models/lancros_interpolation.py

import cv2
import numpy as np

def upsample_lancros(image_x,scale=4):
    h, w = image_x.shape[:2]
    new_w, new_h = int(w * scale), int(h * scale)

    # Upsample using Lanczos interpolation
    pred = cv2.resize(image_x, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4)
    return pred

# def lanczos_kernel(x, a):
#     """Lanczos kernel function."""
#     if x == 0:
#         return 1
#     elif -a < x < a:
#         return a * np.sinc(x) * np.sinc(x / a)
#     else:
#         return 0

# def upsample_lancros_2(image, scale=4, a=3):
#     """
#     Upsample an image using Lanczos interpolation.
    
#     Parameters:
#         image: numpy array (grayscale or RGB)
#         scale: scaling factor (default 4x)
#         a: size of Lanczos window (default 3)

#     Returns:
#         Upsampled image as numpy array.
#     """
#     if image.ndim == 2:  # Grayscale
#         h, w = image.shape
#         channels = 1
#     else:  # RGB
#         h, w, channels = image.shape

#     new_h, new_w = int(h * scale), int(w * scale)
#     output = np.zeros((new_h, new_w, channels)) if channels > 1 else np.zeros((new_h, new_w))

#     for y_new in range(new_h):
#         for x_new in range(new_w):
#             print(y_new,x_new)
#             # Map new pixel to original image space
#             x_orig = x_new / scale
#             y_orig = y_new / scale

#             x0 = int(np.floor(x_orig))
#             y0 = int(np.floor(y_orig))

#             # Accumulators
#             pixel = np.zeros(channels) if channels > 1 else 0.0
#             norm = 0.0

#             for j in range(y0 - a + 1, y0 + a + 1):
#                 for i in range(x0 - a + 1, x0 + a + 1):
#                     if 0 <= i < w and 0 <= j < h:
#                         wx = lanczos_kernel(x_orig - i, a)
#                         wy = lanczos_kernel(y_orig - j, a)
#                         weight = wx * wy
#                         if channels > 1:
#                             pixel += image[j, i] * weight
#                         else:
#                             pixel += image[j, i] * weight
#                         norm += weight

#             if norm > 0:
#                 output[y_new, x_new] = pixel / norm

#     if channels == 1:
#         output = output.astype(image.dtype)
#     else:
#         output = output.clip(0, 255).astype(image.dtype)

#     return output