def IterationStandalone(iWhile):
crossref = g.crossref
captureQ = g.captureQ
captureR = g.captureR
if common.MY_DEBUG_STDOUT:
common.DebugPrint("Entered IterationStandalone(): crossref=%s, captureQ=%s, "\
"captureR=%s, refined_crossref=%s, warp_p=%s, "
"x0=%s, y0=%s, start=%s, t=%d, iWhile=%d." % \
(str(crossref), str(captureQ), str(captureR), \
str(g.refined_crossref), str(g.warp_p), \
str(g.x0), str(g.y0), str(g.start), g.t, iWhile))
common.DebugPrint("IterationStandalone(): id(g)=%s" % str(id(g)))
r_path = captureQ
q_path = captureR
x0 = g.x0
y0 = g.y0
"""
x0 = crossref[5: -5, 0].T;
y0 = crossref[5: -5, 1].T;
"""
"""
x0 = np.array(range(10));
y0 = np.array(range(10));
"""
start = g.start
#start = y0.T; #% Alex: So start is related to crossref(:,2) from the end of dp3.m
#x_init = 0;
refined_crossref = g.refined_crossref
#refined_crossref = crossref.copy();
##refined_crossref = np.array([]);
#% fprintf('\nRetrieval using visual dictionary and inverted index list...(VD method)\n');
"""
if config.USE_ECC_FROM_OPENCV:
H = np.eye(3, dtype=np.float32); #% use feature matching if you need a good initialization
else:
H = np.eye(3); #% use feature matching if you need a good initialization
warp_p = H; #%initial warp
"""
warp_p = g.warp_p
#%initial warp
t = g.t
#t = 0; #%initial subframe correction
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR == False:
fit = []
#if config.pixel_select == 1:
# config.weighted_flag = 0;
try:
common.DebugPrint("SpatialAlignment.IterationStandalone(iWhile=%d)\n" % \
iWhile)
"""
if i < 5:
tic
"""
if config.affine_time == 0:
if config.seq2seq == 0:
common.DebugPrint(
"Alex: NOT affine temporal model, NOT seq2seq")
#% frame-to-subframe scheme (one query frame against a reference subsequence
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
fitecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC)
else:
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC)
else: # seq2seq == 1, affine_time == 0
common.DebugPrint(
"Alex: NOT affine temporal model, seq2seq == 1")
#% sequence-to-sequence alignment (one temporal parameter)
fit[iWhile].ecc = ecc_homo_spacetime_seq(
start[iWhile], x0[iWhile], warp_p, t, iterECC, levels,
r_path, q_path, nof, time_flag, weighted_flag,
pixel_select, config.imformat, verbose)
else: #% affine temporal model (two parameters) for the case frame-to-subframe
common.DebugPrint("Alex: Affine temporal model\n")
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_affine_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC)
"""
if i < 5:
toc;
"""
iterECC0 = config.iterECC - 1
if fitecc[0][iterECC0].t == None:
while iterECC0 > 0:
iterECC0 -= 1
if fitecc[0][iterECC0].t != None:
break
#!!!!TODO: understand ecc_homo_spacetime - should we use fitecc[config.levelsECC - 1][iterECC0].t instead of fitecc[0][iterECC0].t? (when levelsECC != 1)?
