def align(groupid, dat):
res = []
translations = []
for i, group in enumerate(zip(*dat)):
if i == 0:
# We want to look at everything for the title
left_border = 0
else:
# Skip the voting target for the rest
#left_border = 80
left_border = 0
Iref_orig = sh.standardImread(group[0], flatten=True)
Iref = Iref_orig[:, left_border:]
r = []
r_img = []
for i in range(len(group)):
I_orig = sh.standardImread(group[i], flatten=True)
I = I_orig[:, left_border:]
Inorm = make_norm(I, Iref)
(H, imres, err) = imagesAlign.imagesAlign(Inorm,
Iref,
trfm_type='translation')
r_img.append((make_norm(I_orig, Iref_orig), H))
r.append(translate(group[i]))
translations.append(r_img)
res.append(r)
translated_images = []
for contest in zip(*translations):
c_res = []
"""
print 'new'
arr = np.zeros((3,3))
for _,H in contest:
arr += H
arr /= len(contest)
print arr
"""
for img, H in contest:
translated = sh.imtransform(np.copy(img), H, fillval=np.nan)
align_res = np.nan_to_num(translated)
c_res.append(align_res)
translated_images.append(c_res)
translated_images = zip(*translated_images)
return res, translated_images
def align(groupid, dat):
res = []
translations = []
for i,group in enumerate(zip(*dat)):
if i == 0:
# We want to look at everything for the title
left_border = 0
else:
# Skip the voting target for the rest
#left_border = 80
left_border = 0
Iref_orig=sh.standardImread(group[0],flatten=True)
Iref = Iref_orig[:,left_border:]
r = []
r_img = []
for i in range(len(group)):
I_orig=sh.standardImread(group[i],flatten=True)
I=I_orig[:,left_border:]
Inorm = make_norm(I, Iref)
(H,imres,err)=imagesAlign.imagesAlign(Inorm,Iref,trfm_type='translation')
r_img.append((make_norm(I_orig, Iref_orig), H))
r.append(translate(group[i]))
translations.append(r_img)
res.append(r)
translated_images = []
for contest in zip(*translations):
c_res = []
"""
print 'new'
arr = np.zeros((3,3))
for _,H in contest:
arr += H
arr /= len(contest)
print arr
"""
for img,H in contest:
translated = sh.imtransform(np.copy(img), H, fillval=np.nan)
align_res = np.nan_to_num(translated)
c_res.append(align_res)
translated_images.append(c_res)
translated_images = zip(*translated_images)
return res, translated_images
def cluster_imgpatchesV2(imgpaths, bb_map, init_clusters=None, THRESHOLD=0.95):
""" Given a list of imgpaths, and bounding boxes for each image,
cluster the bounding boxes from each image.
Input:
list imgpaths:
dict bb_map: maps {str imgpath: (y1,y2,x1,x2)}
list init_clusters: An initial set of cluster centers, of the form:
{imgpath_i: (imgpath_i, bb_i)}
float THRESHOLD: A cutoff value to use when determining if the patch
was found. Ranges from [0.0, 1.0], where higher values is 'stricter',
i.e. THRESHOLD=1.0 means only accept exact matches (not recommended).
Higher values also means more computation time.
Output:
A dict of the form:
{c_imgpath: [(imgpath_i, bb_i, score), ...}
where each c_imgpath is the 'center' of a given cluster C.
"""
clusters = {}
unlabeled_imgpaths = list(imgpaths)
while unlabeled_imgpaths:
curimgpath = unlabeled_imgpaths.pop()
bb = bb_map[curimgpath]
I = shared.standardImread(curimgpath, flatten=True)
_t = time.time()
print "...calling find_patch_matchesV1..."
