manager()
resQueue = manager.Queue()
multiProcess = []
# VID dataloader
dataset = ImagenetDataset(args.dataset, is_val=True)
true_case_stat, all_gb_boxes = group_annotation_by_class(dataset)
for f in range(len(dataset)):
frame, first_frame = dataset.get_image(f)
if first_frame:
# keyframe need to be uploaded to cloud
print('first frame upload to cloud')
# close the last multiprocessing
for i in range(len(multiProcess)):
multiProcess[i].join()
# send frame to cloud
payload = {"type":"data","contents":{"frame":frame}}
print("####################",payload)
ecci_client.publish(payload, "cloud")
# get rect from cloud
cloud_data = ecci_client.get_sub_data_payload_queue().get()
print("###########recieve data from cloud",cloud_data)
bbox= cloud_data["bbox"]
label = cloud_data["label"]
probs = cloud_data["probs"]
# wirte txt
for i in range(len(bbox)):
write_txt(dataset, f, bbox[i], label[i], probs[i])
# # start multiprocessing
multiProcess = []
for i in range(len(bbox)):
multiProcess.append(build_multitracker(model,label[i],probs[i],resQueue))
for i in range(len(multiProcess)):
init_rect = [bbox[i][0],bbox[i][1],bbox[i][2]-bbox[i][0],bbox[i][3]-bbox[i][1]]
multiProcess[i].init(frame, init_rect)
multiProcess[i].start()
key_frame = frame
first_frame = False
index = 1
# elif is_key(key_frame, frame):
elif index % 5== 0:
if is_key(key_frame, frame) or index % 15 ==0 :
# keyframe need to be uploaded to cloud ##### outputs, time ######
print('key frame upload to cloud')
# close the last multiprocessing
for i in range(len(multiProcess)):
multiProcess[i].join()
# send frame to cloud
payload = {"type":"data","contents":{"frame":frame}}
print("####################",payload)
ecci_client.publish(payload, "cloud")
# get rect from cloud
cloud_data = ecci_client.get_sub_data_payload_queue().get()
print("###########recieve data from cloud",cloud_data)
bbox= cloud_data["bbox"]
label = cloud_data["label"]
probs = cloud_data["probs"]
# wirte txt
for i in range(len(bbox)):
write_txt(dataset, f, bbox[i], label[i], probs[i])
# # start multiprocessing
multiProcess = []
for i in range(len(bbox)):
multiProcess.append(build_multitracker(model,label[i],probs[i],resQueue))
for i in range(len(multiProcess)):
init_rect = [bbox[i][0],bbox[i][1],bbox[i][2]-bbox[i][0],bbox[i][3]-bbox[i][1]]
multiProcess[i].init(frame, init_rect)
multiProcess[i].start()
key_frame = frame
index = 1
else:
print('track locally')
for i in range(len(multiProcess)):
multiProcess[i].track(frame)
for i in range(len(multiProcess)):
resDict = resQueue.get()
resDict['bbox'] = [resDict['bbox'][0],resDict['bbox'][1],resDict['bbox'][0]+resDict['bbox'][2],resDict['bbox'][1]+resDict['bbox'][3]]
write_txt(dataset, f, resDict['bbox'], resDict['label'], resDict['probs']-0.1)
index += 1
else:
print('track locally')
for i in range(len(multiProcess)):
multiProcess[i].track(frame)
t= time.time()
for i in range(len(multiProcess)):
resDict = resQueue.get()
resDict['bbox'] = [resDict['bbox'][0],resDict['bbox'][1],resDict['bbox'][0]+resDict['bbox'][2],resDict['bbox'][1]+resDict['bbox'][3]]
write_txt(dataset, f, resDict['bbox'], resDict['label'], resDict['probs']-0.1)
print(time.time()-t)
index +=1
map_compute()
def main():
# Initialize ecci sdk and connect to the broker in edge-cloud
ecci_client = Client()
mqtt_thread = threading.Thread(target=ecci_client.initialize)
mqtt_thread.start()
ecci_client.wait_for_ready()
print('edge start --------')
# load config
cfg.merge_from_file(args.config)
cfg.CUDA = torch.cuda.is_available() and cfg.CUDA
device = torch.device('cuda' if cfg.CUDA else 'cpu')
# create model
model = ModelBuilder()
# load model
checkpoint = torch.load(args.snapshot)
model.load_state_dict(checkpoint)
for param in model.parameters():
param.requires_grad = False
model.eval().to(device)
#multiprocessing
manager = mp.Manager()
resQueue = manager.Queue()
