本文整理汇总了Python中scipy.ndimage.zoom方法的典型用法代码示例。如果您正苦于以下问题：Python ndimage.zoom方法的具体用法？Python ndimage.zoom怎么用？Python ndimage.zoom使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块scipy.ndimage的用法示例。

在下文中一共展示了ndimage.zoom方法的25个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: predict_multiscale

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def predict_multiscale(net, image, tile_size, scales, classes, flip_evaluation, recurrence):

"""

Predict an image by looking at it with different scales.

We choose the "predict_whole_img" for the image with less than the original input size,

for the input of larger size, we would choose the cropping method to ensure that GPU memory is enough.

"""

image = image.data

N_, C_, H_, W_ = image.shape

full_probs = np.zeros((H_, W_, classes))

for scale in scales:

scale = float(scale)

print("Predicting image scaled by %f" % scale)

scale_image = ndimage.zoom(image, (1.0, 1.0, scale, scale), order=1, prefilter=False)

scaled_probs = predict_whole(net, scale_image, tile_size, recurrence)

if flip_evaluation == True:

flip_scaled_probs = predict_whole(net, scale_image[:,:,:,::-1].copy(), tile_size, recurrence)

scaled_probs = 0.5 * (scaled_probs + flip_scaled_probs[:,::-1,:])

full_probs += scaled_probs

full_probs /= len(scales)

return full_probs

开发者ID:speedinghzl，项目名称:pytorch-segmentation-toolbox，代码行数:22，

示例2: predict_multiscale

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def predict_multiscale(net, image, tile_size, scales, classes, flip_evaluation):

"""

Predict an image by looking at it with different scales.

We choose the "predict_whole_img" for the image with less than the original input size,

for the input of larger size, we would choose the cropping method to ensure that GPU memory is enough.

"""

image = image.data

N_, C_, H_, W_ = image.shape

full_probs = np.zeros((H_, W_, classes))

for scale in scales:

scale = float(scale)

print("Predicting image scaled by %f" % scale)

scale_image = ndimage.zoom(image, (1.0, 1.0, scale, scale), order=1, prefilter=False)

scaled_probs = predict_whole(net, scale_image, tile_size)

if flip_evaluation == True:

flip_scaled_probs = predict_whole(net, scale_image[:, :, :, ::-1].copy(), tile_size)

scaled_probs = 0.5 * (scaled_probs + flip_scaled_probs[:, ::-1, :])

full_probs += scaled_probs

full_probs /= len(scales)

return full_probs

开发者ID:lxtGH，项目名称:Fast_Seg，代码行数:22，

示例3: clipped_zoom

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def clipped_zoom(img, zoom_factor):

h = img.shape[0]

# ceil crop height(= crop width)

ch = int(np.ceil(h / zoom_factor))

top = (h - ch) // 2

img = scizoom(img[top:top + ch, top:top + ch], (zoom_factor, zoom_factor, 1), order=1)

# trim off any extra pixels

trim_top = (img.shape[0] - h) // 2

return img[trim_top:trim_top + h, trim_top:trim_top + h]

# /// End Distortion Helpers ///

# /// Distortions ///

开发者ID:hendrycks，项目名称:robustness，代码行数:19，

示例4: generate

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def generate(values, nb_classes, batch_size, input_size, image_dir, anno_dir):

while 1:

random.shuffle(values)

images, labels = update_inputs(batch_size=batch_size,

input_size=input_size, num_classes=nb_classes)

for i, d in enumerate(values):

img = imresize(imread(os.path.join(image_dir, d['image']), mode='RGB'), input_size)

y = imread(os.path.join(anno_dir, d['anno']), mode='L')

h, w = input_size

y = zoom(y, (1.*h/y.shape[0], 1.*w/y.shape[1]), order=1, prefilter=False)

y = (np.arange(nb_classes) == y[:,:,None]).astype('float32')

assert y.shape[2] == nb_classes

images[i % batch_size] = img

labels[i % batch_size] = y

if (i + 1) % batch_size == 0:

yield images, labels

images, labels = update_inputs(batch_size=batch_size,

input_size=input_size, num_classes=nb_classes)

