def test_car_length(self):
cc = CameraCalibration()
X1, Y1 = cc.image2world(661.498, 465.096)
X2, Y2 = cc.image2world(799.19, 617.622)
length = np.sqrt((X2 - X1)**2 + (Y2 - Y1)**2)
real_length = 11
assert np.abs(length - real_length) < 4
def test_car_width(self):
cc = CameraCalibration()
X1, Y1 = cc.image2world(914.768, 603.941)
X2, Y2 = cc.image2world(799.19, 617.622)
width = np.sqrt((X2 - X1)**2 + (Y2 - Y1)**2)
real_width = 2.55
assert np.abs(width - real_width) < 0.2
class CalculateSpeed(object):
def __init__(self, filename=join(dirname(dirname(realpath(__file__))), "data/cctv_ftg1_SPEED_000000_carLoc.csv"),
top_obs_rec=906, bottom_obs_rec=439,
window_size=10, start_time_stamp='2018-01-01T08:00'):
self.start_time_stamp = start_time_stamp
self.start_time = []
self.car_type = []
self.track_coord = []
with open(filename, 'r') as f:
for line in f:
dt = line.strip().split("; ")
self.start_time.append(float(dt[0]) * 0.1 / 3)
self.car_type.append(dt[1])
self.track_coord.append([eval(x) for x in dt[2:]])
self.start_time = np.array(self.start_time)
self.car_type = np.array(self.car_type)
self.top_obs_rec = top_obs_rec
self.bottom_obs_rec = bottom_obs_rec
self.window_size = window_size
if pd.to_datetime(self.start_time_stamp) < pd.to_datetime('2018-09-11T16:00'):
self.cc = CameraCalibration(default=False)
self.scale = 1
self.top_obs_rec = 931.761
self.bottom_obs_rec = 439.723
else:
self.cc = CameraCalibration(
p1=(787.919, 640.099), p2=(594.782, 404.630),
p3=(675.065, 399.513), p4=(913.843, 619.450),
u2=703.475, u4=823.241, default=False
)
self.scale = 31.05945702896952 / 51.07394876776243
self.top_obs_rec = 793.968
self.bottom_obs_rec = 337.152
def coord_image2word(self):
self.Us = []
self.Vs = []
self.times = []
for i, track in enumerate(self.track_coord):
dt = np.dtype('float,float')
track = np.array(track, dtype=dt)
U = track['f0']
V = track['f1']
time = self.start_time[i] + np.arange(U.shape[0]) * 0.1
idx = ((V >= self.bottom_obs_rec) & (V <= self.top_obs_rec))
U = U[idx]
V = V[idx]
time = time[idx]
self.Us.append(U)
self.Vs.append(V)
self.times.append(time)
self.Xs = []
self.Ys = []
for U, V in zip(self.Us, self.Vs):
X, Y = self.cc.image2world(U, V)
self.Xs.append(X)
self.Ys.append(Y)
return self.Xs, self.Ys, self.times
def get_speed(self):
Xs, Ys, times = self.coord_image2word()
self.speeds = []
for X, Y, time in zip(Xs, Ys, times):
# self.speeds.append(
# ((np.sqrt(
# (X[self.window_size:] - X[:-self.window_size])**2 +
# (Y[self.window_size:] - Y[:-self.window_size])**2
# ) / (time[self.window_size:] - time[:-self.window_size]) * 3.6)).mean()
# )
if len(time) > 1:
self.speeds.append(np.sqrt((X[-1] - X[0])**2 + (Y[-1] - Y[0])**2) / (time[-1] - time[0]) * 3.6 * self.scale)
# m/s -> km/h
return self.speeds
def get_df(self, save=False):
df = pd.DataFrame(columns=['time', 'speed', 'car_type'])
# all_speeds = np.hstack(self.speeds)
all_speeds = self.speeds
# all_times = [(tm[self.window_size:] + tm[:-self.window_size]) / 2 for tm in self.times]
all_times = [(tm[-1] + tm[0]) / 2 for tm in self.times if len(tm) > 1]
all_times = np.hstack(all_times).astype('timedelta64[s]') + np.datetime64(self.start_time_stamp)
df.speed = all_speeds
df.time = all_times
i = 0
for j in range(len(self.times)):
# df.loc[i: i+self.speeds[j].shape[0], 'car_type'] = self.car_type[j]
# i = i + self.speeds[j].shape[0]
if len(self.times[j]) > 1:
df.loc[i, 'car_type'] = self.car_type[j]
i += 1
if save:
df.to_csv(join(dirname(dirname(realpath(__file__))), f'result/speeds_{self.start_time_stamp}.csv'), index=False)
return df
def draw_result(self):
for i, (car_type, speed, time) in enumerate(zip(self.car_type, self.speeds, self.times)):
plt.close()
plt.plot((time[self.window_size:] + time[:-self.window_size]) / 2, speed, 'o-')
plt.title(f"{car_type} speed")
plt.xlabel('time (s)')
plt.ylabel('speed (km/h)')
plt.savefig(join(dirname(dirname(realpath(__file__))), f'result/speed/{i}.pdf'))
plt.close()
for i, (car_type, X, Y) in enumerate(zip(self.car_type, self.Xs, self.Ys)):
plt.close()
plt.plot(X, Y)
plt.title(f"{car_type} track")
plt.xlabel('X (m)')
plt.ylabel('Y (m)')
plt.savefig(join(dirname(dirname(realpath(__file__))), f'result/track/{i}.pdf'))
plt.close()
for i, (car_type, u, v) in enumerate(zip(self.car_type, self.Us, self.Vs)):
plt.close()
plt.plot(u, v)
plt.title(f"{car_type} track")
plt.xlabel('U (pixel)')
plt.ylabel('V (pixel)')
plt.savefig(join(dirname(dirname(realpath(__file__))),f'result/track_image/{i}.pdf'))
plt.close()
return 0
