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('coco')
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
cam.start()
# open_window(WINDOW_NAME, args.image_width, args.image_height,
# 'Camera TensorRT YOLOv3 Demo')
vis = BBoxVisualization(cls_dict)
# for video
# loop_and_detect(cam, trt_yolov3, conf_th=0.3, vis=vis)
# for single file
detect_demo(cam, trt_yolov3, conf_th=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('ERROR: bad category_num (%d)!' % args.category_num)
if not os.path.isfile('yolo/%s.trt' % args.model):
raise SystemExit('ERROR: file (yolo/%s.trt) not found!' % args.model)
cam = Camera(args)
cam.open()
if not cam.is_opened:
raise SystemExit('ERROR: failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
yolo_dim = args.model.split('-')[-1]
if 'x' in yolo_dim:
dim_split = yolo_dim.split('x')
if len(dim_split) != 2:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
w, h = int(dim_split[0]), int(dim_split[1])
else:
h = w = int(yolo_dim)
if h % 32 != 0 or w % 32 != 0:
raise SystemExit('ERROR: bad yolo_dim (%s)!' % yolo_dim)
trt_yolo = TrtYOLO(args.model, (h, w), args.category_num)
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT YOLO Demo')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_yolo, conf_th=0.3, 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)
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():
args = parse_args()
cam = Camera(args)
cam.open()
# import pdb
# pdb.set_trace()
if not cam.is_opened:
sys.exit('[INFO] Failed to open camera!')
cls_dict = get_cls_dict('coco')
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
print('[INFO] Camera: starting')
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'TensorRT YOLOv3 Detector')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam,
args.runtime,
trt_yolov3,
conf_th=0.3,
vis=vis,
window_name=WINDOW_NAME)
print('[INFO] Program: stopped')
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
if args.category_num <= 0:
raise SystemExit('Bad category_num: %d!' % args.category_num)
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cls_dict = get_cls_dict(args.category_num)
yolo_dim = int(args.model.split('-')[-1])
if yolo_dim not in (288, 416, 608):
raise SystemExit('Bad yolo_dim: %d!\nPlease make sure the model file name contains the correct dimension...' % yolo_dim)
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim), args.category_num)
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT YOLOv3 Demo')
vis = BBoxVisualization(cls_dict)
loop_and_detect(cam, trt_yolov3, conf_th=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
#YOLO INIT
#cls_dict = get_cls_dict('coco')
cls_dict = get_cls_dict('deepfamily')
print("classes count : ", len(cls_dict))
yolo_dim = int(args.model.split('-')[-1]) # 416 or 608
print("yolo_dim : ", yolo_dim)
trt_yolov3 = TrtYOLOv3(args.model, (yolo_dim, yolo_dim))
#CAMERA
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cam.start()
#CAM-WINDOW
open_window(WINDOW_NAME, args.image_width, args.image_height,
'DEEPFAMILY PROJECT - TensorRT YOLOv3')
vis = BBoxVisualization(cls_dict)
#DETECT-LOOP
loop_and_detect(cam, trt_yolov3, conf_th=0.95, vis=vis)
#loop_and_detect(cam, trt_yolov3, conf_th=0.95)
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!')
