def createPatchTuplesMAP(balL, attr2pat, R, flip=False):
"""
Sort of creates 'tasks' for groupImagesWorkerMAP, where each task
is a tuple of the form:
(imgpatch_i, [attrpatch_i, ...], attrval_i, side_i, isflip_i)
And you create one task for each side of a voted ballot (i.e. one
for side0, another for side1, ...), to figure out the imgorder.
Note that there can be multiple exemplar attrpatches.
Input:
tuple balL: (sidepath_i, ...)
dict attr2pat: maps {str attrval: [(str imgpath, obj imgpatch_i), ...]}
tuple R: (y1, y2, x1, x2). A 'super' region.
Output:
((obj imgpatch_i, [obj attrpatch_i0, ...], str attrval_i, int side_i, int isflip_i), ...)
"""
pFac = 1
patchTuples = []
for idx in range(len(balL)):
balP = balL[idx]
I = sh.standardImread(balP, flatten=True)
if flip:
I = sh.fastFlip(I)
(rOut, rOff) = sh.expand(R[0], R[1], R[2], R[3], I.shape[0],
I.shape[1], pFac)
I1 = I[rOut[0]:rOut[1], rOut[2]:rOut[3]]
for attrval, exemplar_pairs in attr2pat.iteritems():
exmpl_patches = [t[1] for t in exemplar_pairs]
patchTuples.append((I1, exmpl_patches, attrval, idx, flip))
return patchTuples
def createPatchTuplesMAP(balL,attr2pat,R,flip=False):
"""
Sort of creates 'tasks' for groupImagesWorkerMAP, where each task
is a tuple of the form:
(imgpatch_i, [attrpatch_i, ...], attrval_i, side_i, isflip_i)
And you create one task for each side of a voted ballot (i.e. one
for side0, another for side1, ...), to figure out the imgorder.
Note that there can be multiple exemplar attrpatches.
Input:
tuple balL: (sidepath_i, ...)
dict attr2pat: maps {str attrval: [(str imgpath, obj imgpatch_i), ...]}
tuple R: (y1, y2, x1, x2). A 'super' region.
Output:
((obj imgpatch_i, [obj attrpatch_i0, ...], str attrval_i, int side_i, int isflip_i), ...)
"""
pFac=1;
patchTuples=[];
for idx in range(len(balL)):
balP=balL[idx]
I=sh.standardImread(balP,flatten=True)
if flip:
I = sh.fastFlip(I)
(rOut,rOff)=sh.expand(R[0],R[1],R[2],R[3],I.shape[0],I.shape[1],pFac)
I1=I[rOut[0]:rOut[1],rOut[2]:rOut[3]]
for attrval, exemplar_pairs in attr2pat.iteritems():
exmpl_patches = [t[1] for t in exemplar_pairs]
patchTuples.append((I1, exmpl_patches, attrval, idx, flip))
return patchTuples
def main():
patchpath = 'patch_tmp.png'
regionpath = 'allpatches_tmp.png'
imgpatch = shared.standardImread(patchpath, flatten=True)
h, w = imgpatch.shape
bb = [0, h-1, 0, w-1]
matches = shared.find_patch_matchesV1(imgpatch, bb, (regionpath,))
print 'Num Matches:', len(matches)
def main():
patchpath = 'patch_tmp.png'
regionpath = 'allpatches_tmp.png'
imgpatch = shared.standardImread(patchpath, flatten=True)
h, w = imgpatch.shape
bb = [0, h - 1, 0, w - 1]
matches = shared.find_patch_matchesV1(imgpatch, bb, (regionpath, ))
print 'Num Matches:', len(matches)
def templateSSWorker(job):
# dict attr2pat: maps {str attrval: [[str imgpath_i, obj patch_i], ...]}
(attr2pat, attrval, superRegion, sStep, img2flip) = job
attr2pat1 = attr2pat.copy()
# TODO: Arbitrarily chooses a patch_i
(imgpath, patch) = attr2pat1[attrval][0]
isflip = img2flip[imgpath]
I = sh.standardImread(imgpath, flatten=True)
superRegionNp = np.array(superRegion)
patchTuples = createPatchTuples(I, attr2pat1, superRegionNp, flip=isflip)
sc0 = sh.resizeOrNot(patch.shape, sh.MAX_PRECINCT_PATCH_DIM)
minSc = sh.resizeOrNot(patch.shape, sh.MIN_PRECINCT_PATCH_DIM)
(scores0, locs, exemplar_idxs) = dist2patches(patchTuples, sc0)
sidx = np.argsort(scores0)
sidx = sidx[::-1]
trackIdx = sidx[0]
# sc1 is the 'scale' that we're currently working with.
sc1 = sc0 - sStep # Starting scale.
