stuff

.gitignore

@@ -162,3 +162,5 @@ cython_debug/
 .venv
 assets/*
 *.pt
+
+big-lama

README.md

@@ -12,8 +12,8 @@ I first realized that a normal mosaic algorithm isn't safe AT ALL seeing this pr
 
 ```bash
 # Step 1: Create and activate virtual environment
-python3 -m venv .venv
+python3.8 -m venv .venv
-source venv/bin/activate
+source .venv/bin/activate
 
 # Step 2: Install the local Python program add the -e flag for development
 pip install .
@@ -21,3 +21,21 @@ pip install .
 # Step 3: Run the secure-pixelation command
 secure-pixelation
 ```
+
+## Setup LaMa
+
+This is the generative ai model to impaint the blacked out areas.
+
+```
+# get the pretrained models
+mkdir -p ./big-lama
+wget https://huggingface.co/smartywu/big-lama/resolve/main/big-lama.zip
+unzip big-lama.zip -d ./big-lama
+rm big-lama.zip
+
+# get the code to run the models
+cd big-lama
+git clone https://github.com/advimman/lama.git
+cd lama
+pip install -r requirements.txt
+```

deblur/deblur.py

@@ -0,0 +1,57 @@
+import numpy as np
+import cv2
+
+
+image = np.array([1, 3, 1, 2, 1, 6, 1], dtype=np.float32)
+kernel = np.array([1, 2, 1], dtype=np.float32) / 4
+
+blurred = np.convolve(image, kernel, mode="same")
+
+print(image)
+print(blurred)
+
+print()
+print("building linalg")
+# https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html
+a = []
+b = []
+
+for i in range(len(blurred)):
+    y = blurred[i]
+
+    shift = i - 1
+    equation = np.zeros(len(image))
+    # Calculate valid range in the output array
+    start_eq = max(0, shift)
+    end_eq = min(len(image), shift + len(kernel))
+
+    # Corresponding range in the kernel
+    start_k = start_eq - shift  # how much to cut from the beginning of the kernel
+    end_k = start_k + (end_eq - start_eq)
+    
+
+    # Assign the clipped kernel segment
+    equation[start_eq:end_eq] = kernel[start_k:end_k]
+
+    a.append(equation)
+    b.append(y)
+    goal = image[i]
+    print(f"{i} ({goal}): {y} = {equation}")
+
+print()
+print("deblurring")
+deblurred = np.linalg.solve(a, b)
+print(deblurred)
+
+
+
+def show_matrix(m):
+    # Resize the image to make it visible (e.g., scale up to 200x200 pixels)
+    scaled_image = cv2.resize(m, (200, 200), interpolation=cv2.INTER_NEAREST)
+
+    # Display the image
+    cv2.imshow('Test Matrix', scaled_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+

