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():
global THREAD_RUNNING
cuda.init() # init pycuda driver
args = parse_args()
cap = open_cam_rtsp(args.cam_name, args.cam_password, args.cam_ip)
if not cap.isOpened():
sys.exit('Failed to open camera!')
#抓取图像子进程
THREAD_RUNNING = True
th = threading.Thread(target=grab_img, args=(cap, ))
th.start()
#目标识别
cls_dict = get_cls_dict(args.model.split('_')[-1])
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT SSD Demo for Jetson Nano')
vis = BBoxVisualization(cls_dict)
condition = threading.Condition()
global IMG_HANDLE
trt_thread = TrtThread(condition, IMG_HANDLE, args.model, conf_th=0.3)
trt_thread.start() # start the child thread
loop_and_display(condition, vis)
trt_thread.stop() # stop the child thread
#关闭图像子进程
THREAD_RUNNING = False
th.join()
cap.release()
cv2.destroyAllWindows()
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 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():
args = parse_args()
cam = Camera(args)
if not cam.get_is_opened():
raise SystemExit('ERROR: failed to open camera!')
cuda.init() # init pycuda driver
cls_dict = get_cls_dict(args.model.split('_')[-1])
open_window(WINDOW_NAME, 'Camera TensorRT SSD Demo', cam.img_width,
cam.img_height)
vis = BBoxVisualization(cls_dict)
condition = threading.Condition()
trt_thread = TrtThread(condition, cam, args.model, conf_th=0.3)
trt_thread.start() # start the child thread
loop_and_display(condition, vis)
trt_thread.stop() # stop the child thread
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()
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.model.split('_')[-1])
cuda.init() # init pycuda driver
cam.start() # let camera start grabbing frames
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT SSD Demo for Jetson Nano')
vis = BBoxVisualization(cls_dict)
condition = threading.Condition()
trt_thread = TrtThread(condition, cam, args.model, conf_th=0.3)
trt_thread.start() # start the child thread
loop_and_display(condition, vis)
trt_thread.stop() # stop the child thread
cam.stop()
cam.release()
cv2.destroyAllWindows()
import cv2
import pycuda.autoinit # This is needed for initializing CUDA driver
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)
def loop_and_detect(cam, trt_ssd, conf_th, robot, model):
"""Loop, grab images from camera, and do object detection.
# Arguments
cam: the camera object (video source).
tf_sess: TensorFlow/TensorRT session to run SSD object detection.
conf_th: confidence/score threshold for object detection.
vis: for visualization.
"""
settings_path = "../dataset/RTIMULib"
logger.info("Using settings file %s", settings_path + ".ini")
if not os.path.exists(settings_path + ".ini"):
logger.error("Settings file does not exist, will be created")
s = RTIMU.Settings(settings_path)
imu = RTIMU.RTIMU(s)
logger.info("IMU Name: %s", imu.IMUName())
if not imu.IMUInit():
logger.error("IMU Init Failed")
else:
logger.info("IMU Init Succeeded")
imu.setSlerpPower(0.02)
imu.setGyroEnable(True)
imu.setAccelEnable(True)
imu.setCompassEnable(True)
poll_interval = imu.IMUGetPollInterval()
logger.info("Recommended Poll Interval: %f", poll_interval)
cls_dict = get_cls_dict(model.split('_')[-1])
fps = 0.0
counter = 58
tic = time.time()
while True:
if imu.IMURead():
gyro = imu.getIMUData().copy()
else:
gyro = []
img = cam.read()
if img is not None:
boxes, confs, clss = trt_ssd.detect(img, conf_th)
toc = time.time()
curr_fps = 1.0 / (toc - tic)
# calculate an exponentially decaying average of fps number
fps = curr_fps if fps == 0.0 else (fps * 0.9 + curr_fps * 0.1)
tic = toc
counter += 1
if counter > fps:
logger.info("fps: %f", fps)
if len(gyro) > 0:
accel = gyro["accel"]
fusion = gyro["fusionPose"]
logger.info(
"r: %f p: %f y: %f" %
(math.degrees(fusion[0]), math.degrees(
fusion[1]), math.degrees(fusion[2])))
logger.info("x: %.2f y: %.2f z: %.2f" %
(accel[0], accel[1], accel[2]))
counter = 0
# compute all detected objects
detections = []
for i, (bb, cf, cl) in enumerate(zip(boxes, confs, clss)):
detections.append({
'bbox': bb,
'confidence': cf,
'label': int(cl)
})
if logger.isEnabledFor(logging.DEBUG) and (len(detections)) > 0:
logger.debug(detections)
# select detections that match selected class label
matching_detections = [
d for d in detections
if d['label'] in v2_coco_labels_to_capture
and d['confidence'] > 0.50
]
if len(matching_detections) > 0:
logger.debug(matching_detections)
# get detection closest to center of field of view and center bot
det = closest_detection(matching_detections,
width=cam.img_width,
height=cam.img_height)
if det is not None:
center = detection_center(det, cam.img_width, cam.img_height)
logger.info("center: %s, on object: %s", center,
cls_dict[det['label']])
move_speed = 2.0 * center[0]
if abs(move_speed) > 0.3:
if move_speed > 0.0:
robot.right(move_speed)
else:
robot.left(abs(move_speed))
else:
robot.stop()