matches = partask.do_partask(findpatchmatches,
unlabeled_imgpaths,
_args=(I,
bb,
bb, THRESHOLD))
print "...finished find_patch_matchesV1 ({0} s)".format(time.time() - _t)
# Manually add in I
matches.append((curimgpath, -1.0, 1.0, None, bb[0], bb[1], bb[2], bb[3], 1.0))
if matches:
# 0.) Retrieve best matches from matches (may have multiple
# matches for the same imagepath)
print "...number of pre-filtered matches: {0}".format(len(matches))
bestmatches = {} # maps {imgpath: (imgpath,sc1,sc2,Ireg,y1,y2,x1,x2,rszFac)}
for (filename,sc1,sc2,Ireg,y1,y2,x1,x2,rszFac) in matches:
y1, y2, x1, x2 = map(lambda c: int(round(c/rszFac)), (y1, y2, x1, x2))
if filename not in bestmatches:
bestmatches[filename] = (filename,sc1,sc2,y1,y2,x1,x2,rszFac)
else:
old_sc2 = bestmatches[filename][2]
if sc2 < old_sc2:
bestmatches[filename] = (filename,sc1,sc2,y1,y2,x1,x2,rszFac)
print "...found {0} matches".format(len(bestmatches))
# 1.) Handle the best matches
for _, (filename,sc1,sc2,y1,y2,x1,x2,rszFac) in bestmatches.iteritems():
if filename in unlabeled_imgpaths:
unlabeled_imgpaths.remove(filename)
clusters.setdefault(curimgpath, []).append((filename, (y1,y2,x1,x2), sc2))
else:
print "...Uh oh, no matches found. This shouldnt' have \
happened."
pdb.set_trace()
print "...Completed clustering. Found {0} clusters.".format(len(clusters))
return clusters
def distance2(img, imgpath2):
""" L2 norm between img1, img2 """
img2 = shared.standardImread(imgpath2, flatten=True)
bb2 = bb_map.get(imgpath2, None)
if bb2 != None:
img2 = img2[bb2[0]:bb2[1], bb2[2]:bb2[3]]
img2 = common.resize_img_norescale(img2, (img.shape[1], img.shape[0]))
diff = np.linalg.norm(img - img2)
return diff
def distance2(img, imgpath2):
""" L2 norm between img1, img2 """
img2 = shared.standardImread(imgpath2, flatten=True)
bb2 = bb_map.get(imgpath2, None)
if bb2 != None:
img2 = img2[bb2[0]:bb2[1], bb2[2]:bb2[3]]
img2 = common.resize_img_norescale(img2, (img.shape[1], img.shape[0]))
diff = np.linalg.norm(img - img2)
return diff
def distance3(img, imgpath2):
""" NCC score between img1, img2. """
imgCv = cv.fromarray(np.copy(img.astype(np.float32)))
img2 = shared.standardImread(imgpath2, flatten=True)
bb2 = bb_map.get(imgpath2, None)
if bb2 != None:
img2 = img2[bb2[0]:bb2[2], bb2[2]:bb2[3]]
img2Cv = cv.fromarray(np.copy(img2.astype(np.float32)))
outCv = cv.CreateMat(imgCv.height - img2Cv.height+1, imgCv.width - img2Cv.width+1,
imgCv.type)
cv.MatchTemplate(imgCv, img2Cv, outCv, cv.CV_TM_CCOEFF_NORMED)
return outCv.max()
def distance3(img, imgpath2):
""" NCC score between img1, img2. """
imgCv = cv.fromarray(np.copy(img.astype(np.float32)))
img2 = shared.standardImread(imgpath2, flatten=True)
bb2 = bb_map.get(imgpath2, None)
if bb2 != None:
img2 = img2[bb2[0]:bb2[2], bb2[2]:bb2[3]]
img2Cv = cv.fromarray(np.copy(img2.astype(np.float32)))
outCv = cv.CreateMat(imgCv.height - img2Cv.height + 1,
imgCv.width - img2Cv.width + 1, imgCv.type)
cv.MatchTemplate(imgCv, img2Cv, outCv, cv.CV_TM_CCOEFF_NORMED)
return outCv.max()
def do_aligning(imgpaths, outdir, idx):
Iref_imgP = imgpaths.pop(idx)
Iref_np = scipy.misc.imread(Iref_imgP, flatten=True)
Iref = shared.standardImread(Iref_imgP, flatten=True)
Iref_crop = cropout_stuff(Iref, 0.2, 0.2, 0.2, 0.2)
Iouts = global_align(Iref_crop, imgpaths)
ref_dir = os.path.join(outdir, 'ref')
try:
os.makedirs(ref_dir)
except:
pass
Iref_imgname = os.path.split(Iref_imgP)[1]
scipy.misc.imsave(os.path.join(ref_dir, Iref_imgname), Iref)
for imgpath, H, Ireg_crop, err in Iouts:
print "For imgpath {0}, err={1:.4f}, H:".format(imgpath, err)
imgname = os.path.splitext(os.path.split(imgpath)[1])[0]
I = scipy.misc.imread(imgpath, flatten=True)
Hc = correctH(Ireg_crop, H)
print Hc
rot_H = Hc[:2, :2]
trans_H = Hc[:2, 2]
"""
Note: imagesAlign's H is of the form:
[a b X]
[c d Y]
where positive Y means go down, and positive X means to right (as usual).