while sc1 > minSc:
try:
(scores, locs, exemplar_idxs) = dist2patches(patchTuples, sc1)
except Exception as e:
d = {'patchTuples': patchTuples, 'sc1': sc1}
path = '_errdict_0'
'''
while os.path.exists(path):
new_i = int(path.split("_")[-1]) + 1
path = '_errdict_{0}'.format(str(new_i))
print '...outputting debug info to:', path
pickle.dump(d, open(path, 'wb'))
'''
print "Exiting."
exit(1)
sidx = np.argsort(scores)
sidx = sidx[::-1]
mid = np.ceil(len(sidx) / 2.0)
dumpIdx = sidx[mid:len(sidx)]
if sum(0 + (dumpIdx == trackIdx)) > 0:
break
else:
# decrease the scale
sc1 = sc1 - sStep
# write scale to file
#toWrite={"scale": min(sc1+sStep,sc0)}
#file = open(fOut, "wb")
#pickle.dump(toWrite, file)
#file.close()
templateSSWorker.queue.put(min(sc1 + sStep, sc0))
def templateSSWorker(job):
# dict attr2pat: maps {str attrval: [[str imgpath_i, obj patch_i], ...]}
(attr2pat, attrval, superRegion, sStep, img2flip) = job
attr2pat1=attr2pat.copy()
# TODO: Arbitrarily chooses a patch_i
(imgpath, patch) = attr2pat1[attrval][0]
isflip = img2flip[imgpath]
I=sh.standardImread(imgpath,flatten=True)
superRegionNp=np.array(superRegion)
patchTuples=createPatchTuples(I,attr2pat1,superRegionNp,flip=isflip)
sc0=sh.resizeOrNot(patch.shape,sh.MAX_PRECINCT_PATCH_DIM)
minSc=sh.resizeOrNot(patch.shape,sh.MIN_PRECINCT_PATCH_DIM)
(scores0,locs,exemplar_idxs)=dist2patches(patchTuples,sc0)
sidx=np.argsort(scores0)
sidx=sidx[::-1]
trackIdx=sidx[0]
# sc1 is the 'scale' that we're currently working with.
sc1=sc0-sStep # Starting scale.
while sc1>minSc:
try:
(scores,locs, exemplar_idxs)=dist2patches(patchTuples,sc1)
except Exception as e:
d = {'patchTuples': patchTuples, 'sc1': sc1}
path = '_errdict_0'
'''
while os.path.exists(path):
new_i = int(path.split("_")[-1]) + 1
path = '_errdict_{0}'.format(str(new_i))
print '...outputting debug info to:', path
pickle.dump(d, open(path, 'wb'))
'''
print "Exiting."
exit(1)
sidx=np.argsort(scores)
sidx=sidx[::-1]
mid=np.ceil(len(sidx)/2.0)
dumpIdx=sidx[mid:len(sidx)]
if sum(0+(dumpIdx==trackIdx))>0:
break
else:
# decrease the scale
sc1=sc1-sStep
# write scale to file
#toWrite={"scale": min(sc1+sStep,sc0)}
#file = open(fOut, "wb")
#pickle.dump(toWrite, file)
#file.close()
templateSSWorker.queue.put(min(sc1+sStep, sc0))
def main():
args = sys.argv[1:]
if not args:
imgA = 'imgA.png'
imgB = 'imgB.png'
else:
imgA = args[0]
imgB = args[1]
Ia = shared.standardImread(imgA, True)
Ib = shared.standardImread(imgB, True)
print "Aligning {0} to {1}...".format(imgA, imgB)
H, Ireg, err = imagesAlign.imagesAlign(Ia, Ib)
Ireg_nonan = np.nan_to_num(Ireg)
scipy.misc.imsave("_IregA.png", Ireg_nonan)
print "Done, outputted result to: _IregA.png"
def main():
regions = ['extracted_digitpatches/precinct/0_exemplar.png',
'extracted_digitpatches/precinct/1_exemplar.png',
'extracted_digitpatches/precinct/2_exemplar.png']
img1 = shared.standardImread(img1path, flatten=True)
(h,w) = img1.shape
bb = [0, h-1, 0, w-1]
print "bb is:", bb
matches = shared.find_patch_matchesV1(img1, bb, regions, threshold=0.7)
print 'Found {0} number of matches.'.format(len(matches))
def main():
I_np = shared.standardImread(Ipath, flatten=True)
I_np = shared.prepOpenCV(I_np)
cv_imgBAD = cv.fromarray(np.copy(I_np))
cv.SaveImage("party_CV_BAD.png", cv_imgBAD)
cv_imgGOOD = cv.fromarray(np.copy(I_np) * 255.0)
cv.SaveImage("party_CV_GOOD.png", cv_imgGOOD)
print "Done."
