Image appear full black doted when I add Gaussian Noise

[Rida-Noor]

def median_filter(data, kernel_size): temp = [] indexer = kernel_size // 2 data_final = [] data_final = np.zeros((len(data),len(data[0]))) for i in range(len(data)):

    for j in range(len(data[0])):

        for z in range(kernel_size):
            if i + z - indexer < 0 or i + z - indexer > len(data) - 1:
                for c in range(kernel_size):
                    temp.append(0)
            else:
                if j + z - indexer < 0 or j + indexer > len(data[0]) - 1:
                    temp.append(0)
                else:
                    for k in range(kernel_size):
                        temp.append(data[i + z - indexer][j + k - indexer])

        temp.sort()
        data_final[i][j] = temp[len(temp) // 2]
        temp = []
return data_final

def add_gaussian_noise(img, sigma): gauss = np.random.normal(0, sigma, np.shape(img)) noisy_img = img + gauss noisy_img[noisy_img < 0] = 0 noisy_img[noisy_img > 255] = 255 return noisy_img

def rgb2gray(rgb): return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])

if name == 'main': # Load image and convert it to gray scale file_name = os.path.join(r'E:\Datasets\Brain Tumor Images Dataset\training_set\hemmorhage_data\030.png') img = rgb2gray(plt.imread(file_name))

# Add Gaussian noise
noisy_img = add_gaussian_noise(img,50)

# Apply Median Filter
removed_noise_3 = median_filter(noisy_img, 3)
removed_noise_5 = median_filter(noisy_img, 5)

# Display results
fig = plt.figure(figsize = (12, 10))
display = [img, noisy_img, removed_noise_3, removed_noise_5]
title = ['Original Image', 'Gaussian Noise Added', '3x3 Median Filter', '5x5 Median Filter']

for i in range(len(display)):
    fig.add_subplot(2, 2, i+1)
    plt.imshow(display[i], cmap = 'gray')
    plt.title(title[i])

plt.show()