def add_motion_blur(image, kernel_size, angle):
"""
adds motion blur at given angel to the image.
:param image: image
:param kernel_size: int
:param angle: an angle in radians in the range [0, pi).
:return: blurred image
"""
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
return scipy.ndimage.filters.convolve(image, kernel)
def add_motion_blur(image, kernel_size, angle):
"""
A method for adding a motion blur effect to the image
:param image: the image to blur
:param kernel_size: the size of the kernel with which we convolve
:param angle: the angle of the motion blur to perform
:return: a blurred picture
"""
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
return ndimage.filters.convolve(image, kernel)
def add_motion_blur(image, kernel_size, angle):
"""
:param image: an image to blur
:param kernel_size: size for the blur kernel
:param angle: an angle between zero to PI
:return: blurred image
"""
return ndimage.filters.convolve(
image, util.motion_blur_kernel(kernel_size, angle))
def add_motion_blur(image, kernel_size, angle):
"""
simulate motion blur on the given image using a square kernel of size kernel_size where the line has the given
angle in radians, measured relative to the positive horizontal axis, e.g. a horizontal line would have a zero angle
:param image: a grayscale image with values in the [0, 1] range of type float64.
:param kernel_size: an odd integer specifying the size of the kernel (even integers are ill-defined).
:param angle: an angle in radians in the range [0, π).
:return: corrupted image
"""
corr_image = convolve(image, sol5_utils.motion_blur_kernel(kernel_size, angle))
return (np.around(corr_image * 255) / 255).clip(0, 1)
def add_motion_blur(image, kernel_size, angle):
"""
Simulate motion blur on the given image using a square kernel with the size "kernel_size" where the line
has the given angle in radians, measured relative to the positive horizontal axis.
:param image: a gray image with values in the range of [0, 1] with type float64.
:param kernel_size: an odd integer specifying the size of the kernel (even integers are ill-defined).
:param angle: an angle in radians with the range [0, π).
:return: image with motion blur.
"""
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
return convolve(image, kernel).astype(np.float64)
def add_motion_blur(image, kernel_size, angle):
'''
this function receives an image and performs a given motion blur on it
:param image: a grayscale image with values in the [0,1] range of type float64
:param kernel_size: an odd integer specifying the size of the kernel (even integers are ill-defined
:param angle: an angle in radians in the range [0,Pi]
:return: a motion blurred image
'''
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
blurred_img = convolve(image, kernel)
return blurred_img
def add_motion_blur(image, kernel_size, angle):
"""
Adds motion blur to an image.
:param image: a grayscale image with values in the [0, 1] range of type float64
:param kernel_size: an odd integer specifying the size of the kernel
:param angle: an angle in radians in the range [0, pi)
:return: the blurred image
"""
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
corrupted = convolve(image, kernel)
return corrupted
def add_motion_blur(image: np.ndarray, kernel_size: int,
angle: float) -> np.ndarray:
"""
simulate motion blurring on a given image
:param image: a grayscale image with values in the [0, 1] range
:param kernel_size: an odd integer specifying the size of the kernel
:param angle: an angle in radians in the range [0, PI)
:return: motion blurred image
"""
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
blurred = filters.convolve(image, kernel, mode='mirror')
return blurred.astype(np.float32)
def add_motion_blur(image, kernel_size, angle):
"""
The function add_motion_blur simulates motion blur on the given image using a square kernel
of size kernel_size where the line (as described above) has the given angle in radians, measured
relative to the positive horizontal axis, e.g. a horizontal line would have a zero angle, and for the
figure above the angle would be 3π/4 (or 135◦ in degrees).
:param image: a grayscale image with values in the [0, 1] range of type float32.
:param kernel_size: an odd integer specifying the size of the kernel (even integers are ill-defined).
:param angle: an angle in radians in the range [0, π).
