ts.start = frame * NUM ts.stop = (frame + 1) * NUM result = zeros((4, rg.shape[0], rg.shape[1])) for i in range(4): be.n = i result[i] = be.synthetic(freq, 3) maxind = argmax(result.max((1, 2))) # WARUM IMMER MAXINDEX = 3 ??? # print('Result Beamforming: Maxindex = ', maxind) Lm = L_p(result[maxind]).reshape(rg.shape).flatten() max_idx = argmax( Lm.flatten()) # position in grid with max source strength max_cartcoord = rg.gpos[:, max_idx] max_idx = argmax( Lm.flatten()) # position in grid with max source strength max_value = amax(Lm.flatten()) temp_azi = arctan2(sin(rg.phi[max_idx]), cos(rg.phi[max_idx])) temp_ele = pi / 2 - rg.theta[max_idx] max_polcoord = [rad2deg(temp_azi), rad2deg(temp_ele)] #### 3D-Plot ### # fig = plt.figure() # ax = fig.add_subplot(121, projection='3d') # ax.set_xlabel('x-Achse') # ax.set_ylabel('y-Achse')
# # hier muss man sich jetzt überlegen, wie man am Besten die sphärischen # Datenpunkte plottet. Scatter ist eine Variante x = rg.gpos[0] y = rg.gpos[1] z = rg.gpos[2] summed_azi_x = 0.0 summed_azi_y = 0.0 summed_ele = 0.0 summed_max_v = 0.0 print('[Azimuth, Elevation], max_value') for frame in range(0, FRAMES): Lm = L_p(next(gen)).reshape( rg.shape) # get next block from generator pipeline max_idx = argmax(Lm.flatten()) # position in grid with max source strength max_cartcoord = rg.gpos[:, max_idx] fig = plt.figure(frame) ax = fig.add_subplot(121, projection='3d') ax.set_xlabel('x-Achse') ax.set_ylabel('y-Achse') ax.set_zlabel('z-Achse') ax.set_xlim(-1, 1) ax.set_ylim(-1, 1) ax.set_zlim(-1, 1) cmhot = plt.get_cmap("hot_r") if frame == 0: cax = ax.scatter(x, y, -z, s=50, c=Lm, cmap=cmhot, marker='.')
def fbeamextraction(mg, rg, ts, be, firstframe, lastframe, csvdata, _name, fbeamplot, fbeamplot_2ndSrc, algoplot, algoplot_2ndSrc): ##################### DeepLearning-Matrix (Framewise) ######################### ################ Elevation Azimuth Frequenzbänder ################### DL_Matrix = zeros((NPOINTS_ELE, NPOINTS_AZI, len(FREQBANDS)), dtype="float32") ## ################################################################################# for frame in range(firstframe, lastframe): if frame in csvdata[:, 0]: fr_index = where(csvdata[:, 0] == frame)[0][0] if DETAILEDINFO_LVL >= 1: print(' FRAME: ', frame - firstframe, ' (', frame, ')') ts.start = frame * NUM ts.stop = (frame + 1) * NUM # Zur Berechnung des Mittelwerts der Richtungswinkel if not (TRAINING): azis = list(zeros(len(FREQBANDS))) azic = list(zeros(len(FREQBANDS))) eles = list(zeros(len(FREQBANDS))) elec = list(zeros(len(FREQBANDS))) azis_2nd = list(zeros(len(FREQBANDS))) azic_2nd = list(zeros(len(FREQBANDS))) eles_2nd = list(zeros(len(FREQBANDS))) elec_2nd = list(zeros(len(FREQBANDS))) #%% 1. Quelle ############################## # Zur Angle-Prediction ohne händischem Algo glob_maxval = 0 glob_maxidx = 0 for freq_index, freq in enumerate(FREQBANDS): be.n = -1 #Eigenwerte sind der Größe