Well something is something
Gasper Spagnolo
parent
182432917f
commit
3a899115a6
|
|
@ -0,0 +1,82 @@
|
|||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
from matplotlib import pyplot as plt
|
||||
import cv2
|
||||
import uz_framework.image as uz_image
|
||||
import uz_framework.text as uz_text
|
||||
|
||||
#################################################################
|
||||
# EXCERCISE 1: Exercise 1: Global approach to image description #
|
||||
#################################################################
|
||||
|
||||
def ex1():
|
||||
#one_a()
|
||||
#two_b()
|
||||
two_c()
|
||||
|
||||
def one_a() -> None:
|
||||
"""
|
||||
Follow the equations above and derive the equations used to compute first and
|
||||
second derivatives with respect to y: Iy(x, y), Iyy(x, y), as well as the mixed derivative
|
||||
Ixy(x, y)
|
||||
"""
|
||||
|
||||
def two_b() -> None:
|
||||
"""
|
||||
Implement a function that computes the derivative of a 1-D Gaussian kernel
|
||||
Implement the function gaussdx(sigma) that works the same as function gauss
|
||||
from the previous assignment. Don’t forget to normalize the kernel. Be careful as
|
||||
the derivative is an odd function, so a simple sum will not do. Instead normalize the
|
||||
kernel by dividing the values such that the sum of absolute values is 1. Effectively,
|
||||
you have to divide each value by sum(abs(gx(x))).
|
||||
"""
|
||||
sigma = 1
|
||||
kernel = uz_image.gaussdx(sigma)
|
||||
print(kernel)
|
||||
|
||||
def two_c() -> None:
|
||||
"""
|
||||
The properties of the filter can be analyzed by using an impulse response function.
|
||||
This is performed as a convolution of the filter with a Dirac delta function. The
|
||||
discrete version of the Dirac function is constructed as a finite image that has all
|
||||
elements set to 0 except the central element, which is set to a high value (e.g. 1).
|
||||
Generate a 1-D Gaussian kernel G and a Gaussian derivative kernel D.
|
||||
What happens if you apply the following operations to the impulse image?
|
||||
(a) First convolution with G and then convolution with GT
|
||||
(b) First convolution with G and then convolution with DT
|
||||
(c) First convolution with D and then convolution with GT
|
||||
(d) First convolution with GT and then convolution with D.
|
||||
(e) First convolution with DT and then convolution with G.
|
||||
Is the order of operations important? Display the images of the impulse responses
|
||||
for different combinations of operations.
|
||||
"""
|
||||
impulse = uz_image.generate_dirac_impulse(50)
|
||||
gauss = np.array([uz_image.get_gaussian_kernel(3)])
|
||||
gaussdx = np.array([uz_image.gaussdx(3)])
|
||||
|
||||
fig, axs = plt.subplots(2, 3)
|
||||
|
||||
# Plot impulse only
|
||||
axs[0, 0].imshow(impulse, cmap='gray')
|
||||
axs[0, 0].set_title('Impulse')
|
||||
|
||||
# Plot impulse after convolution with G and GT
|
||||
g_gt_impulse = impulse.copy()
|
||||
g_gt_impulse = cv2.filter2D(g_gt_impulse, cv2.CV_64F, gauss)
|
||||
g_gt_impulse = cv2.filter2D(g_gt_impulse, cv2.CV_64F, gauss.T)
|
||||
axs[0, 1].imshow(g_gt_impulse, cmap='gray')
|
||||
axs[0, 1].set_title('impulse * G * GT')
|
||||
|
||||
plt.show()
|
||||
|
||||
# ######## #
|
||||
# SOLUTION #
|
||||
# ######## #
|
||||
|
||||
def main():
|
||||
ex1()
|
||||
#ex2()
|
||||
#ex3()
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
|
@ -0,0 +1,500 @@
|
|||
|
||||
import numpy as np
|
||||
import cv2 as cv2
|
||||
from matplotlib import pyplot as plt
|
||||
from PIL import Image
|
||||
from typing import Union
|
||||
import numpy.typing as npt
|
||||
import enum
|
||||
|
||||
class ImageType(enum.Enum):
|
||||
uint8 = 0
|
||||
float64 = 1
|
||||
|
||||
class DistanceMeasure(enum.Enum):
|
||||
euclidian_distance = 0
|
||||
chi_square_distance = 1
|
||||
intersection_distance = 2
|
||||
hellinger_distance = 3
|
||||
|
||||
def imread(path: str, type: ImageType) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Reads an image in RGB order. Image type is transformed from uint8 to float, and
|
||||
range of values is reduced from [0, 255] to [0, 1].
