def test_pf(tname):
##-----------------------------
PF = V3 # default
if tname == '1': PF = V1
if tname == '2': PF = V2
if tname == '3': PF = V3
if tname == '4': PF = V4
SKIP = 0
EVTMAX = 5 + SKIP
DO_PLOT_IMAGE = True
DO_PLOT_CONNECED_PIXELS = True if PF in (V2, V3, V4) else False
DO_PLOT_LOCAL_MAXIMUMS = True if PF in (V3, V4) else False
DO_PLOT_LOCAL_MINIMUMS = True if PF == V3 else False
shape = (500, 500)
mask = np.ones(shape, dtype=np.uint16)
# Pixel image indexes
#arr3d = np.array((1,shape[0],shape[1]))
INDS = np.indices((1, shape[0], shape[1]), dtype=np.int64)
imRow, imCol = INDS[1, :], INDS[2, :]
#iX = np.array(det.indexes_x(evt), dtype=np.int64) #- xoffset
#iY = np.array(det.indexes_y(evt), dtype=np.int64) #- yoffset
fs = (8, 7) # (11,10)
##-----------------------------
fig5, axim5, axcb5, imsh5 = gg.fig_axim_axcb_imsh(
figsize=fs) if DO_PLOT_LOCAL_MINIMUMS else (None, None, None, None)
fig4, axim4, axcb4, imsh4 = gg.fig_axim_axcb_imsh(
figsize=fs) if DO_PLOT_LOCAL_MAXIMUMS else (None, None, None, None)
fig3, axim3, axcb3, imsh3 = gg.fig_axim_axcb_imsh(
figsize=fs) if DO_PLOT_CONNECED_PIXELS else (None, None, None, None)
fig1, axim1, axcb1, imsh1 = gg.fig_axim_axcb_imsh(
figsize=fs) if DO_PLOT_IMAGE else (None, None, None, None)
##-----------------------------
#alg = PyAlgos(windows=None, mask=None, pbits=10) # 0177777)
alg = psalgos.peak_finder_algos(seg=0, pbits=0)
if PF == V1:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=6)
elif PF == V2:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=6)
elif PF == V3:
alg.set_peak_selection_parameters(npix_min=1,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=8)
elif PF == V4:
alg.set_peak_selection_parameters(npix_min=1,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=4)
alg.print_attributes()
for ev in range(EVTMAX):
ev1 = ev + 1
if ev < SKIP: continue
#if ev>=EVTMAX : break
print 50 * '_', '\nEvent %04d' % ev1
add_water_ring = False if PF == V4 else True
img, peaks_sim = image_with_random_peaks(shape, add_water_ring)
# --- for debugging
#np.save('xxx-image', img)
#np.save('xxx-peaks', np.array(peaks_sim))
#img = np.load('xxx-image-crash.npy')
#peaks_sim = np.load('xxx-peaks-crash.npy')
# ---
peaks_gen = [(0, r, c, a, a * s, 9 * s * s)
for r, c, a, s in peaks_sim]
t0_sec = time()
peaks = alg.peak_finder_v3r3_d2(img, mask, rank=4, r0=6, dr=3, nsigm=3) if PF == V3 else\
alg.peak_finder_v4r3_d2(img, mask, thr_low=20, thr_high=40, rank=4, r0=5, dr=3)
#peaks = alg.list_of_peaks_selected()
#peaks_tot = alg.list_of_peaks()
# peaks = alg.peak_finder_v1(img, thr_low=20, thr_high=40, radius=6, dr=2) if PF == V1 else\
# alg.peak_finder_v2r1(img, thr=30, r0=7, dr=2) if PF == V2 else\
# alg.peak_finder_v3r2(img, rank=5, r0=7, dr=2, nsigm=3) if PF == V3 else\
# alg.peak_finder_v4r2(img, thr_low=20, thr_high=40, rank=6, r0=7, dr=2)
# #alg.peak_finder_v4r2(img, thr_low=20, thr_high=40, rank=6, r0=3.3, dr=0)
print 'Time consumed by the peak_finder = %10.6f(sec) number of simulated/found peaks: %d/%d'%\