开发者ID:Vladkryvoruchko，项目名称:PSPNet-Keras-tensorflow，代码行数:20，

示例5: predict_multi_scale

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def predict_multi_scale(full_image, net, scales, sliding_evaluation, flip_evaluation):

"""Predict an image by looking at it with different scales."""

classes = net.model.outputs[0].shape[3]

full_probs = np.zeros((full_image.shape[0], full_image.shape[1], classes))

h_ori, w_ori = full_image.shape[:2]

for scale in scales:

print("Predicting image scaled by %f" % scale)

scaled_img = misc.imresize(full_image, size=scale, interp="bilinear")

if sliding_evaluation:

scaled_probs = predict_sliding(scaled_img, net, flip_evaluation)

else:

scaled_probs = net.predict(scaled_img, flip_evaluation)

# scale probs up to full size

h, w = scaled_probs.shape[:2]

probs = ndimage.zoom(scaled_probs, (1.*h_ori/h, 1.*w_ori/w, 1.),order=1, prefilter=False)

# visualize_prediction(probs)

# integrate probs over all scales

full_probs += probs

full_probs /= len(scales)

return full_probs

开发者ID:Vladkryvoruchko，项目名称:PSPNet-Keras-tensorflow，代码行数:22，

示例6: test_correct_results

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def test_correct_results(self, min_zoom, max_zoom, mode, align_corners, keep_size):

key = "img"

random_zoom = RandZoomd(

key,

prob=1.0,

min_zoom=min_zoom,

max_zoom=max_zoom,

mode=mode,

align_corners=align_corners,

keep_size=keep_size,

)

random_zoom.set_random_state(1234)

zoomed = random_zoom({key: self.imt[0]})

expected = list()

for channel in self.imt[0]:

expected.append(zoom_scipy(channel, zoom=random_zoom._zoom, mode="nearest", order=0, prefilter=False))

expected = np.stack(expected).astype(np.float32)

np.testing.assert_allclose(expected, zoomed[key], atol=1.0)

开发者ID:Project-MONAI，项目名称:MONAI，代码行数:21，

示例7: zoom

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def zoom(x: np.ndarray, scale_factor: AxesParams, axes: AxesLike = None, order: int = 1,

fill_value: Union[float, Callable] = 0) -> np.ndarray:

"""

Rescale ``x`` according to ``scale_factor`` along the ``axes``.

Parameters

----------

x

scale_factor

axes

axes along which the tensor will be scaled. If None - the last ``len(shape)`` axes are used.

order

order of interpolation.

fill_value

value to fill past edges. If Callable (e.g. `numpy.min`) - ``fill_value(x)`` will be used.

"""

scale_factor = fill_by_indices(np.ones(x.ndim, 'float64'), scale_factor, axes)

if callable(fill_value):

fill_value = fill_value(x)

# remove an annoying warning

with warnings.catch_warnings():

warnings.simplefilter('ignore', UserWarning)

return ndimage.zoom(x, scale_factor, order=order, cval=fill_value)

开发者ID:neuro-ml，项目名称:deep_pipe，代码行数:26，

示例8: zoom_to_shape

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def zoom_to_shape(x: np.ndarray, shape: AxesLike, axes: AxesLike = None, order: int = 1,

fill_value: Union[float, Callable] = 0) -> np.ndarray:

"""

Rescale ``x`` to match ``shape`` along the ``axes``.

Parameters

----------

x

shape

final shape.

axes

axes along which the tensor will be scaled. If None - the last ``len(shape)`` axes are used.

order

order of interpolation.

fill_value

value to fill past edges. If Callable (e.g. `numpy.min`) - ``fill_value(x)`` will be used.