|
||||
"""
|
||||
I = Image.open(path).convert('RGB') # PIL image.
|
||||
I = np.asarray(I) # Converting to Numpy array.
|
||||
if type == ImageType.float64:
|
||||
I = I.astype(np.float64) / 255
|
||||
return I
|
||||
elif type == ImageType.uint8:
|
||||
return I
|
||||
|
||||
raise Exception(f"Unrecognized image format! {type}")
|
||||
|
||||
|
||||
def imread_gray(path: str, type: ImageType) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Reads an image in gray. Image type is transformed from uint8 to float, and
|
||||
range of values is reduced from [0, 255] to [0, 1].
|
||||
"""
|
||||
|
||||
I = Image.open(path).convert('L') # PIL image opening and converting to gray.
|
||||
I = np.asarray(I) # Converting to Numpy array.
|
||||
|
||||
if type == ImageType.float64:
|
||||
I = I.astype(np.float64) / 255
|
||||
return I
|
||||
elif type == ImageType.uint8:
|
||||
return I
|
||||
|
||||
raise Exception("Unrecognized image format!")
|
||||
|
||||
def signal_show(*signals):
|
||||
"""
|
||||
Plots all given 1D signals in the same plot.
|
||||
Signals can be Python lists or 1D numpy array.
|
||||
"""
|
||||
for s in signals:
|
||||
if type(s) == np.ndarray:
|
||||
s = s.squeeze()
|
||||
plt.plot(s)
|
||||
plt.show()
|
||||
|
||||
|
||||
def convolve(I: np.ndarray, *ks):
|
||||
"""
|
||||
Convolves input image I with all given kernels.
|
||||
|
||||
:param I: Image, should be of type float64 and scaled from 0 to 1.
|
||||
:param ks: 2D Kernels
|
||||
:return: Image convolved with all kernels.
|
||||
"""
|
||||
for k in ks:
|
||||
k = np.flip(k) # filter2D performs correlation, so flipping is necessary
|
||||
I = cv2.filter2D(I, cv2.CV_64F, k)
|
||||
return I
|
||||
|
||||
def transform_coloured_image_to_grayscale(image: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts float64 picture with three colour channels and returns float64 grayscale image
|
||||
with one channel.
|
||||
"""
|
||||
grayscale_image = np.zeros(image.shape[:2])
|
||||
|
||||
for i in range(image.shape[0]):
|
||||
for j in range(image.shape[1]):
|
||||
grayscale_image[i, j] = (image[i, j, 0] + image[i,j, 1] + image[i, j, 2]) / 3
|
||||
|
||||
|
||||
return grayscale_image
|
||||
|
||||
def invert_coloured_image_part(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], startx: int, endx: int, starty: int, endy: int) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts image, starting position end end position for axes x & y. Returns whole image with inverted part.
|
||||
"""
|
||||
inverted_image = image.copy()
|
||||
|
||||
if image.dtype.type == np.float64:
|
||||
for i in range(startx, endx):
|
||||
for j in range(starty, endy):
|
||||
inverted_image[i, j, 0] = 1 - image[i, j, 0]
|
||||
inverted_image[i, j, 1] = 1 - image[i, j, 1]
|
||||
inverted_image[i, j, 2] = 1 - image[i, j, 2]
|
||||
return inverted_image
|
||||
elif image.dtype.type == np.uint8:
|
||||
for i in range(startx, endx):
|
||||
for j in range(starty, endy):
|
||||
inverted_image[i, j, 0] = 255 - image[i, j, 0]
|
||||
inverted_image[i, j, 1] = 255 - image[i, j, 1]
|
||||
inverted_image[i, j, 2] = 255 - image[i, j, 2]
|
||||
return inverted_image
|
||||
|
||||
raise Exception("Unrecognized image format!")