trt_ssd = TrtSSD(args.model)
cam.start()
# initialize bot
logger.info('initialize robot')
robot = Robot()
logger.info('starting to loop and detect')
loop_and_detect(cam=cam,
trt_ssd=trt_ssd,
conf_th=0.3,
robot=robot,
model=args.model)
logger.info('cleaning up')
robot.stop()
cam.stop()
cam.release()
def main():
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Ask tensorflow logger not to propagate logs to parent (which causes
# duplicated logging)
logging.getLogger('tensorflow').propagate = False
global args
args = parse_args()
logger.info('called with args: %s' % args)
# build the class (index/name) dictionary from labelmap file
logger.info('reading label map')
cls_dict = read_label_map(args.labelmap_file)
pb_path = './data/{}_trt.pb'.format(args.model)
log_path = './logs/{}_trt'.format(args.model)
if args.do_build:
logger.info('building TRT graph and saving to pb: %s' % pb_path)
build_trt_pb(args.model, pb_path)
logger.info('opening camera device/file')
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
logger.info('loading TRT graph from pb: %s' % pb_path)
trt_graph = load_trt_pb(pb_path)
logger.info('starting up TensorFlow session')
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
#tf_sess = tf.Session(config=tf_config, graph=trt_graph) -- Vincent
#Solve : "unable to satfisfy explicit device /dev/CPU:0 -- Vincent
tf_sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=True),
graph=trt_graph)
if args.do_tensorboard:
logger.info('writing graph summary to TensorBoard')
write_graph_tensorboard(tf_sess, log_path)
logger.info('warming up the TRT graph with a dummy image')
od_type = 'faster_rcnn' if 'faster_rcnn' in args.model else 'ssd'
dummy_img = np.zeros((720, 1280, 3), dtype=np.uint8)
_, _, _ = detect(dummy_img, tf_sess, conf_th=.3, od_type=od_type)
cam.start() # ask the camera to start grabbing images
# grab image and do object detection (until stopped by user)
logger.info('starting to loop and detect')
vis = BBoxVisualization(cls_dict)
open_display_window(cam.img_height, cam.img_width)
result = loop_and_detect(cam, tf_sess, args.conf_th, vis, od_type=od_type)
logger.info('cleaning up')
cam.stop() # terminate the sub-thread in camera
tf_sess.close()
cam.release()
cv2.destroyAllWindows()
def main():
cam_config = "config/camera.json"
config = CVConfig("config/config.json")
config.load_cam_config(cam_config)
camera = Camera(config)
config_json = None
try:
camera.start()
while (True):
print "Enter a camera setting to change. (or 'P' to preview, 'X' to quit, 'V' to see current settings, 'S' to save image, 'L' to load config, 'SC' to save config)"
print "1 to save hexagon pic, 2 to save resource pic, 3 to save num pic"
token = raw_input("Input: ")
if token == 'P':
get_picture(camera)
elif token == 'X':
print "Saving config as:", cam_config
config.save_cam_config(cam_config)
break
elif token == 'SC':
print "Saving config as:", cam_config
config.save_cam_config(cam_config)
elif token == 'V':
settings = config.get_cam_all()
print "Settings for: ", cam_config
for key in settings:
print key, ": ", settings[key]
elif token == 'S':
img = get_picture(camera)
path = raw_input("Path: ")
CVUtils.save_img(img, path)
elif token == 'L':
cam_config = raw_input('Config path:')
config_json = CVConfig.load_json(cam_config)
camera._set_config(config_json)
get_picture(camera)
elif token =='1':
img = get_picture(camera, CVConfig.load_json("config/camera_hex.json"))
CVUtils.save_img(img, "images/test_hex.png")
elif token =='2':
img = get_picture(camera)
CVUtils.save_img(img, "images/test_resource.png")
elif token =='3':
img = get_picture(camera, CVConfig.load_json("config/camera_nums.json"))
CVUtils.save_img(img, "images/test_nums.png")
else:
process_token(token, camera)
get_picture(camera)
finally:
camera.stop()
def main():
args = parse_args()
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
mtcnn = TrtMtcnn()
cam.start()
open_window(WINDOW_NAME, width=640, height=480, title='MTCNN Window')
detect_faces(cam, mtcnn)
cam.stop()
cam.release()
cv2.destroyAllWindows()
del mtcnn
def main():
args = parse_args()
cam = Camera(args)
cam.open()
if not cam.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)
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=0.3, vis=vis)
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
labels = np.loadtxt('googlenet/synset_words.txt', str, delimiter='\t')
cam = Camera(args)
cam.open()
if not cam.is_opened:
raise SystemExit('ERROR: failed to open camera!')