def main():
regions = [
'extracted_digitpatches/precinct/0_exemplar.png',
'extracted_digitpatches/precinct/1_exemplar.png',
'extracted_digitpatches/precinct/2_exemplar.png'
]
img1 = shared.standardImread(img1path, flatten=True)
(h, w) = img1.shape
bb = [0, h - 1, 0, w - 1]
print "bb is:", bb
matches = shared.find_patch_matchesV1(img1, bb, regions, threshold=0.7)
print 'Found {0} number of matches.'.format(len(matches))
def debugWorker(job):
(Iref, bbs, fIn, destDir, extractedMeta, contestMeta, f1) = job
I=sh.standardImread(fIn)
# trim down or expand I to fit Iref
I1=np.ones([Iref.shape[0],Iref.shape[1]],dtype='float32')
h=min(Iref.shape[0],I.shape[0])-1; w=min(Iref.shape[1],I.shape[1])-1;
I1[1:h,1:w]=I[1:h,1:w]
# check if ballot is flipped
res=checkBallotFlipped(I1,Iref)
Ichk=res[1]; flipped=res[0]
# extract target and write result
return (Ichk,extractTargets(Ichk,Iref,bbs))
def debugWorker(job):
(Iref, bbs, fIn, destDir, extractedMeta, contestMeta, f1) = job
I = sh.standardImread(fIn)
# trim down or expand I to fit Iref
I1 = np.ones([Iref.shape[0], Iref.shape[1]], dtype='float32')
h = min(Iref.shape[0], I.shape[0]) - 1
w = min(Iref.shape[1], I.shape[1]) - 1
I1[1:h, 1:w] = I[1:h, 1:w]
# check if ballot is flipped
res = checkBallotFlipped(I1, Iref)
Ichk = res[1]
flipped = res[0]
# extract target and write result
return (Ichk, extractTargets(Ichk, Iref, bbs))
def main():
patch = shared.standardImread(imgA, flatten=True)
h, w = patch.shape
bb = [0, h, 0, w]
matches = shared.find_patch_matchesV1(patch, bb, (imgB,), threshold=0.0)
filename,score1,score2,Ireg,y1,y2,x1,x2,rszFac = matches[0]
print "Score1 is: {0}".format(score1)
print " This is low, which is good."
print "Score2 is: {0}".format(score2)
print " This is super low, which is /bad/. The two patches are \
very different."
print "I think this is the root of the problem: if I save the \
Ireg outputted by find_patch_matchesV1, then I get an all-black \
output image."
print "Saving Ireg as: Ireg_out.png"
scipy.misc.imsave('Ireg_out.png', Ireg)
def groupByAttr(bal2imgs,
img2page,
img2flip,
attrName,
side,
attrMap,
patchDestDir,
stopped,
proj,
verbose=False,
deleteall=True):
"""
Input:
dict bal2imgs: maps {str ballotid: (sidepath_i, ...)}
dict IMG2PAGE:
dict IMG2FLIP:
str attrName: the current attribute type
int SIDE:
dict attrMap: maps {str attrtype: {str attrval: (bb, str side, blankpath)}}
str patchDestDir: A directory, i.e. 'extracted_precincts-ballottype', stores
the extracted attribute image patches.
fn stopped:
obj proj:
options:
bool deleteall: if True, this will first remove all output files
before computing.
"""
if deleteall:
if os.path.exists(patchDestDir): shutil.rmtree(patchDestDir)
create_dirs(patchDestDir)
# maps {str attrval: [(str exmpl_imP, obj imagepatch), ...]}
attr2pat = {}
superRegion = (float('inf'), 0, float('inf'), 0)
# attrValMap: {str attrval: (bb, str side, blankpath)}
attrValMap = attrMap[attrName]
