Example #1
0
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.sift(*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
Example #2
0
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.sift(*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
Example #3
0
def stitch(*img):
    if len(img) <= 1:
        return img

    d = feature.sift(*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
Example #4
0
def stitch(*img):
    if len(img) <= 1:
        return img

    d = feature.sift(*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