本文整理汇总了Python中accimage.Image方法的典型用法代码示例。如果您正苦于以下问题:Python accimage.Image方法的具体用法?Python accimage.Image怎么用?Python accimage.Image使用的例子?那么, 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类accimage
的用法示例。
在下文中一共展示了accimage.Image方法的15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,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: adjust_brightness
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def adjust_brightness(img, brightness_factor):
"""Adjust brightness of an Image.
Args:
img (PIL Image): PIL Image to be adjusted.
brightness_factor (float): How much to adjust the brightness. Can be
any non negative number. 0 gives a black image, 1 gives the
original image while 2 increases the brightness by a factor of 2.
Returns:
PIL Image: Brightness adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Brightness(img)
img = enhancer.enhance(brightness_factor)
return img
示例2: adjust_contrast
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def adjust_contrast(img, contrast_factor):
"""Adjust contrast of an Image.
Args:
img (PIL Image): PIL Image to be adjusted.
contrast_factor (float): How much to adjust the contrast. Can be any
non negative number. 0 gives a solid gray image, 1 gives the
original image while 2 increases the contrast by a factor of 2.
Returns:
PIL Image: Contrast adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Contrast(img)
img = enhancer.enhance(contrast_factor)
return img
示例3: adjust_saturation
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def adjust_saturation(img, saturation_factor):
"""Adjust color saturation of an image.
Args:
img (PIL Image): PIL Image to be adjusted.
saturation_factor (float): How much to adjust the saturation. 0 will
give a black and white image, 1 will give the original image while
2 will enhance the saturation by a factor of 2.
Returns:
PIL Image: Saturation adjusted image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
enhancer = ImageEnhance.Color(img)
img = enhancer.enhance(saturation_factor)
return img
示例4: __call__
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def __call__(self, img):
"""Convert a ``numpy.ndarray`` to tensor.
Args:
img (numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if not(_is_numpy_image(img)):
raise TypeError('img should be ndarray. Got {}'.format(type(img)))
if isinstance(img, np.ndarray):
# handle numpy array
if img.ndim == 3:
img = torch.from_numpy(img.transpose((2, 0, 1)).copy())
elif img.ndim == 2:
img = torch.from_numpy(img.copy())
else:
raise RuntimeError('img should be ndarray with 2 or 3 dimensions. Got {}'.format(img.ndim))
# backward compatibility
#return img.float().div(255)
return img.float()
示例5: get_params
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def get_params(img, output_size):
"""Get parameters for ``crop`` for center crop.
Args:
img (numpy.ndarray (C x H x W)): Image to be cropped.
output_size (tuple): Expected output size of the crop.
Returns:
tuple: params (i, j, h, w) to be passed to ``crop`` for center crop.
"""
h = img.shape[0]
w = img.shape[1]
th, tw = output_size
i = int(round((h - th) / 2.))
j = int(round((w - tw) / 2.))
# # randomized cropping
# i = np.random.randint(i-3, i+4)
# j = np.random.randint(j-3, j+4)
return i, j, th, tw
示例6: __call__
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be scaled.
Returns:
PIL.Image: Rescaled image.
"""
if isinstance(self.size, int):
w, h, d = img.size
if (w <= h and w == self.size) or (h <= w and h == self.size):
return img
if w < h:
ow = self.size
oh = int(self.size * h / w)
return img.resize((ow, oh), self.interpolation)
else:
oh = self.size
ow = int(self.size * w / h)
return img.resize((ow, oh), self.interpolation)
else:
return img.resize(self.size, self.interpolation)
示例7: __call__
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def __call__(self, sample):
image, depth = sample['image'], sample['depth']
applied_angle = random.uniform(-self.angle, self.angle)
angle1 = applied_angle
angle1_rad = angle1 * np.pi / 180
# print('before rotating:',image.size)
image = ndimage.interpolation.rotate(
image, angle1, reshape=self.reshape, order=self.order)
depth = ndimage.interpolation.rotate(
depth, angle1, reshape=self.reshape, order=self.order)
image = Image.fromarray(image)
depth = Image.fromarray(depth)
# print('after rotating:',image.shape,depth.shape)
return {'image': image, 'depth': depth}
示例8: changeScale
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def changeScale(self, img, size, interpolation=Image.BILINEAR):
if not _is_pil_image(img):
raise TypeError(
'img should be PIL Image. Got {}'.format(type(img)))
if not (isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)):
raise TypeError('Got inappropriate size arg: {}'.format(size))
if isinstance(size, int):
w, h = img.size
if (w <= h and w == size) or (h <= w and h == size):
return img
if w < h:
ow = size
oh = int(size * h / w)
return img.resize((ow, oh), interpolation)
else:
oh = size
ow = int(size * w / h)
return img.resize((ow, oh), interpolation)
else:
return img.resize(size[::-1], interpolation)
示例9: to_tensor
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def to_tensor(pic):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
See ``ToTensor`` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if not(_is_numpy_image(pic)):
raise TypeError('pic should be ndarray. Got {}'.format(type(pic)))
# handle numpy array
img = torch.from_numpy(pic.transpose((2, 0, 1)))
# backward compatibility
if isinstance(img, torch.ByteTensor) or img.dtype==torch.uint8:
return img.float().div(255)
else:
return img
示例10: resized_crop
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def resized_crop(img, i, j, h, w, size, interpolation=cv2.INTER_LINEAR):
"""Crop the given numpy ndarray and resize it to desired size.