#% synchronization correction
#%synchro with subframe correction
#refined_crossref(i,2)=refined_crossref(i,2)+fit(i).ecc(1,iter).t;
if config.USE_ECC_FROM_OPENCV:
pass
else:
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fitecc[0][iterECC0].t
#fitecc[0][config.iterECC - 1].t;
else:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fit[iWhile].ecc[iterECC0].t
#fit[iWhile].ecc[0, config.iterECC - 1].t;
if common.MY_DEBUG_STDOUT:
common.DebugPrint("Exiting IterationStandalone(iWhile=%d)" %
iWhile)
except:
common.DebugPrintErrorTrace()
def IterationStandalone(iWhile):
crossref = g.crossref;
captureQ = g.captureQ;
captureR = g.captureR;
if common.MY_DEBUG_STDOUT:
common.DebugPrint("Entered IterationStandalone(): crossref=%s, captureQ=%s, "\
"captureR=%s, refined_crossref=%s, warp_p=%s, "
"x0=%s, y0=%s, start=%s, t=%d, iWhile=%d." % \
(str(crossref), str(captureQ), str(captureR), \
str(g.refined_crossref), str(g.warp_p), \
str(g.x0), str(g.y0), str(g.start), g.t, iWhile));
common.DebugPrint("IterationStandalone(): id(g)=%s" % str(id(g)));
r_path = captureQ;
q_path = captureR;
x0 = g.x0;
y0 = g.y0;
"""
x0 = crossref[5: -5, 0].T;
y0 = crossref[5: -5, 1].T;
"""
"""
x0 = np.array(range(10));
y0 = np.array(range(10));
"""
start = g.start;
#start = y0.T; #% Alex: So start is related to crossref(:,2) from the end of dp3.m
#x_init = 0;
refined_crossref = g.refined_crossref;
#refined_crossref = crossref.copy();
##refined_crossref = np.array([]);
#% fprintf('\nRetrieval using visual dictionary and inverted index list...(VD method)\n');
"""
if config.USE_ECC_FROM_OPENCV:
H = np.eye(3, dtype=np.float32); #% use feature matching if you need a good initialization
else:
H = np.eye(3); #% use feature matching if you need a good initialization
warp_p = H; #%initial warp
"""
warp_p = g.warp_p; #%initial warp
t = g.t;
#t = 0; #%initial subframe correction
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR == False:
fit = [];
#if config.pixel_select == 1:
# config.weighted_flag = 0;
try:
common.DebugPrint("SpatialAlignment.IterationStandalone(iWhile=%d)\n" % \
iWhile);
"""
if i < 5:
tic
"""
if config.affine_time == 0:
if config.seq2seq == 0:
common.DebugPrint("Alex: NOT affine temporal model, NOT seq2seq");
#% frame-to-subframe scheme (one query frame against a reference subsequence
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
fitecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC);
else:
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC);
else: # seq2seq == 1, affine_time == 0
common.DebugPrint("Alex: NOT affine temporal model, seq2seq == 1");
#% sequence-to-sequence alignment (one temporal parameter)
fit[iWhile].ecc = ecc_homo_spacetime_seq(start[iWhile],
x0[iWhile], warp_p, t,
iterECC, levels, r_path, q_path, nof, time_flag,
weighted_flag, pixel_select, config.imformat, verbose);
else: #% affine temporal model (two parameters) for the case frame-to-subframe
common.DebugPrint("Alex: Affine temporal model\n");
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_affine_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC);
"""
if i < 5:
toc;
"""
iterECC0 = config.iterECC - 1;
if fitecc[0][iterECC0].t == None:
while iterECC0 > 0:
iterECC0 -= 1;
if fitecc[0][iterECC0].t != None:
break;
#!!!!TODO: understand ecc_homo_spacetime - should we use fitecc[config.levelsECC - 1][iterECC0].t instead of fitecc[0][iterECC0].t? (when levelsECC != 1)?
#% synchronization correction
#%synchro with subframe correction
#refined_crossref(i,2)=refined_crossref(i,2)+fit(i).ecc(1,iter).t;
if config.USE_ECC_FROM_OPENCV:
pass;
else:
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fitecc[0][iterECC0].t;
#fitecc[0][config.iterECC - 1].t;
else:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fit[iWhile].ecc[iterECC0].t;
#fit[iWhile].ecc[0, config.iterECC - 1].t;
if common.MY_DEBUG_STDOUT:
common.DebugPrint("Exiting IterationStandalone(iWhile=%d)" % iWhile);
except:
common.DebugPrintErrorTrace();
def SpatialAlignmentEvangelidis(crossref, captureQ, captureR):
"""
% Alex:
% We entere a bogus crossref, since for the 720x480 MIT video Matlab
% crashed with out of memory - it ended up using 1GB of data
%crossref=zeros(1594,2);
%for i=1:size(crossref,1)
% crossref(i,1)=1000+i;
% crossref(i,2)=2000+i;
%end
%crossref
"""
#fprintf(1, 'Alex: Entered alignment_script\n');
#% Alex: tic starts a stopwatch timer to measure performance. The function records the internal time at execution of the tic command. Display the elapsed time with the toc function.
#tic
#% alignment parameters
"""
% Alex: From [TPAMI_2013] paper:
% "Given a pair (m; n), we consider the temporally local
%subsequence In-mu;...,In+mu where mu is a small integer. After
%defining Phi(), we look for the image warped in space and time
%from the above subsequence that aligns with Im. To this
%end, we extend the ECC alignment algorithm [3] to the
%space-time dimensions, i.e., the extended scheme estimates
%the spatiotemporal parameterspthat maximize the correla-
%tion coefficient between the input frame Iq(x) and the
%warped reference subframeIr(Phi(x_hat; p)."