deblur/deblur_2d.py

@@ -0,0 +1,419 @@
+import numpy as np
+from scipy.signal import convolve2d
+from scipy.sparse import lil_matrix
+from scipy.sparse.linalg import spsolve
+from scipy.optimize import curve_fit
+import cv2
+import matplotlib
+import matplotlib.pyplot as plt
+from pathlib import Path
+from scipy.ndimage import correlate
+from skimage.restoration import richardson_lucy
+import os
+
+
+matplotlib.use('qtagg')
+
+"""
+https://setosa.io/ev/image-kernels/
+https://openaccess.thecvf.com/content/CVPR2021/papers/Tran_Explore_Image_Deblurring_via_Encoded_Blur_Kernel_Space_CVPR_2021_paper.pdf
+"""
+
+def show(img):
+    cv2.imshow('image',img.astype(np.uint8))
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+def demo(image_file):
+    # Define 2D image and kernel
+    image = cv2.imread(image_file, 0)
+    image = cv2.resize(image, (200, 200), interpolation= cv2.INTER_LINEAR)
+
+    kernel = np.array([
+        [1, 2, 1],
+        [2, 4, 2],
+        [1, 2, 1]
+    ], dtype=np.float32) 
+    kernel /= kernel.sum()  # Normalize
+
+    print(kernel)
+
+    # Perform 2D convolution (blurring)
+    blurred = convolve2d(image, kernel, mode="same", boundary="fill", fillvalue=0)
+
+    h, w = image.shape
+    kh, kw = kernel.shape
+    pad_h, pad_w = kh // 2, kw // 2
+
+
+    show(image)
+    show(blurred)
+
+    print("Original image:\n", image)
+    print("\nBlurred image:\n", blurred)
+
+    print("\nBuilding linear system for deconvolution...")
+
+    # Step 2: Build sparse matrix A
+    N = h * w
+    A = lil_matrix((N, N), dtype=np.float32)
+    b = blurred.flatten()
+
+    def index(y, x):
+        return y * w + x
+
+    for y in range(h):
+        for x in range(w):
+            row_idx = index(y, x)
+            for ky in range(kh):
+                for kx in range(kw):
+                    iy = y + ky - pad_h
+                    ix = x + kx - pad_w
+                    if 0 <= iy < h and 0 <= ix < w:
+                        col_idx = index(iy, ix)
+                        A[row_idx, col_idx] += kernel[ky, kx]
+
+    # Step 3: Solve the sparse system A * x = b
+    x = spsolve(A.tocsr(), b)
+    deblurred = x.reshape((h, w))
+
+    print("\nDeblurred image:\n", np.round(deblurred, 2))
+
+    show(deblurred)
+
+
+def get_mask(image_file):
+    mask_file = Path(image_file)
+    mask_file = mask_file.with_name("mask_" + mask_file.name)
+
+    if mask_file.exists():
+        return cv2.imread(str(mask_file), 0)
+
+    drawing = False  # True when mouse is pressed
+    brush_size = 5
+    image = cv2.imread(image_file)
+    mask = np.zeros(image.shape[:2], dtype=np.uint8)
+    clone = image.copy()
+
+    def draw_mask(event, x, y, flags, param):
+        nonlocal drawing, mask, brush_size
+
+        if event == cv2.EVENT_LBUTTONDOWN:
+            drawing = True
+        elif event == cv2.EVENT_MOUSEMOVE:
+            if drawing:
+                cv2.circle(mask, (x, y), brush_size, 255, -1)
+                cv2.circle(image, (x, y), brush_size, (0, 0, 255), -1)
+        elif event == cv2.EVENT_LBUTTONUP:
+            drawing = False
+
+
+    cv2.namedWindow("Draw Mask")
+    cv2.setMouseCallback("Draw Mask", draw_mask)
+
+    while True:
+        display = image.copy()
+        cv2.putText(display, f'Brush size: {brush_size}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)
+        cv2.imshow("Draw Mask", display)
