def siftManyImg(fn, nbcpu=None,cut=2000):
result={}
if not nbcpu:
nbcpu = multiprocessing.cpu_count()
for i in range((len(fn)-1)//nbcpu+1):
lf=[fn[0]]+[fn[j+1] for j in range(i*nbcpu,(i+1)*nbcpu) if j<len(fn)-1]
print lf
ld=[img2array(j)[cut:] for j in lf]
dr=feature.siftn(*ld,verbose=False,vs_first=True)
for k,v in dr.items():
result[(lf[k[0]],lf[k[1]])]=v
print(lf[k[0]],lf[k[1]],calcShift(v))
return result
def siftManyImg(fn, nbcpu=None, cut=2000):
result = {}
if not nbcpu:
nbcpu = multiprocessing.cpu_count()
for i in range((len(fn) - 1) // nbcpu + 1):
lf = [fn[0]] + [
fn[j + 1]
for j in range(i * nbcpu, (i + 1) * nbcpu) if j < len(fn) - 1
]
print lf
ld = [img2array(j)[cut:] for j in lf]
dr = feature.siftn(*ld, verbose=False, vs_first=True)
for k, v in dr.items():
result[(lf[k[0]], lf[k[1]])] = v
print(lf[k[0]], lf[k[1]], calcShift(v))
return result
def stitch(*img):
if len(img) <= 1:
return img
d = feature.siftn(*img, verbose=True)
t0 = time.time()
for i in d:
print i, "calcShift", calcShift(d[i])
so = setOfOffsets(d)
bigShape0 = max([i.shape[0] for i in img])
bigShape1 = max([i.shape[1] for i in img])
minPos = [0, 0]
maxPos = list(img[0].shape)
shifts = [(0, 0)]
for i in range(1, len(img)):
bp = so.bestPath(0, i)[-1]
print ((0, i), bp)
s = bp["shift"]
shifts.append(s)
if s[0] < minPos[0]:
minPos[0] = s[0]
if s[1] < minPos[1]:
minPos[1] = s[1]
if s[0] + img[i].shape[0] > maxPos[0]:
maxPos[0] = s[0] + img[i].shape[0]
if s[1] + img[i].shape[1] > maxPos[1]:
maxPos[1] = s[1] + img[i].shape[1]
print minPos, maxPos, shifts
print "Alignement:", time.time() - t0
big = numpy.zeros((maxPos[0] - minPos[0] + 1, maxPos[1] - minPos[1] + 1))
print big.shape
for i in range(len(img)):
s0 = int(round(shifts[i][0] - minPos[0]))
s1 = int(round(shifts[i][1] - minPos[1]))
print img[i].shape
print s0, s0 + img[i].shape[0], s1, s1 + img[i].shape[1]
big[s0:s0 + img[i].shape[0],
s1:s1 + img[i].shape[1]] = img[i]
print big.shape
print big
return big
def stitch(*img):
if len(img) <= 1:
return img
d = feature.siftn(*img, verbose=True)
t0 = time.time()
for i in d:
print i, "calcShift", calcShift(d[i])
so = setOfOffsets(d)
bigShape0 = max([i.shape[0] for i in img])
bigShape1 = max([i.shape[1] for i in img])
minPos = [0, 0]
maxPos = list(img[0].shape)
shifts = [(0, 0)]
for i in range(1, len(img)):
bp = so.bestPath(0, i)[-1]
print((0, i), bp)
s = bp["shift"]
shifts.append(s)
if s[0] < minPos[0]:
minPos[0] = s[0]
if s[1] < minPos[1]:
minPos[1] = s[1]
if s[0] + img[i].shape[0] > maxPos[0]:
maxPos[0] = s[0] + img[i].shape[0]
if s[1] + img[i].shape[1] > maxPos[1]:
maxPos[1] = s[1] + img[i].shape[1]
print minPos, maxPos, shifts
print "Alignement:", time.time() - t0
big = numpy.zeros((maxPos[0] - minPos[0] + 1, maxPos[1] - minPos[1] + 1))
print big.shape
for i in range(len(img)):
s0 = int(round(shifts[i][0] - minPos[0]))
s1 = int(round(shifts[i][1] - minPos[1]))
print img[i].shape
print s0, s0 + img[i].shape[0], s1, s1 + img[i].shape[1]
big[s0:s0 + img[i].shape[0], s1:s1 + img[i].shape[1]] = img[i]
print big.shape
print big
return big