:return: corrupted - The corrupted image
"""
ker = sol5_utils.motion_blur_kernel(kernel_size, angle)
return convolve(image, ker, mode='reflect').astype(np.float32)
def add_motion_blur(image, kernel_size, angle):
'''
Adding motion blur to a given image
:param image:Grayscale image in the [0,1] range of float64
:param kernel_size: The size of the kernel.An ood integer
:param angle: angle in range [0,pi)
:return: Image blurred
'''
blur_kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
corrupted = convolve(image, blur_kernel)
corrupted = np.divide(np.round(corrupted * (255)), (255))
corrupted = np.clip(corrupted, 0, 1)
return corrupted
def add_motion_blur(image, kernel_size, angle):
"""
Adds a motion blur to an image according to angle and kernel size.
:param image: a grayscale image with values in the [0, 1] range of type float64
:type image: np array
:param kernel_size: an odd integer specifying the size of the kernel (even integers are ill-defined)
:type kernel_size: int
:param angle: an angle in radians in the range [0, π).
:type angle: radians
"""
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
return scipy.ndimage.filters.convolve(image, kernel)
def add_motion_blur(image, kernel_size, angle):
"""
adds motion blur to an image.
:param image: a grayscale image with values in the [0, 1] range of type float64.
:param kernel_size: an odd integer specifying the size of the kernel
:param angle: an angle in radians in the range [0 ,π)
:return: The corrupted image
"""
motion_blur_kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
blurred_image = scipy.ndimage.filters.convolve(image,
motion_blur_kernel,
mode='nearest')
return np.clip(np.round(blurred_image * 255) / 255, 0, 1)
def add_motion_blur(image, kernel_size, angle):
"""
Motion blur by convolving the image with a kernel made of a single line crossing its center, where
the direction of the motion blur is given by the angle of the line.
:param image: a grayscale image with values in the [0, 1] range of type float64.
:param kernel_size: an odd integer specifying the size of the kernel (even integers are ill-defined).
:param angle: an angle in radians in the range [0, π)
:return: blurred image
"""
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
blurred_im = image.copy()
if len(image.shape) == 3:
blurred_im[:, :, 0] = convolve(image[:, :, 0], kernel)
else:
blurred_im = convolve(image, kernel)
blurred_im = np.around(255 * blurred_im) / 255.0
return blurred_im.clip(0, 1).astype(np.float64)
def add_motion_blur(image, kernel_size, angle):
"""
Motion blur can be simulated by convolving the image with a kernel made of
a single line crossing its center, where the direction of the motion blur
is given by the angle of the line.
:param image: a grayscale image with values in the [0, 1] range of type
float64.
:param kernel_size: an odd integer specifying the size of the
kernel (even integers are ill-defined).
:param angle: an angle in radians in the range [0, π).
:return: a blured image.
"""
blur_kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
im = scipy.ndimage.filters.convolve(image, blur_kernel)
im *= 255
im /= 255
return im.clip(min=0, max=1)
def add_motion_blur(image, kernel_size, angle):
kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
return convolve(image, kernel)
def add_motion_blur(image, kernel_size, angle):
"""
adds motion blur to a given image
"""
return flt.convolve(image, sut.motion_blur_kernel(kernel_size, angle))
def add_motion_blur(image, kernel_size, angle):
blur_kernel = sol5_utils.motion_blur_kernel(kernel_size, angle)
return ndimage.filters.convolve(image, blur_kernel)
def add_motion_blur(image, kernel_size, angle):
kernel2d = sol5_utils.motion_blur_kernel(kernel_size, angle)
#kernel2d = kernel2d.reshape(kernel2d.shape[0], kernel2d.shape[1])
return convolve(image, kernel2d).astype(np.float32)
def add_motion_blur(image, kernel_size, angle):
# create filter:
filter = sol5_utils.motion_blur_kernel(kernel_size, angle)
# do the bluring:
return scipy.ndimage.filters.convolve(image, filter)