+        key = cv2.waitKey(1) & 0xFF
+        if key == 13:  # Enter to finish
+            break
+        elif key == ord('+') or key == ord('='):  # `=` for some keyboard layouts
+            brush_size = min(100, brush_size + 1)
+        elif key == ord('-') or key == ord('_'):
+            brush_size = max(1, brush_size - 1)
+
+
+    cv2.destroyAllWindows()
+
+    cv2.imwrite(str(mask_file), mask)
+
+    # Apply mask
+    masked_image = cv2.bitwise_and(clone, clone, mask=mask)
+
+    cv2.imshow("Masked Image", masked_image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+
+def color_edge_detection(image, threshold=30):
+    img_lab = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)
+    L, A, B = cv2.split(img_lab)
+
+    def gradient_magnitude(channel):
+        gx = cv2.Sobel(channel, cv2.CV_64F, 1, 0, ksize=3)
+        gy = cv2.Sobel(channel, cv2.CV_64F, 0, 1, ksize=3)
+        return gx, gy
+
+    gxL, gyL = gradient_magnitude(L)
+    gxA, gyA = gradient_magnitude(A)
+    gxB, gyB = gradient_magnitude(B)
+
+    gx_total = gxL**2 + gxA**2 + gxB**2
+    gy_total = gyL**2 + gyA**2 + gyB**2
+    magnitude = np.sqrt(gx_total + gy_total)
+
+    magnitude = cv2.normalize(magnitude, None, 0, 255, cv2.NORM_MINMAX)
+    edges = (magnitude > threshold).astype(np.uint8) * 255
+    return edges, magnitude
+
+
+# === Step 2: Extract Vertical Profile ===
+def extract_vertical_profile(image, center_x, center_y, length=21):
+    half_len = length // 2
+    y_range = np.clip(np.arange(center_y - half_len, center_y + half_len + 1), 0, image.shape[0] - 1)
+    profile = image[y_range, center_x].astype(np.float64)
+    profile -= profile.min()
+    if profile.max() > 0:
+        profile /= profile.max()
+    return profile, y_range - center_y  # profile, x-axis
+
+# === Step 3: Fit Gaussian ===
+def gaussian(x, amp, mu, sigma):
+    return amp * np.exp(-(x - mu)**2 / (2 * sigma**2))
+
+def fit_gaussian(profile, x_vals):
+    p0 = [1.0, 0.0, 2.0]  # initial guess: amp, mu, sigma
+    popt, _ = curve_fit(gaussian, x_vals, profile, p0=p0)
+    return popt  # amp, mu, sigma
+
+# === Step 4: Create Gaussian Kernel ===
+def create_gaussian_kernel(sigma):
+    ksize = int(sigma * 6) | 1  # ensure odd size
+    kernel_1d = cv2.getGaussianKernel(ksize, sigma)
+    kernel_2d = kernel_1d @ kernel_1d.T
+    return kernel_2d
+
+
+def kernel_detection(blurred, mask, edge_threshold=30, profile_length=21):
+    edges, gradient_mag = color_edge_detection(blurred, threshold=edge_threshold)
+    edges = cv2.bitwise_and(edges, edges, mask=mask)
+    # show(edges)
+
+
+     # Find central edge pixel
+    y_idxs, x_idxs = np.where(edges > 0)
+    if len(x_idxs) == 0:
+        raise RuntimeError("No edges found.")
+    idx = len(x_idxs) // 2
+    cx, cy = x_idxs[idx], y_idxs[idx]
+
+    gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
+    profile, x_vals = extract_vertical_profile(gray, cx, cy, length=profile_length)
+    popt = fit_gaussian(profile, x_vals)
+    amp, mu, sigma = popt
+
+    print(f"Estimated Gaussian sigma: {sigma:.2f}")
+
+    kernel = create_gaussian_kernel(sigma)
+
+    # print(kernel)
+
+    return kernel / kernel.sum()
+
+
+
+def kernel_detection_box(blurred, mask, edge_threshold=30, profile_length=21):
+    def box_function(x, amp, center, width):
+        """Simple box profile: flat region with sharp transitions."""