# 0.) First, grab an exemplar patch for each attrval. Add them to
# attr2pat.
# multexemplars_map: maps {attrtype: {attrval: ((str patchpath_i, str blankpath_i, (x1,y1,x2,y2)), ...)}}
multexemplars_map = pickle.load(
open(pathjoin(proj.projdir_path, proj.multexemplars_map), 'rb'))
exemplar_dict = multexemplars_map[attrName]
for attrval, exemplars in exemplar_dict.iteritems():
# Sort, in order to provide a canonical ordering
exemplars_sorted = sorted(exemplars, key=lambda t: t[0])
for (patchpath, blankpath, (x1, y1, x2, y2)) in exemplars_sorted:
P = sh.standardImread(patchpath, flatten=True)
attr2pat.setdefault(attrval, []).append((blankpath, P))
superRegion = sh.bbUnion(superRegion, (y1, y2, x1, x2))
for _attr, patches in attr2pat.iteritems():
print 'for attr {0}, there are {1} exemplars'.format(
_attr, len(patches))
# 1.) Estimate smallest viable scale (for performance)
if len(attr2pat) > 2:
scale = estimateScale(attr2pat, img2flip, superRegion,
sh.MAX_PRECINCT_PATCH_DIM, stopped)
else:
scale = sh.resizeOrNot(P.shape, sh.MAX_PRECINCT_PATCH_DIM)
print 'ATTR: ', attrName, ': using starting scale:', scale
# 2.) Generate jobs for the multiprocessing
nProc = sh.numProcs()
#nProc = 1
manager = mp.Manager()
queue = manager.Queue()
queue_progress = manager.Queue() # Used for MyGauge updates
pool = mp.Pool(processes=nProc)
jobs = []
for ballotid in bal2imgs.keys():
imgpaths = bal2imgs[ballotid]
imgpaths_ordered = sorted(imgpaths, key=lambda imP: img2page[imP])
imgpath_in = imgpaths_ordered[side]
isflip = img2flip[imgpath_in]
jobs.append([
ballotid, [imgpath_in], attrName, superRegion, attr2pat, isflip,
scale, patchDestDir, queue, queue_progress
])
print "Number of jobs:", len(jobs)
# 3.) Perform jobs.
if nProc < 2:
# default behavior for non multiproc machines
for job in jobs:
if stopped():
return False
groupImagesWorkerMAP(job)
else:
print 'using ', nProc, ' processes'
it = [False]
def imdone(x):
it[0] = True
print "I AM DONE NOW! WOW"
pool.map_async(groupImagesWorkerMAP,
jobs,
callback=lambda x: imdone(it))
i = 0
while i < len(jobs):
job_status, job_ballotid = queue_progress.get(
) # Blocks until value is ready
if job_status == False:
print "...Uhoh, ballotid={0} had a grouping failure.".format(
job_ballotid)
if wx.App.IsMainLoopRunning():
wx.CallAfter(Publisher().sendMessage, "signals.MyGauge.tick",
(JOBID_GROUPING_IMGBASED, ))
i += 1
while not it[0]:
if stopped():
print ' UHOH, stopped'
pool.terminate()
return False
time.sleep(.1)
print "HERE"
pool.close()
pool.join()
print "GOT HERE."
# list RESULTS: [[int ballotid, attrtype, dict outdict], ...]
results = []
cnt = 0
while cnt < len(jobs):
res = queue.get()
if type(res) in (str, unicode):
print "OH NO, badness happened."
else:
results.append(res)
cnt += 1
print 'cnt: ', cnt
# TODO: quarantine on grouping errors. For now, just let alignment check handle it
print 'ATTR: ', attrName, ': done'
return results
def groupByAttr(bal2imgs, img2page, img2flip, attrName, side, attrMap,
patchDestDir, stopped, proj, verbose=False, deleteall=True):
"""
Input:
dict bal2imgs: maps {str ballotid: (sidepath_i, ...)}
dict IMG2PAGE:
dict IMG2FLIP:
str attrName: the current attribute type
int SIDE:
dict attrMap: maps {str attrtype: {str attrval: (bb, str side, blankpath)}}
str patchDestDir: A directory, i.e. 'extracted_precincts-ballottype', stores
the extracted attribute image patches.
fn stopped:
obj proj:
options:
bool deleteall: if True, this will first remove all output files
before computing.