"""
#nof = 5; #%number of frames for sub-sequences
#cropflag = 0; #% flag for cropped images (use 0)
#iterECC = 15; #%iterations of ECC
#levelsECC = 1; #%levels of multi-resolution ECC (use 1)
#verboseECC = 1; #% save/see the spatial alignment (and if want, uncomment to get a pause per frame)
if config.TESTING_IDENTICAL_MATLAB:
#q_path = "video_data/input/";
#r_path = "video_data/reference/";
q_path = "Videos/input/"
r_path = "Videos/reference/"
if not os.path.exists(q_path):
os.makedirs(q_path)
if not os.path.exists(r_path):
os.makedirs(r_path)
else:
q_path = captureQ
r_path = captureR
#% reject a few frames in the beginning and at the end, because of the sub-sequence
#% Alex: crossref comes from the synchro_script (via the global scope)
#x0=crossref(6:end-5,1)';
x0 = crossref[5:-5, 0].T
#y0=crossref(6:end-5,2)';
y0 = crossref[5:-5, 1].T
#imformat = "png"; #"jpeg"; #%image format
start = y0.T
#% Alex: So start is related to crossref(:,2) from the end of dp3.m
x_init = 0
refined_crossref = crossref.copy()
#% fprintf('\nRetrieval using visual dictionary and inverted index list...(VD method)\n');
if config.USE_ECC_FROM_OPENCV:
H = np.eye(3, dtype=np.float32)
#% use feature matching if you need a good initialization
else:
H = np.eye(3)
#% use feature matching if you need a good initialization
warp_p = H
#%initial warp
t = 0
#%initial subframe correction
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR == False:
fit = []
#pixel_select=0; #% when 1, it considers only pixels around salient points
#time_flag=0; #% when 0, it does only spatial alignment even in seq2seq
#weighted_flag=1; #% when 1, it considers a self-weighted version of ECC, not explained in PAMI paper
#%fprintf(1, 'Am here before\n');
if config.pixel_select == 1:
config.weighted_flag = 0
"""
% !!!!NEEDS NORMALLY MEX: concat_3d, etc (UNLESS on Win64, for which we have binaries for the MEXes)
% when affine_time is 1, an affine temporal model is considered with the frame-to-subframe scheme
% Alex: affine temporal model means two parameters, for the case frame-to-subframe
% Alex: if affine_time == 0 --> it has one temporal parameter. We can also use here sequence-to-sequence alignment
% Alex: From [TPAMI_2013] paper: "there will be a global affine temporal transformation
% t=alpha * t_hat + tau t_hat determines corre-spondences between the
% indices t and t_hat, regardless of scene content or camera motion."
"""
#config.affine_time = 0;
"""
% Alex: From [TPAMI_2013] paper:
%"Note, however, that if the input frames are weakly textured,
%sequence-to-sequence schemes may be preferable to image-to-sequence counterparts."