+        return amp * ((x >= (center - width / 2)) & (x <= (center + width / 2))).astype(float)
+
+    def fit_box(profile, x_vals):
+        # Initial guess: full amplitude, centered at 0, small width
+        p0 = [1.0, 0.0, 5.0]
+        bounds = ([0, -10, 1], [1.5, 10, len(x_vals)])  # reasonable bounds
+        popt, _ = curve_fit(box_function, x_vals, profile, p0=p0, bounds=bounds)
+        return popt  # amp, center, width
+
+    def create_box_kernel(width):
+        """Generate a normalized 2D box kernel."""
+        ksize = int(round(width))
+        if ksize < 1:
+            ksize = 1
+        if ksize % 2 == 0:
+            ksize += 1  # ensure odd size
+        kernel = np.ones((ksize, ksize), dtype=np.float32)
+        return kernel / kernel.sum()
+
+
+    edges, gradient_mag = color_edge_detection(blurred, threshold=edge_threshold)
+    edges = cv2.bitwise_and(edges, edges, mask=mask)
+
+    y_idxs, x_idxs = np.where(edges > 0)
+    if len(x_idxs) == 0:
+        raise RuntimeError("No edges found.")
+    idx = len(x_idxs) // 2
+    cx, cy = x_idxs[idx], y_idxs[idx]
+
+    gray = cv2.cvtColor(blurred, cv2.COLOR_BGR2GRAY)
+    profile, x_vals = extract_vertical_profile(gray, cx, cy, length=profile_length)
+    popt = fit_box(profile, x_vals)
+    amp, mu, width = popt
+
+    print(f"Estimated box width: {width:.2f} pixels")
+
+    kernel = create_box_kernel(width)
+    return kernel
+
+
+
+def deconvolution(image_file, edge_threshold=30, profile_length=21):
+    image = cv2.imread(image_file)
+    mask = get_mask(image_file)
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32) / 255.0
+
+    kernel = kernel_detection_box(image, mask, edge_threshold=edge_threshold, profile_length=profile_length)
+
+
+    # Apply Richardson-Lucy to each channel
+    num_iter = 30
+    deblurred_channels = []
+    for i in range(3):  # R, G, B
+        channel = image_rgb[..., i]
+        deconv = richardson_lucy(channel, kernel, num_iter=num_iter)
+        deblurred_channels.append(deconv)
+
+    # Stack back into an RGB image
+    deblurred_rgb = np.stack(deblurred_channels, axis=2)
+    deblurred_rgb = np.clip(deblurred_rgb, 0, 1)
+
+    
+    # Show result
+    plt.figure(figsize=(10, 5))
+    plt.subplot(1, 2, 1)
+    plt.imshow(image_rgb)
+    plt.title("Blurred Image")
+    plt.axis('off')
+
+    plt.subplot(1, 2, 2)
+    plt.imshow(deblurred_rgb)
+    plt.title("Deconvolved Image")
+    plt.axis('off')
+    plt.show()
+
+
+def sharpness_heatmap(image, block_size=32, threshold=30):
+    """
+    Compute a sharpness heatmap using color-aware Laplacian variance over blocks,
+    generate a binary mask highlighting blurred areas, and smooth the edges of the mask.
+
+    Args:
+        image: BGR or RGB image (NumPy array).
+        block_size: Size of the square block to compute sharpness.
+        sigma: Standard deviation for Gaussian smoothing of the heatmap.
+        threshold: Sharpness threshold to define blurred regions (between 0 and 1).
+        smoothing_sigma: Standard deviation for Gaussian smoothing of the binary mask edges.
+
+    Returns:
+        blurred_mask: Binary mask highlighting blurred areas (0 = sharp, 255 = blurred).
+    """
+    if image.ndim != 3 or image.shape[2] != 3:
+        raise ValueError("Input must be a color image (3 channels)")
+
+    h, w, _ = image.shape
+    heatmap = np.zeros((h // block_size, w // block_size))
+
