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116 lines
4.1 KiB
116 lines
4.1 KiB
import numpy as np
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import os
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import pickle as pkl
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from PIL import Image
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import json
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import yaml
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# import ffmpeg
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import matplotlib.pyplot as plt
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# import cv2
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import sys
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import glob
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# sys.path.append('/radar-imaging-dataset/mmfn_project/mmfn_scripts/team_code/mmfn_utils/sim_radar_utils/')
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# sys.path.append('/radar-imaging-dataset/mmfn_project/mmfn_scripts/team_code/e2e_agent_sem_lidar2shenron_package/')
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from e2e_agent_sem_lidar2shenron_package.lidar import run_lidar
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from sim_radar_utils.radar_processor import RadarProcessor
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from sim_radar_utils.utils_radar import *
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from sim_radar_utils.transform_utils import *
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def convert2D_img_func(sim_radar, limit = 75):
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'''
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converts the 3D radar raw data to 2d range-angle image of dimension 256X256
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'''
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radarProcessor = RadarProcessor()
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# radar = sim_radar[0]
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radar = sim_radar
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chirpLevelData = reformat_adc_shenron(radar)
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rangeProfile = radarProcessor.cal_range_fft(chirpLevelData)
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# dopplerProfile = radarProcessor.cal_doppler_fft(rangeProfile)
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aoaProfile = radarProcessor.cal_angle_fft(rangeProfile)
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range_angle = get_range_angle(aoaProfile)
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cart_cord = polar_to_cart(range_angle, limit = limit)
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# print(cart_cord_man.shape)
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# return [cart_cord]
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return cart_cord
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def convert_sem_lidar_2D_img_func(sim_radar, invert_angle, limit = 75):
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'''
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converts the semantic_lidar data to the range angle 2D image of dimension 256X256
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'''
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# convert semantic lidar to raw 3d radar data
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# with open('/radar-imaging-dataset/mmfn_project/mmfn_scripts/team_code/e2e_agent_sem_lidar2shenron_package/simulator_configs.yaml', 'r') as f:
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# sim_config = yaml.safe_load(f)
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# with open(glob.glob('../carla_garage_radar/team_code/e2e_agent_sem_lidar2shenron_package/simulator_configs.yaml'), 'r') as f:
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# sim_config = yaml.safe_load(f)
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with open('/radar-imaging-dataset/carla_garage_radar/team_code/e2e_agent_sem_lidar2shenron_package/simulator_configs.yaml', 'r') as f:
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sim_config = yaml.safe_load(f)
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sim_config['INVERT_ANGLE'] = invert_angle
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radar = run_lidar(sim_config, sim_radar)
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radarProcessor = RadarProcessor()
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# radar = sim_radar[0]
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# radar = sim_radar
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chirpLevelData = reformat_adc_shenron(radar)
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rangeProfile = radarProcessor.cal_range_fft(chirpLevelData)
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# dopplerProfile = radarProcessor.cal_doppler_fft(rangeProfile)
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aoaProfile = radarProcessor.cal_angle_fft(rangeProfile)
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range_angle = get_range_angle(aoaProfile)
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cart_cord = polar_to_cart(range_angle, limit = limit)
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# print(cart_cord_man.shape)
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# return [cart_cord]
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return cart_cord
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def main():
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radarProcessor = RadarProcessor()
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# with open('/mnt/intA-ssdr1-4tb/satyam53/mmfn/data1/pro_train_fnss_0702/0.pkl', 'rb') as f:
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# pkl_data = pkl.load(f)
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# print(type(pkl_data))
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# print(pkl_data.keys())
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# sim_radar = pkl_data['sim_radar']
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# # sim_radar = pkl_data['vectormaps']
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# print(type(sim_radar[0]))
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# print(len(sim_radar))
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# print(sim_radar[0])
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# radar_man = sim_radar[0]
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# # radar_man = np.load(f'{path_sim_man}/{files_sim_manual[i]}')
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# chirpLevelData = reformat_adc_shenron(radar_man)
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# rangeProfile = radarProcessor.cal_range_fft(chirpLevelData)
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# # dopplerProfile = radarProcessor.cal_doppler_fft(rangeProfile)
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# aoaProfile = radarProcessor.cal_angle_fft(rangeProfile)
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# range_angle = get_range_angle(aoaProfile)
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# cart_cord_man = polar_to_cart(range_angle, limit = 50)
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# print(cart_cord_man.shape)
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# fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(15,5))
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# # ax1.imshow(cart_cord, aspect='auto')
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# # ax1.set_title('radar data simulated using shenron')
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# ax2.imshow(cart_cord_man, aspect='auto')
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# ax2.set_title('radar data simulated manually')
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# # plt.show()
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# # ax3.scatter(lidar[:, 0], lidar[:, 1], s = 0.1, c = lidar[:, 5])
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# # ax3.set_title('lidar data')
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# plt.tight_layout()
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# plt.show()
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# # plt.savefig(f'{save_path}/{i}.png')
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# plt.close()
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if __name__=="__main__":
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main()
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