# initialize the tensorrt googlenet engine
net = PyTrtGooglenet(DEPLOY_ENGINE, ENGINE_SHAPE0, ENGINE_SHAPE1)
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT GoogLeNet Demo for Jetson Nano')
loop_and_classify(cam, net, labels, args.crop_center)
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!')
mtcnn = TrtMtcnn()
cam.start()
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT MTCNN Demo for Jetson TX2')
loop_and_detect(cam, mtcnn, args.minsize)
cam.stop()
cam.release()
cv2.destroyAllWindows()
del (mtcnn)
def main():
args = parse_args()
labels = np.loadtxt('googlenet/synset_words.txt', str, delimiter='\t')
cam = Camera(args)
cam.open()
if not cam.is_opened:
sys.exit('Failed to open camera!')
cam.start() # let camera start grabbing frames
open_window(WINDOW_NAME, args.image_width, args.image_height,
'Camera TensorRT GoogLeNet Demo for Jetson Nano')
condition = threading.Condition()
trt_thread = TrtGooglenetThread(condition, cam, labels, args.crop_center)
trt_thread.start() # start the child thread
loop_and_display(condition)
trt_thread.stop() # stop the child thread
cam.stop()
cam.release()
cv2.destroyAllWindows()
def main():
args = parse_args()
cam = Camera(args)
cam.open()
if not cam.is_opened:
raise SystemExit('ERROR: 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()
def loop_and_detect(cam, tf_sess, conf_th, vis, od_type):
"""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.
"""
show_fps = True
full_scrn = False
fps = 0.0
#tic = time.time()
tic = 0
toc = 0
global rects, ct, temp
global person_x, person_y
global leave_zone
global leave_zone_2
global leave_zone_counter
global enter_zone
zone_x_bed = 0
zone_y_bed = 0
zone_x_clean = 0
zone_y_clean = 0
#Boundary boxes for RTSP (low resolution)
zone_x_min_bed, zone_y_min_bed, zone_x_max_bed, zone_y_max_bed = 366, 369, 521, 667
zone_x_min_clean, zone_y_min_clean, zone_x_max_clean, zone_y_max_clean = 194, 300, 330, 420
zone_x_min_door, zone_y_min_door, zone_x_max_door, zone_y_max_door = 620, 151, 674, 414
zone_x_min_alchol, zone_y_min_alchol, zone_x_max_alchol, zone_y_max_alchol = 430, 329, 470, 356
distance_thres_bed = 165
distance_thres_clean = 105
distance_thres_alchol = 100
counter_msg = 0
fail_msg = 0
pass_msg = 0
global hand_wash_status, args, client
hd = 0
wash_delay = 0
invalid_id = []
invalid_id.append(999)
enter, leave = False, False
restart_flag = False #restart issue
backup_label = None #restart issue
none_buff = 0 #restart issue
previous_id = 999
personal_status = []
for i in range(0, 1000):
personal_status.append(0)
#CSV Log File
if (5 > 2):
while True:
#if cv2.getWindowProperty(WINDOW_NAME, 0) < 0:
# Check to see if the user has closed the display window.
# If yes, terminate the while loop.
# break
if (restart_flag == True):
cam = Camera(args)
cam.open()
cam.start()
print("Camera is opened!")
dummy_img = np.zeros((720, 1280, 3), dtype=np.uint8)
_, _, _ = detect(dummy_img,
tf_sess,
conf_th=.3,
od_type=od_type)
print("Loading dummy image!")