(time()-t0_sec, len(peaks_sim), len(peaks))
#map3 = reshape_to_2d(alg.maps_of_connected_pixels()) if DO_PLOT_CONNECED_PIXELS else None # np.zeros((10,10))
#map4 = reshape_to_2d(alg.maps_of_local_maximums()) if DO_PLOT_LOCAL_MAXIMUMS else None # np.zeros((10,10))
#map5 = reshape_to_2d(alg.maps_of_local_minimums()) if DO_PLOT_LOCAL_MINIMUMS else None # np.zeros((10,10))
map3 = reshape_to_2d(alg.connected_pixels(
)) if DO_PLOT_CONNECED_PIXELS else None # np.zeros((10,10))
map4 = reshape_to_2d(alg.local_maxima(
)) if DO_PLOT_LOCAL_MAXIMUMS else None # np.zeros((10,10))
map5 = reshape_to_2d(alg.local_minima(
)) if DO_PLOT_LOCAL_MINIMUMS else None # np.zeros((10,10))
#print_arr(map3, 'map_of_connected_pixels')
#maps.shape = shape
#for i, (r0, c0, a0, sigma) in enumerate(peaks_sim) :
# print ' %04d row=%6.1f col=%6.1f amp=%6.1f sigma=%6.3f' % (i, r0, c0, a0, sigma)
#plot_image(img)
#print 'Found peaks:'
#print hdr
reg = 'IMG'
peaks_rec = []
if False:
for pk in peaks:
seg,row,col,npix,amax,atot,rcent,ccent,rsigma,csigma,\
rmin,rmax,cmin,cmax,bkgd,rms,son = pk.parameters()
#rmin,rmax,cmin,cmax,bkgd,rms,son = pk[0:17]
rec = fmt % (ev, reg, seg, row, col, npix, amax, atot, rcent, ccent, rsigma, csigma,\
rmin, rmax, cmin, cmax, bkgd, rms, son) #,\
#imrow, imcol, xum, yum, rum, phi)
peaks_rec.append((seg, row, col, amax, atot, npix))
print rec
#peaks_gen = [(0, r, c, a, a*s, 9*s*s) for r,c,a,s in peaks_sim]
#peaks_rec = [(p.seg, p.row, p.col, p.amp_max, p.amp_tot, p.npix) for p in peaks]
#s, r, c, amax, atot, npix = rec[0:6]
if DO_PLOT_CONNECED_PIXELS:
cmin, cmax = (map3.min(),
map3.max()) if map3 is not None else (None, None)
#print 'Connected pixel groups min/max:', cmin, cmax
gg.plot_imgcb(fig3,
axim3,
axcb3,
imsh3,
map3,
amin=cmin,
amax=cmax,
title='Connected pixel groups, ev: %04d' % ev1)
gg.move_fig(fig3, x0=100, y0=30)
if DO_PLOT_LOCAL_MAXIMUMS:
gg.plot_imgcb(fig4,
axim4,
axcb4,
imsh4,
map4,
amin=0,
amax=10,
title='Local maximums, ev: %04d' % ev1)
gg.move_fig(fig4, x0=200, y0=30)
if DO_PLOT_LOCAL_MINIMUMS:
gg.plot_imgcb(fig5,
axim5,
axcb5,
imsh5,
map5,
amin=0,
amax=10,
title='Local minimums, ev: %04d' % ev1)
gg.move_fig(fig5, x0=300, y0=30)
if DO_PLOT_IMAGE:
#nda = maps_of_conpix_arc
#nda = maps_of_conpix_equ
#img = det.image(evt, nda)[xoffset:xoffset+xsize,yoffset:yoffset+ysize]
#img = det.image(evt, mask_img*nda)[xoffset:xoffset+xsize,yoffset:yoffset+ysize]
#img = det.image(evt, maps_of_conpix_equ)[xoffset:xoffset+xsize,yoffset:yoffset+ysize]
ave, rms = img.mean(), img.std()
amin, amax = ave - 1 * rms, ave + 8 * rms
axim1.clear()
if imsh1 is not None: del imsh1
imsh1 = None
gg.plot_imgcb(fig1,
axim1,
axcb1,
imsh1,
img,
amin=amin,
amax=amax,
title='Image, ev: %04d' % ev1)
gg.move_fig(fig1, x0=400, y0=30)
gg.plot_peaks_on_img(peaks_gen,
axim1,
imRow,
imCol,
color='g',
lw=2) #, pbits=3)
gg.plot_peaks_on_img(peaks_rec, axim1, imRow, imCol,