"""

old_shape = np.array(x.shape, 'float64')

new_shape = np.array(fill_by_indices(x.shape, shape, axes), 'float64')

return zoom(x, new_shape / old_shape, order=order, fill_value=fill_value)

开发者ID:neuro-ml，项目名称:deep_pipe，代码行数:22，

示例9: proportional_zoom_to_shape

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def proportional_zoom_to_shape(x: np.ndarray, shape: AxesLike, axes: AxesLike = None,

padding_values: Union[AxesParams, Callable] = 0, order: int = 1) -> np.ndarray:

"""

Proportionally rescale ``x`` to fit ``shape`` along ``axes`` then pad it to that shape.

Parameters

----------

x

shape

final shape.

axes

axes along which ``x`` will be padded. If None - the last ``len(shape)`` axes are used.

padding_values

values to pad with.

order

order of interpolation.

"""

axes = expand_axes(axes, shape)

scale_factor = (np.array(shape, 'float64') / extract(x.shape, axes)).min()

return pad_to_shape(zoom(x, scale_factor, axes, order), shape, axes, padding_values)

开发者ID:neuro-ml，项目名称:deep_pipe，代码行数:22，

示例10: image_dataset_phase_2

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def image_dataset_phase_2(repository, image_number, x, y, feature_offsets, R_offsets, delta):

img = makesize(snd.zoom(readimage(repository, image_number), delta), 1)

(h, w) = img.shape

mask = np.ones((h, w), 'bool')

mask[:, 0] = 0

mask[0, :] = 0

mask[h - 1, :] = 0

mask[:, w - 1] = 0

(nroff, blc) = R_offsets.shape

h -= 2

w -= 2

x += 1

y += 1

rep = np.zeros((nroff, 2))

number = image_number

xs = (x + R_offsets[:, 0]).astype('int')

ys = (y + R_offsets[:, 1]).astype('int')

rep[:, 0] = R_offsets[:, 0]

rep[:, 1] = R_offsets[:, 1]

dataset = dataset_from_coordinates(img, xs, ys, feature_offsets)

return dataset, rep, number

开发者ID:cytomine，项目名称:Cytomine-python-datamining，代码行数:23，

示例11: agregation_phase_2

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def agregation_phase_2(repository, image_number, ip, probability_maps, reg, delta, feature_offsets, filter_size, beta, n_iterations):

img = makesize(snd.zoom(readimage(repository, image_number), delta), 1)

(h, w, nldms) = probability_maps.shape

nldms -= 1

mh = h - 1

mw = w - 1

for iteration in range(n_iterations):

y, x = np.where(probability_maps[:, :, ip] >= beta * np.max(probability_maps[:, :, ip]))

dataset = dataset_from_coordinates(img, x + 1, y + 1, feature_offsets)

offsets = reg.predict(dataset)

n_x = (x - offsets[:, 0]).clip(min=0, max=mw)

n_y = (y - offsets[:, 1]).clip(min=0, max=mh)

new_pmap = np.zeros((h, w))

for i in range(n_x.size):

new_pmap[n_y[i], n_x[i]] += probability_maps[y[i], x[i], ip]

probability_maps[:, :, ip] = new_pmap

probability_maps[0, :, ip] = 0

probability_maps[:, 0, ip] = 0

probability_maps[mh, :, ip] = 0

probability_maps[:, mw, ip] = 0

return filter_perso(probability_maps[:, :, ip], filter_size)

开发者ID:cytomine，项目名称:Cytomine-python-datamining，代码行数:24，

示例12: test_uint64_max

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def test_uint64_max():

# Test interpolation respects uint64 max. Reported to fail at least on

# win32 (due to the 32 bit visual C compiler using signed int64 when

# converting between uint64 to double) and Debian on s390x.

# Interpolation is always done in double precision floating point, so we

# use the largest uint64 value for which int(float(big)) still fits in

# a uint64.

big = 2**64-1025

arr = np.array([big, big, big], dtype=np.uint64)

