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)