import numpy as np
import cv2


image = np.array([1, 3, 1, 2, 1, 6, 1], dtype=np.float32)
kernel = np.array([1, 2, 1], dtype=np.float32) / 4

blurred = np.convolve(image, kernel, mode="same")

print(image)
print(blurred)

print()
print("building linalg")
# https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html
a = []
b = []

for i in range(len(blurred)):
    y = blurred[i]

    shift = i - 1
    equation = np.zeros(len(image))
    # Calculate valid range in the output array
    start_eq = max(0, shift)
    end_eq = min(len(image), shift + len(kernel))

    # Corresponding range in the kernel
    start_k = start_eq - shift  # how much to cut from the beginning of the kernel
    end_k = start_k + (end_eq - start_eq)
    

    # Assign the clipped kernel segment
    equation[start_eq:end_eq] = kernel[start_k:end_k]

    a.append(equation)
    b.append(y)
    goal = image[i]
    print(f"{i} ({goal}): {y} = {equation}")

print()
print("deblurring")
deblurred = np.linalg.solve(a, b)
print(deblurred)



def show_matrix(m):
    # Resize the image to make it visible (e.g., scale up to 200x200 pixels)
    scaled_image = cv2.resize(m, (200, 200), interpolation=cv2.INTER_NEAREST)

    # Display the image
    cv2.imshow('Test Matrix', scaled_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()