scipy's affine_transform's offset should be offset=(y,x), but where
positive y is go UP, and positive x is to the LEFT.
"""
#Itrans = scipy.ndimage.interpolation.affine_transform(I, rot_H,
# offset=(-trans_H[1], -trans_H[0]),
# output_shape=I.shape)
#Icv = cv.LoadImageM(imgpath, cv.CV_LOAD_IMAGE_GRAYSCALE)
#Icv_trans = cv.CreateMat(Icv.rows, Icv.cols, Icv.type)
#H_cv = cv.fromarray(Hc.astype(np.float32)[:2,:])
#cv.WarpAffine(Icv, Icv_trans, H_cv)
#Itrans = np.asarray(Icv)
Itrans = imtransform(I, H)
Itrans = np.nan_to_num(Itrans)
outP = os.path.join(outdir, "{0}_err{1:.4f}.png".format(imgname, err))
Ioverlay = np.zeros(Itrans.shape)
Ioverlay[:, :] = Itrans
Ioverlay[:, :] += Iref_np
scipy.misc.imsave(outP, Ioverlay)
def closest_label(imgpath, exemplars):
mindist = None
bestmatch = None
img = shared.standardImread(imgpath, flatten=True)
bb = bb_map.get(imgpath, None)
if bb != None:
img = img[bb[0]:bb[1], bb[2]:bb[3]]
for label, imgpaths in exemplars.iteritems():
for imgpathB in imgpaths:
dist = distance2(img, imgpathB)
if mindist == None or dist < mindist:
bestmatch = label
mindist = dist
return bestmatch, mindist
def do_aligning(imgpaths, outdir, idx):
Iref_imgP = imgpaths.pop(idx)
Iref_np = scipy.misc.imread(Iref_imgP, flatten=True)
Iref = shared.standardImread(Iref_imgP, flatten=True)
Iref_crop = cropout_stuff(Iref, 0.2, 0.2, 0.2, 0.2)
Iouts = global_align(Iref_crop, imgpaths)
ref_dir = os.path.join(outdir, 'ref')
try:
os.makedirs(ref_dir)
except: pass
Iref_imgname = os.path.split(Iref_imgP)[1]
scipy.misc.imsave(os.path.join(ref_dir, Iref_imgname), Iref)
for imgpath, H, Ireg_crop, err in Iouts:
print "For imgpath {0}, err={1:.4f}, H:".format(imgpath, err)
imgname = os.path.splitext(os.path.split(imgpath)[1])[0]
I = scipy.misc.imread(imgpath, flatten=True)
Hc = correctH(Ireg_crop, H)
print Hc
rot_H = Hc[:2, :2]
trans_H = Hc[:2,2]
"""
Note: imagesAlign's H is of the form:
[a b X]
[c d Y]
where positive Y means go down, and positive X means to right (as usual).
scipy's affine_transform's offset should be offset=(y,x), but where
positive y is go UP, and positive x is to the LEFT.
