def run(self):
"""Run until 'running' flag is set to False by main thread.
NOTE: CUDA context is created here, i.e. inside the thread
which calls CUDA kernels. In other words, creating CUDA
context in __init__() doesn't work.
"""
global s_img, s_boxes, s_confs, s_clss
print('TrtThread: loading the TRT SSD engine...')
self.cuda_ctx = cuda.Device(0).make_context() # GPU 0
self.trt_ssd = TrtSSD(self.model, INPUT_HW)
print('TrtThread: start running...')
self.running = True
while self.running:
img = self.cam.read()
if img is None:
break
boxes, confs, clss = self.trt_ssd.detect(img, self.conf_th)
with self.condition:
s_img, s_boxes, s_confs, s_clss = img, boxes, confs, clss
self.condition.notify()
del self.trt_ssd
self.cuda_ctx.pop()
del self.cuda_ctx
print('TrtThread: stopped...')
def main():
args = parse_args()
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.model)
trt_ssd = TrtSSD(args.model, INPUT_HW)
cam.start()
if args.use_console:
loop_and_detect_console(cam,
trt_ssd,
conf_th=0.3,
loop=args.loop,
cls_dict=cls_dict)
else:
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT SSD Demo for Jetson Nano')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_ssd, conf_th=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def detect(q1, q2):
model_config = parser.parse_args()
print("Loading detector")
model = "ssd_mobilenet_v2_coco"
conf_th = 0.3
INPUT_HW = (300, 300)
cls_dict = get_cls_dict("coco")
vis = BBoxVisualization(cls_dict)
trt_ssd = TrtSSD(model, INPUT_HW)
print("Loading detector complete")
if model_config.ui == 1:
cv2.startWindowThread()
cv2.namedWindow("window")
while 1:
try:
frame, frame_time = q1.get()
delay = time.time() - frame_time
if delay > 0.4:
print("Skipping frame")
continue
boxes, confs, clss = trt_ssd.detect(frame, conf_th)
print([get_cls_dict("coco")[c] for c in clss])
if model_config.ui == 1:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img = vis.draw_bboxes(frame, boxes, confs, clss)
cv2.imshow('window', img[..., ::-1])
except Exception as e:
traceback.print_exc()
def main():
# Parse arguments and get input
args = parse_args()
cam = Camera(args)
if not cam.isOpened():
raise SystemExit('ERROR: failed to open camera!')
# Create NN1 and NN2 models and load into memory
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
mtcnn = TrtMtcnn()
# Create Preview Window
open_window(WINDOW_NAME, 'Camera Preview', cam.img_width, cam.img_height)
vis = BBoxVisualization(cls_dict)
# Enter Detection Mode
while True:
# Get Image
img = cam.read()
out.write(img)
nn1_results = []
# Run Neural Networks
img, nn1_results, nn2_results, nn3_results = loop_and_detect(
img, mtcnn, args.minsize, trt_ssd, conf_th=0.3, vis=vis)
# Communicate to Arduino
if (nn1_results != []):
img = robot_drive(img, nn1_results)
else:
serial_port.write("N".encode())
print("N")
# Display and save output
cv2.imshow(WINDOW_NAME, img)
outNN.write(img)
# User/Keyboard Input
key = cv2.waitKey(1)
if key == ord('q'):
out.release()
outNN.release()
break
# Clean up and exit
cam.release()
cv2.destroyAllWindows()
serial_port.close()
def main():
args = parse_args()
cam = Camera(args)
if not cam.get_is_opened():
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
open_window(WINDOW_NAME, 'Camera TensorRT SSD Demo', cam.img_width,
cam.img_height)
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_ssd, conf_th=0.3, vis=vis)
cam.release()
cv2.destroyAllWindows()
def main():
# Parse arguments and get input
args = parse_args()
# Create NN1 and NN2 models
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
mtcnn = TrtMtcnn()
# Create Preview Window
vis = BBoxVisualization(cls_dict)
imageNum = 10
# Enter Detection Mode
while True:
# Get Image
imageName = "/home/nvidia/Pictures/test13.jpg"
#imageName = "/media/E76F-73E0/Faces/1 (" + str(imageNum) + ").jpg"
#imageName = "/home/nvidia/Pictures/Benchmarks/Pedestrians/PennPed000" + str(imageNum) + ".png"
imageNum = imageNum + 1
#print(imageName)
img = cv2.imread(imageName)
cv2.imshow(WINDOW_NAME, img)
# Run Neural Networks
img, nn1_results, nn2_results, nn3_results = loop_and_detect(
img, mtcnn, args.minsize, trt_ssd, conf_th=0.3, vis=vis)
# Display Results
cv2.imshow(WINDOW_NAME, img)
#cv2.waitKey(0)
