def show_data(list_dat, num=4):
from pylab import plt
for dat in np.random.choice(list_dat, num):
print dat
im=cv2.imread(dat['filepath'])[:,:,::-1]
plt.figure(1)
plt.imshow(im)
for bbox in dat['bboxes']:
plt.gca().add_patch(plt.Rectangle((bbox['x1'], bbox['y1']),
bbox['x2'] - bbox['x1'],
bbox['y2'] - bbox['y1'], fill=False,
edgecolor='red', linewidth=1) )
for idx, bbox in enumerate(dat['bboxes']):
ann = np.array(Image.open(bbox['ann_path']))
if len(ann.shape)==3: ann = ann[:,:,0] # Make sure ann is a two dimensional np array.
plt.figure(11+idx)
plt.imshow(ann)
plt.show()
def show_data(list_dat, num=4):
from pylab import plt
for dat in np.random.choice(list_dat, num):
print dat
im = cv2.imread(dat['filepath'])[:, :, ::-1]
plt.figure(1)
plt.imshow(im)
for bbox in dat['bboxes']:
plt.gca().add_patch(
plt.Rectangle((bbox['x1'], bbox['y1']),
bbox['x2'] - bbox['x1'],
bbox['y2'] - bbox['y1'],
fill=False,
edgecolor='red',
linewidth=1))
for idx, bbox in enumerate(dat['bboxes']):
ann = cv2.imread(bbox['ann_path'], cv2.IMREAD_GRAYSCALE)
plt.figure(11 + idx)
plt.imshow(ann)
plt.show()
# ..............................................................
# ............ Subject exploration .............................
# ..............................................................
rn = [ ( rd.random() * 2 - 1, rd.random() * 2 - 1 ) for _ in range( 5000 )]
rn = np.array( rn )
distance = np.sqrt( ( rn**2 ).sum( axis = 1 ) )
frac = ( distance <= 1.0 ).sum( ) / len( distance )
pi_mcs = frac * 4
#....... PLOT .............................
fig = plt.figure( figsize = ( 8, 8 ) )
ax = fig.add_subplot( 1, 1, 1 )
circ = plt.Circle( (0,0), radius = 1, edgecolor = 'g', lw = 2.0, facecolor = 'None' )
box = plt.Rectangle( (-1,-1), 2,2, edgecolor = 'b', alpha = 0.3 )
ax.add_patch( circ )
ax.add_patch( box )
plt.plot( rn[ :, 0], rn[:, 1 ], 'r.' )
plt.xlim( -1.1, 1.1)
plt.ylim( -1.1, 1.1)
# ..............................................................
# ............ MonteCarlo function: NumPy.......................
# ..............................................................
def mcs_pi_np( n ):
rn = [ ( rd.random() * 2 - 1, rd.random() * 2 - 1 ) for _ in range( n )]
rn = np.array( rn )
distance = np.sqrt( ( rn**2 ).sum( axis = 1 ) )
frac = ( distance <= 1.0 ).sum( ) / n
ttxt = 'case {} and test function {}: benchmarking {}'.format(
nc, nc, eat)
ttxt += '\nbest error: mean {} and std {} after {} evaluations'.format(
mean(y[:, -1]), std(y[:, -1]), mean(neval[:, -1]))
date = give_datestring()
plt.figure()
for i, xval in enumerate(x[0]):
if i == 0:
rxmin, rxmax = xval - 0.004, xval + 0.004
else:
rxmin, rxmax = xval - 0.03, xval + 0.03
rymin, rymax = np.min(y[:, i]), np.max(y[:, i])
rect = plt.Rectangle((rxmin, rymin),
rxmax - rxmin,
rymax - rymin,
facecolor='grey',
alpha=0.4)
plt.gca().add_patch(rect)
for i, sc in enumerate(loaded):
plt.plot(x[i], y[i])
if nc != 8: plt.semilogy()
#if nc in yldict: plt.ylim(yldict[nc])
plt.xlabel('FES / maxFES')
plt.ylabel(r'error = $f_i(x)-f_i(x^*)$')
plt.suptitle(ttxt, x=0.5, y=0.98, ha='center', va='top', fontsize=10)
plt.suptitle(date, x=0.97, y=0.02, ha='right', va='bottom', fontsize=8)
plt.savefig(
join(
plotloc, 'allruns_c' + str(nc).zfill(3) + '_' + eat + '_' +
df[:-1] + '.png'))