% when seq2seq is 1, it considers a sequence-to-sequence alignment (seq2seq in PAMI paper)
% Alex: the PAMI paper seems to be [1] Y. Caspi and M. Irani,
Spatio-Temporal Alignment of Se-quences,
IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 24, no. 11, pp. 1409-1424, Nov. 2002
"""
#config.seq2seq = 0;
#% if seq2seq==1:
#% pixel_select = 0;
if common.MY_DEBUG_STDOUT:
common.DebugPrint("SpatialAlignmentEvangelidis(): crossref.shape = %s" % \
str(crossref.shape))
common.DebugPrint("SpatialAlignmentEvangelidis(): crossref = %s" %
str(crossref))
common.DebugPrint("SpatialAlignmentEvangelidis(): x0.shape = %s" %
str(x0.shape))
common.DebugPrint("SpatialAlignmentEvangelidis(): x0 = %s" % str(x0))
common.DebugPrint("SpatialAlignmentEvangelidis(): y0.shape = %s" %
str(y0.shape))
common.DebugPrint("SpatialAlignmentEvangelidis(): y0 = %s" % str(y0))
common.DebugPrint("SpatialAlignmentEvangelidis(): start.shape = %s" % \
str(start.shape))
common.DebugPrint("SpatialAlignmentEvangelidis(): (used to generate reference " \
"frame to read) start = %s" % str(start))
if config.USE_MULTITHREADING == True:
global g
g.crossref = crossref
g.captureQ = captureQ
g.captureR = captureR
g.x0 = x0
g.y0 = y0
g.start = start
g.refined_crossref = refined_crossref
g.warp_p = warp_p
g.t = t
# We consider only this case:
assert config.affine_time == 0
assert config.seq2seq == 0
if common.MY_DEBUG_STDOUT:
common.DebugPrint("SpatialAlignmentEvangelidis(): id(g)=%s" %
str(id(g)))
common.DebugPrint("SpatialAlignmentEvangelidis(): g.crossref=%s, " \
"g.captureQ=%s, g.captureR=%s." % \
(g.crossref, g.captureQ, g.captureR))
"""
Start worker processes to use on multi-core processor (able to run
in parallel - no GIL issue if each core has it's own VM)
"""
pool = multiprocessing.Pool(processes=config.numProcesses)
"""
common.DebugPrint("SpatialAlignment(): Spawning a new " \
"IterationStandalone(iWhile=%d) thread" % iWhile);
"""
print("SpatialAlignment(): Spawned a pool of %d workers" % \
config.numProcesses)
if False:
# The multithreaded solution is not great, since it encounters the GIL limitation
#NOT_TODO: this is not working here, outside the while loop
t = Thread(target=IterationStandalone, args=(iWhile, ))
t.start()
common.DebugPrint("SpatialAlignment(): __name__ = %s" % str(__name__))
if True:
#listParams = [(crossref, captureQ, captureR) for x in range(1, 8 + 1)];
#listParams = range(1, x0.size + 1);
listParams = range(0, x0.size)
#!!!!TODO: use counterStep
#% listParams is the vector with query frame IDs. y0 with the corresponding ref frames.
# See https://docs.python.org/2/library/multiprocessing.html#module-multiprocessing.pool
res = pool.map(func=IterationStandalone, iterable=listParams, \
chunksize=1)
print("Pool.map returns %s" % str(res))
#x0.size + 1
"""
From https://medium.com/building-things-on-the-internet/40e9b2b36148
close the pool and wait for the work to finish
"""
pool.close()
pool.join()
"""
We passed refined_crossref to the workers, so it should be
updated here. !!!!TODO: check if so
"""
"""
!!!!TODO: check to see if refined_crossref was updated properly by the workers
!!!!TODO: seems you need to use muliprocessing.Value - see http://stackoverflow.com/questions/14124588/python-multiprocessing-shared-memory:
<<Python's multithreading is not suitable for CPU-bound tasks
(because of the GIL), so the usual solution in that case is
to go on multiprocessing.
However, with this solution you need to explicitly share
the data, using multiprocessing.Value and
multiprocessing.Array.>>
From http://stackoverflow.com/questions/10721915/shared-memory-objects-in-python-multiprocessing :
<<If you use an operating system that uses copy-on-write fork() semantics (like any common unix), then as long as you never alter your data structure it will be available to all child processes without taking up additional memory. You will not have to do anything special (except make absolutely sure you don't alter the object).
The most efficient thing you can do for your problem would be to pack your array into an efficient array structure (using numpy or array), place that in shared memory, wrap it with multiprocessing.Array, and pass that to your functions. This answer shows how to do that.
If you want a writeable shared object, then you will need to wrap it with some kind of synchronization or locking. multiprocessing provides two methods of doing this: one using shared memory (suitable for simple values, arrays, or ctypes) or a Manager proxy, where one process holds the memory and a manager arbitrates access to it from other processes (even over a network).
The Manager approach can be used with arbitrary Python objects, but will be slower than the equivalent using shared memory because the objects need to be serialized/deserialized and sent between processes.
There are a wealth of parallel processing libraries and approaches available in Python. multiprocessing is an excellent and well rounded library, but if you have special needs perhaps one of the other approaches may be better.>>
From http://en.wikipedia.org/wiki/Fork_%28system_call%29 :
<<In Unix systems equipped with virtual memory support
(practically all modern variants), the fork operation
creates a separate address space for the child.
The child process has an exact copy of all the memory
segments of the parent process, though if copy-on-write
semantics are implemented, the physical memory need
not be actually copied.