# User/Keyboard Input
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
# Clean up and exit
cv2.destroyAllWindows()
serial_port.close()
def main(args, cam):
# args = parse_args()
is_open = up()
time.sleep(60)
# cam = Camera(args)
if is_open:
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
cam.start()
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_ssd, conf_th=0.9, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main(config):
args = parse_args()
data_sys = Data_sys()
pub = Publish(host=config['Mqtt_pub'])
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
img_list = []
# video
cap = cv2.VideoCapture('xiasha.avi')
i = 0
while cap.isOpened():
frame, img = cap.read()
if img is not None:
img_list.append([img])
result = trt_ssd.detect(img_list[0], conf_th=0.3)
# print(result)
data_sys.dataSynchronization(result, img_list, args.model,
['boundary_intrude', None],
config['Zone'], config['Channel'][0],
config['device_id'], pub,
config['Polygon'])
img_list = []
i = i + 1
print(i)
else:
msg = json.dumps({
"nvr_id": config['Zone'],
"device_id": config['device_id'],
"channel_id": config['Channel'][0]
})
pub.send_msg(topic="zs/ai_spxwfx/rtsp/" + config['Zone'] + "/" +
config['Channel'][0],
msg=msg,
Zone=config['Zone'],
device_id=config['device_id'])
'''
def main():
args = parse_args()
cam = Camera(args)
is_open = up()
#time.sleep(60)
if is_open:
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.model.split('_')[-1])
trt_ssd = TrtSSD(args.model, INPUT_HW)
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT SSD Demo for Jetson Nano')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_ssd, conf_th=0.9, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
check_args(args)
results_file = 'ssd/results_%s_%s.json' % (args.model, args.mode)
if args.mode == 'trt':
ssd = TrtSSD(args.model, INPUT_HW)
else:
ssd = TfSSD(args.model, INPUT_HW)
jpgs = [j for j in os.listdir(args.imgs_dir) if j.endswith('.jpg')]
generate_results(ssd, args.imgs_dir, jpgs, results_file)
# Run COCO mAP evaluation
# Reference: https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocoGt = COCO(args.annotations)
cocoDt = cocoGt.loadRes(results_file)
imgIds = sorted(cocoGt.getImgIds())
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds
cocoEval.evaluate()
cocoEval.accumulate()
print(cocoEval.summarize())
from utils.ssd_classes import get_cls_dict
from utils.ssd import TrtSSD
from utils.visualization import BBoxVisualization
from utils.display import open_window, set_display, show_fps
import time
import numpy as np
model = "ssd_mobilenet_v2_coco"
filename = "./dogs.jpg"
conf_th = 0.3
INPUT_HW = (300, 300)
cls_dict = get_cls_dict("coco")
vis = BBoxVisualization(cls_dict)
img = cv2.imread(filename)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
trt_ssd = TrtSSD(model, INPUT_HW)
# Kick start the model.
for _ in range(20):
boxes, confs, clss = trt_ssd.detect(img, conf_th)
print([get_cls_dict("coco")[c] for c in clss])
img = vis.draw_bboxes(img, boxes, confs, clss)
cv2.imwrite("result.jpg", img[..., ::-1])
times = []
for _ in range(20):
start_time = time.time()
boxes, confs, clss = trt_ssd.detect(img, conf_th)
delta = time.time() - start_time
times.append(delta)
mean_delta = np.array(times).mean()
fps = 1 / mean_delta
'''parse args'''
parser = argparse.ArgumentParser()
parser = add_camera_args(parser)
parser.add_argument('--model', type=str, default='ssd_mobilenet_v1_digger')
parser.add_argument('--image_resize', default=300, type=int)
parser.add_argument('--det_conf_thresh', default=0.8, type=float)
parser.add_argument('--seq_dir', default="sequence/")
parser.add_argument('--sort_max_age', default=5, type=int)
parser.add_argument('--sort_min_hit', default=3, type=int)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
trt_ssd = TrtSSD(args.model, (args.image_resize, args.image_resize))
mot_tracker = Sort(args.sort_max_age, args.sort_min_hit)
video = '/home/mengjun/xianlu/data/digger.mp4'
cap = cv2.VideoCapture(video)
colours = np.random.rand(32, 3) * 255
fps = 0.0
tic = time.time()
while cap.isOpened():
ret, frame = cap.read()
boxes, confs, clss = trt_ssd.detect(frame, args.det_conf_thresh)
#print(boxes, confs, clss)
if len(boxes) != 0:
result = []
for bb, cf, cl in zip(boxes, confs, clss):