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#!/usr/bin/env python
#
# file: $ISIP_EXP/tuh_dpath/exp_0074/scripts/decode.py
#
# revision history:
# 20190925 (TE): first version
#
# usage:
# python decode.py odir mfile data
#
# arguments:
# odir: the directory where the hypotheses will be stored
# mfile: input model file
# data: the input data list to be decoded
#
# This script decodes data using a simple MLP model.
#------------------------------------------------------------------------------
# import pytorch modules
#
import torch
from tqdm import tqdm
# visualize:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
matplotlib.style.use('ggplot')
import sys
import os
################ customized parameters #################
################ please modify them based on your dataset #################
DATASET_ODIR = "~/semantic2d_data/2024-04-04-12-16-41" # the directory path of the raw data
DATASET_NAME = "train" # select the train, dev, and test
SEMANTIC_MASK_ODIR = "./output"
# Hokuyo UTM-30LX-EW:
POINTS = 1081 # the number of lidar points
AGNLE_MIN = -2.356194496154785
AGNLE_MAX = 2.356194496154785
RANGE_MAX = 60.0
# # WLR-716:
# POINTS = 811 # the number of lidar points
# AGNLE_MIN = -2.356194496154785
# AGNLE_MAX = 2.356194496154785
# RANGE_MAX = 25.0
# # RPLIDAR-S2:
# POINTS = 1972 # the number of lidar points
# AGNLE_MIN = -3.1415927410125732
# AGNLE_MAX = 3.1415927410125732
# RANGE_MAX = 16.0
################# read dataset ###################
NEW_LINE = "\n"
# for reproducibility, we seed the rng
#
class Semantic2DLidarDataset(torch.utils.data.Dataset):
def __init__(self, img_path, file_name):
# initialize the data and labels
# read the names of image data:
self.scan_file_names = []
self.intensity_file_names = []
self.vel_file_names = []
self.label_file_names = []
# parameters:
self.s_max = 30
self.s_min = 0
# open train.txt or dev.txt:
fp_file = open(img_path+'/'+file_name+'.txt', 'r')
# for each line of the file:
for line in fp_file.read().split(NEW_LINE):
if('.npy' in line):
self.scan_file_names.append(img_path+'/scans_lidar/'+line)
self.intensity_file_names.append(img_path+'/intensities_lidar/'+line)
self.label_file_names.append(img_path+'/semantic_label/'+line)
# close txt file:
fp_file.close()
self.length = len(self.scan_file_names)
print("dataset length: ", self.length)
def __len__(self):
return self.length
def __getitem__(self, idx):
# get the index of start point:
scan = np.zeros((1, POINTS))
intensity = np.zeros((1, POINTS))
label = np.zeros((1, POINTS))
# get the scan data:
scan_name = self.scan_file_names[idx]
scan = np.load(scan_name)
# get the intensity data:
intensity_name = self.intensity_file_names[idx]
intensity = np.load(intensity_name)
# get the semantic label data:
label_name = self.label_file_names[idx]
label = np.load(label_name)
# initialize:
scan[np.isnan(scan)] = 0.
scan[np.isinf(scan)] = 0.
intensity[np.isnan(intensity)] = 0.
intensity[np.isinf(intensity)] = 0.
scan[scan >= 15] = 0.
label[np.isnan(label)] = 0.
label[np.isinf(label)] = 0.
# transfer to pytorch tensor:
scan_tensor = torch.FloatTensor(scan)
intensity_tensor = torch.FloatTensor(intensity)
label_tensor = torch.FloatTensor(label)
data = {
'scan': scan_tensor,
'intensity': intensity_tensor,
'label': label_tensor,
}
return data
#------------------------------------------------------------------------------
#
# the main program starts here
#
#------------------------------------------------------------------------------
# function: main
#
# arguments: none
#
# return: none
#
# This method is the main function.
#
if __name__ == '__main__':
# input parameters:
dataset_odir = DATASET_ODIR
dataset_name = DATASET_NAME
semantic_mask_odir = SEMANTIC_MASK_ODIR
# create the folder for the semantic label mask:
if not os.path.exists(semantic_mask_odir):
os.makedirs(semantic_mask_odir)
# read dataset:
eval_dataset = Semantic2DLidarDataset(dataset_odir, dataset_name)
eval_dataloader = torch.utils.data.DataLoader(eval_dataset, batch_size=1, num_workers=2, \
shuffle=False, drop_last=True, pin_memory=True)
# for each batch in increments of batch size:
cnt = 0
cnt_m = 0
# get the number of batches (ceiling of train_data/batch_size):
num_batches = int(len(eval_dataset)/eval_dataloader.batch_size)
for i, batch in tqdm(enumerate(eval_dataloader), total=num_batches):
# collect the samples as a batch: 10 timesteps
if(i % 200 == 0):
scans = batch['scan']
scans = scans.detach().cpu().numpy()
labels = batch['label']
labels = labels.detach().cpu().numpy()
# lidar data:
r = scans.reshape(POINTS)
theta = np.linspace(AGNLE_MIN, AGNLE_MAX, num=POINTS, endpoint='true')
## plot semantic label:
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(1,1,1, projection='polar', facecolor='seashell')
smap = labels.reshape(POINTS)
# add the background label:
theta = np.insert(theta, -1, np.pi)
r = np.insert(r, -1, 1)
smap = np.insert(smap, -1, 0)
label_val = np.unique(smap).astype(int)
print("label_values: ", label_val)
colors = smap
area = 6
scatter = ax.scatter(theta, r, c=colors, s=area, cmap='nipy_spectral', alpha=0.95, linewidth=10)
ax.set_xticks(np.linspace(AGNLE_MIN, AGNLE_MAX, 8, endpoint='true'))
ax.set_thetamin(-135)
ax.set_thetamax(135)
ax.set_yticklabels([])
# produce a legend with the unique colors from the scatter
classes = ['Other', 'Chair', 'Door', 'Elevator', 'Person', 'Pillar', 'Sofa', 'Table', 'Trash bin', 'Wall']
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.legend(handles=scatter.legend_elements(num=[j for j in label_val])[0], labels=[classes[j] for j in label_val], bbox_to_anchor=(0.5, -0.08), loc='lower center', fontsize=18)
ax.grid(False)
ax.set_theta_offset(np.pi/2)
input_img_name = semantic_mask_odir + "/semantic_mask" + str(i)+ ".png"
plt.savefig(input_img_name, bbox_inches='tight')
plt.show()
print(i)
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