+    # Calculate sharpness for each block
+    for y in range(0, h - block_size + 1, block_size):
+        for x in range(0, w - block_size + 1, block_size):
+            block = image[y:y + block_size, x:x + block_size, :]
+            sharpness_vals = []
+
+            for c in range(3):  # For R, G, B channels
+                channel = block[..., c]
+                lap_var = cv2.Laplacian(channel, cv2.CV_64F).var()
+                sharpness_vals.append(lap_var)
+
+            # Use average sharpness across color channels
+            heatmap[y // block_size, x // block_size] = np.mean(sharpness_vals)
+
+    print(heatmap)
+
+    # Threshold the heatmap to create a binary mask (blurred regions)
+    mask = heatmap < threshold
+    mask = (mask * 255).astype(np.uint8)  # Convert to binary mask (0, 255)
+
+    # Display Heatmap
+    plt.subplot(1, 2, 1)
+    plt.imshow(heatmap, cmap='hot', interpolation='nearest')
+    plt.title("Sharpness Heatmap")
+    plt.colorbar(label='Sharpness')
+
+    # Display Smoothed Mask
+    plt.subplot(1, 2, 2)
+    plt.imshow(mask, cmap='gray', interpolation='nearest')
+    plt.title("Smoothed Mask for Blurred Areas")
+    plt.colorbar(label='Blurred Mask')
+
+    plt.tight_layout()
+    plt.show()
+
+    return smoothed_mask
+
+
+def graininess_heatmap(image, block_size=32, threshold=100):
+    """
+    Compute a graininess heatmap using local variance (texture/noise) over blocks.
+    No smoothing or blurring is applied.
+
+    Args:
+        image: BGR or RGB image (NumPy array).
+        block_size: Size of the square block to compute variance (graininess).
+
+    Returns:
+        graininess_map: Heatmap highlighting the graininess (texture/noise) in the image.
+    """
+    if image.ndim != 3 or image.shape[2] != 3:
+        raise ValueError("Input must be a color image (3 channels)")
+
+    h, w, _ = image.shape
+    graininess_map = np.zeros((h // block_size, w // block_size))
+
+    # Calculate variance for each block
+    for y in range(0, h - block_size + 1, block_size):
+        for x in range(0, w - block_size + 1, block_size):
+            block = image[y:y + block_size, x:x + block_size, :]
+            variance_vals = []
+
+            for c in range(3):  # For R, G, B channels
+                channel = block[..., c]
+                variance = np.var(channel)
+                variance_vals.append(variance)
+
+            # Use average variance across color channels for graininess
+            graininess_map[y // block_size, x // block_size] = np.mean(variance_vals)
+
+
+    mask = graininess_map < threshold
+    mask = (mask * 255).astype(np.uint8)  # Convert to binary mask (0, 255)
+
+    # Display graininess_map
+    plt.subplot(1, 2, 1)
+    plt.imshow(graininess_map, cmap='hot', interpolation='nearest')
+    plt.title("Graininess Heatmap")
+    plt.colorbar(label='Graininess')
+
+    # Display Smoothed Mask
+    plt.subplot(1, 2, 2)
+    plt.imshow(mask, cmap='gray', interpolation='nearest')
+    plt.title("Mask for Blurred Areas")
+    plt.colorbar(label='Blurred Mask')
+
+    plt.tight_layout()
+    plt.show()
+
+    return graininess_map
+
+
+
+if __name__ == "__main__":
+    img_file = "assets/real_test.jpg"
+
+    #demo("assets/omas.png")
+    # deconvolution(img_file, edge_threshold=5)
+
+    image = cv2.imread(img_file)
+    test = graininess_heatmap(image)
+    heatmap = sharpness_heatmap(image)