"""
#Itrans = scipy.ndimage.interpolation.affine_transform(I, rot_H,
# offset=(-trans_H[1], -trans_H[0]),
# output_shape=I.shape)
#Icv = cv.LoadImageM(imgpath, cv.CV_LOAD_IMAGE_GRAYSCALE)
#Icv_trans = cv.CreateMat(Icv.rows, Icv.cols, Icv.type)
#H_cv = cv.fromarray(Hc.astype(np.float32)[:2,:])
#cv.WarpAffine(Icv, Icv_trans, H_cv)
#Itrans = np.asarray(Icv)
Itrans = imtransform(I, H)
Itrans = np.nan_to_num(Itrans)
outP = os.path.join(outdir, "{0}_err{1:.4f}.png".format(imgname, err))
Ioverlay = np.zeros(Itrans.shape)
Ioverlay[:,:] = Itrans
Ioverlay[:,:] += Iref_np
scipy.misc.imsave(outP, Ioverlay)
def closest_label(imgpath, exemplars):
mindist = None
bestmatch = None
img = shared.standardImread(imgpath, flatten=True)
bb = bb_map.get(imgpath, None)
if bb != None:
img = img[bb[0]:bb[1], bb[2]:bb[3]]
for label, imgpaths in exemplars.iteritems():
for imgpathB in imgpaths:
dist = distance2(img, imgpathB)
if mindist == None or dist < mindist:
bestmatch = label
mindist = dist
return bestmatch, mindist
def closest_label(imgpath, bb, exemplars):
bestlabel = None
mindist = None
bbBest = None
img = shared.standardImread(imgpath, flatten=True)
for label, tuples in exemplars.iteritems():
imgpaths2, bbs2 = [], []
for imgpath2, bb2 in tuples:
imgpaths2.append(imgpath2)
bbs2.append(bb2)
closestdist, bbOut = get_closest_ncclk(imgpath, img, bb, imgpaths2, bbs2)
if bestlabel == None or closestdist < mindist:
bestlabel = label
mindist = closestdist
bbBest = bbOut
return bestlabel, mindist, bbBest
def closest_label(imgpath, bb, exemplars):
bestlabel = None
mindist = None
bbBest = None
img = shared.standardImread(imgpath, flatten=True)
for label, tuples in exemplars.iteritems():
imgpaths2, bbs2 = [], []
for imgpath2, bb2 in tuples:
imgpaths2.append(imgpath2)
bbs2.append(bb2)
closestdist, bbOut = get_closest_ncclk(
imgpath, img, bb, imgpaths2, bbs2)
if bestlabel is None or closestdist < mindist:
bestlabel = label
mindist = closestdist
bbBest = bbOut
return bestlabel, mindist, bbBest
def global_align(Iref, imgpaths):
""" Using IREF as a reference, aligns every image in IMGPATHS to IREF.
Input:
IplImage IREF: An OpenCV IplImage instance, i.e. the reference
image we will align against.
list IMGPATHS: A list of image paths.
Output:
list IOUTS. [(str imgpath, nparray H, IplImage Ireg, float err), ...].
IOUTS:
A list of tuples containing the aligned image Ireg, along with
the discovered transformation matrix H, alignment error ERR, and
the path to the ballot image IMGPATH.
"""
Iouts = [] # [(imgpath, H, Ireg, err), ...]
for imgpath in imgpaths:
I = shared.standardImread(imgpath, flatten=True)
Icrop = cropout_stuff(I, 0.2, 0.2, 0.2, 0.2)
H, Ireg, err = imagesAlign(Icrop, Iref, trfm_type='rigid', rszFac=0.25)
Ireg = np.nan_to_num(Ireg)
Iouts.append((imgpath, H, Ireg, err))
return Iouts
def global_align(Iref, imgpaths):
""" Using IREF as a reference, aligns every image in IMGPATHS to IREF.
