python类rotate()的实例源码

def TF_rotate(x, angle=0.0, target=None):
    assert len(x.shape) == 3
    h, w, nc = x.shape

    # Rotate using edge fill mode
    return rotate(x, angle, mode='edge', order=1)

def rotate_image(inImgFilename, outImgFilename, rotAngle):
    """
    Rotates a copy of inImgFilename by angle rotAngle (in degrees)

    Args:
        inImgFilename (str): path to the input mammogram in PNG format
        outImgFilename (str): directory where the output image must be saved in PNG format
        rotAngle (float): angle to rotate image (in degrees)
    """

    raw = misc.imread(inImgFilename)
    aug = rotate(raw, rotAngle)
    imsave(outImgFilename, aug)

def Data_iterate_minibatches(inputs, targets, batchsize, arg=False, genSetting=None, shuffle=False, warpMode=None):
    # assert len(inputs[0]) == len(targets[0])
    if shuffle:
        rinputs = copy.deepcopy(inputs)
        rtargets = copy.deepcopy(targets)
        indices = np.random.permutation(len(inputs[0]))
        for i in range(len(inputs[0])):
            for idx in range(len(inputs)):
                rinputs[idx][i] = inputs[idx][indices[i]]
            for idx in range(len(targets)):
                rtargets[idx][i] = targets[idx][indices[i]]
        inputs = rinputs
        targets = rtargets
        # inputs[:] = inputs[indices]
        # targets[:] = targets[indices]

    init = True
    global input_tmp
    global target_tmp
    global isOK
    for start_idx in range(0, len(inputs[0]) - batchsize*2 + 1, batchsize):
        # if (isOK == False) and (two == False):
        #     inputsbatch, targetsbatch = read_pics(inputs[start_idx:start_idx + batchsize], targets[start_idx:start_idx + batchsize], batchsize, crop, mirror, flip, rotate)
        # else:
        while isOK == False:
            if init:
                sl = range(start_idx,start_idx + batchsize)
                thread.start_new_thread(Data_readPics_thread, ([itemgetter(*sl)(i) for i in inputs], [itemgetter(*sl)(i) for i in targets], batchsize, genSetting, arg, warpMode))
                init = False
                # inputsbatch, targetsbatch = read_pics(inputs[start_idx:start_idx + batchsize], targets[start_idx:start_idx + batchsize], batchsize, crop, mirror, flip, rotate)
            time.sleep(0.01)
        inputsbatch, targetsbatch = input_tmp, target_tmp
        isOK = False
        sl = range(start_idx  + batchsize,start_idx + 2 * batchsize)
        thread.start_new_thread(Data_readPics_thread, ([itemgetter(*sl)(i) for i in inputs], [itemgetter(*sl)(i) for i in targets], batchsize, genSetting, arg, warpMode))
        # yield itertools.chain(inputsbatch, targetsbatch)
        yield inputsbatch + targetsbatch
    while isOK == False:
        time.sleep(0.01)
    inputsbatch, targetsbatch = input_tmp, target_tmp
    isOK = False
    # yield itertools.chain(inputsbatch, targetsbatch)
    yield inputsbatch + targetsbatch
    # len(inputs) - batchsize*2 + 1

def randomrotate(angle=90, randomstate=None, invert=False, padding=0, extrapadding=0):
    ignorechannels=True

    if isinstance(randomstate, int):
        rng = np.random.RandomState(randomstate)
    elif isinstance(randomstate, np.random.RandomState):
        rng = randomstate
    else:
        rng = np.random.RandomState(None)

    def _randomrot90(im):
        k = rng.randint(0, 4)
        if not invert:
            return np.rot90(im, k=k)
        else:
            return np.rot90(im, k=(4-k))

    def _randomrot45(im):
        assert im.shape[0] == im.shape[1], "45 degree rotations are tested only for square images."

        k = int(rng.choice(a=[0, 1, 3, 5, 7, 8], size=1))
        # Rotation angle (in degrees)
        rotangle = 45 * k

        if not invert:
            if k == 0 or k == 8:
                im = np.pad(im, (padding + extrapadding), mode='reflect') if padding > 0 else im
                return im
            else:
                im = _rotate(im, angle=rotangle, resize=True, mode='reflect')
                im = np.pad(im, padding, mode='reflect') if padding > 0 else im
                return im
        else:
            if k == 0 or k == 8:
                im = im[(padding + extrapadding):-(padding + extrapadding),
                     (padding + extrapadding):-(padding + extrapadding)] if padding > 0 else im
                return im
            else:
                im = im[padding:-padding, padding:-padding] if padding > 0 else im
                # For some reason, _rotate doesn't like if it's values are larger than +1 or smaller than -1.
                # Scale
                scale = np.max(np.abs(im))
                im *= (1./scale)
                # Process
                im = _rotate(im, angle=(360 - rotangle), resize=True, mode='reflect')
                # Rescale
                im *= scale
                # Edges of im are now twice as large as they were in the original image. Crop.
                cropstart = im.shape[0]/4
                cropstop = cropstart * 3
                im = im[cropstart:cropstop, cropstart:cropstop]
                return im

    if angle == 45:
        return image2batchfunc(_randomrot45, ignorechannels=ignorechannels)
    elif angle == 90:
        return image2batchfunc(_randomrot90, ignorechannels=ignorechannels)
    else:
        raise NotImplementedError("Curently implemented rotation angles are 45 and 90 degrees.")

# Function for random flips of the image