def thread2_function(name):
global Q
## Detection
logging.info("Thread %s: starting ", name)
detector = yoloDetection(args["yolo"], args["input"], args["confidence"],
args["threshold"], args["bbox"])
detector.prepareModel()
start = datetime.datetime.now()
# numFrames = 0
while True:
### GEtting rid of frames.
### TO be done while recording.
for i in range(14):
Q.get()
frame = Q.get()
print("Thread 2: starting ", Q.qsize())
# numFrames += 1
out = detector.runInference(frame)
if args["bbox"] is True:
cv2.imshow('detector', out)
if cv2.waitKey(1) & 0xFF == ord('q'):
print("FPS: ",
numF / (datetime.datetime.now() - start).total_seconds())
break
else:
print(out)
logging.info("Thread %s: finishing", name)
type=float,
default=0.5,
help="minimum probability to filter weak detections")
ap.add_argument("-t",
"--threshold",
type=float,
default=0.3,
help="threshold when applyong non-maxima suppression")
ap.add_argument("-b",
"--bbox",
required=False,
default=True,
help="turn on bounding box")
args = vars(ap.parse_args())
detector = yoloDetection(args["yolo"], args["input"], args["confidence"],
args["threshold"], args["bbox"])
detector.prepareModel()
imgPath = 'images/'
images = os.listdir(imgPath)
# for image in images:
# img = cv2.imread(imgPath+image)
# out = detector.runInference(img)
# if args["bbox"] is True:
# cv2.imshow(''.format(image), out)
# else:
# print(out)