Input:
IplImage IREF: An OpenCV IplImage instance, i.e. the reference
image we will align against.
list IMGPATHS: A list of image paths.
Output:
list IOUTS. [(str imgpath, nparray H, IplImage Ireg, float err), ...].
IOUTS:
A list of tuples containing the aligned image Ireg, along with
the discovered transformation matrix H, alignment error ERR, and
the path to the ballot image IMGPATH.
"""
Iouts = [] # [(imgpath, H, Ireg, err), ...]
for imgpath in imgpaths:
I = shared.standardImread(imgpath, flatten=True)
Icrop = cropout_stuff(I, 0.2, 0.2, 0.2, 0.2)
H, Ireg, err = imagesAlign(Icrop, Iref, trfm_type='rigid', rszFac=0.25)
Ireg = np.nan_to_num(Ireg)
Iouts.append((imgpath, H, Ireg, err))
return Iouts
def temp_match(I, bb, imList, bbSearch=None, bbSearches=None, rszFac=0.75,
padSearch=0.75, padPatch=0.0):
bb = list(bb)
if bbSearch != None:
bbSearch = list(bbSearch)
if bbSearches != None:
bbSearches = list(bbSearches)
matchList = [] # (filename, left,right,up,down)
I = np.round(shared.fastResize(I,rszFac)*255.)/255;
bb[0] = bb[0]*rszFac
bb[1] = bb[1]*rszFac
bb[2] = bb[2]*rszFac
bb[3] = bb[3]*rszFac
[bbOut,bbOff]=shared.expand(bb[0],bb[1],bb[2],bb[3],I.shape[0],I.shape[1],padPatch)
patchFoo = I[bbOut[0]:bbOut[1],bbOut[2]:bbOut[3]]
patch = patchFoo[bbOff[0]:bbOff[1],bbOff[2]:bbOff[3]]
if bbSearch != None:
bbSearch[0] = bbSearch[0]*rszFac
bbSearch[1] = bbSearch[1]*rszFac
bbSearch[2] = bbSearch[2]*rszFac
bbSearch[3] = bbSearch[3]*rszFac
for cur_i, imP in enumerate(imList):
if bbSearches != None:
bbSearch = map(lambda c: c*rszFac, bbSearches[cur_i])
I1 = shared.standardImread(imP,flatten=True)
I1 = np.round(shared.fastResize(I1,rszFac)*255.)/255.
# crop to region if specified
if bbSearch != None:
[bbOut1,bbOff1]=shared.expand(bbSearch[0],bbSearch[1],
bbSearch[2],bbSearch[3],
I1.shape[0],I1.shape[1],padSearch)
I1=I1[bbOut1[0]:bbOut1[1],bbOut1[2]:bbOut1[3]]
if I1.shape[0] < patch.shape[0] or I1.shape[1] < patch.shape[1]:
w_big = max(I1.shape[0], patch.shape[0])
h_big = max(I1.shape[1], patch.shape[1])
I1_big = np.zeros((w_big, h_big)).astype('float32')
I1_big[0:I1.shape[0], 0:I1.shape[1]] = I1
I1 = I1_big
patchCv=cv.fromarray(np.copy(patch))
ICv=cv.fromarray(np.copy(I1))
outCv=cv.CreateMat(abs(I1.shape[0]-patch.shape[0])+1,abs(I1.shape[1]-patch.shape[1])+1, cv.CV_32F)
cv.MatchTemplate(ICv,patchCv,outCv,cv.CV_TM_CCOEFF_NORMED)
Iout=np.asarray(outCv)
Iout[Iout==1.0]=0.995; # opencv bug
score1 = Iout.max() # NCC score
YX=np.unravel_index(Iout.argmax(),Iout.shape)
i1=YX[0]; i2=YX[0]+patch.shape[0]
j1=YX[1]; j2=YX[1]+patch.shape[1]
(err,diff,Ireg)=shared.lkSmallLarge(patch,I1,i1,i2,j1,j2, minArea=np.power(2, 17))
score2 = err / diff.size # pixel reg score
if bbSearch != None:
matchList.append((imP,score1,score2,Ireg,
i1+bbOut1[0],i2+bbOut1[0],
j1+bbOut1[2],j2+bbOut1[2],rszFac))
else:
matchList.append((imP,score1,score2,Ireg,
i1,i2,j1,j2,rszFac))
return matchList
def do_digit_group(b2imgs, img2b, partitions_map, partitions_invmap,
partition_exmpls, badballotids,
img2page, img2flip, attrinfo, digitexemplars_map,
digitpatch_outdir, voteddir_root, digpatch2imgpath_outP,
mode=GRP_PER_PARTITION):
"""
Input:
dict B2IMGS:
dict IMG2B:
dict PARTITIONS_MAP:
dict PARTITIONS_INVMAP:
dict PARTITIONS_EXMPLS:
list BADBALLOTIDS: List of quarantined/discarded ballot ids.