restart_flag = False
rects = []
img = cam.read()
if img is not None:
img = cv2.flip(img, 0)
#check mqtt status
#client.on_message = on_message
#client.loop_forever()
box, conf, cls = detect(img, tf_sess, conf_th, od_type=od_type)
img, hd = vis.draw_bboxes(img, box, conf, cls)
cv2.rectangle(img, (zone_x_min_bed, zone_y_min_bed),
(zone_x_max_bed, zone_y_max_bed),
(255, 102, 255), 2)
zone_x_bed = int((zone_x_min_bed + zone_x_max_bed) / 2.0)
zone_y_bed = int((zone_y_min_bed + zone_y_max_bed) / 2.0)
cv2.circle(img, (zone_x_bed, zone_y_bed), 4, (255, 102, 255),
-1)
cv2.putText(img, "Patient", (zone_x_bed - 40, zone_y_bed - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 102, 255), 1)
cv2.rectangle(img, (zone_x_min_clean, zone_y_min_clean),
(zone_x_max_clean, zone_y_max_clean),
(255, 255, 51), 2)
zone_x_clean = int((zone_x_min_clean + zone_x_max_clean) / 2.0)
zone_y_clean = int((zone_y_min_clean + zone_y_max_clean) / 2.0)
cv2.circle(img, (zone_x_clean, zone_y_clean), 4,
(255, 255, 51), -1)
cv2.putText(img, "CLEANING ZONE",
(zone_x_clean - 110, zone_y_clean - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 51), 1)
cv2.rectangle(img, (zone_x_min_door, zone_y_min_door),
(zone_x_max_door, zone_y_max_door),
(127, 0, 255), 2)
zone_x_door = int((zone_x_min_door + zone_x_max_door) / 2.0)
zone_y_door = int((zone_y_min_door + zone_y_max_door) / 2.0)
cv2.circle(img, (zone_x_door, zone_y_door), 4, (127, 0, 255),
-1)
cv2.putText(img, "ENTRANCE",
(zone_x_door - 40, zone_y_door - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (127, 0, 255), 1)
cv2.rectangle(img, (zone_x_min_alchol, zone_y_min_alchol),
(zone_x_max_alchol, zone_y_max_alchol),
(255, 255, 51), 2)
zone_x_alchol = int(
(zone_x_min_alchol + zone_x_max_alchol) / 2.0)
zone_y_alchol = int(
(zone_y_min_alchol + zone_y_max_alchol) / 2.0)
cv2.circle(img, (zone_x_alchol, zone_y_alchol), 4,
(255, 255, 51), -1)
cv2.putText(img, "CLEANING ZONE",
(zone_x_alchol - 35, zone_y_alchol - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 51), 1)
#ROI
cv2.rectangle(img, (160, 130), (697, 721), (0, 255, 255), 2)
distance_bed = 0
distance_clean = 0
#Detection Zone
objects, valid_checker = ct.update(rects)
flag = False
leave_zone_counter = 0
for ((objectID, centroid),
(objectID, valid)) in zip(objects.items(),
valid_checker.items()):
# draw both the ID of the object and the centroid of the
# object on the output frame
#text_id = "ID {}".format(objectID)
text_id = "id = 0"
backup_label = str(objectID)
text = "staff"
cv2.putText(img, text,
(centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.putText(img, text_id,
(centroid[0] - 10, centroid[1] - 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.circle(img, (centroid[0], centroid[1]), 4, (0, 255, 0),
-1)
distance_bed = int(
math.sqrt((centroid[0] - zone_x_bed)**2 +
(centroid[1] - zone_y_bed)**2))
distance_clean = int(
math.sqrt((centroid[0] - zone_x_clean)**2 +
(centroid[1] - zone_y_clean)**2))
distance_clean_alchol = int(
math.sqrt((centroid[0] - zone_x_alchol)**2 +
(centroid[1] - zone_y_alchol)**2))
enter = ct.display_enter_status(objectID)
leave = ct.display_leave_status(objectID)
flag = ct.display_hygiene(objectID)
if (distance_clean_alchol <= distance_thres_alchol):
cv2.line(img, (centroid[0], centroid[1]),
(zone_x_alchol, zone_y_alchol), (0, 0, 255),
1)
# if(hand_wash_status == 1):
# personal_status[objectID] = 1
# ct.update_wash(True,objectID)
if (distance_bed <= distance_thres_bed):
#personal_status[0] = 0
hand_wash_status = 0
cv2.line(img, (centroid[0], centroid[1]),
(zone_x_bed, zone_y_bed), (0, 255, 0), 1)
#Update Hygiene Status as the staff is originally cleaned
ct.update_hygiene(False, objectID)
"""
#Never enter
if(enter == False):
ct.update_enter(True,objectID)
#If the staff did not wash hand and go to patient directly
hand_wash_flag = ct.display_wash(objectID)
if(hand_wash_flag == False):
ct.update_valid(False,objectID)
m = 0
match = True
#Check whether this ID is marked as fail or not (on the screen)
while(m<len(invalid_id)):
if(objectID==invalid_id[m]):
#ObjectID is found in the invalid bank
match = True
else:
match = False
m+=1
