def noise_estimation(im: np.ndarray):
"""
Given an image, this function determines the variance in its pixel
values and displays a histogram of the image along with a fitted normal
distribution.
"""
# Calculate histogram
im_hist, bin_edges = np.histogram(im, 30)
bin_centers = bin_edges[:-1] + (np.diff(bin_edges) / 2)
# Normalize histogram to make a PDF
im_hist = im_hist.astype(float)
im_hist /= np.sum(im_hist)
# Calculate the mean and variance values
mean = np.sum(im_hist * bin_centers)
var = np.sum(im_hist * (bin_centers**2)) - (mean**2)
# Calculate the values on the normal distribution PDF
norm_vals = np.exp(-((bin_centers - mean) ** 2) / (2 * var)) \
/ np.sqrt(2 * np.pi * var)
# Normalize the norm_vals
norm_vals /= np.sum(norm_vals)
# Rescale variance
var /= (255**2)
print('Variance: {}'.format(var))
dip.figure()
dip.bar(bin_centers, im_hist)
dip.plot(bin_centers, norm_vals, 'r')
dip.legend(['Fitted Gaussian PDF', 'Histogram of image'])
dip.xlabel('Pixel value')
dip.ylabel('Occurrence')
dip.show()
def hist25(image):
dim = image.shape
x = np.reshape(image, (dim[0] * dim[1], 1))
dip.hist(x)
dip.grid()
dip.title('Histogram')
dip.xlabel('Pixel value')
dip.ylabel('Pixel frequency')
def _test_noise_mode(self, file, deg_type):
fh = ImageFileHandler()
im = fh.open_image_file_as_matrix(file)
degraded_im = self.degrade(im, degradation_type=deg_type)
dip.figure()
dip.subplot(121)
dip.imshow(im, cmap="gray")
dip.subplot(122)
dip.imshow(degraded_im, cmap="gray")
dip.xlabel("PSNR: {0:.2f}".format(dip.PSNR(im, degraded_im)))
dip.show()
def hist(image, plot):
dim = image.shape
M = dim[0]
N = dim[1]
res = np.zeros(256)
for i in range(0, M):
for j in range(0, N):
res[image[i, j]] += 1
if plot:
dip.bar(np.arange(256), res, width=1)
dip.grid()
dip.title('Histogram')
dip.xlabel('Pixel value')
dip.ylabel('Pixel frequency')
return res
def _test_restore_mode(self, file, deg_type, save_images=False, name=None):
fh = ImageFileHandler()
im_degrader = ImageDegrader()
im = fh.open_image_file_as_matrix(file)
degraded_im = im_degrader.degrade(im,
degradation_type=deg_type,
severity_value=.5)
restored_im, clustered_im, h_params = self.multiplicative_clustering_restore(
degraded_im)
restored_im_2 = self.fast_multiplicative_restore(
degraded_im, h_param=np.mean(h_params), search_window_size=21)
if save_images:
dip.im_write(dip.float_to_im(degraded_im),
"./" + name + "_degraded_image.jpg",
quality=95)
dip.im_write(dip.float_to_im(restored_im),
"./" + name + "_restored_image.jpg",
quality=95)
dip.im_write(dip.float_to_im(clustered_im),
"./" + name + "_clustered_image.jpg",
quality=95)
dip.figure()
dip.subplot(141)
dip.imshow(im, cmap="gray")
dip.subplot(142)
dip.imshow(degraded_im, cmap="gray")
dip.xlabel("PSNR: {0:.2f}, SSIM: {1:.2f}".format(
dip.PSNR(im, degraded_im),
dip.SSIM(im, degraded_im)[0]))
dip.subplot(143)
dip.imshow(restored_im, cmap="gray")
dip.xlabel("PSNR: {0:.2f}, SSIM: {1:.2f}".format(
dip.PSNR(im, restored_im),
dip.SSIM(im, restored_im)[0]))
dip.subplot(144)
dip.imshow(restored_im_2, cmap="gray")
dip.xlabel("PSNR: {0:.2f}, SSIM: {1:.2f}".format(
dip.PSNR(im, restored_im_2),
dip.SSIM(im, restored_im_2)[0]))
dip.show()
x_max = max(x_max, dim[1])
y_max = max(y_max, dim[0])
image_size_distribution = np.zeros((y_max + 100, x_max + 100))
for filename in os.listdir(directory):
im = dip.im_read(directory + filename)
dim = im.shape
image_size_distribution[dim[0]][dim[1]] += 1
image_size_distribution = image_size_distribution / np.amax(
image_size_distribution)
dip.contour(dip.float_to_im(image_size_distribution, 8))
dip.grid()
dip.xlabel('image width')
dip.ylabel('image height')
dip.title('Image size distribution')
###########################
# CONVERTING TO GRAYSCALE #
###########################
for directory in dirs:
for filename in os.listdir(directory):
out = Image.open(directory + filename).convert('L')
filename_without_extension = os.path.splitext(filename)[0]
if directory == dir_no:
out.save("../grayscale_data/no/" + filename_without_extension +
".png")
elif directory == dir_yes:
out.save("../grayscale_data/yes/" + filename_without_extension +