"""
if deleteall:
if os.path.exists(patchDestDir): shutil.rmtree(patchDestDir)
create_dirs(patchDestDir)
# maps {str attrval: [(str exmpl_imP, obj imagepatch), ...]}
attr2pat={}
superRegion=(float('inf'),0,float('inf'),0)
# attrValMap: {str attrval: (bb, str side, blankpath)}
attrValMap=attrMap[attrName]
# 0.) First, grab an exemplar patch for each attrval. Add them to
# attr2pat.
# multexemplars_map: maps {attrtype: {attrval: ((str patchpath_i, str blankpath_i, (x1,y1,x2,y2)), ...)}}
multexemplars_map = pickle.load(open(pathjoin(proj.projdir_path,
proj.multexemplars_map),
'rb'))
exemplar_dict = multexemplars_map[attrName]
for attrval, exemplars in exemplar_dict.iteritems():
# Sort, in order to provide a canonical ordering
exemplars_sorted = sorted(exemplars, key=lambda t: t[0])
for (patchpath, blankpath, (x1,y1,x2,y2)) in exemplars_sorted:
P = sh.standardImread(patchpath, flatten=True)
attr2pat.setdefault(attrval, []).append((blankpath, P))
superRegion = sh.bbUnion(superRegion, (y1,y2,x1,x2))
for _attr, patches in attr2pat.iteritems():
print 'for attr {0}, there are {1} exemplars'.format(_attr, len(patches))
# 1.) Estimate smallest viable scale (for performance)
if len(attr2pat)>2:
scale = estimateScale(attr2pat,img2flip,superRegion,sh.MAX_PRECINCT_PATCH_DIM,stopped)
else:
scale = sh.resizeOrNot(P.shape,sh.MAX_PRECINCT_PATCH_DIM);
print 'ATTR: ', attrName,': using starting scale:',scale
# 2.) Generate jobs for the multiprocessing
nProc=sh.numProcs()
#nProc = 1
manager = mp.Manager()
queue = manager.Queue()
queue_progress = manager.Queue() # Used for MyGauge updates
pool = mp.Pool(processes=nProc)
jobs = []
for ballotid in bal2imgs.keys():
imgpaths = bal2imgs[ballotid]
imgpaths_ordered = sorted(imgpaths, key=lambda imP: img2page[imP])
imgpath_in = imgpaths_ordered[side]
isflip = img2flip[imgpath_in]
jobs.append([ballotid, [imgpath_in], attrName, superRegion, attr2pat, isflip, scale,
patchDestDir, queue, queue_progress])
print "Number of jobs:", len(jobs)
# 3.) Perform jobs.
if nProc < 2:
# default behavior for non multiproc machines
for job in jobs:
if stopped():
return False
groupImagesWorkerMAP(job)
else:
print 'using ', nProc, ' processes'
it = [False]
def imdone(x):
it[0] = True
print "I AM DONE NOW! WOW"
pool.map_async(groupImagesWorkerMAP,jobs, callback=lambda x: imdone(it))
i = 0
while i < len(jobs):
job_status, job_ballotid = queue_progress.get() # Blocks until value is ready
if job_status == False:
print "...Uhoh, ballotid={0} had a grouping failure.".format(job_ballotid)
if wx.App.IsMainLoopRunning():
wx.CallAfter(Publisher().sendMessage, "signals.MyGauge.tick", (JOBID_GROUPING_IMGBASED,))
i += 1
while not it[0]:
if stopped():
print ' UHOH, stopped'
pool.terminate()
return False
time.sleep(.1)
print "HERE"
pool.close()
pool.join()
print "GOT HERE."
# list RESULTS: [[int ballotid, attrtype, dict outdict], ...]
results = []
cnt = 0
while cnt < len(jobs):
res = queue.get()
if type(res) in (str, unicode):
print "OH NO, badness happened."
else:
results.append(res)
cnt += 1
print 'cnt: ', cnt
# TODO: quarantine on grouping errors. For now, just let alignment check handle it
print 'ATTR: ', attrName, ': done'
return results
import shared as sh
import os
import numpy as np
from matplotlib.pyplot import show, imshow, figure, title
# load patch
ballotDir = '../../test-ballots/1side_Ntemplates/cedar2008primary_full/blank'
I = sh.standardImread(os.path.join(ballotDir, '20120608170512502_0001.jpg'),
flatten=True)
bb = [770, 800, 355, 500]
patch = I[bb[0]:bb[1], bb[2]:bb[3]]