# Tests geometric transform (map_coordinates, affine_transform)

inds = np.indices(arr.shape) - 0.1

x = ndimage.map_coordinates(arr, inds)

assert_equal(x[1], int(float(big)))

assert_equal(x[2], int(float(big)))

# Tests zoom / shift

x = ndimage.shift(arr, 0.1)

assert_equal(x[1], int(float(big)))

assert_equal(x[2], int(float(big)))

开发者ID:Relph1119，项目名称:GraphicDesignPatternByPython，代码行数:20，

示例13: deep_dream

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def deep_dream(image, model, iterations, lr, octave_scale, num_octaves):

""" Main deep dream method """

image = preprocess(image).unsqueeze(0).cpu().data.numpy()

# Extract image representations for each octave

octaves = [image]

for _ in range(num_octaves - 1):

octaves.append(nd.zoom(octaves[-1], (1, 1, 1 / octave_scale, 1 / octave_scale), order=1))

detail = np.zeros_like(octaves[-1])

for octave, octave_base in enumerate(tqdm.tqdm(octaves[::-1], desc="Dreaming")):

if octave > 0:

# Upsample detail to new octave dimension

detail = nd.zoom(detail, np.array(octave_base.shape) / np.array(detail.shape), order=1)

# Add deep dream detail from previous octave to new base

input_image = octave_base + detail

# Get new deep dream image

dreamed_image = dream(input_image, model, iterations, lr)

# Extract deep dream details

detail = dreamed_image - octave_base

return deprocess(dreamed_image)

开发者ID:eriklindernoren，项目名称:PyTorch-Deep-Dream，代码行数:24，

示例14: _clipped_zoom_no_scipy_warning

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def _clipped_zoom_no_scipy_warning(img, zoom_factor):

from scipy.ndimage import zoom as scizoom

with warnings.catch_warnings():

warnings.filterwarnings("ignore", ".*output shape of zoom.*")

# clipping along the width dimension:

ch0 = int(np.ceil(img.shape[0] / float(zoom_factor)))

top0 = (img.shape[0] - ch0) // 2

# clipping along the height dimension:

ch1 = int(np.ceil(img.shape[1] / float(zoom_factor)))

top1 = (img.shape[1] - ch1) // 2

img = scizoom(img[top0:top0 + ch0, top1:top1 + ch1],

(zoom_factor, zoom_factor, 1), order=1)

return img

开发者ID:aleju，项目名称:imgaug，代码行数:20，

示例15: im_rescale

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def im_rescale(img, scale_factor):

zoomed_img = np.zeros_like(img, dtype=img.dtype)

zoomed = skimage.transform.rescale(img, scale_factor)

if scale_factor >= 1.0:

shift_x = (zoomed.shape[0] - img.shape[0]) // 2

shift_y = (zoomed.shape[1] - img.shape[1]) // 2

zoomed_img[:,:] = zoomed[shift_x:shift_x+img.shape[0], shift_y:shift_y+img.shape[1]]

else:

shift_x = (img.shape[0] - zoomed.shape[0]) // 2

shift_y = (img.shape[1] - zoomed.shape[1]) // 2

zoomed_img[shift_x:shift_x+zoomed.shape[0], shift_y:shift_y+zoomed.shape[1]] = zoomed

return zoomed_img

# this old version uses ndimage zoom which is unreliable

开发者ID:benanne，项目名称:kaggle-galaxies，代码行数:19，

示例16: im_rescale_old

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def im_rescale_old(img, scale_factor):

zoomed_img = np.zeros_like(img, dtype=img.dtype)

if img.ndim == 2:

z = (scale_factor, scale_factor)

elif img.ndim == 3:

z = (scale_factor, scale_factor, 1)

