parser.add_argument("--balls", type=int, default=3)
args = parser.parse_args()
handleTensorflowSession(memoryLimit=0.2)
gridModel = GridModel("../grid_models/grid_model_submovavg_64x64.h5",
args.balls)
cap = cv2.VideoCapture(0)
framerateChecker = FramerateChecker(expected_fps=30)
while True:
framerateChecker.check()
ret, original_img = cap.read()
if not ret:
print("Couldn't get frame from camera.")
break
else:
height, width, channels = original_img.shape
tocrop = int((width - height) / 2)
original_img = original_img[:, tocrop:-tocrop]
ballsAndHands = gridModel.predict(original_img.copy())
img = cv2.resize(original_img, (256, 256), cv2.INTER_CUBIC)
drawBallsAndHands(img, ballsAndHands)
cv2.imshow('Webcam', img)
cv2.waitKey(1)
cap.release()
cv2.destroyAllWindows()
model = GridModel(
"../grid_models/grid_model_submovavg_64x64_light.h5",
nBalls=args.n_balls,
preprocessType="SUBMOVAVG",
flip=False,
postprocess=True,
)
cap = cv2.VideoCapture(args.path)
ball_ys = np.empty((0, args.n_balls))
with tqdm(total=int(cap.get(cv2.CAP_PROP_FRAME_COUNT))) as pbar:
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
balls_and_hands = model.predict(frame)
balls = balls_and_hands["balls"]
ball_ys = np.vstack((ball_ys, balls[:, 1]))
pbar.update()
cap.release()
fig, (ax1, ax2, ax3) = plt.subplots(3)
xs = np.arange(ball_ys.shape[0])
ball_ys_blurred = gaussian_filter(ball_ys, sigma=(2, 0))
ax1.set_title("Original ball positions")
ax1.set_xlabel("Frame")
ax1.set_ylabel("y position")
for i in range(args.n_balls):
ax1.plot(xs, ball_ys[:, i], "-o", label=f"Ball {i}")
ax1.legend()