#If it is not in the bank,then mark it and fail counter + 1
if(match == False):
fail_msg +=1
invalid_id.append(objectID)
#Enter again with uncleaned => invalid
else:
if(flag == False):
#Re-enter the patient zone
if((enter == True)and(leave == True)):
ct.update_valid(False,objectID)
m = 0
match = True
#Check whether this ID is marked as fail or not (on the screen)
while(m<len(invalid_id)):
if(objectID==invalid_id[m]):
#ObjectID is found in the invalid bank
match = True
else:
match = False
m+=1
#If it is not in the bank,then mark it and fail counter + 1
if(match == False):
fail_msg +=1
invalid_id.append(objectID)
else:
if(enter == True):
ct.update_leave(True,objectID)
"""
if ((distance_clean <= distance_thres_clean) or
(distance_clean_alchol <= distance_thres_alchol)):
if (distance_clean <= distance_thres_clean):
cv2.line(img, (centroid[0], centroid[1]),
(zone_x_clean, zone_y_clean),
(51, 255, 255), 1)
#if(hand_wash_status == 1):
# personal_status[0] = 1
#hand_wash_status = 1
#Update Hygiene Status
ct.update_hygiene(True, objectID)
#Reset IN/OUT Mechanism
ct.update_enter(False, objectID)
ct.update_leave(False, objectID)
ct.update_wash(True, objectID)
#Return hygiene status
flag = ct.display_hygiene(objectID)
#if(previous_id!=objectID):
#hand_wash_status = 0
#personal_status = 0
previous_id = objectID
with open('./path_analyzer/path_log.csv', 'a',
newline='') as csv_log_file:
log_writer = csv.writer(csv_log_file)
log_writer.writerow([
objectID, centroid[0], centroid[1],
int(distance_bed),
int(distance_clean),
int(distance_clean_alchol),
int(0),
int(0)
])
#log_writer.writerow([objectID,centroid[0],centroid[1],int(distance_bed),int(distance_clean),int(distance_clean_alchol),int(hand_wash_status),int(personal_status[objectID])])
if(((centroid[0]>=zone_x_min_door)and(centroid[0]<=zone_x_max_door)) and \
((centroid[1]>=zone_y_min_door)and(centroid[1]<=zone_y_max_door))):
person_x = centroid[0]
person_y = centroid[1]
leave_zone_counter += 1
if ((leave_zone_counter == 0) and (enter_zone == True)
and (leave_zone == False)):
if (leave_zone_2 == False):
print("LEAVE 1!!!\r\n")
leave_zone = True
else:
print("LAEVE 2!!!\r\n")
enter_zone = False
leave_zone_2 = False
#print("leave_zone_counter:",leave_zone_counter)
#print("leave_zone_2:",leave_zone_2)
#print("enter_zone:",enter_zone)
#print("leave_zone:",leave_zone)
#If any id passed through entrance
if (((person_x > 0) and (person_y > 0))
and (leave_zone_counter > 0)):
font = cv2.FONT_HERSHEY_PLAIN
line = cv2.LINE_AA
if (enter_zone == False):
cv2.putText(img, "START", (40, 100), font, 3.0,
(255, 0, 0), 4, line)
print("START\r\n")
enter_zone = True
start_time = time.time()
pp = ' { "sys_status" :"' + str(10) + '"}'
client.publish("MDSSCC/STATUS", pp)
else:
if (leave_zone == True):
cv2.putText(img, "COUNT", (40, 100), font, 3.0,
(255, 0, 0), 4, line)
print("COUNT\r\n")
pp = ' { "sys_status" :"' + str(30) + '"}'
client.publish("MDSSCC/STATUS", pp)
leave_zone = False
leave_zone_2 = True
leave_zone_counter = 0
person_x = 0
person_y = 0
print()
if (restart_flag == False):
if show_fps:
img = draw_help_and_fps(img, fps)
#img = img[200:721, 160:697]
cv2.imshow(WINDOW_NAME, img)
cv2.setMouseCallback(
WINDOW_NAME,
pixel_info) #Receive mouse click on HSV_Picker
#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
else:
print("None Image --> None Buff = {}".format(none_buff))
none_buff += 1
if (none_buff == 1000):
print("[SYSTEM] VSTARCAMERA Restart")
cam.stop() # terminate the sub-thread in camera
#tf_sess.close()
#tf.reset_default_graph()
#tf.contrib.keras.backend.clear_session()
cam.release()
restart_flag = True
none_buff = 0
#img = None
cv2.destroyAllWindows()
hand_wash_status = 0
if (restart_flag == False):
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
elif key == ord('H') or key == ord('h'): # Toggle help/fps
show_fps = not show_fps
elif key == ord('F') or key == ord('f'): # Toggle fullscreen
full_scrn = not full_scrn
set_full_screen(full_scrn)
#client.loop_start()
#client.loop_forever()
client.reconnect()
def loop_and_detect(cam, tf_sess, conf_th, vis, od_type):
"""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.
