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('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('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()
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()
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():
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)