#imshow(patch); show()
# generate image list
imList = []
for root, dirs, files in os.walk(ballotDir):
for f in files:
(f0, ext) = os.path.splitext(f)
if ext != '.jpg':
continue
p1 = os.path.join(root, f)
imList.append(p1)
#results = sh.find_patch_matches(patch,imList,region=bb)
results = sh.find_patch_matchesV1(I, bb, imList)
minOverlay = []
maxOverlay = []
for r1 in results:
if len(minOverlay) == 0:
import imagesAlign as lk
import shared as sh
import os
import numpy as np
from matplotlib.pyplot import show, imshow, figure, title
# load patch
img_dir = '../../test-ballots/carlini/'
Iref = sh.standardImread(os.path.join(img_dir, '0.tif'), flatten=True)
M = np.zeros((Iref.shape[0], Iref.shape[1], 11))
for i in range(11):
I = sh.standardImread(os.path.join(img_dir,
str(i + 1) + '.tif'),
flatten=True)
Inorm = np.zeros(Iref.shape)
# make patch the same size as Iref
min0 = min(I.shape[0], Iref.shape[0])
min1 = min(I.shape[1], Iref.shape[1])
Inorm[0:min0, 0:min1] = I[0:min0, 0:min1]
diff0 = Iref.shape[0] - I.shape[0]
diff1 = Iref.shape[1] - I.shape[1]
if diff0 > 0:
Inorm[I.shape[0]:I.shape[0] + diff0, :] = 1
if diff1 > 0:
Inorm[:, I.shape[1]:I.shape[1] + diff1] = 1
def process_one(args):
try:
imP, digit_hash, imList, bbSearch, nDigits, hspace, rejected_hashes, accepted_hashes, flipmap, queue_progress = args
I1 = sh.standardImread(imP, flatten=True)
if flipmap[imP]:
I1 = sh.fastFlip(I1)
# 0.) For Yolo (and perhaps other elections), upside-down voted
# ballots were problematic. Recall that the ballot straightener
# will pad the voted ballot with a black border if the B isn't
# of the specified canonical size (W,H). Currently, the straightener
# adds the padding to the bottom+right of the image. However, if the
# voted ballot is upside down, then the padding is added to the
# top+left (after undoing the flip), which results in a large shift
# which our algorithms aren't able to gracefully handle.
# ROWS, COLS is number of rows/cols removed from remove_border_topleft
I1, rows, cols = sh.remove_border_topleft(I1)
#I1=sh.prepOpenCV(I1)
E_i1 = 0.10 # factor to expand bbSearch by
E_i2 = 0.33
E_j1 = 0.05
E_j2 = 0.05
h, w = int(bbSearch[1] - bbSearch[0]), int(bbSearch[3] - bbSearch[2])
amt_i1 = int(round(E_i1 * h))
amt_i2 = int(round(E_i2 * h))
amt_j1 = int(round(E_j1 * w))
amt_j2 = int(round(E_j2 * w))
if (bbSearch[0] - amt_i1) < 0:
amt_i1 = bbSearch[0]
if (bbSearch[1] + amt_i2) > (I1.shape[0] - 1):
amt_i2 = (I1.shape[0] - 1 - bbSearch[1])
if (bbSearch[2] - amt_j1) < 0:
amt_j1 = bbSearch[2]
if (bbSearch[3] + amt_j2) > (I1.shape[1] - 1):
amt_j2 = (I1.shape[1] - 1 - bbSearch[3])
bb_patch = [
max(0, bbSearch[0] - amt_i1),
min(I1.shape[0] - 1, bbSearch[1] + amt_i2),
max(0, bbSearch[2] - amt_j1),
min(I1.shape[1] - 1, bbSearch[3] + amt_j2)
]
#I1=I1[bbSearch[0]:bbSearch[1],bbSearch[2]:bbSearch[3]]
I1_patch = I1[bb_patch[0]:bb_patch[1], bb_patch[2]:bb_patch[3]]
#if do_flip == False:
# misc.imsave('_{0}_{1}_bb.png'.format(os.path.splitext(os.path.split(imP)[1])[0],
# str(do_flip)),
# I1)
rejected_hash = rejected_hashes.get(imP,
None) if rejected_hashes else None
accepted_hash = accepted_hashes.get(imP,
None) if accepted_hashes else None
# perform matching for all digits
# return best matching digit
# mask out
# res := (str ocr_str, list patches, list bbs, list scores, list matched_keys)
res = pm2(digit_hash,
I1_patch,
nDigits,
hspace,
rejected_hash=rejected_hash,
accepted_hash=accepted_hash)
#res = pm1(digit_hash,I1,nDigits,hspace,rejected_hash=rejected_hash,accepted_hash=accepted_hash)
