我们从Python开源项目中，提取了以下49个代码示例，用于说明如何使用PIL.Image.html()。
def brightness(x, gamma=1, gain=1, is_random=False): """Change the brightness of a single image, randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). gamma : float, small than 1 means brighter. Non negative real number. Default value is 1. - If is_random is True, gamma in a range of (1-gamma, 1+gamma). gain : float The constant multiplier. Default value is 1. is_random : boolean, default False - If True, randomly change brightness. References ----------- - `skimage.exposure.adjust_gamma <http://scikit-image.org/docs/dev/api/skimage.exposure.html>`_ - `chinese blog <http://www.cnblogs.com/denny402/p/5124402.html>`_ """ if is_random: gamma = np.random.uniform(1-gamma, 1+gamma) x = exposure.adjust_gamma(x, gamma, gain) return x
def brightness(x, gamma=1, gain=1, is_random=False): """Change the brightness of a single image, randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). gamma : float, small than 1 means brighter. Non negative real number. Default value is 1, smaller means brighter. - If is_random is True, gamma in a range of (1-gamma, 1+gamma). gain : float The constant multiplier. Default value is 1. is_random : boolean, default False - If True, randomly change brightness. References ----------- - `skimage.exposure.adjust_gamma <http://scikit-image.org/docs/dev/api/skimage.exposure.html>`_ - `chinese blog <http://www.cnblogs.com/denny402/p/5124402.html>`_ """ if is_random: gamma = np.random.uniform(1-gamma, 1+gamma) x = exposure.adjust_gamma(x, gamma, gain) return x
def erosion(x, radius=3): """ Return greyscale morphological erosion of an image, see `skimage.morphology.erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.erosion>`_. Parameters ----------- x : 2D array image. radius : int for the radius of mask. """ from skimage.morphology import disk, dilation, erosion mask = disk(radius) x = erosion(x, selem=mask) return x ## Object Detection
def trans_image(idx, img_arr): # https://docs.scipy.org/doc/numpy/reference/routines.math.html #img_arr = np.add(np.multiply(255.0, img_arr), 255.0) img_arr = np.multiply(255.0, img_arr) # ????????255 img_arr = np.array(img_arr, dtype=np.uint8) # ???0-255????? img_arr = img_arr.reshape(28, 28) # ???28*28?????? # print(img_arr) # imgplot = plt.imshow(img, cmap='gray') #?????? img_1 = Image.fromarray(img_arr) out_file = out_dir % idx # ???BMP????????????????????????? img_1.save(out_file, format="BMP") return img_arr
def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.): """Rotate an image randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). rg : int or float Degree to rotate, usually 0 ~ 180. is_random : boolean, default False If True, randomly rotate. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’ - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0 - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ Examples --------- >>> x --> [row, col, 1] greyscale >>> x = rotation(x, rg=40, is_random=False) >>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='temp',cmap='gray') """ if is_random: theta = np.pi / 180 * np.random.uniform(-rg, rg) else: theta = np.pi /180 * rg rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]) h, w = x.shape[row_index], x.shape[col_index] transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w) x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval) return x
def shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.): """Shift an image randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). wrg : float Percentage of shift in axis x, usually -0.25 ~ 0.25. hrg : float Percentage of shift in axis y, usually -0.25 ~ 0.25. is_random : boolean, default False If True, randomly shift. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ """ h, w = x.shape[row_index], x.shape[col_index] if is_random: tx = np.random.uniform(-hrg, hrg) * h ty = np.random.uniform(-wrg, wrg) * w else: tx, ty = hrg * h, wrg * w translation_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]]) transform_matrix = translation_matrix # no need to do offset x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval) return x
def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.): """Shear an image randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). intensity : float Percentage of shear, usually -0.5 ~ 0.5 (is_random==True), 0 ~ 0.5 (is_random==False), you can have a quick try by shear(X, 1). is_random : boolean, default False If True, randomly shear. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ """ if is_random: shear = np.random.uniform(-intensity, intensity) else: shear = intensity shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], [0, 0, 1]]) h, w = x.shape[row_index], x.shape[col_index] transform_matrix = transform_matrix_offset_center(shear_matrix, h, w) x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval) return x