"""
show_fps = True
full_scrn = False
fps = 0.0
tic = time.time()
feature_params = dict(maxCorners=1000, qualityLevel=0.1, minDistance=4, blockSize=7)
lk_params = dict(winSize=(15, 15), maxLevel=3, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.02))
tracks = []
track_len = 8
frame_idx = 0
detect_interval = 10
global rects,ct,temp,args,optical_enable #restart issue
frame_buff = 0
none_buff = 0
restart_flag = False #restart issue
backup_label = None #restart issue
while True:
#if cv2.getWindowProperty(WINDOW_NAME, 0) < 0:
# Check to see if the user has closed the display window.
# If yes, terminate the while loop.
# break
if(restart_flag == True):
cam = Camera(args)
cam.open()
cam.start()
print("Camera is opened!")
#pb_path = './data/{}_trt.pb'.format(args.model)
#log_path = './logs/{}_trt'.format(args.model)
#trt_graph = load_trt_pb(pb_path)
#tf_config = tf.ConfigProto()
#tf_config.gpu_options.allow_growth = True
#tf_sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True),graph=trt_graph)
#od_type = 'faster_rcnn' if 'faster_rcnn' in args.model else 'ssd'
dummy_img = np.zeros((720, 1280, 3), dtype=np.uint8)
_, _, _ = detect(dummy_img, tf_sess, conf_th=.3, od_type=od_type)
print("Loading dummy image!")
restart_flag = False
rects = []
img = cam.read()
if img is not None:
optical_flow_image = img
box, conf, cls = detect(img, tf_sess, conf_th, od_type=od_type)
img = vis.draw_bboxes(img, box, conf, cls)
#Optical Flow
if (optical_enable==True):
frame_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if len(tracks) > 0:
img0 , img1 = prev_gray, frame_gray
p0 = np.float32([tr[-1] for tr in tracks]).reshape(-1,1,2)
p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
p0r, _, _ = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
d = abs(p0-p0r).reshape(-1,2).max(-1)
good = d < 1
new_tracks = []
for i, (tr, (x, y), flag) in enumerate(zip(tracks, p1.reshape(-1, 2), good)):
if not flag:
continue
tr.append((x,y))
if len(tr)> track_len:
del tr[0]
new_tracks.append(tr)
cv2.circle(img, (x, y), 2, (0, 255, 0), -1)
tracks = new_tracks
cv2.polylines(img, [np.int32(tr) for tr in tracks], False, (0, 255, 0), 1)
for box_coord in box:
y_min, x_min, y_max, x_max = box_coord[0], box_coord[1], box_coord[2], box_coord[3]
for tr in tracks:
tail = len(tr)
start_point = tr[0]
end_point = tr[tail-1]
if((start_point[0]<=x_max)and(start_point[0]>=x_min))and((end_point[0]<=x_max)and(end_point[0]>=x_min)):
if((start_point[1]<=y_max)and(start_point[1]>=y_min))and((end_point[1]<=y_max)and(end_point[1]>=y_min)):
if(end_point[0]-start_point[0])>40:
cv2.putText(img, "ENTER", (x_min+10,y_min+50),cv2.FONT_HERSHEY_TRIPLEX,1,(255,255,153),2,cv2.LINE_AA)
elif(start_point[0]-end_point[0])>40:
cv2.putText(img, "LEAVE", (x_min+10,y_min+50),cv2.FONT_HERSHEY_TRIPLEX,1,(255,255,153),2,cv2.LINE_AA)
#elif(abs(start_point[0]-end_point[0])<10)and(abs(start_point[1]-end_point[1])<10):