# 1.) Remember to correct for E_i,E_j expansion factor from earlier,
# the cropped-out black border (ROWS,COLS), and also to account
# for bbSearch offset.
newbbs = []
for bb in res[2]:
newbb0 = (max(0, bb[0] + bb_patch[0] + rows),
min(I1.shape[0] - 1, bb[1] + bb_patch[0] + rows),
max(0, bb[2] + bb_patch[2] + cols),
min(I1.shape[1] - 1, bb[3] + bb_patch[2] + cols))
newbbs.append(newbb0)
queue_progress.put((True, None))
return (imP, res[0], res[1], newbbs, res[3])
except Exception as e:
traceback.print_exc()
queue_progress.put((False, (imP, e.message)))
return False
import shared as sh
import os
import numpy as np
from matplotlib.pyplot import show, imshow, figure, title
# load patch
ballotDir = '../../test-ballots/1side_Ntemplates/cedar2008primary_full/blank'
I=sh.standardImread(os.path.join(ballotDir,'20120608170512502_0001.jpg'),flatten=True)
bb=[320,400,500,950]
patch = I[bb[0]:bb[1],bb[2]:bb[3]]
#imshow(patch); show()
# generate image list
imList = []
for root, dirs, files in os.walk(ballotDir):
for f in files:
(f0,ext)=os.path.splitext(f)
if ext != '.jpg':
continue
p1=os.path.join(root,f)
imList.append(p1)
#results = sh.find_patch_matches(patch,imList,region=bb)
results = sh.find_patch_matchesV1(I,bb,imList,bbSearch=list(bb),rszFac=.5)
minOverlay=[]
maxOverlay=[]
for r1 in results:
if len(minOverlay)==0:
minOverlay = r1[3]
maxOverlay = r1[3]
def process_one(args):
try:
imP, digit_hash,imList,bbSearch,nDigits, hspace, rejected_hashes,accepted_hashes, flipmap, queue_progress = args
I1 = sh.standardImread(imP,flatten=True)
if flipmap[imP]:
I1 = sh.fastFlip(I1)
# 0.) For Yolo (and perhaps other elections), upside-down voted
# ballots were problematic. Recall that the ballot straightener
# will pad the voted ballot with a black border if the B isn't
# of the specified canonical size (W,H). Currently, the straightener
# adds the padding to the bottom+right of the image. However, if the
# voted ballot is upside down, then the padding is added to the
# top+left (after undoing the flip), which results in a large shift
# which our algorithms aren't able to gracefully handle.
# ROWS, COLS is number of rows/cols removed from remove_border_topleft
I1, rows, cols = sh.remove_border_topleft(I1)
#I1=sh.prepOpenCV(I1)
E_i1 = 0.10 # factor to expand bbSearch by
E_i2 = 0.33
E_j1 = 0.05
E_j2 = 0.05
h, w = int(bbSearch[1] - bbSearch[0]), int(bbSearch[3]-bbSearch[2])
amt_i1 = int(round(E_i1*h))
amt_i2 = int(round(E_i2*h))
amt_j1 = int(round(E_j1*w))
amt_j2 = int(round(E_j2*w))
if (bbSearch[0] - amt_i1) < 0:
amt_i1 = bbSearch[0]
if (bbSearch[1] + amt_i2) > (I1.shape[0]-1):
amt_i2 = (I1.shape[0]-1 - bbSearch[1])
if (bbSearch[2] - amt_j1) < 0:
amt_j1 = bbSearch[2]
if (bbSearch[3] + amt_j2) > (I1.shape[1]-1):
amt_j2 = (I1.shape[1]-1 - bbSearch[3])
bb_patch = [max(0, bbSearch[0]-amt_i1),
min(I1.shape[0]-1, bbSearch[1]+amt_i2),
max(0, bbSearch[2]-amt_j1),
min(I1.shape[1]-1, bbSearch[3]+amt_j2)]
#I1=I1[bbSearch[0]:bbSearch[1],bbSearch[2]:bbSearch[3]]
I1_patch = I1[bb_patch[0]:bb_patch[1], bb_patch[2]:bb_patch[3]]
#if do_flip == False:
# misc.imsave('_{0}_{1}_bb.png'.format(os.path.splitext(os.path.split(imP)[1])[0],
# str(do_flip)),
# I1)
rejected_hash = rejected_hashes.get(imP, None) if rejected_hashes else None
accepted_hash = accepted_hashes.get(imP, None) if accepted_hashes else None
# perform matching for all digits
# return best matching digit
# mask out
# res := (str ocr_str, list patches, list bbs, list scores, list matched_keys)
res = pm2(digit_hash,I1_patch,nDigits,hspace,rejected_hash=rejected_hash,accepted_hash=accepted_hash)
#res = pm1(digit_hash,I1,nDigits,hspace,rejected_hash=rejected_hash,accepted_hash=accepted_hash)
# 1.) Remember to correct for E_i,E_j expansion factor from earlier,
# the cropped-out black border (ROWS,COLS), and also to account
# for bbSearch offset.
newbbs = []
for bb in res[2]:
newbb0 = (max(0, bb[0]+bb_patch[0]+rows),
min(I1.shape[0]-1, bb[1]+bb_patch[0]+rows),
max(0, bb[2]+bb_patch[2]+cols),
min(I1.shape[1]-1, bb[3]+bb_patch[2]+cols))
newbbs.append(newbb0)
queue_progress.put((True, None))
return (imP,res[0],res[1],newbbs,res[3])
except Exception as e:
traceback.print_exc()
queue_progress.put((False, (imP, e.message)))
return False
import sys
from matplotlib.pyplot import show, imshow
sys.path.append('../')
import shared
"""
Script to show a case where find_patch_matchesV1 returns an
Ireg with a lot of NaN's.