|
||||
|
||||
def invert_coloured_image(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts image and inverts it
|
||||
"""
|
||||
return invert_coloured_image_part(image, 0, image.shape[0], 0, image.shape[1])
|
||||
|
||||
def calculate_best_treshold_using_otsu_method(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> int:
|
||||
"""
|
||||
Accepts image and returns best treshold using otsu method
|
||||
"""
|
||||
|
||||
if image.dtype.type == np.float64:
|
||||
im = image.copy()
|
||||
im = im * (255.0/im.max())
|
||||
elif image.dtype.type == np.uint8:
|
||||
im = image.copy()
|
||||
else:
|
||||
raise Exception("Unrecognized image format!")
|
||||
|
||||
treshold_range = np.arange(np.max(im) + 1)
|
||||
criterias = []
|
||||
|
||||
for treshold in treshold_range:
|
||||
# create the thresholded image
|
||||
thresholded_im = np.zeros(im.shape)
|
||||
|
||||
thresholded_im[im >= treshold] = 1
|
||||
|
||||
# compute weights
|
||||
nb_pixels = im.size
|
||||
nb_pixels1 = np.count_nonzero(thresholded_im)
|
||||
weight1 = nb_pixels1 / nb_pixels
|
||||
weight0 = 1 - weight1
|
||||
|
||||
# if one the classes is empty, eg all pixels are below or above the threshold, that threshold will not be considered
|
||||
# in the search for the best threshold
|
||||
if weight1 == 0 or weight0 == 0:
|
||||
continue
|
||||
|
||||
# find all pixels belonging to each class
|
||||
val_pixels1 = im[thresholded_im == 1]
|
||||
val_pixels0 = im[thresholded_im == 0]
|
||||
|
||||
# compute variance of these classes
|
||||
var0 = np.var(val_pixels0) if len(val_pixels0) > 0 else 0
|
||||
var1 = np.var(val_pixels1) if len(val_pixels1) > 0 else 0
|
||||
|
||||
criterias.append( weight0 * var0 + weight1 * var1)
|
||||
|
||||
best_threshold = treshold_range[np.argmin(criterias)]
|
||||
return best_threshold
|
||||
|
||||
|
||||
def get_image_bins_for_loop(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], number_of_bins: int) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts image in the float64 format or uint8, returns normailzed
|
||||
image bins, histogram
|
||||
"""
|
||||
if image.dtype.type == np.float64 or image.dtype.type == np.uint8:
|
||||
bin_restrictions = np.linspace(np.min(image), np.max(image), num=number_of_bins)
|
||||
else:
|
||||
raise Exception("Unrecognized image format!")
|
||||
|
||||
bins = np.zeros(number_of_bins).astype(np.float64)
|
||||
|
||||
for pixel in image.reshape(-1):
|
||||
# https://stackoverflow.com/a/16244044
|
||||
bins[np.argmax(bin_restrictions > pixel)] += 1
|
||||
|
||||
return bins / np.sum(bins)
|
||||
|
||||
|
||||
# Much faster implementation than for loop
|
||||
def get_image_bins(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], number_of_bins: int) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts image in the float64 format or uint8, returns normailzed
|
||||
image bins, histogram
|
||||
"""
|
||||
if image.dtype.type == np.float64 or image.dtype.type == np.uint8:
|
||||
bins = np.linspace(np.min(image), np.max(image), num=number_of_bins)
|
||||
else:
|
||||
raise Exception("Unrecognized image format!")
