generated from Hazel/python-project
72 lines
1.5 KiB
Python
72 lines
1.5 KiB
Python
import numpy as np
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from scipy.signal import convolve2d
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from scipy.sparse import lil_matrix
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from scipy.sparse.linalg import spsolve
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import cv2
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import matplotlib.pyplot as plt
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def show(img):
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cv2.imshow('image',img.astype(np.uint8))
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# Define 2D image and kernel
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image = cv2.imread('assets/omas.png', 0)
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image = cv2.resize(image, (200, 200), interpolation= cv2.INTER_LINEAR)
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kernel = np.array([
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[1, 2, 1],
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[2, 4, 2],
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[1, 2, 1]
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], dtype=np.float32)
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kernel /= kernel.sum() # Normalize
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print(kernel)
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# Perform 2D convolution (blurring)
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blurred = convolve2d(image, kernel, mode="same", boundary="fill", fillvalue=0)
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h, w = image.shape
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kh, kw = kernel.shape
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pad_h, pad_w = kh // 2, kw // 2
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show(image)
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show(blurred)
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print("Original image:\n", image)
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print("\nBlurred image:\n", blurred)
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print("\nBuilding linear system for deconvolution...")
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# Step 2: Build sparse matrix A
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N = h * w
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A = lil_matrix((N, N), dtype=np.float32)
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b = blurred.flatten()
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def index(y, x):
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return y * w + x
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for y in range(h):
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for x in range(w):
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row_idx = index(y, x)
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for ky in range(kh):
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for kx in range(kw):
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iy = y + ky - pad_h
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ix = x + kx - pad_w
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if 0 <= iy < h and 0 <= ix < w:
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col_idx = index(iy, ix)
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A[row_idx, col_idx] += kernel[ky, kx]
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# Step 3: Solve the sparse system A * x = b
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x = spsolve(A.tocsr(), b)
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deblurred = x.reshape((h, w))
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print("\nDeblurred image:\n", np.round(deblurred, 2))
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show(deblurred)
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