# cv2.putText(img, "IDLE", (x_min+10,y_min-20),cv2.FONT_HERSHEY_TRIPLEX,1,(102,255,178),2,cv2.LINE_AA)
if frame_idx % detect_interval==0:
mask = np.zeros_like(frame_gray)
mask[:] = 255
if frame_idx !=0:
for x,y in [np.int32(tr[-1]) for tr in tracks]:
cv2.circle(mask, (x, y), 5, 0, -1)
p = cv2.goodFeaturesToTrack(frame_gray, mask=mask, **feature_params)
if p is not None:
for x, y in np.float32(p).reshape(-1,2):
tracks.append([(x, y)])
frame_idx+=1
prev_gray = frame_gray
#Optical Flow done
cv2.rectangle(img, (0,980),(1920,1075),(0,0,0),-1)
objects = ct.update(rects)
for (objectID, centroid) in objects.items():
# draw both the ID of the object and the centroid of the
# object on the output frame
text = "ID {}".format(objectID)
cv2.putText(img, text, (centroid[0] - 10, centroid[1] - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,255), 2)
cv2.putText(img, str(objectID), (330,1035),cv2.FONT_HERSHEY_TRIPLEX,1,(255,255,255),2,cv2.LINE_AA)
cv2.circle(img, (centroid[0], centroid[1]), 4, (255,0,255), -1)
backup_label = str(objectID)
if(optical_enable == True):
cv2.putText(img, "ON", (1380,1035), cv2.FONT_HERSHEY_TRIPLEX,1,(255,255,255),2,cv2.LINE_AA)
else:
cv2.putText(img, "OFF", (1380,1035), cv2.FONT_HERSHEY_TRIPLEX,1,(255,255,255),2,cv2.LINE_AA)
sys_clock = str(datetime.datetime.now())
cv2.putText(img, backup_label, (330,1035),cv2.FONT_HERSHEY_TRIPLEX,1,(255,255,255),2,cv2.LINE_AA)
cv2.putText(img, sys_clock, (20,950), cv2.FONT_HERSHEY_TRIPLEX,1,(0,0,255),2,cv2.LINE_AA)
cv2.putText(img, "Traffic Counter: ", (20,1035), cv2.FONT_HERSHEY_TRIPLEX,1,(0,0,255),2,cv2.LINE_AA)
cv2.putText(img, "Detector Type: Human", (400,1035), cv2.FONT_HERSHEY_TRIPLEX,1,(0,0,255),2,cv2.LINE_AA)
cv2.putText(img, "Real Time Optical Trace :", (900,1035), cv2.FONT_HERSHEY_TRIPLEX,1,(0,0,255),2,cv2.LINE_AA)
#print(sys_clock)
if(restart_flag == False):
if show_fps:
img = draw_help_and_fps(img, fps)
cv2.moveWindow(WINDOW_NAME,0,0) #restart issue
cv2.imshow(WINDOW_NAME, img)
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
else:
print("None Image --> None Buff = {}".format(none_buff))
none_buff+=1
if(none_buff == 1000):
print("[SYSTEM] VSTARCAMERA Restart")
cam.stop() # terminate the sub-thread in camera
#tf_sess.close()
#tf.reset_default_graph()
#tf.contrib.keras.backend.clear_session()
cam.release()
restart_flag = True
none_buff = 0
#img = None
cv2.destroyAllWindows()
if(restart_flag== False):
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
elif key == ord('H') or key == ord('h'): # Toggle help/fps
show_fps = not show_fps
elif key == ord('F') or key == ord('f'): # Toggle fullscreen
full_scrn = not full_scrn
set_full_screen(full_scrn)
elif key == ord ('P') or key == ord('p'):
if(optical_enable == True):
optical_enable = False
else:
optical_enable = True
def loop_and_detect(cam, tf_sess, conf_th, vis, od_type):
"""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.
