#!/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)