# Task 1:
import cv2
import numpy as np
import matplotlib.pyplot as plt
import os

# Optional: print working directory
print("Current working directory:", os.getcwd())


# Task 1:
# Load image using full path
img = cv2.imread(r'F:\Img_Processing\Lab2\image2.jpg')
if img is None:
    raise FileNotFoundError("Could not load image.")

# Convert to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Display image and histogram side by side
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.title('Grayscale Image')
plt.imshow(img, cmap='gray')
plt.axis('off')

plt.subplot(1, 2, 2)
plt.title('Histogram')
hist = cv2.calcHist([img], [0], None, [256], [0, 256])
plt.plot(hist)
plt.tight_layout()
plt.show()


# Task 2: 
# Apply 3 filters and display with original
avg_filter = cv2.blur(img, (10, 10))
gaussian_filter = cv2.GaussianBlur(img, (11, 11), 0)
median_filter = cv2.medianBlur(img, 11)

# Display original and filtered images
plt.figure(figsize=(12, 4))
plt.subplot(1, 4, 1)
plt.title('Original')
plt.imshow(img, cmap='gray')
plt.axis('off')

plt.subplot(1, 4, 2)
plt.title('Averaging')
plt.imshow(avg_filter, cmap='gray')
plt.axis('off')

plt.subplot(1, 4, 3)
plt.title('Gaussian')
plt.imshow(gaussian_filter, cmap='gray')
plt.axis('off')

plt.subplot(1, 4, 4)
plt.title('Median')
plt.imshow(median_filter, cmap='gray')
plt.axis('off')

plt.tight_layout()
plt.show()

# Helper function to show image and histogram side by side
def show_image_and_hist(image, title):
    plt.figure(figsize=(10, 4))
    plt.subplot(1, 2, 1)
    plt.title(title)
    plt.imshow(image, cmap='gray')
    plt.axis('off')

    plt.subplot(1, 2, 2)
    plt.title('Histogram')
    hist = cv2.calcHist([image], [0], None, [256], [0, 256])
    plt.plot(hist)
    plt.tight_layout()
    plt.show()

# Task 3: Add noises and show with histogram

# Gaussian Noise
gauss_noise = np.random.normal(0, 25, img.shape).astype(np.float32)
gauss_noisy_img = img.astype(np.float32) + gauss_noise
gauss_noisy_img = np.clip(gauss_noisy_img, 0, 255).astype(np.uint8)
show_image_and_hist(gauss_noisy_img, "Gaussian Noisy Image")

# Salt & Pepper Noise
sp_img = img.copy()
prob = 0.02  # 2% noise
num_salt = np.ceil(prob * img.size * 0.5)
num_pepper = np.ceil(prob * img.size * 0.5)

# Add salt (white)
coords = [np.random.randint(0, i - 1, int(num_salt)) for i in img.shape]
sp_img[tuple(coords)] = 255

# Add pepper (black)
coords = [np.random.randint(0, i - 1, int(num_pepper)) for i in img.shape]
sp_img[tuple(coords)] = 0

salt_noisy_img = sp_img
show_image_and_hist(salt_noisy_img, "Salt and Pepper Noise Image")

# Periodic Noise
per_noise = np.zeros_like(img, dtype=np.uint8)
for i in range(0, img.shape[0], 10):
    for j in range(0, img.shape[1], 10):
        per_noise[i, j] = 255
per_noisy_img = cv2.add(img, per_noise)
show_image_and_hist(per_noisy_img, "Periodic Noise Image")


# Task 4: Apply all 3 filters to each noisy image
def apply_filters_and_show(noisy_img, title_prefix):
    # Apply filters
    avg = cv2.blur(noisy_img, (10, 10))
    gaussian = cv2.GaussianBlur(noisy_img, (11, 11), 0)
    median = cv2.medianBlur(noisy_img, 11)

    # Display
    plt.figure(figsize=(12, 4))
    plt.subplot(1, 4, 1)
    plt.title(f'{title_prefix} Noisy')
    plt.imshow(noisy_img, cmap='gray')
    plt.axis('off')

    plt.subplot(1, 4, 2)
    plt.title('Averaging')
    plt.imshow(avg, cmap='gray')
    plt.axis('off')

    plt.subplot(1, 4, 3)
    plt.title('Gaussian')
    plt.imshow(gaussian, cmap='gray')
    plt.axis('off')

    plt.subplot(1, 4, 4)
    plt.title('Median')
    plt.imshow(median, cmap='gray')
    plt.axis('off')

    plt.tight_layout()
    plt.show()

# Apply to each noisy image
apply_filters_and_show(gauss_noisy_img, "Gaussian Noise")
apply_filters_and_show(salt_noisy_img, "Salt Noise")
apply_filters_and_show(per_noisy_img, "Periodic Noise")