color='w') #, pbits=3)
fig1.canvas.draw() # re-draw figure content
#gg.plotHistogram(nda, amp_range=(-100,100), bins=200, title='Event %d' % i)
gg.show(mode='do not hold')
gg.show()
def test01(tname='1', NUMBER_OF_EVENTS=5, DO_PRINT=False):
print 'local extrema : %s' % ('minimums' if tname=='1'\
else 'maximums' if tname=='2'\
else 'maximums runk=1 cross' if tname=='3'\
else 'two-threshold maximums')
from time import time
from pyimgalgos.GlobalUtils import print_ndarr
import pyimgalgos.GlobalGraphics as gg
#sh, fs = (200,200), (11,10)
sh, fs = (1000, 1000), (11, 10)
#sh, fs = (185,388), (11,5)
fig1, axim1, axcb1, imsh1 = gg.fig_axim_axcb_imsh(figsize=fs)
fig2, axim2, axcb2, imsh2 = gg.fig_axim_axcb_imsh(figsize=fs)
print 'Image shape: %s' % str(sh)
mu, sigma = 200, 25
for evnum in range(NUMBER_OF_EVENTS):
data = np.array(mu + sigma * np.random.standard_normal(sh),
dtype=np.float64)
mask = np.ones(sh, dtype=np.uint16)
extrema = np.zeros(sh, dtype=np.uint16)
rank = 5
thr_low = mu + 3 * sigma
thr_high = mu + 4 * sigma
nmax = 0
if DO_PRINT: print_ndarr(data, 'input data')
t0_sec = time()
#----------
if tname == '1':
nmax = algos.local_minima_2d(data, mask, rank, extrema)
elif tname == '2':
nmax = algos.local_maxima_2d(data, mask, rank, extrema)
elif tname == '3':
nmax = algos.local_maxima_rank1_cross_2d(data, mask, extrema)
else:
nmax = algos.threshold_maxima_2d(data, mask, rank, thr_low,
thr_high, extrema)
#----------
print 'Event: %4d, consumed time = %10.6f(sec), nmax = %d' % (
evnum, time() - t0_sec, nmax)
if DO_PRINT: print_ndarr(extrema, 'output extrema')
img1 = data
img2 = extrema
axim1.clear()
if imsh1 is not None: del imsh1
imsh1 = None
axim2.clear()
if imsh2 is not None: del imsh2
imsh2 = None
ave, rms = img1.mean(), img1.std()
amin, amax = ave - 1 * rms, ave + 5 * rms
gg.plot_imgcb(fig1,
axim1,
axcb1,
imsh1,
img1,
amin=amin,
amax=amax,
title='Event: %d, Data' % evnum,
cmap='inferno')
gg.move_fig(fig1, x0=400, y0=30)
gg.plot_imgcb(fig2,
axim2,
axcb2,
imsh2,
img2,
amin=0,
amax=5,
title='Event: %d, Local extrema' % evnum,
cmap='inferno')
gg.move_fig(fig2, x0=0, y0=30)
gg.show(mode='DO_NOT_HOLD')
gg.show()
imCol = np.select([iY < yoffset], [0], default=iY - yoffset)
# Pixel coordinates [um] (transformed as needed)
Xum = det.coords_y(runnum)
Yum = -det.coords_x(runnum)
# Derived pixel raduius in [um] and angle phi[degree]
Rum = np.sqrt(Xum * Xum + Yum * Yum)
Phi = np.arctan2(Yum, Xum) * 180 / np.pi
imRow.shape = imCol.shape = \
Xum.shape = Yum.shape = \
Rum.shape = Phi.shape = det.shape()
#------------------------------
fig1, axim1, axcb1, imsh1 = gg.fig_axim_axcb_imsh(figsize=(12, 11))
#------------------------------
t0_sec_evloop = time()
nda = None
peaks = None
# loop over events in data set
myrun = next(ds.runs())
for evnum, evt in enumerate(myrun.events()):
if evnum % 100 == 0: print 'Event %d' % (evnum)
if evnum < SKIP: continue
if evnum >= EVTMAX: break