def imresize(x, size=[100, 100], interp='bilinear', mode=None): """Resize an image by given output size and method. Warning, this function will rescale the value to [0, 255]. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). size : int, float or tuple (h, w) - int, Percentage of current size. - float, Fraction of current size. - tuple, Size of the output image. interp : str, optional Interpolation to use for re-sizing (‘nearest’, ‘lanczos’, ‘bilinear’, ‘bicubic’ or ‘cubic’). mode : str, optional The PIL image mode (‘P’, ‘L’, etc.) to convert arr before resizing. Returns -------- imresize : ndarray The resized array of image. References ------------ - `scipy.misc.imresize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.imresize.html>`_ """ if x.shape[-1] == 1: # greyscale x = scipy.misc.imresize(x[:,:,0], size, interp=interp, mode=mode) return x[:, :, np.newaxis] elif x.shape[-1] == 3: # rgb, bgr .. return scipy.misc.imresize(x, size, interp=interp, mode=mode) else: raise Exception("Unsupported channel %d" % x.shape[-1]) # normailization
def channel_shift(x, intensity, is_random=False, channel_index=2): """Shift the channels of an image, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). intensity : float Intensity of shifting. is_random : boolean, default False If True, randomly shift. channel_index : int Index of channel, default 2. """ if is_random: factor = np.random.uniform(-intensity, intensity) else: factor = intensity x = np.rollaxis(x, channel_index, 0) min_x, max_x = np.min(x), np.max(x) channel_images = [np.clip(x_channel + factor, min_x, max_x) for x_channel in x] x = np.stack(channel_images, axis=0) x = np.rollaxis(x, 0, channel_index+1) return x # x = np.rollaxis(x, channel_index, 0) # min_x, max_x = np.min(x), np.max(x) # channel_images = [np.clip(x_channel + np.random.uniform(-intensity, intensity), min_x, max_x) # for x_channel in x] # x = np.stack(channel_images, axis=0) # x = np.rollaxis(x, 0, channel_index+1) # return x
def channel_shift_multi(x, intensity, channel_index=2): """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_ . Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Parameters ----------- x : list of numpy array List of images with dimension of [n_images, row, col, channel] (default). others : see ``channel_shift``. """ if is_random: factor = np.random.uniform(-intensity, intensity) else: factor = intensity results = [] for data in x: data = np.rollaxis(data, channel_index, 0) min_x, max_x = np.min(data), np.max(data) channel_images = [np.clip(x_channel + factor, min_x, max_x) for x_channel in x] data = np.stack(channel_images, axis=0) data = np.rollaxis(x, 0, channel_index+1) results.append( data ) return np.asarray(results) # noise
def apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0.): """Return transformed images by given transform_matrix from ``transform_matrix_offset_center``. Parameters ---------- x : numpy array Batch of images with dimension of 3, [batch_size, row, col, channel]. transform_matrix : numpy array Transform matrix (offset center), can be generated by ``transform_matrix_offset_center`` channel_index : int Index of channel, default 2. fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’ - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0 - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ Examples -------- - See ``rotation``, ``shift``, ``shear``, ``zoom``. """ x = np.rollaxis(x, channel_index, 0) final_affine_matrix = transform_matrix[:2, :2] final_offset = transform_matrix[:2, 2] channel_images = [ndi.interpolation.affine_transform(x_channel, final_affine_matrix, final_offset, order=0, mode=fill_mode, cval=cval) for x_channel in x] x = np.stack(channel_images, axis=0) x = np.rollaxis(x, 0, channel_index+1) return x
def array_to_img(x, dim_ordering=(0,1,2), scale=True): """Converts a numpy array to PIL image object (uint8 format). Parameters ---------- x : numpy array A image with dimension of 3 and channels of 1 or 3. dim_ordering : list or tuple of 3 int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). scale : boolean, default is True If True, converts image to [0, 255] from any range of value like [-1, 2]. References ----------- - `PIL Image.fromarray <http://pillow.readthedocs.io/en/3.1.x/reference/Image.html?highlight=fromarray>`_ """ from PIL import Image # if dim_ordering == 'default': # dim_ordering = K.image_dim_ordering() # if dim_ordering == 'th': # theano # x = x.transpose(1, 2, 0) x = x.transpose(dim_ordering) if scale: x += max(-np.min(x), 0) x_max = np.max(x) if x_max != 0: # print(x_max) # x /= x_max x = x / x_max x *= 255 if x.shape[2] == 3: # RGB return Image.fromarray(x.astype('uint8'), 'RGB') elif x.shape[2] == 1: # grayscale return Image.fromarray(x[:, :, 0].astype('uint8'), 'L') else: raise Exception('Unsupported channel number: ', x.shape[2]) ## Sequence
def shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1): """Shift an image randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). wrg : float Percentage of shift in axis x, usually -0.25 ~ 0.25. hrg : float Percentage of shift in axis y, usually -0.25 ~ 0.25. is_random : boolean, default False If True, randomly shift. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ """ h, w = x.shape[row_index], x.shape[col_index] if is_random: tx = np.random.uniform(-hrg, hrg) * h ty = np.random.uniform(-wrg, wrg) * w else: tx, ty = hrg * h, wrg * w translation_matrix = np.array([[1, 0, tx], [0, 1, ty], [0, 0, 1]]) transform_matrix = translation_matrix # no need to do offset x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order) return x
def imresize(x, size=[100, 100], interp='bicubic', mode=None): """Resize an image by given output size and method. Warning, this function will rescale the value to [0, 255]. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). size : int, float or tuple (h, w) - int, Percentage of current size. - float, Fraction of current size. - tuple, Size of the output image. interp : str, optional Interpolation to use for re-sizing (‘nearest’, ‘lanczos’, ‘bilinear’, ‘bicubic’ or ‘cubic’). mode : str, optional The PIL image mode (‘P’, ‘L’, etc.) to convert arr before resizing. Returns -------- imresize : ndarray The resized array of image. References ------------ - `scipy.misc.imresize <https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.imresize.html>`_ """ if x.shape[-1] == 1: # greyscale x = scipy.misc.imresize(x[:,:,0], size, interp=interp, mode=mode) return x[:, :, np.newaxis] elif x.shape[-1] == 3: # rgb, bgr .. return scipy.misc.imresize(x, size, interp=interp, mode=mode) else: raise Exception("Unsupported channel %d" % x.shape[-1]) # value scale
def channel_shift_multi(x, intensity, is_random=False, channel_index=2): """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_ . Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Parameters ----------- x : list of numpy array List of images with dimension of [n_images, row, col, channel] (default). others : see ``channel_shift``. """ if is_random: factor = np.random.uniform(-intensity, intensity) else: factor = intensity results = [] for data in x: data = np.rollaxis(data, channel_index, 0) min_x, max_x = np.min(data), np.max(data) channel_images = [np.clip(x_channel + factor, min_x, max_x) for x_channel in x] data = np.stack(channel_images, axis=0) data = np.rollaxis(x, 0, channel_index+1) results.append( data ) return np.asarray(results) # noise
def array_to_img(x, dim_ordering=(0,1,2), scale=True): """Converts a numpy array to PIL image object (uint8 format). Parameters ---------- x : numpy array A image with dimension of 3 and channels of 1 or 3. dim_ordering : list or tuple of 3 int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). scale : boolean, default is True If True, converts image to [0, 255] from any range of value like [-1, 2]. References ----------- - `PIL Image.fromarray <http://pillow.readthedocs.io/en/3.1.x/reference/Image.html?highlight=fromarray>`_ """ from PIL import Image # if dim_ordering == 'default': # dim_ordering = K.image_dim_ordering() # if dim_ordering == 'th': # theano # x = x.transpose(1, 2, 0) x = x.transpose(dim_ordering) if scale: x += max(-np.min(x), 0) x_max = np.max(x) if x_max != 0: # print(x_max) # x /= x_max x = x / x_max x *= 255 if x.shape[2] == 3: # RGB return Image.fromarray(x.astype('uint8'), 'RGB') elif x.shape[2] == 1: # grayscale return Image.fromarray(x[:, :, 0].astype('uint8'), 'L') else: raise Exception('Unsupported channel number: ', x.shape[2])
def find_contours(x, level=0.8, fully_connected='low', positive_orientation='low'): """ Find iso-valued contours in a 2D array for a given level value, returns list of (n, 2)-ndarrays see `skimage.measure.find_contours <http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.find_contours>`_ . Parameters ------------ x : 2D ndarray of double. Input data in which to find contours. level : float. Value along which to find contours in the array. fully_connected : str, {‘low’, ‘high’}. Indicates whether array elements below the given level value are to be considered fully-connected (and hence elements above the value will only be face connected), or vice-versa. (See notes below for details.) positive_orientation : either ‘low’ or ‘high’. Indicates whether the output contours will produce positively-oriented polygons around islands of low- or high-valued elements. If ‘low’ then contours will wind counter-clockwise around elements below the iso-value. Alternately, this means that low-valued elements are always on the left of the contour. """ return skimage.measure.find_contours(x, level, fully_connected='low', positive_orientation='low')
