Added Multiple things

models/lancros_interpolation.py

@@ -9,68 +9,68 @@ def upsample_lancros(image_x,scale=4):
     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 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.
+# 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)
+#     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
+#     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))
+#     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
+#     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))
+#             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
+#             # 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
+#             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 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)
+#     if channels == 1:
+#         output = output.astype(image.dtype)
+#     else:
+#         output = output.clip(0, 255).astype(image.dtype)
 
-    return output
+#     return output