def whenSelected(): if getSelection(): if pcol.get() or p3d.get(): if (checkValidInt(sf, 0) and checkValidInt(ws, 1) and checkValidInt(ch, 2) and checkBandPass(hi, lo)): for file in files: freqs, time, data = stft.main(params, file) # print(data.shape) if pcol.get(): plotting.plotPColor(data, file, params[2], freqs, time) if p3d.get(): plotting.plot3D(data, file, params[2], freqs, time) else: mbox.showerror("No graph type selected", errors[-1])
# if(constants.URL.__contains__('sonar')) : # best_fitness = output(best_fitness) # print("BEST FITNESS ::",output(best_fitness)) print("BEST FITNESS ::", (best_fitness)) print("labelled :: \n", labelled) ############################## #### FMEASURE if (constants.URL.__contains__('ionosphere') or constants.URL.__contains__('iris') or constants.URL.__contains__('wine') or constants.URL.__contains__('parkinsons') or constants.URL.__contains__('sonar') or constants.URL.__contains__('segmentation') or constants.URL.__contains__('glass')): # classdata = pd.read_csv(constants.cURL) classdata = np.array(y_dataset_glob) #print(classdata) fmeasure(labelled, classdata) ############################## if constants.URL.__contains__('random2'): plot2D(got_back[best_index], pop_distri) if constants.URL.__contains__('random3'): plot3D(got_back[best_index], pop_distri)
bkgTauMin, bkgAMin, sortedTimes, meanSigma) #Calculating pdf function with background area2 = trapz(pdf2, sortedTimes) #Calculating area of new pdf using trapezium rule #Calculating errors TauAErrors = minim.bkgError(bkgTaus, bkgAs, bkgTauMin, bkgAMin, bkgNllMin, funcs.bkgNll) meanTauError = np.mean([TauAErrors[0], TauAErrors[1]]) meanAError = np.mean([TauAErrors[2], TauAErrors[3]]) ### CREATING AND DISPLAYING PLOTS ### pt.plotContour(bkgTaus, bkgAs, funcs.bkgNll, levels=np.arange(bkgNllMin + 0.5, bkgNllMin + 0.5 + 100, 10)) pt.plot3D(bkgTaus, bkgAs, funcs.bkgNll) intersect, m, c = pt.plotErrorsVSReadings(sizes, sizeErrors) pt.plotNLL(taus, likelihoods, tauMin, nllMin) pt.plotHist(times, bins=100, sortedTimes=sortedTimes, pdfs=[pdf, pdf2]) ### PRINTING RESULTS ### print "Area under initial PDF (without background):", area print "Area under refined PDF (with background):", area2 print "NLL at Minimum 1D:", nllMin print "Tau at Minimum 1D:", tauMin, "picoseconds" print "Positive Tau Error 1D:", posError, "picoseconds" print "Negative Tau Error 1D:", negError, "picoseconds" print "Mean Tau Error 1D:", meanError, "picoseconds" print "Last Parabolic Estimate Error of Tau:", parabError, "picoseconds" print "Intersect of extrapolation of gradient", m, "and intercept", c, "gives approximately", int( 10**intersect[0]), "readings required for accuracy of 0.001ps"
def test_plot3d(self): data = dict([(i, dict([(j, random.random()) for j in range(10)])) for i in range(5)]) plot3D(data) plt.show()
labelled = fitness.get_pop_distri_label(file, centroid, constants.K) print("labelled :: \n", labelled) ############################## #### FMEASURE if (constants.URL.__contains__('ionosphere') or constants.URL.__contains__('iris') or constants.URL.__contains__('wine') or constants.URL.__contains__('parkinsons') or constants.URL.__contains__('sonar') or constants.URL.__contains__('segmentation') or constants.URL.__contains__('glass')): # classdata = pd.read_csv(constants.cURL) classdata = np.array(y_dataset) #print(classdata) fmeasure(labelled, classdata) ############################## if constants.URL.__contains__('random2'): plot2D(centroid, pop_distri) if constants.URL.__contains__('random3'): plot3D(centroid, pop_distri) #data = np.array(data) #print(dist(centroids[0],data[0]),dist(centroids[1],data[0]),dist(centroids[2],data[0])) #print(dist(centroids[0],data[55]),dist(centroids[1],data[55]),dist(centroids[2],data[55])) #print(dist(centroids[0],data[127]),dist(centroids[1],data[127]),dist(centroids[2],data[127]))