Notably used in :class:`~torchvision.transforms.RandomResizedCrop`.
Args:
img (numpy ndarray): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
size (sequence or int): Desired output size. Same semantics as ``scale``.
interpolation (int, optional): Desired interpolation. Default is
``cv2.INTER_CUBIC``.
Returns:
PIL Image: Cropped image.
"""
assert _is_numpy_image(img), 'img should be numpy image'
img = crop(img, i, j, h, w)
img = resize(img, size, interpolation=interpolation)
return img
示例11: adjust_brightness
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def adjust_brightness(img, brightness_factor):
"""Adjust brightness of an Image.
Args:
img (numpy ndarray): numpy ndarray to be adjusted.
brightness_factor (float): How much to adjust the brightness. Can be
any non negative number. 0 gives a black image, 1 gives the
original image while 2 increases the brightness by a factor of 2.
Returns:
numpy ndarray: Brightness adjusted image.
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
table = np.array([ i*brightness_factor for i in range (0,256)]).clip(0,255).astype('uint8')
# same thing but a bit slower
# cv2.convertScaleAbs(img, alpha=brightness_factor, beta=0)
if img.shape[2]==1:
return cv2.LUT(img, table)[:,:,np.newaxis]
else:
return cv2.LUT(img, table)
示例12: adjust_contrast
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def adjust_contrast(img, contrast_factor):
"""Adjust contrast of an mage.
Args:
img (numpy ndarray): numpy ndarray to be adjusted.
contrast_factor (float): How much to adjust the contrast. Can be any
non negative number. 0 gives a solid gray image, 1 gives the
original image while 2 increases the contrast by a factor of 2.
Returns:
numpy ndarray: Contrast adjusted image.
"""
# much faster to use the LUT construction than anything else I've tried
# it's because you have to change dtypes multiple times
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
table = np.array([ (i-74)*contrast_factor+74 for i in range (0,256)]).clip(0,255).astype('uint8')
# enhancer = ImageEnhance.Contrast(img)
# img = enhancer.enhance(contrast_factor)
if img.shape[2]==1:
return cv2.LUT(img, table)[:,:,np.newaxis]
else:
return cv2.LUT(img,table)
示例13: adjust_saturation
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def adjust_saturation(img, saturation_factor):
"""Adjust color saturation of an image.
Args:
img (numpy ndarray): numpy ndarray to be adjusted.
saturation_factor (float): How much to adjust the saturation. 0 will
give a black and white image, 1 will give the original image while
2 will enhance the saturation by a factor of 2.
Returns:
numpy ndarray: Saturation adjusted image.
"""
# ~10ms slower than PIL!
if not _is_numpy_image(img):
raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
img = Image.fromarray(img)
enhancer = ImageEnhance.Color(img)
img = enhancer.enhance(saturation_factor)
return np.array(img)
示例14: to_grayscale
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def to_grayscale(img, num_output_channels=1):
"""Convert image to grayscale version of image.
Args:
img (numpy ndarray): Image to be converted to grayscale.
Returns:
numpy ndarray: Grayscale version of the image.
if num_output_channels = 1 : returned image is single channel
if num_output_channels = 3 : returned image is 3 channel with r = g = b
"""
if not _is_numpy_image(img):
raise TypeError('img should be numpy ndarray. Got {}'.format(type(img)))
if num_output_channels==1:
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis]
elif num_output_channels==3:
# much faster than doing cvtColor to go back to gray
img = np.broadcast_to(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)[:,:,np.newaxis], img.shape)
return img
示例15: __call__
# 需要导入模块: import accimage [as 别名]
# 或者: from accimage import Image [as 别名]
def __call__(self, img):
"""
Args:
img (PIL.Image): Image to be scaled.
Returns:
PIL.Image: Rescaled image.
"""
if isinstance(self.size, int):
w, h = img.size
if (w <= h and w == self.size) or (h <= w and h == self.size):
return img
if w < h:
ow = self.size
oh = int(self.size * h / w)
return img.resize((ow, oh), self.interpolation)
else:
oh = self.size
ow = int(self.size * w / h)
return img.resize((ow, oh), self.interpolation)
else:
return img.resize(self.size, self.interpolation)