|
||||
|
||||
# Put pixels into classes
|
||||
# ex. binsize = 10 then 0.4 would map into 4
|
||||
binarray = np.digitize(image.reshape(-1), bins).astype(np.uint8)
|
||||
|
||||
# Now count those values
|
||||
binarray = np.unique(binarray, return_counts=True)
|
||||
|
||||
counts = binarray[1].astype(np.float64) # Get the counts out of tuple
|
||||
|
||||
# Check if there is any empty bin
|
||||
empty_bins = []
|
||||
bins = binarray[0]
|
||||
for i in range(1, number_of_bins + 1):
|
||||
if i not in bins:
|
||||
empty_bins.append(i)
|
||||
|
||||
# Add empty bins with zeros
|
||||
if empty_bins != []:
|
||||
for i in empty_bins:
|
||||
counts = np.insert(counts, i - 1, 0)
|
||||
|
||||
return counts
|
||||
|
||||
def get_image_bins_ND(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], number_of_bins: int) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts image in the float64 format or uint8 and number of bins
|
||||
Returns normailzed image histogram bins
|
||||
"""
|
||||
bs = []
|
||||
hist = np.zeros((number_of_bins, number_of_bins, number_of_bins))
|
||||
bins = np.linspace(np.min(image), np.max(image), num=number_of_bins)
|
||||
|
||||
for i in range(image.shape[2]):
|
||||
v = image[:, :, i].reshape(-1)
|
||||
bs.append(np.digitize(v, bins).astype(np.uint32))
|
||||
|
||||
for i in range(len(bs[0])):
|
||||
hist[bs[2][i] -1, bs[1][i] -1, bs[0][i] - 1] += 1
|
||||
|
||||
return hist / np.sum(hist)
|
||||
|
||||
def compare_two_histograms(h1: npt.NDArray[np.float64], h2: npt.NDArray[np.float64], method: DistanceMeasure) -> float:
|
||||
"""
|
||||
Accepts two histograms and method of comparison
|
||||
Returns distance between them
|
||||
"""
|
||||
|
||||
if method == DistanceMeasure.euclidian_distance:
|
||||
d = np.sqrt(np.sum(np.square(h1 - h2)))
|
||||
elif method == DistanceMeasure.chi_square_distance:
|
||||
d = 0.5 * np.sum(np.square(h1 - h2) / (h1 + h2 + np.finfo(float).eps))
|
||||
elif method == DistanceMeasure.intersection_distance:
|
||||
d = 1 - np.sum(np.minimum(h1, h2))
|
||||
elif method == DistanceMeasure.hellinger_distance:
|
||||
d = np.sqrt(0.5 * np.sum(np.square(np.sqrt(h1) - np.sqrt(h2))))
|
||||
else:
|
||||
raise Exception('Unsuported method chosen!')
|
||||
|
||||
return d.astype(float)
|
||||
|
||||
|
||||
def apply_mask_on_image(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], mask: npt.NDArray[np.uint8]) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts image and applys mask to image
|
||||
"""
|
||||
image = image.copy()
|
||||
|
||||
mask = np.expand_dims(mask, axis=2)
|
||||
image = mask * image
|
||||
|
||||
return image
|
||||
|
||||
def simple_convolution(signal: npt.NDArray[np.float64], kernel: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts: signal & kernel
|
||||
|
||||
Returns: convolved signal with a kernel
|
||||
"""
|
||||
N = int(np.ceil(len(kernel) / 2 - 1))
|
||||
n_conv = signal.size - kernel.size + 1
|
||||
print(N, signal.size -N, n_conv)
|
||||
|
||||
convolved_signal = np.zeros(len(signal))
|
||||
rev_kernel = kernel[::-1].copy()
|
||||
|
||||
for i in range(n_conv):
|
||||
convolved_signal[i] = np.dot(signal[i: i+kernel.size], rev_kernel) # Well if you would add i+N then you wuold shift this
|
||||
|
||||
return convolved_signal
|
||||
|
||||
def simple_convolution_improved(signal: npt.NDArray[np.float64], kernel: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts: signal & kernel
|
||||
|
||||
Returns: convolved signal with a kernel
|
||||
Improved method replicates edges of an signal
|
||||
"""
|
||||
signal_len = signal.size
|
||||
kernel_len = kernel.size
|
||||
signal = signal.copy()
|
||||
|
||||
# Calculate which values to fill in and range
|
||||
EDGE_FRONT = signal[0]
|
||||
EDGE_BACK = signal[-1]
|
||||
EXTEND_RANGE = int(np.floor(kernel.size / 2))
|
||||
|
||||
# Append end insert edges
|
||||
front = [ EDGE_FRONT for _ in range(EXTEND_RANGE)]
|
||||
back = [ EDGE_BACK for _ in range(EXTEND_RANGE)]
|
||||
signal = np.insert(signal, 1, front, axis=0)
|
||||
signal = np.append(signal, back, axis=0)
|
||||
|
||||
convolved_signal = np.zeros(signal_len)
|
||||
rev_kernel = kernel[::-1].copy()
|
||||
n_conv = signal_len - kernel_len + 1
|
||||
|
||||
for i in range(n_conv):
|
||||
convolved_signal[i] = np.dot(signal[i: i+kernel.size], rev_kernel)
|
||||
|
||||
return convolved_signal
|
||||
|
||||
def get_gaussian_kernel(sigma: float) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts sigma
|
||||
Returns gaussian kernel
|
||||
"""
|
||||
# https://github.com/mikepound/convolve/blob/master/run.gaussian.py
|
||||
kernel_size = int(2 * np.ceil(3*sigma) + 1)
|
||||
k_min_max = np.ceil(3*sigma)
|
||||
k_interval = np.arange(-k_min_max, k_min_max + 1.)