Instead, virtual memory pages in both processes may refer to
the same pages of physical memory until one of them writes
to such a page: then it is copied.>>
From http://en.wikipedia.org/wiki/Copy-on-write
<<Copy-on-write finds its main use in virtual memory operating systems; when a process creates a copy of itself, the pages in memory that might be modified by either the process or its copy are marked copy-on-write. When one process modifies the memory, the operating system's kernel intercepts the operation and copies the memory thus a change in the memory of one process is not visible in another's.>>
"""
return refined_crossref
#i=1;
# IMPORTANT NOTE: We substitute i - 1 --> iWhile (since array numbering
# starts with 0, not like in Matlab from 1)
iWhile = 0
#% x0 is the vector with query frame IDs. y0 with the corresponding ref frames.
#while(i<(numel(x0)+1))
while iWhile < x0.size:
try:
common.DebugPrint(
"SpatialAlignmentEvangelidis(): Iteration(iWhile=%d)\n" %
iWhile)
"""
if i < 5:
tic
"""
#if ~affine_time
if config.affine_time == 0:
if config.seq2seq == 0:
common.DebugPrint(
"SpatialAlignmentEvangelidis(): NOT affine temporal model, NOT seq2seq"
)
#% We do spatial alignment for query frame x0(i) and ref frame start(i) = y0(i)
#% frame-to-subframe scheme (one query frame against a reference subsequence
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
fitecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC)
else:
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC)
else:
common.DebugPrint(
"SpatialAlignmentEvangelidis(): NOT affine temporal model, seq2seq == 1"
)
#% sequence-to-sequence alignment (one temporal parameter)
fit[iWhile].ecc = ecc_homo_spacetime_seq(
start[iWhile], x0[iWhile], warp_p, t, iterECC, levels,
r_path, q_path, nof, time_flag, weighted_flag,
pixel_select, config.imformat, verbose)
else: #% affine temporal model (two parameters) for the case frame-to-subframe
common.DebugPrint(
"SpatialAlignmentEvangelidis(): Affine temporal model\n")
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_affine_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC)
"""
if i < 5:
toc;
"""
#% synchronization correction
#%synchro with subframe correction
#refined_crossref(i,2)=refined_crossref(i,2)+fit(i).ecc(1,iter).t;
if config.USE_ECC_FROM_OPENCV:
pass
else:
iterECC0 = config.iterECC - 1
if fitecc[0][iterECC0].t == None:
while iterECC0 > 0:
iterECC0 -= 1
if fitecc[0][iterECC0].t != None:
break
if fitecc[0][iterECC0].t == None:
continue
#!!!!TODO: understand ecc_homo_spacetime - should we use fitecc[config.levelsECC - 1][iterECC0].t instead of fitecc[0][iterECC0].t? (when levelsECC != 1)?
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fitecc[0][iterECC0].t
else:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fit[iWhile].ecc[0, iterECC0].t
common.DebugPrint(
"SpatialAlignment(): Finished iteration iWhile=%d" % \
iWhile)
common.DebugPrint("SpatialAlignmentEvangelidis(): warp_p " \
"(at end of iteration) = %s" % \
str(warp_p))
# Inspired from http://effbot.org/zone/stupid-exceptions-keyboardinterrupt.htm
except (KeyboardInterrupt, SystemExit):
raise
except:
common.DebugPrintErrorTrace()
quit()
iWhile += 1
"""
We stop the spatial alignment after generating the
first "pair" of visuals.
"""
#break;
#print("SpatialAlignment(): while loop: iWhile = %d" % iWhile);
#% fit struct containes the results of the spatial alignment.
#toc
return refined_crossref
def SpatialAlignmentEvangelidis(crossref, captureQ, captureR):
"""
% Alex:
% We entere a bogus crossref, since for the 720x480 MIT video Matlab
% crashed with out of memory - it ended up using 1GB of data
%crossref=zeros(1594,2);
%for i=1:size(crossref,1)
% crossref(i,1)=1000+i;
% crossref(i,2)=2000+i;
%end
%crossref
"""
#fprintf(1, 'Alex: Entered alignment_script\n');
#% Alex: tic starts a stopwatch timer to measure performance. The function records the internal time at execution of the tic command. Display the elapsed time with the toc function.