"""
show_fps = True
full_scrn = False
fps = 0.0
tic = time.time()
tracks = []
global rects, ct, temp, args
frame_buff = 0
none_buff = 0
restart_flag = False
backup_label = None
while True:
#if cv2.getWindowProperty(WINDOW_NAME, 0) < 0:
# Check to see if the user has closed the display window.
# If yes, terminate the while loop.
# break
if (restart_flag == True):
cam = Camera(args)
cam.open()
cam.start()
#pb_path = './data/{}_trt.pb'.format(args.model)
#log_path = './logs/{}_trt'.format(args.model)
#trt_graph = load_trt_pb(pb_path)
#tf_config = tf.ConfigProto()
#tf_config.gpu_options.allow_growth = True
#tf_sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=True),graph=trt_graph)
#od_type = 'faster_rcnn' if 'faster_rcnn' in args.model else 'ssd'
dummy_img = np.zeros((720, 1280, 3), dtype=np.uint8)
_, _, _ = detect(dummy_img, tf_sess, conf_th=.3, od_type=od_type)
restart_flag = False
rects = []
img = cam.read()
optical_flow_image = img
if img is not None:
box, conf, cls = detect(img, tf_sess, conf_th, od_type=od_type)
img = vis.draw_bboxes(img, box, conf, cls)
objects = ct.update(rects)
cv2.rectangle(img, (0, 980), (1920, 1075), (0, 0, 0), -1)
for (objectID, centroid) in objects.items():
# draw both the ID of the object and the centroid of the
# object on the output frame
text = "ID {}".format(objectID)
cv2.putText(img, text, (centroid[0] - 10, centroid[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2)
cv2.circle(img, (centroid[0], centroid[1]), 4, (255, 0, 255),
-1)
backup_label = str(objectID)
cv2.putText(img, backup_label, (330, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (255, 255, 255), 2,
cv2.LINE_AA)
sys_clock = str(
datetime.datetime.now()) + " Frame_buff=" + str(frame_buff)
print(sys_clock)
cv2.putText(img, sys_clock, (20, 950), cv2.FONT_HERSHEY_TRIPLEX, 1,
(0, 0, 255), 2, cv2.LINE_AA)
cv2.putText(img, "Traffic Counter: ", (20, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.putText(img, "Detector Type: Human", (400, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.putText(img, "Real Time Optical Trace :", (900, 1035),
cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.putText(img, "OFF", (1380, 1035), cv2.FONT_HERSHEY_TRIPLEX, 1,
(255, 255, 255), 2, cv2.LINE_AA)
"""
if(frame_buff == 2000):
print("[SYSTEM] VSTARCAMERA Restart")
cam.stop() # terminate the sub-thread in camera
#tf_sess.close()
#tf.reset_default_graph()
#tf.contrib.keras.backend.clear_session()
cam.release()
restart_flag = True
frame_buff = 0
img = None
cv2.destroyAllWindows()
frame_buff += 1
"""
if (restart_flag == False):
if show_fps:
img = draw_help_and_fps(img, fps)
#set_full_screen(full_scrn)
cv2.moveWindow(WINDOW_NAME, 0, 0)
cv2.imshow(WINDOW_NAME, img)
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
else:
print("None Image --> None Buff = {}".format(none_buff))
none_buff += 1
if (none_buff == 500):
print("[SYSTEM] VSTARCAMERA Restart")
cam.stop() # terminate the sub-thread in camera
#tf_sess.close()
#tf.reset_default_graph()
#tf.contrib.keras.backend.clear_session()
cam.release()
restart_flag = True
none_buff = 0
img = None
cv2.destroyAllWindows()
if (restart_flag == False):
key = cv2.waitKey(1)
if key == 27: # ESC key: quit program
break
elif key == ord('H') or key == ord('h'): # Toggle help/fps
show_fps = not show_fps
elif key == ord('F') or key == ord('f'): # Toggle fullscreen
full_scrn = not full_scrn
set_full_screen(full_scrn)