|
||||
|
||||
result = (1. / (np.sqrt(2. * np.pi )* sigma)) * np.exp(- (np.square(k_interval)) / (2.*np.square(sigma)))
|
||||
|
||||
assert(kernel_size == len(result))
|
||||
|
||||
return result / np.sum(result)
|
||||
|
||||
def sharpen_image(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], sharpen_factor=1.0) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts: image & sharpen factor
|
||||
|
||||
Returns: sharpened image
|
||||
https://blog.demofox.org/2022/02/26/image-sharpening-convolution-kernels/ <-- sharpening kernel, but also on slides
|
||||
good explanation
|
||||
"""
|
||||
sharpened_image = image.copy()
|
||||
|
||||
KERNEL = np.array([[-1, -1, -1],
|
||||
[-1, 17, -1],
|
||||
[-1, -1,-1]]) * 1./9. * sharpen_factor
|
||||
|
||||
if image.dtype.type == np.float64:
|
||||
sharpened_image = cv2.filter2D(sharpened_image, cv2.CV_64F, KERNEL)
|
||||
elif image.dtype.type == np.uint8:
|
||||
sharpened_image = cv2.filter2D(sharpened_image, cv2.CV_8U, KERNEL)
|
||||
|
||||
return sharpened_image
|
||||
|
||||
def gaussfilter2D(image: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], sigma: float) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts: image, sigma
|
||||
Applies gaussian noise on image
|
||||
returns: filtered_image
|
||||
"""
|
||||
filtered_image = image.copy()
|
||||
kernel = np.array([get_gaussian_kernel(sigma)])
|
||||
filtered_image = cv2.filter2D(filtered_image, cv2.CV_64F, kernel)
|
||||
filtered_image = cv2.filter2D(filtered_image, cv2.CV_64F, kernel.T)
|
||||
|
||||
return filtered_image
|
||||
|
||||
def gaussdx(sigma: float) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts sigma
|
||||
Returns gaussian kernel
|
||||
"""
|
||||
kernel_size = int(2 * np.ceil(3*sigma) + 1)
|
||||
k_min_max = np.ceil(3*sigma)
|
||||
k_interval = np.arange(-k_min_max, k_min_max + 1.)
|
||||
|
||||
result = - (1. / (np.sqrt(2. * np.pi )* np.power(sigma, 3))) * k_interval* np.exp(- (np.square(k_interval)) / (2.*np.square(sigma)))
|
||||
|
||||
assert(kernel_size == len(result))
|
||||
|
||||
return result / np.sum(np.abs(result))
|
||||
|
||||
|
||||
def simple_median(signal: npt.NDArray[np.float64], width: int) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts: signal & width
|
||||
returns signal improved using median filter
|
||||
"""
|
||||
if width % 2 == 0:
|
||||
raise Exception('No u won\'t do that')
|
||||
|
||||
signal = signal.copy()
|
||||
for i in range(len(signal) - int(np.ceil(width/2))):
|
||||
middle_element = int(i + np.floor(width/2))
|
||||
signal[middle_element] = np.median(signal[i:i+width])
|
||||
return signal
|
||||
|
||||
def apply_median_method_2D(image:Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], width: int) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts: image & filter width
|
||||
returns: image with median filter applied
|
||||
"""
|
||||
if width % 2 == 0:
|
||||
raise Exception('No u won\'t do that')
|
||||
|
||||
image = image.copy()
|
||||
W_HALF = int(np.floor(width/2))
|
||||
padded_image = np.pad(image, W_HALF, mode='edge')
|
||||
IMAGE_HEIGHT = image.shape[0] # y
|
||||
IMAGE_WIDTH = image.shape[1] # x
|
||||
|
||||
for x in range(W_HALF, IMAGE_WIDTH):
|
||||
for y in range(W_HALF, IMAGE_HEIGHT):
|
||||
median_filter = np.zeros(0)
|
||||
STARTX = x - W_HALF
|
||||
STARTY = y - W_HALF
|
||||
for m in range(width):
|
||||
