def inc(fn,title,do_plot=0,eyl=1): l = AttrDict(load(fn+"pred.npz")) Ym, Ypred, expa = l.Ym, l.Ypred, l.expa print title print 'RMSEP',RMSEP(Ym, Ypred), 'BIAS',BIAS(Ym, Ypred), 'SE',SE(Ym, Ypred) #print len(Ym),len(Ypred),len(expa) if not do_plot: return persons = Persons(expa) ax = fig_pred.subplot(do_plot) Ymm = [0,max(Ym)] ax.plot(Ymm,Ymm,'g-') persons.plot( ax, Ym, Ypred ) ax.set_title(title) ax.set_xlabel('IS, measured, mg/L') if eyl: ax.set_ylabel('IS, predicted, mg/L') ax = fig_ba.subplot(do_plot) mean_mp = (Ym+Ypred)/2. diff_mp = Ym-Ypred mm_mean_mp = [min(mean_mp),max(mean_mp)] md,s2 = mean(diff_mp), 2*diff_mp.std() #print mm_mean_mp,md,s2 #ax.clabel(contour,'AAA')#ax.set_xticklabels(['EE'],minor=True) def hline(l,txt): ax.axhline(y=l,ls=':',c='g') if txt: ax.annotate(txt,(mm_mean_mp[0],l),color='g') hline(md-s2,'Mean-2*SD') hline(md,'') hline(md+s2,'Mean+2*SD') persons.plot( ax, mean_mp, diff_mp ) ax.set_title(title+', Bland-Altman') ax.set_xlabel('IS, (measured+predicted)/2, mg/L') if eyl: ax.set_ylabel('IS, (measured-predicted), mg/L')
def __init__(self, length, breadth): """Make an enemy.""" Persons.__init__(self, length, breadth) self.nextX = None self.nextY = None self.direction = None self.matrix = [['{', '*', '*', '}'], [' ', ']', '[', ' ']]
def __init__(self): rospy.loginfo("Connecting to person detection service ...") self.pd = PersonDetection(service_name='what_am_i_looking_at') rospy.loginfo("Connecting to pose estimation service ...") self.pe = PoseEstimation() rospy.loginfo("Connecting to camera ...") self.camera = CameraStreamer(image_topic="/usb_cam/image_raw", scale=1.0) self.persons = Persons()
def build_model(): print("Initialisation...") face_recognizator = FaceRecognizator() face_recognizator.init() persons = Persons(face_recognizator) print("\n\nDémarrage...") face_detector = FaceDetector(persons) return face_detector
def build(self): self.theme_cls.primary_palette = "Gray" builder = Builder.load_file('main.kv') self.persons = Persons() #při startu vytvoří testovací data, kvůli testování aplikace je momentálně zaneprázdněna znaménky #, # ať se mi do toho nemotá ####self.persons.create_test_data()### builder.ids.navigation.ids.tab_manager.screens[0].add_widget( self.persons) return builder
X4fit, Y4fit, expa4fit = X[a4fit ,:], Y[a4fit ,:], expa[a4fit ] X4test, Y4test, expa4test = X[a4test,:], Y[a4test,:], expa[a4test] pls = PLSRegression(n_components=n_components,algorithm='svd',scale=False) pls.fit(X=X4fit,Y=Y4fit) print "predict..." Y_pred = pls.predict(X4test.copy()) dis4test = Y4test[:,0] dis_pred = Y_pred[:,0] dis_max = max(disa) #dis_pred = where(dis_pred<dis_max+1,where(dis_pred<-1,-1,dis_pred),dis_max+1) persons = Persons(expa4test) #print ia4test, ia4fit, logical_not(a4fit & good_std) #print expa4test.shape, dis4test.shape, dis_pred.shape, Y.shape, Y4test.shape, Y_pred.shape plt.plot([0,dis_max],[0,dis_max],'g-') persons.plot(plt,dis4test,dis_pred) plt.savefig(out_pre+"pred.png") plt.cla() print "fit..." pls = PLSRegression(n_components=n_components,algorithm='svd',scale=False) pls.fit(X=X,Y=Y) print "save..."