#tic
#% alignment parameters
"""
% Alex: From [TPAMI_2013] paper:
% "Given a pair (m; n), we consider the temporally local
%subsequence In-mu;...,In+mu where mu is a small integer. After
%defining Phi(), we look for the image warped in space and time
%from the above subsequence that aligns with Im. To this
%end, we extend the ECC alignment algorithm [3] to the
%space-time dimensions, i.e., the extended scheme estimates
%the spatiotemporal parameterspthat maximize the correla-
%tion coefficient between the input frame Iq(x) and the
%warped reference subframeIr(Phi(x_hat; p)."
"""
#nof = 5; #%number of frames for sub-sequences
#cropflag = 0; #% flag for cropped images (use 0)
#iterECC = 15; #%iterations of ECC
#levelsECC = 1; #%levels of multi-resolution ECC (use 1)
#verboseECC = 1; #% save/see the spatial alignment (and if want, uncomment to get a pause per frame)
if config.TESTING_IDENTICAL_MATLAB:
#q_path = "video_data/input/";
#r_path = "video_data/reference/";
q_path = "Videos/input/";
r_path = "Videos/reference/";
if not os.path.exists(q_path):
os.makedirs(q_path);
if not os.path.exists(r_path):
os.makedirs(r_path);
else:
q_path = captureQ;
r_path = captureR;
#% reject a few frames in the beginning and at the end, because of the sub-sequence
#% Alex: crossref comes from the synchro_script (via the global scope)
#x0=crossref(6:end-5,1)';
x0 = crossref[5: -5, 0].T;
#y0=crossref(6:end-5,2)';
y0 = crossref[5: -5, 1].T;
#imformat = "png"; #"jpeg"; #%image format
start = y0.T; #% Alex: So start is related to crossref(:,2) from the end of dp3.m
x_init = 0;
refined_crossref = crossref.copy();
#% fprintf('\nRetrieval using visual dictionary and inverted index list...(VD method)\n');
if config.USE_ECC_FROM_OPENCV:
H = np.eye(3, dtype=np.float32); #% use feature matching if you need a good initialization
else:
H = np.eye(3); #% use feature matching if you need a good initialization
warp_p = H; #%initial warp
t = 0; #%initial subframe correction
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR == False:
fit = [];
#pixel_select=0; #% when 1, it considers only pixels around salient points
#time_flag=0; #% when 0, it does only spatial alignment even in seq2seq
#weighted_flag=1; #% when 1, it considers a self-weighted version of ECC, not explained in PAMI paper
#%fprintf(1, 'Am here before\n');
if config.pixel_select == 1:
config.weighted_flag = 0;
"""
% !!!!NEEDS NORMALLY MEX: concat_3d, etc (UNLESS on Win64, for which we have binaries for the MEXes)
% when affine_time is 1, an affine temporal model is considered with the frame-to-subframe scheme
% Alex: affine temporal model means two parameters, for the case frame-to-subframe
% Alex: if affine_time == 0 --> it has one temporal parameter. We can also use here sequence-to-sequence alignment
% Alex: From [TPAMI_2013] paper: "there will be a global affine temporal transformation
% t=alpha * t_hat + tau t_hat determines corre-spondences between the
% indices t and t_hat, regardless of scene content or camera motion."
"""
#config.affine_time = 0;
"""
% Alex: From [TPAMI_2013] paper:
%"Note, however, that if the input frames are weakly textured,
%sequence-to-sequence schemes may be preferable to image-to-sequence counterparts."
