def generate_spectrum_from_RDC(filename, numFrames=500, numADCSamples=128, numTxAntennas=3, numRxAntennas=4, numLoopsPerFrame=128, numAngleBins=64, chirpPeriod=0.06, logGabor=False, accumulate=True, save_full=False): numChirpsPerFrame = numTxAntennas * numLoopsPerFrame # ============================================================================= # numADCSamples = number of range bins # numLoopsPerFrame = number of doppler bins # ============================================================================= range_resolution, bandwidth = dsp.range_resolution(numADCSamples) doppler_resolution = dsp.doppler_resolution(bandwidth) if filename[-4:] != '.bin': filename += '.bin' adc_data = np.fromfile(filename, dtype=np.int16) adc_data = adc_data.reshape(numFrames, -1) adc_data = np.apply_along_axis(DCA1000.organize, 1, adc_data, num_chirps=numChirpsPerFrame, num_rx=numRxAntennas, num_samples=numADCSamples) print("Data Loaded!") dataCube = adc_data micro_doppler_data = np.zeros((numFrames, numLoopsPerFrame, numADCSamples), dtype=np.float64) theta_data = np.zeros((numFrames, numLoopsPerFrame, numTxAntennas * numRxAntennas, numADCSamples), dtype=np.complex) for i, frame in enumerate(dataCube): # (2) Range Processing from mmwave.dsp.utils import Window radar_cube = dsp.range_processing(frame, window_type_1d=Window.BLACKMAN) assert radar_cube.shape == ( numChirpsPerFrame, numRxAntennas, numADCSamples), "[ERROR] Radar cube is not the correct shape!" # (3) Doppler Processing det_matrix, theta_data[i] = dsp.doppler_processing( radar_cube, num_tx_antennas=3, clutter_removal_enabled=True, window_type_2d=Window.HAMMING) # --- Shifts & Store det_matrix_vis = np.fft.fftshift(det_matrix, axes=1) micro_doppler_data[i, :, :] = det_matrix_vis # Data should now be ready. Needs to be in micro_doppler_data, a 3D-numpy array with shape [numDoppler, numRanges, numFrames] # LOG GABOR if logGabor: if accumulate: image = micro_doppler_data.sum(axis=1).T else: image = micro_doppler_data.T from LogGabor import LogGabor import holoviews as hv lg = LogGabor("default_param.py") lg.set_size(image) lg.pe.datapath = 'database/' image = lg.normalize(image, center=True) # display input image # hv.Image(image) # display log gabor'd image image = lg.whitening(image) * lg.mask hv.Image(image) uDoppler = image elif accumulate: uDoppler = micro_doppler_data.sum(axis=1).T else: uDoppler = micro_doppler_data.T if save_full: return range_resolution, doppler_resolution, uDoppler, theta_data else: return range_resolution, doppler_resolution, uDoppler
import holoviews as hv import os fig_width = 12 figsize=(fig_width, .618*fig_width) lg = LogGabor("default_param.py") lg.set_size(image) lg.pe.datapath = 'database/' image = lg.normalize(image, center=True) # display input image # hv.Image(image) # display log gabor'd image image = lg.whitening(image)*lg.mask hv.Image(image) uDoppler = image elif accumulate: uDoppler = micro_doppler_data.sum(axis=1).T else: uDoppler = micro_doppler_data[:,80,:].T plt.figure(1) plt.title("micro-Doppler Accumulated") plt.ylabel("Velocity (m/s)") plt.xlabel("Time (seconds)") plt.imshow(uDoppler,origin='lower',extent=(0,chirpPeriod*micro_doppler_data[:,120,:].shape[0],-micro_doppler_data[:,120,:].shape[1]*doppler_resolution/2,micro_doppler_data[:,120,:].shape[1]*doppler_resolution/2)) #