generated from Hazel/python-project
feat: added option to paint mask
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Hazel Noack
parent
aaa706264d
commit
ed650dcc5d
@ -4,6 +4,7 @@ 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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from pathlib import Path
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"""
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https://setosa.io/ev/image-kernels/
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@ -16,61 +17,183 @@ def show(img):
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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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def demo(image_file):
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# Define 2D image and kernel
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image = cv2.imread(image_file, 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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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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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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# 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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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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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("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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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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# 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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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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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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# 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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print("\nDeblurred image:\n", np.round(deblurred, 2))
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show(deblurred)
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show(deblurred)
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def get_mask(image_file):
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mask_file = Path(image_file)
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mask_file = mask_file.with_name("mask_" + mask_file.name)
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if mask_file.exists():
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return cv2.imread(str(mask_file), 0)
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drawing = False # True when mouse is pressed
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brush_size = 5
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image = cv2.imread(image_file)
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mask = np.zeros(image.shape[:2], dtype=np.uint8)
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clone = image.copy()
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def draw_mask(event, x, y, flags, param):
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nonlocal drawing, mask, brush_size
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if event == cv2.EVENT_LBUTTONDOWN:
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drawing = True
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elif event == cv2.EVENT_MOUSEMOVE:
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if drawing:
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cv2.circle(mask, (x, y), brush_size, 255, -1)
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cv2.circle(image, (x, y), brush_size, (0, 0, 255), -1)
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elif event == cv2.EVENT_LBUTTONUP:
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drawing = False
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cv2.namedWindow("Draw Mask")
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cv2.setMouseCallback("Draw Mask", draw_mask)
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while True:
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display = image.copy()
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cv2.putText(display, f'Brush size: {brush_size}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
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cv2.imshow("Draw Mask", display)
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key = cv2.waitKey(1) & 0xFF
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if key == 13: # Enter to finish
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break
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elif key == ord('+') or key == ord('='): # `=` for some keyboard layouts
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brush_size = min(100, brush_size + 1)
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elif key == ord('-') or key == ord('_'):
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brush_size = max(1, brush_size - 1)
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cv2.destroyAllWindows()
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cv2.imwrite(str(mask_file), mask)
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# Apply mask
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masked_image = cv2.bitwise_and(clone, clone, mask=mask)
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cv2.imshow("Masked Image", masked_image)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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def deconvolution(image_file):
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image = cv2.imread(image_file, 0)
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# image = cv2.resize(image, (200, 200), interpolation= cv2.INTER_LINEAR)
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mask = get_mask(image_file)
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# Define 2D image and kernel
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kernel = np.array([
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[1, 1, 1],
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[1, 1, 1],
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[1, 1, 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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return
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# Perform 2D convolution (blurring)
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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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print("Original image:\n", image)
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print("\nBlurred image:\n", image)
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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 = image.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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if __name__ == "__main__":
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img_file = "assets/real_test.jpg"
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#demo("assets/omas.png")
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deconvolution(img_file)
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