# else fail

zoomed = ndimage.zoom(img, z)

if scale_factor >= 1.0:

shift_x = (zoomed.shape[0] - img.shape[0]) // 2

shift_y = (zoomed.shape[1] - img.shape[1]) // 2

zoomed_img[:,:] = zoomed[shift_x:shift_x+img.shape[0], shift_y:shift_y+img.shape[1]]

else:

shift_x = (img.shape[0] - zoomed.shape[0]) // 2

shift_y = (img.shape[1] - zoomed.shape[1]) // 2

zoomed_img[shift_x:shift_x+zoomed.shape[0], shift_y:shift_y+zoomed.shape[1]] = zoomed

return zoomed_img

开发者ID:benanne，项目名称:kaggle-galaxies，代码行数:22，

示例17: render

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def render(self, mode='human', close=False):

if close:

if self.viewer is not None:

self.viewer.close()

self.viewer = None

img = self.get_obs()[0]

if mode == 'rgb_array':

return img

elif mode == 'human':

from gym.envs.classic_control import rendering

from scipy.ndimage import zoom

if self.viewer is None:

self.viewer = rendering.SimpleImageViewer()

img = zoom(img, [5, 5, 1], order=0)

self.viewer.imshow(img)

else:

raise NotImplementedError

# Generate ground truth Q-frames by finding the smallest number of steps towards all coordinates given a window position.

开发者ID:fabiopardo，项目名称:qmap，代码行数:21，

示例18: handleBands

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def handleBands(data, snapshape):

import numpy as np

from scipy.ndimage import zoom

try:

data[np.where(data.mask == True)] = data.min()

except AttributeError:

pass

if data.shape != snapshape:

data = handleArrays(data)

data = zoom(data, 2 * snapshape[1] / data.shape[1], order=1)

data = ((np.roll(data, 1, axis=0) + data) / 2)[1:]

else:

data = handleArrays(data)

return data

开发者ID:mapbox，项目名称:grib-doctor，代码行数:18，

示例19: _log_density

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def _log_density(self, stimulus):

smap = self.parent_model.log_density(stimulus)

target_shape = (stimulus.shape[0],

stimulus.shape[1])

if smap.shape != target_shape:

if self.verbose:

print("Resizing saliency map", smap.shape, target_shape)

x_factor = target_shape[1] / smap.shape[1]

y_factor = target_shape[0] / smap.shape[0]

smap = zoom(smap, [y_factor, x_factor], order=1, mode='nearest')

smap -= logsumexp(smap)

assert smap.shape == target_shape

return smap

开发者ID:matthias-k，项目名称:pysaliency，代码行数:21，

示例20: conditional_log_density

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None):

smap = self.parent_model.conditional_log_density(stimulus, x_hist, y_hist, t_hist, attributes=attributes, out=out)

target_shape = (stimulus.shape[0],

stimulus.shape[1])

if smap.shape != target_shape:

if self.verbose:

print("Resizing saliency map", smap.shape, target_shape)

x_factor = target_shape[1] / smap.shape[1]

y_factor = target_shape[0] / smap.shape[0]

smap = zoom(smap, [y_factor, x_factor], order=1, mode='nearest')

smap -= logsumexp(smap)

assert smap.shape == target_shape

return smap

开发者ID:matthias-k，项目名称:pysaliency，代码行数:21，

示例21: _saliency_map

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def _saliency_map(self, stimulus):

smap = self.parent_model.saliency_map(stimulus)

target_shape = (stimulus.shape[0],

stimulus.shape[1])

if smap.shape != target_shape:

if self.verbose:

print("Resizing saliency map", smap.shape, target_shape)

x_factor = target_shape[1] / smap.shape[1]

y_factor = target_shape[0] / smap.shape[0]

smap = zoom(smap, [y_factor, x_factor], order=1, mode='nearest')

assert smap.shape == target_shape

return smap

开发者ID:matthias-k，项目名称:pysaliency，代码行数:19，

示例22: clipped_zoom

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def clipped_zoom(x: np.ndarray, zoom_factor: float) -> np.ndarray:

"""

Helper function for zoom blur.