multiProcess = []
label = []
probs = []
for i in range(10):
multiProcess.append(build_multitracker(model, label, probs, resQueue))
multiProcess[i].start()
first_frame = True
filename = "./demo/video.avi"
camera = cv2.VideoCapture(filename)
fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', '2')
demo_out = cv2.VideoWriter('./demo/output/video.avi', fourcc, 25,
(640, 368))
while camera.isOpened():
ret, frame = camera.read()
if ret == True:
print(frame.shape)
if first_frame:
# keyframe need to be uploaded to cloud
print('first frame')
key_frame = frame
# communication
payload = {"type": "data", "contents": {"frame": frame}}
print("####################", payload)
ecci_client.publish(payload, "cloud")
cloud_data = ecci_client.get_sub_data_payload_queue().get()
print("###########recieve data from cloud", cloud_data)
bbox = cloud_data["bbox"]
label = cloud_data["label"]
probs = cloud_data["probs"]
num_process = len(bbox)
t_detect_start = time.time()
for i in range(num_process):
cv2.rectangle(frame, (int(bbox[i][0]), int(bbox[i][1])),
(int(bbox[i][2]), int(bbox[i][3])),
(0, 0, 255), 3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[i][0], bbox[i][1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255),
thickness=1)
init_rect = [
bbox[i][0], bbox[i][1], bbox[i][2] - bbox[i][0],
bbox[i][3] - bbox[i][1]
]
multiProcess[i].init(frame, init_rect, label[i], probs[i])
t_detect_end = time.time()
print("detect fps : ", 1 / (t_detect_end - t_detect_start))
first_frame = False
index = 1
elif index % 5 == 0:
if is_key(key_frame, frame) or index % 10 == 0:
# keyframe need to be uploaded to cloud ##### outputs, time ######
print('key frame')
key_frame = frame
# communication
payload = {"type": "data", "contents": {"frame": frame}}
print("####################", payload)
ecci_client.publish(payload, "cloud")
cloud_data = ecci_client.get_sub_data_payload_queue().get()
print("###########recieve data from cloud", cloud_data)
bbox = cloud_data["bbox"]
label = cloud_data["label"]
probs = cloud_data["probs"]
num_process = len(bbox)
t_detect_start = time.time()
for i in range(num_process):
cv2.rectangle(frame,
(int(bbox[i][0]), int(bbox[i][1])),
(int(bbox[i][2]), int(bbox[i][3])),
(0, 0, 255), 3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[i][0], bbox[i][1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255),
thickness=1)
init_rect = [
bbox[i][0], bbox[i][1], bbox[i][2] - bbox[i][0],
bbox[i][3] - bbox[i][1]
]
multiProcess[i].init(frame, init_rect, label[i],
probs[i])
t_detect_end = time.time()
print("detect fps : ", 1 / (t_detect_end - t_detect_start))
index = 1
else:
print('non-key frame')
t_track_start = time.time()
for i in range(num_process):
multiProcess[i].track(frame)
t_track_end = time.time()
print("track fps : ", 1 / (t_track_end - t_track_start))
for i in range(num_process):
resDict = resQueue.get()
print(resDict)
bbox = list(map(int, resDict['bbox']))
cv2.rectangle(frame, (bbox[0], bbox[1]),
(bbox[0] + bbox[2], bbox[1] + bbox[3]),
(0, 255, 0), 3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[0], bbox[1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 255, 0),
thickness=1)
index += 1
else:
print('non-key frame')
t_track_start = time.time()
for i in range(num_process):
multiProcess[i].track(frame)
t_track_end = time.time()
print("track fps : ", 1 / (t_track_end - t_track_start))
for i in range(num_process):
resDict = resQueue.get()
print(resDict)
bbox = list(map(int, resDict['bbox']))
cv2.rectangle(frame, (bbox[0], bbox[1]),
(bbox[0] + bbox[2], bbox[1] + bbox[3]),
(0, 255, 0), 3)
cv2.putText(frame,
'%s: %.3f' % (label[i], probs[i]),
(bbox[0], bbox[1] - 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 255, 0),
thickness=1)
index += 1
print("writing")
demo_out.write(frame)
else:
break
camera.release()
demo_out.release()
cv2.destroyAllWindows()