% when seq2seq is 1, it considers a sequence-to-sequence alignment (seq2seq in PAMI paper)
% Alex: the PAMI paper seems to be [1] Y. Caspi and M. Irani,
Spatio-Temporal Alignment of Se-quences,
IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 24, no. 11, pp. 1409-1424, Nov. 2002
"""
#config.seq2seq = 0;
#% if seq2seq==1:
#% pixel_select = 0;
if common.MY_DEBUG_STDOUT:
common.DebugPrint("SpatialAlignmentEvangelidis(): crossref.shape = %s" % \
str(crossref.shape));
common.DebugPrint("SpatialAlignmentEvangelidis(): crossref = %s" % str(crossref));
common.DebugPrint("SpatialAlignmentEvangelidis(): x0.shape = %s" % str(x0.shape));
common.DebugPrint("SpatialAlignmentEvangelidis(): x0 = %s" % str(x0));
common.DebugPrint("SpatialAlignmentEvangelidis(): y0.shape = %s" % str(y0.shape));
common.DebugPrint("SpatialAlignmentEvangelidis(): y0 = %s" % str(y0));
common.DebugPrint("SpatialAlignmentEvangelidis(): start.shape = %s" % \
str(start.shape));
common.DebugPrint("SpatialAlignmentEvangelidis(): (used to generate reference " \
"frame to read) start = %s" % str(start));
if config.USE_MULTITHREADING == True:
global g;
g.crossref = crossref;
g.captureQ = captureQ;
g.captureR = captureR;
g.x0 = x0;
g.y0 = y0;
g.start = start;
g.refined_crossref = refined_crossref;
g.warp_p = warp_p;
g.t = t;
# We consider only this case:
assert config.affine_time == 0;
assert config.seq2seq == 0;
if common.MY_DEBUG_STDOUT:
common.DebugPrint("SpatialAlignmentEvangelidis(): id(g)=%s" % str(id(g)));
common.DebugPrint("SpatialAlignmentEvangelidis(): g.crossref=%s, " \
"g.captureQ=%s, g.captureR=%s." % \
(g.crossref, g.captureQ, g.captureR));
"""
Start worker processes to use on multi-core processor (able to run
in parallel - no GIL issue if each core has it's own VM)
"""
pool = multiprocessing.Pool(processes=config.numProcesses);
"""
common.DebugPrint("SpatialAlignment(): Spawning a new " \
"IterationStandalone(iWhile=%d) thread" % iWhile);
"""
print("SpatialAlignment(): Spawned a pool of %d workers" % \
config.numProcesses);
if False:
# The multithreaded solution is not great, since it encounters the GIL limitation
#NOT_TODO: this is not working here, outside the while loop
t = Thread(target=IterationStandalone, args=(iWhile, ));
t.start();
common.DebugPrint("SpatialAlignment(): __name__ = %s" % str(__name__));
if True:
#listParams = [(crossref, captureQ, captureR) for x in range(1, 8 + 1)];
#listParams = range(1, x0.size + 1);
listParams = range(0, x0.size); #!!!!TODO: use counterStep
#% listParams is the vector with query frame IDs. y0 with the corresponding ref frames.
# See https://docs.python.org/2/library/multiprocessing.html#module-multiprocessing.pool
res = pool.map(func=IterationStandalone, iterable=listParams, \
chunksize=1);
print("Pool.map returns %s" % str(res)); #x0.size + 1
"""
From https://medium.com/building-things-on-the-internet/40e9b2b36148
close the pool and wait for the work to finish
"""
pool.close();
pool.join();
"""
We passed refined_crossref to the workers, so it should be
updated here. !!!!TODO: check if so
"""
"""
!!!!TODO: check to see if refined_crossref was updated properly by the workers
!!!!TODO: seems you need to use muliprocessing.Value - see http://stackoverflow.com/questions/14124588/python-multiprocessing-shared-memory:
<<Python's multithreading is not suitable for CPU-bound tasks
(because of the GIL), so the usual solution in that case is
to go on multiprocessing.
However, with this solution you need to explicitly share
the data, using multiprocessing.Value and
multiprocessing.Array.>>
From http://stackoverflow.com/questions/10721915/shared-memory-objects-in-python-multiprocessing :
<<If you use an operating system that uses copy-on-write fork() semantics (like any common unix), then as long as you never alter your data structure it will be available to all child processes without taking up additional memory. You will not have to do anything special (except make absolutely sure you don't alter the object).
The most efficient thing you can do for your problem would be to pack your array into an efficient array structure (using numpy or array), place that in shared memory, wrap it with multiprocessing.Array, and pass that to your functions. This answer shows how to do that.
If you want a writeable shared object, then you will need to wrap it with some kind of synchronization or locking. multiprocessing provides two methods of doing this: one using shared memory (suitable for simple values, arrays, or ctypes) or a Manager proxy, where one process holds the memory and a manager arbitrates access to it from other processes (even over a network).
The Manager approach can be used with arbitrary Python objects, but will be slower than the equivalent using shared memory because the objects need to be serialized/deserialized and sent between processes.
There are a wealth of parallel processing libraries and approaches available in Python. multiprocessing is an excellent and well rounded library, but if you have special needs perhaps one of the other approaches may be better.>>
From http://en.wikipedia.org/wiki/Fork_%28system_call%29 :
<<In Unix systems equipped with virtual memory support
(practically all modern variants), the fork operation
creates a separate address space for the child.
The child process has an exact copy of all the memory
segments of the parent process, though if copy-on-write
semantics are implemented, the physical memory need
not be actually copied.