def binary_dilation(x, radius=3): """ Return fast binary morphological dilation of an image. see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`_. Parameters ----------- x : 2D array image. radius : int for the radius of mask. """ from skimage.morphology import disk, binary_dilation mask = disk(radius) x = binary_dilation(x, selem=mask) return x
def dilation(x, radius=3): """ Return greyscale morphological dilation of an image, see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`_. Parameters ----------- x : 2D array image. radius : int for the radius of mask. """ from skimage.morphology import disk, dilation mask = disk(radius) x = dilation(x, selem=mask) return x
def channel_shift_multi(x, intensity, channel_index=2): """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`_ . Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Parameters ----------- x : list of numpy array List of images with dimension of [n_images, row, col, channel] (default). others : see ``channel_shift``. """ if is_random: factor = np.random.uniform(-intensity, intensity) else: factor = intensity results = [] for data in x: data = np.rollaxis(data, channel_index, 0) min_x, max_x = np.min(data), np.max(data) channel_images = [np.clip(x_channel + factor, min_x, max_x) for x_channel in x] data = np.stack(channel_images, axis=0) data = np.rollaxis(x, 0, channel_index+1) results.append( data ) return np.asarray(results) # manual transform
def swirl(x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False, is_random=False): """Swirl an image randomly or non-randomly, see `scikit-image swirl API <http://scikit-image.org/docs/dev/api/skimage.transform.html#skimage.transform.swirl>`_ and `example <http://scikit-image.org/docs/dev/auto_examples/plot_swirl.html>`_. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). center : (row, column) tuple or (2,) ndarray, optional Center coordinate of transformation. strength : float, optional The amount of swirling applied. radius : float, optional The extent of the swirl in pixels. The effect dies out rapidly beyond radius. rotation : float, (degree) optional Additional rotation applied to the image, usually [0, 360], relates to center. output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See skimage.transform.warp for detail. mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional Points outside the boundaries of the input are filled according to the given mode, with ‘constant’ used as the default. Modes match the behaviour of numpy.pad. cval : float, optional Used in conjunction with mode ‘constant’, the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float. is_random : boolean, default False If True, random swirl. - random center = [(0 ~ x.shape[0]), (0 ~ x.shape[1])] - random strength = [0, strength] - random radius = [1e-10, radius] - random rotation = [-rotation, rotation] Examples --------- >>> x --> [row, col, 1] greyscale >>> x = swirl(x, strength=4, radius=100) """ assert radius != 0, Exception("Invalid radius value") rotation = np.pi / 180 * rotation if is_random: center_h = int(np.random.uniform(0, x.shape[0])) center_w = int(np.random.uniform(0, x.shape[1])) center = (center_h, center_w) strength = np.random.uniform(0, strength) radius = np.random.uniform(1e-10, radius) rotation = np.random.uniform(-rotation, rotation) max_v = np.max(x) if max_v > 1: # Note: the input of this fn should be [-1, 1], rescale is required. x = x / max_v swirled = skimage.transform.swirl(x, center=center, strength=strength, radius=radius, rotation=rotation, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range) if max_v > 1: swirled = swirled * max_v return swirled
def elastic_transform(x, alpha, sigma, mode="constant", cval=0, is_random=False): """Elastic deformation of images as described in `[Simard2003] <http://deeplearning.cs.cmu.edu/pdfs/Simard.pdf>`_ . Parameters ----------- x : numpy array, a greyscale image. alpha : scalar factor. sigma : scalar or sequence of scalars, the smaller the sigma, the more transformation. Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. mode : default constant, see `scipy.ndimage.filters.gaussian_filter <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.filters.gaussian_filter.html>`_. cval : float, optional. Used in conjunction with mode ‘constant’, the value outside the image boundaries. is_random : boolean, default False Examples --------- >>> x = elastic_transform(x, alpha = x.shape[1] * 3, sigma = x.shape[1] * 0.07) References ------------ - `Github <https://gist.github.com/chsasank/4d8f68caf01f041a6453e67fb30f8f5a>`_. - `Kaggle <https://www.kaggle.com/pscion/ultrasound-nerve-segmentation/elastic-transform-for-data-augmentation-0878921a>`_ """ if is_random is False: random_state = np.random.RandomState(None) else: random_state = np.random.RandomState(int(time.time())) # is_3d = False if len(x.shape) == 3 and x.shape[-1] == 1: x = x[:,:,0] is_3d = True elif len(x.shape) == 3 and x.shape[-1] != 1: raise Exception("Only support greyscale image") assert len(x.shape)==2 shape = x.shape dx = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode=mode, cval=cval) * alpha dy = gaussian_filter((random_state.rand(*shape) * 2 - 1), sigma, mode=mode, cval=cval) * alpha x_, y_ = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing='ij') indices = np.reshape(x_ + dx, (-1, 1)), np.reshape(y_ + dy, (-1, 1)) if is_3d: return map_coordinates(x, indices, order=1).reshape((shape[0], shape[1], 1)) else: return map_coordinates(x, indices, order=1).reshape(shape)
def rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1): """Rotate an image randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). rg : int or float Degree to rotate, usually 0 ~ 180. is_random : boolean, default False If True, randomly rotate. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’ - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0 order : int, optional The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ Examples --------- >>> x --> [row, col, 1] greyscale >>> x = rotation(x, rg=40, is_random=False) >>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='temp',cmap='gray') """ if is_random: theta = np.pi / 180 * np.random.uniform(-rg, rg) else: theta = np.pi /180 * rg rotation_matrix = np.array([[np.cos(theta), -np.sin(theta), 0], [np.sin(theta), np.cos(theta), 0], [0, 0, 1]]) h, w = x.shape[row_index], x.shape[col_index] transform_matrix = transform_matrix_offset_center(rotation_matrix, h, w) x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order) return x
def shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1): """Shear an image randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). intensity : float Percentage of shear, usually -0.5 ~ 0.5 (is_random==True), 0 ~ 0.5 (is_random==False), you can have a quick try by shear(X, 1). is_random : boolean, default False If True, randomly shear. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ References ----------- - `Affine transformation <https://uk.mathworks.com/discovery/affine-transformation.html>`_ """ if is_random: shear = np.random.uniform(-intensity, intensity) else: shear = intensity shear_matrix = np.array([[1, -np.sin(shear), 0], [0, np.cos(shear), 0], [0, 0, 1]]) h, w = x.shape[row_index], x.shape[col_index] transform_matrix = transform_matrix_offset_center(shear_matrix, h, w) x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order) return x
def shear2(x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1): """Shear an image randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). shear : tuple of two floats Percentage of shear for height and width direction (0, 1). is_random : boolean, default False If True, randomly shear. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ References ----------- - `Affine transformation <https://uk.mathworks.com/discovery/affine-transformation.html>`_ """ assert len(shear) == 2, "shear should be tuple of 2 floats, or you want to use tl.prepro.shear rather than tl.prepro.shear2 ?" if is_random: shear[0] = np.random.uniform(-shear[0], shear[0]) shear[1] = np.random.uniform(-shear[1], shear[1]) shear_matrix = np.array([[1, shear[0], 0], [shear[1], 1, 0], [0, 0, 1]]) h, w = x.shape[row_index], x.shape[col_index] transform_matrix = transform_matrix_offset_center(shear_matrix, h, w) x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order) return x
def zoom(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1): """Zoom in and out of a single image, randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). zoom_range : list or tuple - If is_random=False, (h, w) are the fixed zoom factor for row and column axies, factor small than one is zoom in. - If is_random=True, it is (min zoom out, max zoom out) for x and y with different random zoom in/out factor. e.g (0.5, 1) zoom in 1~2 times. is_random : boolean, default False If True, randomly zoom. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See ``apply_transform``. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ """ if len(zoom_range) != 2: raise Exception('zoom_range should be a tuple or list of two floats. ' 'Received arg: ', zoom_range) if is_random: if zoom_range[0] == 1 and zoom_range[1] == 1: zx, zy = 1, 1 print(" random_zoom : not zoom in/out") else: zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2) else: zx, zy = zoom_range # print(zx, zy) zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]]) h, w = x.shape[row_index], x.shape[col_index] transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w) x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval, order) return x
