def main():
args = parse_args()
tracker = Tracker(path=args.path,
showResult=True,
saveData=args.saveData,
border=10)
cap = cv2.VideoCapture(args.input)
bbox = None
objectID = args.id
if args.output is not None:
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(os.path.join(args.output, args.id + '.avi'),
fourcc, 30.0, (1280, 720))
while True:
ret, frame = cap.read()
if not ret:
break
frame = cv2.resize(frame, dsize=(1280, 720))
key = cv2.waitKey(1) & 0xFF
tracked_frame, bbox = tracker.update(frame, objectID, bbox)
cv2.imshow('image', frame)
if args.output is not None:
out.write(frame)
if key == ord('s'):
bbox = cv2.selectROI('image', frame, fromCenter=False)
tracker.init(frame, bbox)
print('Selected {}'.format(bbox))
continue
if key == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if args.output is not None:
out.release()
boxes.append([x, y, w, h]) # 배열로 계속 넣어줌.
confidences.append(float(confidence))
indexes = cv2.dnn.NMSBoxes(boxes, confidences, min_confidence, 0.4) # 박스 중복을 줄여주는 거.
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
rects.append([x, y, x+w, y+h]) # 이 박스안에 중복된 내용이 있을수도 없을수도.
label = '{:,.2%}'.format(confidences[i])
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 1) # 박스처리해줌.
cv2.putText(frame, label, (x + 5, y + 15), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 1)
# use Tracker
objects = tracker.update(rects)
total = len(objects)
# loop over the trackable objects
# 아디하고 centroid 좌표가 온다.
for (objectID, centroid) in objects.items():
# check if a trackable object exists with the object ID
# 트랙킹 되고 있는 알고리즘.
trackable = trackables.get(objectID, None)
# 아디 값 오면 trackable이라는 객체가 생성됨.
if trackable is None:
trackable = Trackable(objectID, centroid)
# store the trackable object in our dictionary
trackables[objectID] = trackable