dict IMG2PAGE:
dict IMG2FLIP: maps {str imgpath: bool isflip}
dict ATTRINFO: [x1,y1,x2,y2,attrtype,page,numdigits,digitdist]
dict DIGITEXEMPLARS_MAP: maps {str digitval: [[str regionP, (y1,y2,x1,x2), str digitpatch_i], ...]}
str DIGITPATCH_OUTDIR: Root dir of extracted digit patches.
str VOTEDDIR_ROOT: Root dir of voted ballots.
int MODE:
Output:
dict DRESULTS. maps {int ID: [str digitstr, imgpath, [[str digit_i, (x1,y1,x2,y2), score_i, digpatchP_i], ...]]},
where ID is partitionID/ballotID depending on MODE.
"""
x1, y1, x2, y2, attrtype, page, numdigits, digitdist = attrinfo
# 0.) Depending on MODE, grab the image paths to work with.
d_imgpaths = [] # imgpaths with the digit attribute present
flip_map = {} # maps {str imgpath: bool isflip}
if mode == GRP_PER_PARTITION:
for partitionID, ballotIDs in partition_exmpls.iteritems():
imgpaths = b2imgs[ballotIDs[0]]
imgpaths_ordered = sorted(imgpaths, key=lambda imP: img2page[imP])
d_imgpaths.append(imgpaths_ordered[page])
for imgpath in imgpaths_ordered:
flip_map[imgpath] = img2flip[imgpath]
else:
for ballotID, imgpaths in b2imgs.iteritems():
if ballotID in badballotids:
continue
imgpaths_ordered = sorted(imgpaths, key=lambda imP: img2page[imP])
d_imgpaths.append(imgpaths_ordered[page])
flip_map = img2flip
# 1.) Load the digit exemplars
digit_ex_imgs = {} # maps {(str digit, str meta): nparray digit_img}
for digit, exemplars in digitexemplars_map.iteritems():
for i, (regionP, bb, digitpatch) in enumerate(exemplars):
I = shared.standardImread(digitpatch, flatten=True)
digit_ex_imgs[(digit, i)] = I
# 2.) Invoke digitParse
bb = (y1, y2, x1, x2)
rejected_hashes = {}
accepted_hashes = {}
# RESULTS: [(imgpath_i, ocrstr_i, imgpatches_i, patchcoords_i, scores_i), ...]
pm_results = part_match.digitParse(digit_ex_imgs, d_imgpaths, bb, numdigits,
flipmap=flip_map, rejected_hashes=rejected_hashes,
accepted_hashes=accepted_hashes,
hspace=digitdist)
# 3.) Finally, munge PM_RESULTS into DRESULTS, and also extract
# each digit patch into proj.digitpatch_out.
dresults = {}
extract_jobs = [] # [[imgpath, (x1,y1,x2,y2), outpath], ...]