def run(): global pts3 global pts4 if simulation and view: plot.plot3D(data3D, 'Original 3D Data') if view: plot.plot2D(pts1_raw, name='First Statics (Noise not shown)') plot.plot2D(pts2_raw, name='Second Statics (Noise not shown)') # FUNDAMENTAL MATRIX F = getFundamentalMatrix(pts1, pts2) # ESSENTIAL MATRIX (HZ 9.12) E, w, u, vt = getEssentialMatrix(F, K1, K2) # PROJECTION/CAMERA MATRICES from E (HZ 9.6.2) P1, P2 = getNormalisedPMatrices(u, vt) P1_mat = np.mat(P1) P2_mat = np.mat(P2) # FULL PROJECTION MATRICES (with K) P = K[Rt] KP1 = K1 * P1_mat KP2 = K2 * P2_mat print "\n> KP1:\n", KP1 print "\n> KP2:\n", KP2 # SYNCHRONISATION + CORRECTION if rec_data and simulation is False: print "---Synchronisation---" pts3, pts4 = synchroniseGeometric(pts3, pts4, F) pts3 = pts3.reshape((1, -1, 2)) pts4 = pts4.reshape((1, -1, 2)) newPoints3, newPoints4 = cv2.correctMatches(F, pts3, pts4) pts3 = newPoints3.reshape((-1, 2)) pts4 = newPoints4.reshape((-1, 2)) elif simulation: print "> Simulation: Use whole point set for reconstruction" pts3 = pts1 pts4 = pts2 # Triangulate the trajectory p3d = triangulateCV(KP1, KP2, pts3, pts4) # Triangulate goalposts if simulation is False: goalPosts = triangulateCV(KP1, KP2, postPts1, postPts2) # SCALING AND PLOTTING if simulation: if view: plot.plot3D(p3d, 'Simulation Reconstruction') reprojectionError(K1, P1_mat, K2, P2_mat, pts3, pts4, p3d) p3d = simScale(p3d) if view: plot.plot3D(p3d, 'Scaled Simulation Reconstruction') else: # add the post point data into the reconstruction for context if len(postPts1) == 4: print "> Concatenate goal posts to trajectory" pts3_gp = np.concatenate((postPts1, pts3), axis=0) pts4_gp = np.concatenate((postPts2, pts4), axis=0) p3d_gp = np.concatenate((goalPosts, p3d), axis=0) scale = getScale(goalPosts) scaled_gp_only = [[a * scale for a in inner] for inner in goalPosts] scaled_gp = [[a * scale for a in inner] for inner in p3d_gp] scaled = [[a * scale for a in inner] for inner in p3d] if view: plot.plot3D(scaled_gp, 'Scaled 3D Reconstruction') reprojectionError(K1, P1_mat, K2, P2_mat, pts3_gp, pts4_gp, p3d_gp) getMetrics(scaled, scaled_gp_only) scaled_gp = transform(scaled_gp) if view: plot.plot3D(scaled_gp, 'Final (Reorientated) 3D Reconstruction') if ground_truth_provided: reconstructionError(data3D, scaled_gp) # write X Y Z to file outfile = open('sessions/' + clip + '/3d_out.txt', 'w') for p in scaled_gp: p0 = round(p[0], 2) p1 = round(p[1], 2) p2 = round(p[2], 2) string = str(p0) + ' ' + str(p1) + ' ' + str(p2) outfile.write(string + '\n') outfile.close()
def main(): path = sys.argv[1] folder = os.path.dirname(path) data_3d = getData(path) data_3d = np.array(data_3d, dtype='float32') # Artificial cameras - both have the same intrinsics K = tools.CalibArray(1000, 640, -360) dist = (0, 0, 0, 0) # Some numbers we use a lot rt2 = math.sqrt(2) rt2on2 = rt2 / 2 # Lots of rotation matrices... # naming: z45cc = 45deg counter-clockwise rotation about z z45cc = np.mat([[1 / rt2, -1 / rt2, 0], [1 / rt2, 1 / rt2, 0], [0, 0, 1]], dtype='float32') nothing = np.mat([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype='float32') z90cc = np.mat([[0, -1, 0], [1, 0, 0], [0, 0, 1]], dtype='float32') z90cw = np.mat([[0, 1, 0], [-1, 0, 0], [0, 0, 1]], dtype='float32') z180 = np.mat([[-1, 0, 0], [0, -1, 0], [0, 0, 1]], dtype='float32') x180 = np.mat([[1, 0, 0], [0, -1, 0], [0, 0, -1]], dtype='float32') y180 = np.mat([[-1, 0, 0], [0, 1, 0], [0, 0, -1]], dtype='float32') x90cw = np.mat([[1, 0, 0], [0, 0, 1], [0, -1, 0]], dtype='float32') x90cc = np.mat([[1, 0, 0], [0, 0, -1], [0, 1, 0]], dtype='float32') y90cc = np.mat([[0, 0, 1], [0, 1, 0], [-1, 0, 0]], dtype='float32') y90cw = np.mat([[0, 0, -1], [0, 1, 0], [1, 0, 0]], dtype='float32') z90cc_vec, jacobian = cv2.Rodrigues(z90cc) z90cc_vec, jacobian = cv2.Rodrigues(z90cc) y90cc_vec, jacobian = cv2.Rodrigues(y90cc) x90cc_vec, jacobian = cv2.Rodrigues(x90cc) y45cc = np.mat([[rt2on2, 0, rt2on2], [0, 1, 0], [-rt2on2, 0, rt2on2]], dtype='float32') y45cw = np.mat([[rt2on2, 0, -rt2on2], [0, 1, 0], [rt2on2, 0, rt2on2]], dtype='float32') # cameras have different poses tvec1 = (-5, 1, 14) tvec2 = (5, 1, 13) # Project the model into each camera img_pts1 = project(data_3d, K, y45cc, tvec1) img_pts2 = project(data_3d, K, y45cw, tvec2) img_pts1 = np.reshape(img_pts1, (len(img_pts1), 2, 1)) img_pts2 = np.reshape(img_pts2, (len(img_pts2), 2, 1)) # Plot the model and images plot.plot3D(data_3d) plotImagePoints(img_pts1) plotImagePoints(img_pts2) # Add noise to the image data if desired try: noise = sys.argv[2] img_pts1 = addNoise(float(noise), img_pts1) img_pts2 = addNoise(float(noise), img_pts2) except indexError: continue writeData(folder, img_pts1, img_pts2)
def test_plot3d(self): data = dict([(i, dict([(j, random.random())for j in range(10)])) for i in range(5)]) plot3D(data) plt.show()