deblur/symetric_kernel.py

@@ -0,0 +1,251 @@
+import sys
+import numpy as np
+import cv2
+from PyQt5.QtWidgets import (
+    QApplication, QWidget, QLabel, QSlider, QVBoxLayout,
+    QHBoxLayout, QGridLayout, QPushButton, QFileDialog
+)
+from PyQt5.QtCore import Qt
+from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
+from matplotlib.figure import Figure
+import scipy.signal
+from scipy.signal import convolve2d
+
+import os
+os.environ.pop("QT_QPA_PLATFORM_PLUGIN_PATH", None)
+
+
+def generate_box_kernel(size):
+    return np.ones((size, size), dtype=np.float32) / (size * size)
+
+def generate_disk_kernel(radius):
+    size = 2 * radius + 1
+    y, x = np.ogrid[-radius:radius+1, -radius:radius+1]
+    mask = x**2 + y**2 <= radius**2
+    kernel = np.zeros((size, size), dtype=np.float32)
+    kernel[mask] = 1
+    kernel /= kernel.sum()
+    return kernel
+
+def generate_kernel(radius, sigma=None):
+    """
+    Generate a 2D Gaussian kernel with a given radius.
+
+    Parameters:
+    - radius: int, the radius of the kernel (size will be 2*radius + 1)
+    - sigma: float (optional), standard deviation of the Gaussian. If None, sigma = radius / 3
+
+    Returns:
+    - kernel: 2D numpy array of shape (2*radius+1, 2*radius+1)
+    """
+    size = 2 * radius + 1
+    if sigma is None:
+        sigma = radius / 3.0  # Common default choice
+    print(f"radius: {radius}, sigma: {sigma}")
+
+    # Create a grid of (x,y) coordinates
+    ax = np.arange(-radius, radius + 1)
+    xx, yy = np.meshgrid(ax, ax)
+
+    # Apply the 2D Gaussian formula
+    kernel = np.exp(-(xx**2 + yy**2) / (2 * sigma**2))
+    kernel /= 2 * np.pi * sigma**2  # Normalize based on Gaussian PDF
+    kernel /= kernel.sum()          # Normalize to sum to 1
+
+    return kernel
+
+
+def wiener_deconvolution(blurred, kernel, K=0.1):
+    """
+    Perform Wiener deconvolution on a 2D image.
+
+    Parameters:
+    - blurred: 2D numpy array (blurred image)
+    - kernel: 2D numpy array (PSF / blur kernel)
+    - K: float, estimated noise-to-signal ratio
+
+    Returns:
+    - deconvolved: 2D numpy array (deblurred image)
+    """
+    # Pad kernel to image size
+    kernel /= np.sum(kernel)
+    pad = [(0, blurred.shape[0] - kernel.shape[0]),
+           (0, blurred.shape[1] - kernel.shape[1])]
+    kernel_padded = np.pad(kernel, pad, 'constant')
+
+    # FFT of image and kernel
+    H = np.fft.fft2(kernel_padded)
+    G = np.fft.fft2(blurred)
+
+    # Avoid division by zero
+    H_conj = np.conj(H)
+    denominator = H_conj * H + K
+    F_hat = H_conj / denominator * G
+
+    # Inverse FFT to get result
+    deconvolved = np.fft.ifft2(F_hat)
+    deconvolved = np.abs(deconvolved)
+    deconvolved = np.clip(deconvolved, 0, 255)
+    return deconvolved.astype(np.uint8)
+
+
+def richardson_lucy(image, psf, iterations=30, clip=True):
+    image = image.astype(np.float32) + 1e-6
+    psf = psf / psf.sum()
+    estimate = np.full(image.shape, 0.5, dtype=np.float32)
+
+    psf_mirror = psf[::-1, ::-1]
+
+    for _ in range(iterations):
+        conv = convolve2d(estimate, psf, mode='same', boundary='wrap')
+        relative_blur = image / (conv + 1e-6)
+        estimate *= convolve2d(relative_blur, psf_mirror, mode='same', boundary='wrap')
+
+    if clip:
+        estimate = np.clip(estimate, 0, 255)
+
+    return estimate
+
+
+class KernelVisualizer(QWidget):
+    def __init__(self, image_path=None):
+        super().__init__()
+        self.setWindowTitle("Gaussian Kernel Visualizer")
+        self.image = None
+        self.deconvolved = None
+
+        self.load_button = QPushButton("Load Image")
+        self.load_button.clicked.connect(self.load_image)
+
+        self.radius_slider = QSlider(Qt.Horizontal)
+        self.radius_slider.setRange(1, 100)
+        self.radius_slider.setValue(5)
+
+        self.sigma_slider = QSlider(Qt.Horizontal)
+        self.sigma_slider.setRange(1, 300)
+        self.sigma_slider.setValue(15)
+
+        self.radius_slider.valueChanged.connect(self.update_visualization)
+        self.sigma_slider.valueChanged.connect(self.update_visualization)
+
+        self.kernel_fig = Figure(figsize=(3, 3))
+        self.kernel_canvas = FigureCanvas(self.kernel_fig)
+
+        self.image_fig = Figure(figsize=(6, 3))