Parameters

----------

x

Instance to be perturbed.

zoom_factor

Zoom strength.

Returns

-------

Cropped and zoomed instance.

"""

h = x.shape[0]

ch = int(np.ceil(h / float(zoom_factor))) # ceil crop height(= crop width)

top = (h - ch) // 2

x = zoom(x[top:top + ch, top:top + ch], (zoom_factor, zoom_factor, 1), order=1)

trim_top = (x.shape[0] - h) // 2 # trim off any extra pixels

return x[trim_top:trim_top + h, trim_top:trim_top + h]

开发者ID:SeldonIO，项目名称:alibi-detect，代码行数:23，

示例23: multi_scale_predict

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def multi_scale_predict(model, image, scales, num_classes, device, flip=False):

input_size = (image.size(2), image.size(3))

upsample = nn.Upsample(size=input_size, mode='bilinear', align_corners=True)

total_predictions = np.zeros((num_classes, image.size(2), image.size(3)))

image = image.data.data.cpu().numpy()

for scale in scales:

scaled_img = ndimage.zoom(image, (1.0, 1.0, float(scale), float(scale)), order=1, prefilter=False)

scaled_img = torch.from_numpy(scaled_img).to(device)

scaled_prediction = upsample(model(scaled_img).cpu())

if flip:

fliped_img = scaled_img.flip(-1).to(device)

fliped_predictions = upsample(model(fliped_img).cpu())

scaled_prediction = 0.5 * (fliped_predictions.flip(-1) + scaled_prediction)

total_predictions += scaled_prediction.data.cpu().numpy().squeeze(0)

total_predictions /= len(scales)

return total_predictions

开发者ID:yassouali，项目名称:pytorch_segmentation，代码行数:21，

示例24: zoom_image

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def zoom_image(img, source_rect, target_shape=None):

"""Zooms pixels from the source_rect of img to target_shape."""

import warnings

from scipy.ndimage import zoom

if target_shape is None:

target_shape = img.shape

st, sb, sl, sr = source_rect

source = img[st:sb, sl:sr]

if source.shape == target_shape:

return source

zoom_tuple = tuple(float(t) / s

for t, s in zip(target_shape, source.shape[:2])

) + (1,) * (img.ndim - 2)

with warnings.catch_warnings():

warnings.simplefilter('ignore', UserWarning) # "output shape of zoom"

target = zoom(source, zoom_tuple)

assert target.shape[:2] == target_shape, (target.shape, target_shape)

return target

开发者ID:CSAILVision，项目名称:gandissect，代码行数:20，

示例25: predict_whole_img_

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# 需要导入模块: from scipy import ndimage [as 别名]

# 或者: from scipy.ndimage import zoom [as 别名]

def predict_whole_img_(net, image, scale):

"""

Parameters

----------

net : nn.Module

image : torch.Tensor

shape [batch_size, c, h, w]

scale : scalar

Return

------

full_probs : numpy.ndarray

shape [batch_size, classes, h, w]

Description

-----------

Predict the whole image w/o using multiple crops.

The scale specify whether rescale the input image before predicting the results.

"""

#image = image.cpu().numpy()

_, _, H_, W_ = image.shape

if scale != 1:

#scaled_img = ndimage.zoom(image, (1.0, 1.0, scale, scale), order=1, prefilter=False)

scaled_img = F.interpolate(image, scale_factor=scale, mode='bilinear', align_corners=True)

else:

scaled_img = image

full_probs_ = net(scaled_img)

if isinstance(full_probs_, list):

full_probs = full_probs_[-1]

else:

full_probs = full_probs_

full_depths = net.inference(full_probs) * scale

full_depths = F.interpolate(full_depths, size=(H_, W_), mode='nearest')

return full_depths

开发者ID:miraiaroha，项目名称:ACAN，代码行数:36，

注：本文中的scipy.ndimage.zoom方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。