digpatch2imgpath = {} # maps {str digpatchpath: (str imgpath, int idx)}
for (imgpath, ocrstr, imgpatches, patchcoords, scores) in pm_results:
ballotid = img2b[imgpath]
# Recreate directory structure
rp = os.path.relpath(os.path.abspath(imgpath), os.path.abspath(voteddir_root))
imgname = os.path.splitext(os.path.split(imgpath)[1])[0]
if mode == GRP_PER_PARTITION:
id = partitions_invmap[ballotid]
else:
id = ballotid
entry = []
for i,digit in enumerate(ocrstr):
y1, y2, x1, x2 = patchcoords[i]
digpatchP = pathjoin(digitpatch_outdir, rp, "{0}_dig{1}_{2}.png".format(imgname, digit, i))
entry.append([digit, (x1,y1,x2,y2), scores[i], digpatchP])
extract_jobs.append((imgpath, (x1,y1,x2,y2), digpatchP, img2flip[imgpath]))
digpatch2imgpath[digpatchP] = (imgpath, i)
row = (ocrstr, imgpath, entry)
dresults[id] = row
pickle.dump(digpatch2imgpath, open(digpatch2imgpath_outP, 'wb'),
pickle.HIGHEST_PROTOCOL)
print "...Extracting DigitPatches..."
t = time.time()
do_extract_digitpatches(extract_jobs)
dur = time.time() - t
print "...Finished extracting digit patches ({0} s).".format(dur)
return dresults
def temp_match(I, bb, imList, bbSearch=None, bbSearches=None, rszFac=0.75,
padSearch=0.75, padPatch=0.0):
bb = list(bb)
if bbSearch is not None:
bbSearch = list(bbSearch)
if bbSearches is not None:
bbSearches = list(bbSearches)
matchList = [] # (filename, left,right,up,down)
I = np.round(shared.fastResize(I, rszFac) * 255.) / 255
bb[0] = bb[0] * rszFac
bb[1] = bb[1] * rszFac
bb[2] = bb[2] * rszFac
bb[3] = bb[3] * rszFac
[bbOut, bbOff] = shared.expand(bb[0], bb[1], bb[2], bb[3], I.shape[
0], I.shape[1], padPatch)
patchFoo = I[bbOut[0]:bbOut[1], bbOut[2]:bbOut[3]]
patch = patchFoo[bbOff[0]:bbOff[1], bbOff[2]:bbOff[3]]
if bbSearch is not None:
bbSearch[0] = bbSearch[0] * rszFac
bbSearch[1] = bbSearch[1] * rszFac
bbSearch[2] = bbSearch[2] * rszFac
bbSearch[3] = bbSearch[3] * rszFac
for cur_i, imP in enumerate(imList):
if bbSearches is not None:
bbSearch = map(lambda c: c * rszFac, bbSearches[cur_i])
I1 = shared.standardImread(imP, flatten=True)
I1 = np.round(shared.fastResize(I1, rszFac) * 255.) / 255.