median_filter = np.append(median_filter, padded_image[STARTY + m][STARTX: STARTX + width], axis=0)
|
||||
if image.dtype.type == np.uint8:
|
||||
image[y][x] = int(np.mean(median_filter))
|
||||
else:
|
||||
image[y][x] = np.mean(median_filter)
|
||||
return image
|
||||
|
||||
def filter_laplace(image:Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], sigma: float) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts: image & sigma
|
||||
returns: image with laplace filter applied
|
||||
"""
|
||||
# Prepare unit impulse and gauss kernel
|
||||
unit_impulse = np.zeros((1, 2 * int(np.ceil(3*sigma)) + 1))
|
||||
unit_impulse[0][int(np.ceil(unit_impulse.size /2)) - 1]= 1
|
||||
gauss_kernel = np.array([get_gaussian_kernel(sigma)])
|
||||
assert(len(gauss_kernel[0]) == len(unit_impulse[0]))
|
||||
|
||||
laplacian_filter = unit_impulse - gauss_kernel[0]
|
||||
|
||||
# Now apply laplacian filter
|
||||
applied_by_x = cv2.filter2D(image, -1, laplacian_filter)
|
||||
applied_by_y = cv2.filter2D(applied_by_x, -1, laplacian_filter.T)
|
||||
|
||||
return applied_by_y
|
||||
|
||||
def gauss_noise(I, magnitude=.1) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts: image & magnitude
|
||||
Returns: image with gaussian noise applied
|
||||
"""
|
||||
# input: image, magnitude of noise
|
||||
# output: modified image
|
||||
I = I.copy()
|
||||
|
||||
return I + np.random.normal(size=I.shape) * magnitude
|
||||
|
||||
|
||||
def sp_noise(I, percent=.1) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts: image & percent
|
||||
Returns: image with salt and pepper noise applied
|
||||
"""
|
||||
# input: image, percent of corrupted pixels
|
||||
# output: modified image
|
||||
|
||||
res = I.copy()
|
||||
res[np.random.rand(I.shape[0], I.shape[1]) < percent / 2] = 1
|
||||
res[np.random.rand(I.shape[0], I.shape[1]) < percent / 2] = 0
|
||||
|
||||
return res
|
||||
|
||||
def sp_noise1D(signal, percent=.1) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts: signal & percent
|
||||
Returns: signal with salt and pepper noise applied
|
||||
"""
|
||||
signal = signal.copy()
|
||||
signal[np.random.rand(signal.shape[0]) < percent / 2] = 2
|
||||
signal[np.random.rand(signal.shape[0]) < percent / 2] = 1
|
||||
signal[np.random.rand(signal.shape[0]) < percent / 2] = 4
|
||||
signal[np.random.rand(signal.shape[0]) < percent / 2] = 0.4
|
||||
return signal
|
||||
|
||||
def sum_two_grayscale_images(image_a: Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]], image_b :Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]) -> Union[npt.NDArray[np.float64], npt.NDArray[np.uint8]]:
|
||||
"""
|
||||
Accepts: image_a, image_b
|
||||
Returns: image_a + image_b
|
||||
"""
|
||||
# Merge image_a and image_b
|
||||
return (image_a + image_b)/ 2
|
||||
|
||||
def generate_dirac_impulse(size: int) -> npt.NDArray[np.float64]:
|
||||
"""
|
||||
Accepts: size
|
||||
Returns: dirac impulse of size
|
||||
"""
|
||||
|
||||
dirac_impulse = np.zeros((size, size))
|
||||
dirac_impulse[int(size/2), int(size/2)] = 1
|
||||
|
||||
return dirac_impulse
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
import numpy as np
|
||||
|
||||
def read_data(filename: str):
|
||||
# reads a numpy array from a text file
|
||||
with open(filename) as f:
|
||||
s = f.read()
|
||||
|
||||
return np.fromstring(s, sep=' ')
|
||||
|
||||
Loading…
Reference in New Issue