nach sortiert! -> größter Eigenwert (default) Lm = L_p(be.synthetic(freq, 3)).reshape(rg.shape).flatten() DL_Matrix[:, :, freq_index] = Lm.reshape(rg.shape).T if PLOTBEAMMAPS or DETAILEDINFO_LVL >= 3 or not (TRAINING): max_idx = argmax(Lm.flatten( )) # position in grid with max source strength max_value = amax(Lm.flatten()) if glob_maxval < max_value: # TODO: 'and freq != 500:' !?! glob_maxidx = max_idx glob_maxval = max_value # min_value = amin(Lm.flatten()) max_cartcoord = rg.gpos[:, max_idx] temp_azi = arctan2(sin(rg.phi[max_idx]), cos(rg.phi[max_idx])) temp_ele = pi / 2 - rg.theta[max_idx] max_polcoord = [rad2deg(temp_azi), rad2deg(temp_ele)] azis[freq_index] = sin(temp_azi) azic[freq_index] = cos(temp_azi) eles[freq_index] = sin(temp_ele) elec[freq_index] = cos(temp_ele) if PLOTBEAMMAPS: #### 3D-Plot ### fig = plt.figure() ax = fig.add_subplot(121, projection='3d') ax.set_xlabel('x-Achse') ax.set_ylabel('y-Achse') ax.set_zlabel('z-Achse') ax.set_xlim(-1, 1) ax.set_ylim(-1, 1) ax.set_zlim(-1, 1) cmhot = plt.get_cmap("hot_r") if frame == firstframe: ax.scatter(rg.gpos[0], rg.gpos[1], -rg.gpos[2], s=50, c=Lm, cmap=cmhot, marker='.') # Mikros ax.scatter(mg.mpos[0], mg.mpos[1], mg.mpos[2], 'o') ax.scatter(max_cartcoord[0], max_cartcoord[1], max_cartcoord[2], s=60, c='blue') ### 3D-Plot Ende ### ### 2D-Map ### ax2 = fig.add_subplot(122) ax2.set_xticks(arange(-180, 270, step=90)) ax2.set_yticks(arange(-90, 135, step=45)) cax2 = ax2.imshow(Lm.reshape(rg.shape).T, cmap=cmhot, vmin=Lm.max() - 6, vmax=Lm.max(), extent=[-180, 180, -90, 90]) ax2.plot(max_polcoord[0], max_polcoord[1], 'bo') fig.set_figheight(4) fig.set_figwidth(8) fig.colorbar(cax2) fig.tight_layout(pad=3.0) fig.suptitle(_name + ' ' + str(frame) + ' ' + str(freq)) plt.show() ### 2D-Map Ende ### if DETAILEDINFO_LVL >= 3: print(' {:4d} [{:7.2f}, {:7.2f}] {}'.format( freq, round(max_polcoord[0], 2), round(max_polcoord[1], 2), round(max_value, 2))) #print(' ',freq, max_cartcoord, max_value) if DETAILEDINFO_LVL >= 2 and TRAINING: print(" .csv-Values: Class Azi Ele") print(" ", '{:4d}'.format(csvdata[fr_index, 1]), '{:4d}'.format(csvdata[fr_index, 2]), '{:4d}'.format(csvdata[fr_index, 3])) if TRAINING: feature_dict = { 'inputmap': _float_list_feature(DL_Matrix), 'class': _int64_feature(csvdata[fr_index, 1]), 'azi': _int64_feature(csvdata[fr_index, 2]), 'ele': _int64_feature(csvdata[fr_index, 3]), } if not (TRAINING): # Prediction nur übers globale Maximum azi_pred = arctan2(sin(rg.phi[glob_maxidx]), cos(rg.phi[glob_maxidx])) ele_pred = pi / 2 - rg.theta[glob_maxidx] fbeamplot.append([frame, rad2deg(azi_pred), rad2deg(ele_pred)]) # Calculation per Algorithmus (basierend auf Ergebnissen des fbeamformings) azi_algo, ele_algo = angle_calc_algo(azis, azic, eles, elec) algoplot.append([frame, rad2deg(azi_algo), rad2deg(ele_algo)]) feature_dict = {'inputmap': _float_list_feature(DL_Matrix)} yield feature_dict #%% 2. Quelle ############################## # Nur, wenn nicht bereits am Ende des Arrays (wenn bei letztem aktiven Frame nur 1 Quelle) if not (fr_index + 1 == len(csvdata)): if not (JUST_1SOURCE_FRAMES or TRAINING) and (frame == csvdata[fr_index + 1, 0]): glob_maxval = 0 glob_maxidx = 0 if DETAILEDINFO_LVL >= 1: print(' FRAME: ', frame - firstframe, ' (', frame, '), 2nd Src') for freq_index, freq in enumerate(FREQBANDS): be.n = -2 # zweitgrößter Eigenwert -> zweitstärkste Quelle Lm = L_p(be.synthetic(freq, 3)).reshape(rg.shape).flatten() DL_Matrix[:, :, freq_index] = Lm.reshape(rg.shape).T max_idx = argmax(Lm.flatten( )) # position in grid with max source strength max_value = amax(Lm.flatten()) if glob_maxval < max_value: # TODO: 'and freq != 500:' !?! glob_maxidx = max_idx glob_maxval = max_value max_cartcoord = rg.gpos[:, max_idx] temp_azi = arctan2(sin(rg.phi[max_idx]), cos(rg.phi[max_idx])) temp_ele = pi / 2 - rg.theta[max_idx] max_polcoord = [rad2deg(temp_azi), rad2deg(temp_ele)] azis_2nd[freq_index] = sin(temp_azi) azic_2nd[freq_index] = cos(temp_azi) eles_2nd[freq_index] = sin(temp_ele) elec_2nd[freq_index] = cos(temp_ele) if PLOTBEAMMAPS: #### 3D-Plot ### fig = plt.figure() ax = fig.add_subplot(121, projection='3d') ax.set_xlabel('x-Achse') ax.set_ylabel('y-Achse') ax.set_zlabel('z-Achse') ax.set_xlim(-1, 1) ax.set_ylim(-1, 1) ax.set_zlim(-1, 1) cmhot = plt.get_cmap("hot_r") if frame == firstframe: ax.scatter(rg.gpos[0], rg.gpos[1], -rg.gpos[2], s=50, c=Lm, cmap=cmhot, marker='.') # Mikros ax.scatter(mg.mpos[0], mg.mpos[1], mg.mpos[2], 'o') ax.scatter(max_cartcoord[0], max_cartcoord[1], max_cartcoord[2], s=60, c='blue') ### 3D-Plot Ende ### ### 2D-Map ### ax2 = fig.add_subplot(122) ax2.set_xticks(arange(-180, 270, step=90)) ax2.set_yticks(arange(-90, 135, step=45)) cax2 = ax2.imshow(Lm.reshape(rg.shape).T, cmap=cmhot, vmin=Lm.max() - 6, vmax=Lm.max(), extent=[-180, 180, -90, 90]) ax2.plot(max_polcoord[0], max_polcoord[1], 'bo') fig.set_figheight(4) fig.set_figwidth(8) fig.colorbar(cax2) fig.tight_layout(pad=3.0) fig.suptitle(_name + ' (Frame:' + str(frame) + ', Freq:' + str(freq) + ') 2nd Source') plt.show() ### 2D-Map Ende ### if DETAILEDINFO_LVL >= 3: print(' {:4d} [{:7.2f}, {:7.2f}] {} (2nd Src)'. format(freq, round(max_polcoord[0], 2), round(max_polcoord[1], 2), round(max_value, 2))) #print(' ',freq, max_cartcoord, max_value) # Prediction nur übers globale Maximum azi_pred_2nd = arctan2(sin(rg.phi[glob_maxidx]), cos(rg.phi[glob_maxidx])) ele_pred_2nd = pi / 2 - rg.theta[glob_maxidx] fbeamplot_2ndSrc.append( [frame, rad2deg(azi_pred_2nd), rad2deg(ele_pred_2nd)]) # Calculation per Algorithmus (basierend auf Ergebnissen des fbeamformings) azi_algo_2nd, ele_algo_2nd = angle_calc_algo( azis_2nd, azic_2nd, eles_2nd, elec_2nd) algoplot_2ndSrc.append( [frame, rad2deg(azi_algo_2nd), rad2deg(ele_algo_2nd)]) feature_dict = {'inputmap': _float_list_feature(DL_Matrix)} yield feature_dict