+        self.image_canvas = FigureCanvas(self.image_fig)
+
+        self.iter_slider = QSlider(Qt.Horizontal)
+        self.iter_slider.setRange(1, 50)
+        self.iter_slider.setValue(10)
+
+        self.apply_button = QPushButton("Do Deconvolution.")
+        self.apply_button.clicked.connect(self.apply_kernel)
+        
+        layout = QVBoxLayout()
+        layout.addWidget(self.load_button)
+
+
+        sliders_layout = QGridLayout()
+        sliders_layout.addWidget(QLabel("Radius:"), 0, 0)
+        sliders_layout.addWidget(self.radius_slider, 0, 1)
+        sliders_layout.addWidget(QLabel("Sigma:"), 1, 0)
+        sliders_layout.addWidget(self.sigma_slider, 1, 1)
+
+        sliders_layout.addWidget(QLabel("Iterations:"), 2, 0)
+        sliders_layout.addWidget(self.iter_slider, 2, 1)
+        sliders_layout.addWidget(self.apply_button, 3, 1)
+
+        layout.addLayout(sliders_layout)
+        layout.addWidget(QLabel("Kernel Visualization:"))
+        layout.addWidget(self.kernel_canvas)
+        layout.addWidget(QLabel("Original and Deconvolved Image:"))
+        layout.addWidget(self.image_canvas)
+
+        self.setLayout(layout)
+        
+        if image_path:
+            self.load_image(image_path)
+        else:
+            self.update_visualization()
+
+    def load_image(self, image_path=None):
+        if not image_path:
+            fname, _ = QFileDialog.getOpenFileName(self, "Open Image", "", "Images (*.png *.jpg *.bmp *.jpeg)")
+            image_path = fname
+        
+        if image_path:
+            img = cv2.imread(image_path)
+            if img is not None:
+                self.image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+                self.update_visualization()
+
+    def load_image(self, image_path=None):
+        if not image_path:
+            fname, _ = QFileDialog.getOpenFileName(self, "Open Image", "", "Images (*.png *.jpg *.bmp *.jpeg)")
+            image_path = fname
+        
+        if image_path:
+            img = cv2.imread(image_path)
+            if img is not None:
+                self.image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+                self.image = cv2.resize(self.image, (200, 200))
+                self.update_visualization()
+
+    def apply_kernel(self):
+        radius = self.radius_slider.value()
+        sigma = self.sigma_slider.value() / 10.0
+        iterations = self.iter_slider.value()
+
+        kernel = generate_kernel(radius, sigma)
+
+        self.deconvolved = richardson_lucy(self.image, kernel, iterations=iterations)
+
+        self.update_visualization()
+
+    def update_visualization(self):
+        radius = self.radius_slider.value()
+        sigma = self.sigma_slider.value() / 10.0 * (radius / 3)
+        kernel = generate_kernel(radius, sigma)
+        iterations = self.iter_slider.value()
+
+
+        # Kernel Visualization
+        self.kernel_fig.clear()
+        ax = self.kernel_fig.add_subplot(111)
+        cax = ax.imshow(kernel, cmap='hot')
+        self.kernel_fig.colorbar(cax, ax=ax)
+        ax.set_title(f"Kernel (r={radius}, σ={sigma:.2f})")
+        self.kernel_canvas.draw()
+
+        if self.image is not None:
+            self.image_fig.clear()
+            ax1 = self.image_fig.add_subplot(131)
+            ax1.imshow(self.image, cmap='gray')
+            ax1.set_title("Original")
+            ax1.axis('off')
+
+            if self.deconvolved is not None:
+                ax3 = self.image_fig.add_subplot(133)
+                ax3.imshow(self.deconvolved, cmap='gray')
+                ax3.set_title(f"Deconvolved (RL, {iterations} iter)")
+                ax3.axis('off')
+
+            self.image_canvas.draw()
+        else:
+            self.image_fig.clear()
+            ax = self.image_fig.add_subplot(111)
+            ax.text(0.5, 0.5, "No image loaded", fontsize=14, ha='center', va='center')
+            ax.axis('off')
+            self.image_canvas.draw()
+
+
+if __name__ == "__main__":
+    image_path = None
+    if len(sys.argv) > 1:
+        image_path = sys.argv[1]  # Get image path from command-line argument
+        print(image_path)
+
+    app = QApplication(sys.argv)
+    viewer = KernelVisualizer(image_path=image_path)
+    viewer.show()
+    sys.exit(app.exec_())

