2 MiB
2 MiB
Visualise tracking data¶
This notebook is an adapted version of process.py as it is included in the Trajectron++ package. It can be used to
- Visualise the recorded trajectories, which are normally parsed to a Trajectron++ Node
In [1]:
import sys
import os
import numpy as np
import pandas as pd
import dill
import tqdm
import matplotlib.pyplot as plt
#sys.path.append("../../")
from trajectron.environment import Environment, Scene, Node
from trajectron.environment import derivative_of
from trap.tracker import Smoother
In [2]:
smoothing = True
desired_max_time = 100
pred_indices = [2, 3]
state_dim = 6
frame_diff = 10
desired_frame_diff = 1
dt = 0.1
min_track_length = 20
standardization = {
'PEDESTRIAN': {
'position': {
'x': {'mean': 0, 'std': 1},
'y': {'mean': 0, 'std': 1}
},
'velocity': {
'x': {'mean': 0, 'std': 2},
'y': {'mean': 0, 'std': 2}
},
'acceleration': {
'x': {'mean': 0, 'std': 1},
'y': {'mean': 0, 'std': 1}
}
}
}
data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
# desired_source = 'EXPERIMENTS/raw/hof-meter-maskrcnn2'
# desired_source = 'EXPERIMENTS/20240424-hof-meter-maskrcnn2'
# desired_source = 'EXPERIMENTS/20240426-hof-yolo'
desired_source = 'EXPERIMENTS/raw/hof3'
In [3]:
# run some tests
if not os.path.exists(desired_source):
raise FileNotFoundError(f"Path does not exist {desired_source=}")
In [ ]:
from matplotlib.axes import Axes
from trap.frame_emitter import DetectionState
if smoothing:
smoother = Smoother(window_len=12, convolution=False)
def parse_txt(data_path, dt, axes: Axes, axes2: Axes) -> dict[str, pd.DataFrame]:
skipped_for_error = 0
created = 0
data = pd.read_csv(data_path, sep='\t', index_col=False, header=None)
data.columns = ['frame_id', 'track_id', 'l','t', 'w','h', 'pos_x', 'pos_y', 'state']
# data['frame_id'] = pd.to_numeric(data['frame_id'], downcast='integer')
data['track_id'] = pd.to_numeric(data['track_id'], downcast='integer')
data['frame_id'] = data['frame_id'] // 10
data['frame_id'] -= data['frame_id'].min()
data['node_type'] = 'PEDESTRIAN'
data['node_id'] = data['track_id'].astype(str)
data['state'] = data['state'].apply(lambda x: (eval(x) if type(x) is str else DetectionState(x)).value)
data.sort_values('frame_id', inplace=True)
# Mean Position
print("Means: x:", data['pos_x'].mean(), "y:", data['pos_y'].mean())
data['pos_x'] = data['pos_x'] - data['pos_x'].mean()
data['pos_y'] = data['pos_y'] - data['pos_y'].mean()
max_timesteps = data['frame_id'].max()
nodes = {}
# only keep tentative and confirmed detections
# print(data)
data = data.loc[data['state'] != DetectionState.Lost.value]
# print(data[['track_id', 'node_id', 'state']])
for node_id in tqdm.tqdm(pd.unique(data['node_id'])):
node_df = data[data['node_id'] == node_id]
if not np.all(np.diff(node_df['frame_id']) == 1):
# print(f"Interval in {node_id} not always 1")
# print(node_df['frame_id'])
# print(np.diff(node_df['frame_id']) != 1)
# mask=np.append(False, np.diff(node_df['frame_id']) != 1)
# print(node_df[mask]['frame_id'])
skipped_for_error += 1
continue
node_values = node_df[['pos_x', 'pos_y']].values
if node_values.shape[0] <= min_track_length:
continue
new_first_idx = node_df['frame_id'].iloc[0]
x = smoother.smooth(node_values[:, 0])
y = smoother.smooth(-node_values[:, 1]) # OpenCV a right-handed one (y looking down) coordinate system
vx = derivative_of(x, dt)
vy = derivative_of(y, dt)
ax = derivative_of(vx, dt)
ay = derivative_of(vy, dt)
axes.plot(x, y, alpha=.3)
axes.scatter(x, y, marker='x', alpha=.3)
nv = node_df[['l','t', 'w', 'h']].values
x = smoother.smooth(nv[:, 0] + .5*nv[:, 2])
y = smoother.smooth(nv[:, 1] + nv[:, 3])
axes2.plot(x, y, alpha=.3)
axes2.scatter(x, y, marker='x', alpha=.3)
nodes[node_id] = [x,y]
# data_dict = {'node_id': node_id, ('position', 'x'): x,
# ('position', 'y'): y,
# ('velocity', 'x'): vx,
# ('velocity', 'y'): vy,
# ('acceleration', 'x'): ax,
# ('acceleration', 'y'): ay}
# node_data = pd.DataFrame(data_dict, columns=data_columns)
# nodes[node_id] = node_data
return nodes
In [9]:
fig = plt.figure(figsize=(20,16))
ax1, ax2 = fig.subplots(2)
import cv2
# ax = fig1.gca()
# ax2 = fig2.gca()
im = cv2.imread("../DATASETS/hof2/output.png")
ax2.imshow(im)
ax1.set_aspect(1)
ax2.set_aspect(1)
nodes = []
for data_class in ['train', 'val', 'test']:
target_dir = os.path.join(desired_source, data_class)
for subdir, dirs, files in os.walk(target_dir):
for file in files:
if file.endswith('.txt'):
print(data_class, file)
input_data_dict = dict()
full_data_path = os.path.join(subdir, file)
nodes.extend(parse_txt(full_data_path, dt, ax1, ax2))
# break
# break
# break
fig.show()
len(nodes)
Out[9]:
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