"""
patch = shared.standardImread('lang_en.png', flatten=True)
img = shared.standardImread('bad_english_nans.png', flatten=True)
h, w = patch.shape
x1, y1 = 0, 0
x2, y2 = w - 1, h - 1
bb = [y1, y2, x1, x2]
matches = shared.find_patch_matchesV1(patch,
bb, ('bad_english_nans.png', ),
threshold=0.6)
(path, score1, score2, Ireg, y1, y2, x1, x2, rszFac) = matches[0]
print "Ireg is:"
print Ireg
imshow(Ireg)
show()
import shared as sh
import part_match as pm
import os
import numpy as np
from matplotlib.pyplot import show, imshow, figure, title
ballotDir = '../../test-ballots/small_orange/339_100/'
digitDir = '../../test-ballots/small_orange/digit-source/'
# load all digit patches
digit_hash = {}
digit_hash["0"] = sh.standardImread(os.path.join(digitDir, '0.png'),
flatten=True)
digit_hash["1"] = sh.standardImread(os.path.join(digitDir, '1.png'),
flatten=True)
digit_hash["2"] = sh.standardImread(os.path.join(digitDir, '2.png'),
flatten=True)
digit_hash["3"] = sh.standardImread(os.path.join(digitDir, '3.png'),
flatten=True)
digit_hash["4"] = sh.standardImread(os.path.join(digitDir, '4.png'),
flatten=True)
digit_hash["5"] = sh.standardImread(os.path.join(digitDir, '5.png'),
flatten=True)
digit_hash["6"] = sh.standardImread(os.path.join(digitDir, '6.png'),
flatten=True)
digit_hash["7"] = sh.standardImread(os.path.join(digitDir, '7.png'),
flatten=True)
digit_hash["8"] = sh.standardImread(os.path.join(digitDir, '8.png'),
flatten=True)
digit_hash["9"] = sh.standardImread(os.path.join(digitDir, '9.png'),
flatten=True)
import cProfile
import cv2
from scipy import misc
from shared import standardImread
from imagesAlign import imagesAlign, pttransform
import pdb
import numpy as np
import matplotlib.pyplot as plt
import pstats
# convert to double
I = standardImread('lena_t.png')
Iref = standardImread('lena.png')
IO = imagesAlign(I, Iref)
#p = pstats.Stats('profile_imagesalign1')
#p.strip_dirs().sort_stats('cumulative').print_stats(25)
viz = True
if viz:
H = IO[0]
# figure(0)
# imshow(np.abs(Iref-I),cmap='gray');
plt.figure(1)
plt.imshow(np.abs(Iref - IO[1]), cmap='gray')
pt0 = np.array([155, 65, 1])
plt.figure(2)
plt.imshow(I, cmap='gray')
import imagesAlign as lk
import shared as sh
import os
import numpy as np
from matplotlib.pyplot import show, imshow, figure, title
# load patch
img_dir = '../../test-ballots/carlini/'
Iref=sh.standardImread(os.path.join(img_dir,'0.tif'),flatten=True)
M = np.zeros((Iref.shape[0],Iref.shape[1], 11))
for i in range(11):
I=sh.standardImread(os.path.join(img_dir,str(i+1)+'.tif'),flatten=True)
Inorm = np.zeros(Iref.shape)
# make patch the same size as Iref
min0 = min(I.shape[0],Iref.shape[0])
min1 = min(I.shape[1],Iref.shape[1])
Inorm[0:min0,0:min1] = I[0:min0,0:min1]
diff0 = Iref.shape[0] - I.shape[0]
diff1 = Iref.shape[1] - I.shape[1]
if diff0 > 0:
Inorm[I.shape[0]:I.shape[0]+diff0,:] = 1
if diff1 > 0:
Inorm[:,I.shape[1]:I.shape[1]+diff1] = 1
res=lk.imagesAlign(Inorm,Iref)
M[:,:,i] = res[1]