Python random_rotation示例

示例#1

文件： utilsCAE.py 项目： roseorenbuch/imageCCA

def getImage(sampSize, newSize, imPath, _=''):
    ''' randomly samples an image  '''
    f1 = int(np.sign(np.random.rand() - .5))
    f2 = int(np.sign(np.random.rand() - .5))
    im = image.load_img(imPath).resize((newSize, newSize))
    r = int(np.random.rand() * (newSize - sampSize))
    c = int(np.random.rand() * (newSize - sampSize))
    im = im.crop((r, c, r + sampSize, c + sampSize))
    im = image.random_rotation(im, 5)
    im = image.img_to_array(im)[:, ::-f1, ::-f2]
    im = (im - im.min()) / (im.max() - im.min())
    return im

示例#2

文件： CAE_classify_output_GTEx.py 项目： Ebony-Watson/imageCCA

def getOne(batchSize, imPath):
    allIms = np.zeros((batchSize, 3, sampSize, sampSize))
    for i in xrange(batchSize):
        f1 = int(np.sign(np.random.rand() - .5))
        f2 = int(np.sign(np.random.rand() - .5))
        im = image.load_img(imPath).resize((newSize, newSize))
        r = int(np.random.rand() * (newSize - sampSize))
        c = int(np.random.rand() * (newSize - sampSize))
        im = im.crop((r, c, r + sampSize, c + sampSize))
        im = image.random_rotation(im, 5)
        allIms[i, :, :, :] = image.img_to_array(im)
        allIms[i, :, :, :] = allIms[i, :, ::-f1, ::-f2] / 255.
    return allIms

示例#3

def getBatch(batchSize, path):
	allPaths = image.list_pictures(path)
	allIms = np.zeros((batchSize, 3, sampSize, sampSize))
	for i in xrange(batchSize):
		f1 = int(np.sign(np.random.rand() - .5))
		f2 = int(np.sign(np.random.rand() - .5))
		im = image.load_img(allPaths[int(np.random.rand() * len(allPaths))]).resize((newSize, newSize))
		r  = int(np.random.rand() * (newSize - sampSize))
		c  = int(np.random.rand() * (newSize - sampSize))
		im = im.crop((r, c, r + sampSize, c + sampSize))
		im = image.random_rotation(im, 5)
		allIms[i, :, :, :] = image.img_to_array(im)
		allIms[i, :, :, :] = allIms[i, :, ::-f1, ::-f2]
	return allIms/255.

示例#4

def getBatch(batchSize, allPaths, numClass):
    allIms = np.zeros((batchSize, 3, sampSize, sampSize))
    Y = np.zeros((batchSize, 1)).astype('int')
    rIms = np.zeros((batchSize, 1)).astype('str')
    for i in xrange(batchSize):
        f1 = int(np.sign(np.random.rand() - .5))
        f2 = int(np.sign(np.random.rand() - .5))
        rIm = allPaths[int(np.random.rand() * len(allPaths))]
        rIms[i] = rIm
        im = image.load_img(rIm).resize((newSize, newSize))
        Y[i] = labels[rIm.split('/')[-1]]
        r = int(np.random.rand() * (newSize - sampSize))
        c = int(np.random.rand() * (newSize - sampSize))
        im = im.crop((r, c, r + sampSize, c + sampSize))
        im = image.random_rotation(im, 5)
        allIms[i, :, :, :] = image.img_to_array(im)
        allIms[i, :, :, :] = allIms[i, :, ::-f1, ::-f2]
    if numClass > 2:
        Y2 = np.zeros((batchSize, numClass)).astype('int')
        for i in xrange(batchSize):
            Y2[i, Y[i][0]] = 1
        Y = Y2
    return allIms, Y, rIms