Instead, virtual memory pages in both processes may refer to
the same pages of physical memory until one of them writes
to such a page: then it is copied.>>
From http://en.wikipedia.org/wiki/Copy-on-write
<<Copy-on-write finds its main use in virtual memory operating systems; when a process creates a copy of itself, the pages in memory that might be modified by either the process or its copy are marked copy-on-write. When one process modifies the memory, the operating system's kernel intercepts the operation and copies the memory thus a change in the memory of one process is not visible in another's.>>
"""
return refined_crossref;
#i=1;
# IMPORTANT NOTE: We substitute i - 1 --> iWhile (since array numbering
# starts with 0, not like in Matlab from 1)
iWhile = 0;
#% x0 is the vector with query frame IDs. y0 with the corresponding ref frames.
#while(i<(numel(x0)+1))
while iWhile < x0.size:
try:
common.DebugPrint("SpatialAlignmentEvangelidis(): Iteration(iWhile=%d)\n" % iWhile);
"""
if i < 5:
tic
"""
#if ~affine_time
if config.affine_time == 0:
if config.seq2seq == 0:
common.DebugPrint("SpatialAlignmentEvangelidis(): NOT affine temporal model, NOT seq2seq");
#% We do spatial alignment for query frame x0(i) and ref frame start(i) = y0(i)
#% frame-to-subframe scheme (one query frame against a reference subsequence
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
fitecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC);
else:
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC);
else:
common.DebugPrint("SpatialAlignmentEvangelidis(): NOT affine temporal model, seq2seq == 1");
#% sequence-to-sequence alignment (one temporal parameter)
fit[iWhile].ecc = ecc_homo_spacetime_seq(start[iWhile],
x0[iWhile], warp_p, t,
iterECC, levels, r_path, q_path, nof, time_flag,
weighted_flag, pixel_select, config.imformat, verbose);
else: #% affine temporal model (two parameters) for the case frame-to-subframe
common.DebugPrint("SpatialAlignmentEvangelidis(): Affine temporal model\n");
fit[iWhile].ecc = ecc_homo_spacetime.ecc_homo_affine_spacetime( \
img_index=start[iWhile],
tmplt_index=x0[iWhile],
p_init=warp_p,
t0=t,
n_iters=config.iterECC,
levels=config.levelsECC,
r_capture=r_path,
q_capture=q_path,
nof=config.nof,
time_flag=config.time_flag,
weighted_flag=config.weighted_flag,
pixel_select=config.pixel_select,
mode="ecc",
imformat=config.imformat,
save_image=config.verboseECC);
"""
if i < 5:
toc;
"""
#% synchronization correction
#%synchro with subframe correction
#refined_crossref(i,2)=refined_crossref(i,2)+fit(i).ecc(1,iter).t;
if config.USE_ECC_FROM_OPENCV:
pass;
else:
iterECC0 = config.iterECC - 1;
if fitecc[0][iterECC0].t == None:
while iterECC0 > 0:
iterECC0 -= 1;
if fitecc[0][iterECC0].t != None:
break;
if fitecc[0][iterECC0].t == None:
continue;
#!!!!TODO: understand ecc_homo_spacetime - should we use fitecc[config.levelsECC - 1][iterECC0].t instead of fitecc[0][iterECC0].t? (when levelsECC != 1)?
if DONT_USE_MATLAB_FIT_ECC_RECORD_SYNTACTIC_SUGAR:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fitecc[0][iterECC0].t;
else:
refined_crossref[iWhile, 1] = refined_crossref[iWhile, 1] + \
fit[iWhile].ecc[0, iterECC0].t;
common.DebugPrint(
"SpatialAlignment(): Finished iteration iWhile=%d" % \
iWhile);
common.DebugPrint("SpatialAlignmentEvangelidis(): warp_p " \
"(at end of iteration) = %s" % \
str(warp_p));
# Inspired from http://effbot.org/zone/stupid-exceptions-keyboardinterrupt.htm
except (KeyboardInterrupt, SystemExit):
raise;
except:
common.DebugPrintErrorTrace();
quit();
iWhile += 1;
"""
We stop the spatial alignment after generating the
first "pair" of visuals.
"""
#break;
#print("SpatialAlignment(): while loop: iWhile = %d" % iWhile);
#% fit struct containes the results of the spatial alignment.
#toc
return refined_crossref;
