# Create a Tracking Scenario scenario = TrackingScenario(trackers) # Track the video using the preconfigured scenario retval, tl = scenario.track(video_path, pix_per_m=4441, start_frame=160, end_frame=210) # Computing the trajectory of the led referred to the center pivot center, led = tl led_centered = led - center led_centered.id = 'led' # Computing the trajectory of the wheel referred to the center pivot wheel_centered = led_centered.copy() wheel_centered.add_polar_offset(0.039, 0) wheel_centered.id = 'wheel' plot_trajectories([wheel_centered, led_centered]) # Computing the trajectory of the wheel referred to its initial position wheel = wheel_centered - wheel_centered.r[0] # Computing the linear velocity in optimal conditions (omega x r) v_opt = 4 * 0.07 # Computing the linear velocity by the results of the tracking v_meas = wheel.v.norm #Computing the efficiency eff = v_meas/v_opt # Plotting the linear displacement of the wheel import matplotlib.pyplot as plt
points = self.r[:, :, i]
trajs.append(
Trajectory(points=points,
dt=self.dt,
t=self.t,
traj_id=f"LangevinSolution {i + 1}"))
return trajs
if __name__ == '__main__':
from yupi.analyzing import plot_trajectories
# set parameter values
T = 500 # total time (number of time steps if dt==1)
dim = 2 # dimension of the walker trajectories
N = 3 # number of random walkers
dt = 1 # time step
# probability of every action to be taken
# according to every axis
prob = [
[.5, .1, .4], # x-axis
[.5, 0, .5]
] # y-axis
# get RandomWalk object and get position vectors
rw = RandomWalkGenerator(T, dim, N, dt, prob)
tr = rw.generate()
plot_trajectories(tr)
from yupi.tracking import ROI, ObjectTracker, TrackingScenario
from yupi.tracking import ColorMatching
from yupi.analyzing import plot_trajectories
# Initialize main tracking objects
algorithm = ColorMatching((180, 125, 35), (190, 135, 45))
blue_ball = ObjectTracker('blue', algorithm, ROI((100, 100)))
scenario = TrackingScenario([blue_ball])
# Track the video using the preconfigured scenario
retval, tl = scenario.track('resources/videos/demo.avi', pix_per_m=10)
plot_trajectories(tl)
noise_scale_adim = np.sqrt(2 * dt_adim) # scale parameter of noise pdf v0_adim = np.random.randn(dim, N) # initial dimensionaless speeds ## 2. Simulating the process lg = LangevinGenerator(tt_adim, dim, N, dt_adim, v0=v0_adim) lg.set_scale(v_scale=vr, r_scale=lr, t_scale=tr) trajs = lg.generate() ## 3. Data analysis and plots plt.figure(figsize=(9, 5)) # Spacial trajectories ax1 = plt.subplot(231) ypa.plot_trajectories(trajs[:5], legend=False, show=False) # velocity histogram v = ypa.estimate_velocity_samples(trajs, step=1) ax2 = plt.subplot(232) ypa.plot_velocity_hist(v, bins=20, show=False) # turning angles theta = ypa.estimate_turning_angles(trajs) ax3 = plt.subplot(233, projection='polar') ypa.plot_angle_distribution(theta, show=False) # mean square displacement lag_msd = 30 msd, msd_std = ypa.estimate_msd(trajs, time_avg=True, lag=lag_msd) ax4 = plt.subplot(234)