def __init__(self, length, breadth): Persons.__init__(self, length, breadth) self.matrix = [['[', '^', '^', ']'], [' ', ']', '[', ' ']]
class WaveDetector: def __init__(self): rospy.loginfo("Connecting to person detection service ...") self.pd = PersonDetection(service_name='what_am_i_looking_at') rospy.loginfo("Connecting to pose estimation service ...") self.pe = PoseEstimation() rospy.loginfo("Connecting to camera ...") self.camera = CameraStreamer(image_topic="/usb_cam/image_raw", scale=1.0) self.persons = Persons() def process(self): cv2.namedWindow("image", cv2.WINDOW_NORMAL) while True: start_time = time.time() ret, image = self.camera.read() if not ret: rospy.logwarn("Frame skipped !") continue bodies = self.filter_human_bodies(self.pd.detect(image), self.pe.detect(image)) for body in bodies: person = body["person"] if person[1] < 0.6: continue points = body["points"] if '5' not in points: continue points = limit_points(points) selected_person, exists = self.persons.find_nearest( points['1']) cv2.circle(image, (selected_person.x, selected_person.y), 4, BLUE_COLOR, -1) selected_person.add_point(points) if not exists: self.persons.add(selected_person) else: self.persons.update(selected_person.person_id, points['1']) visualize_points(image, points) x1, y1, x2, y2 = person[0] text = "{:.2f}, {}, {}".format( person[1], selected_person.person_id, selected_person.get_wave_label()) text_size = cv2.getTextSize(text, cv2.FONT_HERSHEY_PLAIN, 1.0, 1) cv2.rectangle( image, (x1 - 1, y1), (x1 + text_size[0][0], y1 - text_size[0][1] - 15), RED_COLOR, -1) cv2.putText(image, text, (x1, y1 - 10), cv2.FONT_HERSHEY_PLAIN, 1.0, WHITE_COLOR) cv2.rectangle(image, (x1, y1), (x2, y2), RED_COLOR, 2) process_time = time.time() - start_time cv2.putText(image, "Process time : {:.2f}".format(process_time), (10, 20), cv2.FONT_HERSHEY_PLAIN, 1.0, RED_COLOR) cv2.imshow("image", image) key = cv2.waitKey(1) & 0xFF if key == ord('q'): rospy.logwarn("The q key has been pressed !") break @staticmethod def filter_human_bodies(persons_list, humans_list): output = [] for person in persons_list: x1, y1, x2, y2 = person[0] for body_parts in humans_list: for part in body_parts: x, y = body_parts[part] if x1 < x < x2 and y1 < y < y2: output.append({"person": person, "points": body_parts}) break return output
n_components = 14 from numpy import load, array, where from numpy.linalg import norm from linre_tools import AttrDict from persons import Persons import matplotlib.pyplot as plt l = AttrDict(load('linre_big'+'2'+'.npz')) mds = AttrDict(load("out23/pred.npz")) X, Y = mds.X, mds.Y Ypred4n = mds.Ypred4n_components Ym, Ypred = Y[:,0], Ypred4n[:,n_components-1] eee = l.ema==l.exa Xeee = X[:,eee] #efm = Xeee.mean(axis=1) #efm = [norm(s) for s in Xeee] #efm = [len(where(s)[0]) for s in X_err] #print Ym.shape, efm.shape persons = Persons(mds.expa) persons.plot( plt, efm, Ypred - Ym) plt.show()
var_count = X.shape[1] ma = empty((var_count,)) disa_pred4var = empty_like(X) for varn in range(var_count): Xc = X[:,varn] loo = KFold( n=len(disa), k=group_count, indices=False ) for fit, test in loo: slope, intercept, r_value, p_value, stderr = linregress(Xc[fit],disa[fit]) disa_pred4var[test,varn] = Xc[test] * slope + intercept ma[varn] = RMSEP(disa,disa_pred4var[:,varn]) ia = argmin(ma) print ma[ia] l = AttrDict(load('linre_big2.npz')) print l.exa[ia], l.ema[ia] persons = Persons(expa) Ymm = [0,max(disa)] plt.plot(Ymm,Ymm,'g-') persons.plot(plt,disa,disa_pred4var[:,ia]) plt.title('Univariate, K-Fold, '+str(len(disa))+' samples') plt.xlabel('IS, measured, mg/L') plt.ylabel('IS, predicted, mg/L') plt.savefig("out26/pred.png") Ym, Ypred = disa, disa_pred4var[:,ia] print 'RMSEP', RMSEP(Ym, Ypred) print 'BIAS', BIAS(Ym, Ypred) print 'SE', SE(Ym, Ypred) savez('out26/pred.npz',Ym=Ym,Ypred=Ypred,expa=expa)
import os import os.path import re from persons import Persons from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker #engine = create_engine('postgresql://*****:*****@localhost:5432/enron_emails', echo = True) # create database session Session = sessionmaker() Session.configure(bind=engine) session = Session() for directory in os.listdir( 'C:\\Users\\MGOW\\Documents\\PythonProject\\maildir'): name_row = Persons(name=directory) session.add(name_row) session.commit()
X_orig = flum.T X_err, X_orig = find_peaks(X_orig,exa) ## exclude outliers PC = PCA(n_components=2).fit_transform(X_orig.copy()) #mean inside PC1 = PC[:,0] good_std = PC1 < PC1.std() a4fit = arange(len(X_orig)) >= samples_in_testing_set ia4fit, = where( a4fit & good_std ) X4fit = X_orig[ia4fit,:] disa4fit = disa[ia4fit] pca = PCA(n_components=n_components) PC = pca.fit_transform(X4fit.copy()) dis_mean = disa4fit.mean() #print PC.shape,(disa4fit-dis_mean).shape (a,residues,rank,s) = lstsq(PC,disa4fit-dis_mean) PC = pca.transform(X_orig.copy()) mdis = PC.dot(a[:,None])[:,0] + dis_mean from persons import Persons persons = Persons(expa) import matplotlib.pyplot as plt plt.figure() dis_max = max(disa) plt.plot([0,dis_max],[0,dis_max],'g-') persons.plot(plt,disa,where(mdis<dis_max+1,where(mdis<-1,-1,mdis),dis_max+1)) plt.show()
parser.add_argument("--enactments", help="argument for sync enactments", action='store_const', const='True', default=False) parser.add_argument("--threads", help="argument for amount of threads for sync enactments", type=int, default=4) args = parser.parse_args() chrome_options = Options() #chrome_options.add_argument("--headless") chrome_options.add_argument("--no-sandbox") driver = webdriver.Chrome(chrome_options=chrome_options) deputats = None date_from = datetime.datetime.strptime(args.fromd, "%d.%m.%Y") date_to = datetime.datetime.strptime(args.tod, "%d.%m.%Y") if args.deputats: print('--deputats = True: sync deputats') persons = Persons() deputats = persons.sync(driver) persons.save() else: print('--deputats = False: load deputats') persons = Persons() deputats = persons.load() if args.enactments: print('--enactment not None: sync enactments') enactment = Enactment(driver) enactment.sync(date_from, date_to) enactment.save() threads_amount = args.threads list_deps = devide_array(deputats, threads_amount)