# crop to region if specified
if bbSearch is not None:
[bbOut1, bbOff1] = shared.expand(bbSearch[0], bbSearch[1],
bbSearch[2], bbSearch[3],
I1.shape[0], I1.shape[1], padSearch)
I1 = I1[bbOut1[0]:bbOut1[1], bbOut1[2]:bbOut1[3]]
if I1.shape[0] < patch.shape[0] or I1.shape[1] < patch.shape[1]:
w_big = max(I1.shape[0], patch.shape[0])
h_big = max(I1.shape[1], patch.shape[1])
I1_big = np.zeros((w_big, h_big)).astype('float32')
I1_big[0:I1.shape[0], 0:I1.shape[1]] = I1
I1 = I1_big
patchCv = cv.fromarray(np.copy(patch))
ICv = cv.fromarray(np.copy(I1))
outCv = cv.CreateMat(abs(I1.shape[
0] - patch.shape[0]) + 1, abs(I1.shape[1] - patch.shape[1]) + 1, cv.CV_32F)
cv.MatchTemplate(ICv, patchCv, outCv, cv.CV_TM_CCOEFF_NORMED)
Iout = np.asarray(outCv)
Iout[Iout == 1.0] = 0.995 # opencv bug
score1 = Iout.max() # NCC score
YX = np.unravel_index(Iout.argmax(), Iout.shape)
i1 = YX[0]
i2 = YX[0] + patch.shape[0]
j1 = YX[1]
j2 = YX[1] + patch.shape[1]
(err, diff, Ireg) = shared.lkSmallLarge(
patch, I1, i1, i2, j1, j2, minArea=np.power(2, 17))
score2 = err / diff.size # pixel reg score
if bbSearch is not None:
matchList.append((imP, score1, score2, Ireg,
i1 + bbOut1[0], i2 + bbOut1[0],
j1 + bbOut1[2], j2 + bbOut1[2], rszFac))
else:
matchList.append((imP, score1, score2, Ireg,
i1, i2, j1, j2, rszFac))
return matchList
def cluster_imgpatchesV2(imgpaths, bb_map, init_clusters=None, THRESHOLD=0.95):
""" Given a list of imgpaths, and bounding boxes for each image,
cluster the bounding boxes from each image.
Input:
list imgpaths:
dict bb_map: maps {str imgpath: (y1,y2,x1,x2)}
list init_clusters: An initial set of cluster centers, of the form:
{imgpath_i: (imgpath_i, bb_i)}
float THRESHOLD: A cutoff value to use when determining if the patch
was found. Ranges from [0.0, 1.0], where higher values is 'stricter',
i.e. THRESHOLD=1.0 means only accept exact matches (not recommended).
Higher values also means more computation time.
Output:
A dict of the form:
{c_imgpath: [(imgpath_i, bb_i, score), ...}
where each c_imgpath is the 'center' of a given cluster C.
"""
clusters = {}
unlabeled_imgpaths = list(imgpaths)
while unlabeled_imgpaths:
curimgpath = unlabeled_imgpaths.pop()
bb = bb_map[curimgpath]
I = shared.standardImread(curimgpath, flatten=True)
_t = time.time()
print "...calling find_patch_matchesV1..."
matches = partask.do_partask(findpatchmatches,
unlabeled_imgpaths,
_args=(I, bb, bb, THRESHOLD))
print "...finished find_patch_matchesV1 ({0} s)".format(time.time() -
_t)
# Manually add in I
matches.append(
(curimgpath, -1.0, 1.0, None, bb[0], bb[1], bb[2], bb[3], 1.0))
if matches:
# 0.) Retrieve best matches from matches (may have multiple
# matches for the same imagepath)
print "...number of pre-filtered matches: {0}".format(len(matches))
bestmatches = {
} # maps {imgpath: (imgpath,sc1,sc2,Ireg,y1,y2,x1,x2,rszFac)}
for (filename, sc1, sc2, Ireg, y1, y2, x1, x2, rszFac) in matches:
y1, y2, x1, x2 = map(lambda c: int(round(c / rszFac)),
(y1, y2, x1, x2))
if filename not in bestmatches:
bestmatches[filename] = (filename, sc1, sc2, y1, y2, x1,
x2, rszFac)
else:
old_sc2 = bestmatches[filename][2]
if sc2 < old_sc2:
bestmatches[filename] = (filename, sc1, sc2, y1, y2,
x1, x2, rszFac)
print "...found {0} matches".format(len(bestmatches))
# 1.) Handle the best matches
for _, (filename, sc1, sc2, y1, y2, x1, x2,
rszFac) in bestmatches.iteritems():
if filename in unlabeled_imgpaths:
unlabeled_imgpaths.remove(filename)
clusters.setdefault(curimgpath, []).append(
(filename, (y1, y2, x1, x2), sc2))
else:
print "...Uh oh, no matches found. This shouldnt' have \
happened."
pdb.set_trace()
print "...Completed clustering. Found {0} clusters.".format(len(clusters))
return clusters