def projective_transform_by_points(x, src, dst, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False): """Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). src : list or numpy The original coordinates, usually 4 coordinates of (width, height). dst : list or numpy The coordinates after transformation, the number of coordinates is the same with src. map_args : dict, optional Keyword arguments passed to inverse_map. output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified. order : int, optional The order of interpolation. The order has to be in the range 0-5: - 0 Nearest-neighbor - 1 Bi-linear (default) - 2 Bi-quadratic - 3 Bi-cubic - 4 Bi-quartic - 5 Bi-quintic mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad. cval : float, optional Used in conjunction with mode ‘constant’, the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float. Examples -------- >>> Assume X is an image from CIFAR 10, i.e. shape == (32, 32, 3) >>> src = [[0,0],[0,32],[32,0],[32,32]] # [w, h] >>> dst = [[10,10],[0,32],[32,0],[32,32]] >>> x = projective_transform_by_points(X, src, dst) References ----------- - `scikit-image : geometric transformations <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_ - `scikit-image : examples <http://scikit-image.org/docs/dev/auto_examples/index.html>`_ """ if type(src) is list: # convert to numpy src = np.array(src) if type(dst) is list: dst = np.array(dst) if np.max(x)>1: # convert to [0, 1] x = x/255 m = transform.ProjectiveTransform() m.estimate(dst, src) warped = transform.warp(x, m, map_args=map_args, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range) return warped # Numpy and PIL
def zoom(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.): """Zoom in and out of a single image, randomly or non-randomly. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). zoom_range : list or tuple - If is_random=False, (h, w) are the fixed zoom factor for row and column axies, factor small than one is zoom in. - If is_random=True, (min zoom out, max zoom out) for x and y with different random zoom in/out factor. e.g (0.5, 1) zoom in 1~2 times. is_random : boolean, default False If True, randomly zoom. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. - `scipy ndimage affine_transform <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.ndimage.interpolation.affine_transform.html>`_ """ if len(zoom_range) != 2: raise Exception('zoom_range should be a tuple or list of two floats. ' 'Received arg: ', zoom_range) if is_random: if zoom_range[0] == 1 and zoom_range[1] == 1: zx, zy = 1, 1 print(" random_zoom : not zoom in/out") else: zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2) else: zx, zy = zoom_range # print(zx, zy) zoom_matrix = np.array([[zx, 0, 0], [0, zy, 0], [0, 0, 1]]) h, w = x.shape[row_index], x.shape[col_index] transform_matrix = transform_matrix_offset_center(zoom_matrix, h, w) x = apply_transform(x, transform_matrix, channel_index, fill_mode, cval) return x
def projective_transform_by_points(x, src, dst, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False): """Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_. Parameters ----------- x : numpy array An image with dimension of [row, col, channel] (default). src : list or numpy The original coordinates, usually 4 coordinates of (x, y). dst : list or numpy The coordinates after transformation, the number of coordinates is the same with src. map_args : dict, optional Keyword arguments passed to inverse_map. output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified. order : int, optional The order of interpolation. The order has to be in the range 0-5: - 0 Nearest-neighbor - 1 Bi-linear (default) - 2 Bi-quadratic - 3 Bi-cubic - 4 Bi-quartic - 5 Bi-quintic mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad. cval : float, optional Used in conjunction with mode ‘constant’, the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float. Examples -------- >>> Assume X is an image from CIFAR 10, i.e. shape == (32, 32, 3) >>> src = [[0,0],[0,32],[32,0],[32,32]] >>> dst = [[10,10],[0,32],[32,0],[32,32]] >>> x = projective_transform_by_points(X, src, dst) References ----------- - `scikit-image : geometric transformations <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_ - `scikit-image : examples <http://scikit-image.org/docs/dev/auto_examples/index.html>`_ """ if type(src) is list: # convert to numpy src = np.array(src) if type(dst) is list: dst = np.array(dst) if np.max(x)>1: # convert to [0, 1] x = x/255 m = transform.ProjectiveTransform() m.estimate(dst, src) warped = transform.warp(x, m, map_args=map_args, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range) return warped # Numpy and PIL
