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6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac 6ee8d9f e261fac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import numpy as np
def pad_reflect(image, pad_size):
imsize = image.shape
height, width = imsize[:2]
new_img = np.zeros([height+pad_size*2, width+pad_size*2, imsize[2]]).astype(np.uint8)
new_img[pad_size:-pad_size, pad_size:-pad_size, :] = image
new_img[0:pad_size, pad_size:-pad_size, :] = np.flip(image[0:pad_size, :, :], axis=0)
new_img[-pad_size:, pad_size:-pad_size, :] = np.flip(image[-pad_size:, :, :], axis=0)
new_img[:, 0:pad_size, :] = np.flip(new_img[:, pad_size:pad_size*2, :], axis=1)
new_img[:, -pad_size:, :] = np.flip(new_img[:, -pad_size*2:-pad_size, :], axis=1)
return new_img
def unpad_image(image, pad_size):
return image[pad_size:-pad_size, pad_size:-pad_size, :]
def unpad_patches(image_patches, padding_size):
return image_patches[:, padding_size:-padding_size, padding_size:-padding_size, :]
def split_image_into_overlapping_patches(image_array, patch_size, padding_size=2):
xmax, ymax, _ = image_array.shape
x_extend = (patch_size - (xmax % patch_size)) % patch_size
y_extend = (patch_size - (ymax % patch_size)) % patch_size
extended_image = np.pad(image_array, ((0, x_extend), (0, y_extend), (0, 0)), 'edge')
padded_image = np.pad(extended_image, ((padding_size, padding_size), (padding_size, padding_size), (0, 0)), 'edge')
xmax_pad, ymax_pad, _ = padded_image.shape
patches = []
# PERBAIKAN 1: Hitung jumlah patch per baris secara akurat dari loop
n_patches_per_row = len(range(padding_size, ymax_pad - padding_size, patch_size))
for x in range(padding_size, xmax_pad - padding_size, patch_size):
for y in range(padding_size, ymax_pad - padding_size, patch_size):
patch = padded_image[x-padding_size:x+patch_size+padding_size, y-padding_size:y+patch_size+padding_size, :]
patches.append(patch)
return np.array(patches), padded_image.shape, n_patches_per_row
def stich_together(patches, padded_image_shape, target_shape, padding_size=4, n_patches_per_row=None):
xmax, ymax, _ = padded_image_shape
patches = unpad_patches(patches, padding_size)
patch_size = patches.shape[1]
if n_patches_per_row is None:
n_patches_per_row = max(1, round(ymax / patch_size))
complete_image = np.zeros((xmax, ymax, 3))
row = -1
col = 0
for i in range(len(patches)):
if i % n_patches_per_row == 0:
row += 1
col = 0
# PERBAIKAN 2: Gunakan min() untuk mencegah assignment melebihi batas array (mencegah broadcast error)
x_start = row * patch_size
x_end = min(x_start + patch_size, xmax)
y_start = col * patch_size
y_end = min(y_start + patch_size, ymax)
patch_x_end = x_end - x_start
patch_y_end = y_end - y_start
complete_image[x_start:x_end, y_start:y_end, :] = patches[i][:patch_x_end, :patch_y_end, :]
col += 1
return complete_image[0: target_shape[0], 0: target_shape[1], :] |