pyproject.toml

@@ -2,7 +2,14 @@
 name = "secure_pixelation"
 version = "0.0.0"
 dependencies = [
+    "torch==2.1.2",
+    "torchvision==0.16.2",
+    
     "opencv_python~=4.11.0.86",
+    "numpy<2.0.0",
+    "hf_transfer==0.1.8",
+    "huggingface_hub==0.25.1",
+
     "ultralytics~=8.3.114",
 ]
 authors = []

secure_pixelation/__main__.py

@@ -5,4 +5,6 @@ from .pixelation_process import pixelate
 def cli():
     print(f"Running secure_pixelation")
 
-    pixelate("assets/human_detection/test.png")
+    pixelate("assets/human_detection/test.png", generative_impaint=True)
+    pixelate("assets/human_detection/humans.png", generative_impaint=False)
+    pixelate("assets/human_detection/rev1.png", generative_impaint=False)

secure_pixelation/pixelation_process.py

@@ -2,12 +2,16 @@ from __future__ import annotations
 
 from typing import Optional
 from pathlib import Path
+import subprocess
+import sys
+import os
 
 import cv2
 import numpy as np
 
 from .data_classes import RawImage
-
+from .simple_lama_bindings import SimpleLama
+# https://github.com/okaris/simple-lama/tree/main
 
 def blackout(raw_image: RawImage) -> np.ndarray:
     image = raw_image.get_image()
@@ -18,31 +22,29 @@ def blackout(raw_image: RawImage) -> np.ndarray:
     return image
 
 
-def impaint(raw_image: RawImage, image: Optional[np.ndarray] = None) -> np.ndarray:
+def get_mask(raw_image: RawImage) -> np.ndarray:
-    image = image if image is not None else raw_image.get_image()
+    mask = np.zeros(raw_image.image.shape[:2], dtype=np.uint8)
-    
-    # Create a mask where blacked-out areas are marked as 255 (white)
-    mask = np.zeros(image.shape[:2], dtype=np.uint8)
-
     for (x, y, w, h) in raw_image.bounding_boxes:
         mask[y:y+h, x:x+w] = 255
 
+    return mask
+
+
+def quick_impaint(raw_image: RawImage, image: Optional[np.ndarray] = None) -> np.ndarray:
+    image = image if image is not None else raw_image.get_image()
+    
+    mask = get_mask(raw_image)
+
     # Apply inpainting using the Telea method
     return cv2.inpaint(image, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
 
 
 def do_generative_impaint(raw_image: RawImage, image: Optional[np.ndarray] = None) -> np.ndarray:
     image = image if image is not None else raw_image.get_image()
+    mask = get_mask(raw_image)
 
-    # Create a mask where blacked-out areas are marked as 255 (white)
+    lama = SimpleLama()
-    mask = np.zeros(image.shape[:2], dtype=np.uint8)
+    return lama(image=image, mask=mask)
-
-    for (x, y, w, h) in raw_image.bounding_boxes:
-        mask[y:y+h, x:x+w] = 255
-
-    # Apply inpainting using the Telea method
-    return cv2.inpaint(image, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
-
 
 
 def pixelate_regions(raw_image: RawImage, image: Optional[np.ndarray] = None, pixel_size: int = 10) -> np.ndarray:
@@ -60,7 +62,7 @@ def pixelate_regions(raw_image: RawImage, image: Optional[np.ndarray] = None, pi
     return image
 
 
-def pixelate(to_detect: str, generative_impaint: bool = True, debug_drawings: bool = True):
+def pixelate(to_detect: str, generative_impaint: bool = True, debug_drawings: bool = False):
     raw_image = RawImage(to_detect)
 
     step_dir = raw_image.get_dir("steps")
@@ -82,9 +84,12 @@ def pixelate(to_detect: str, generative_impaint: bool = True, debug_drawings: bo
 
     if generative_impaint:
         step_2 = do_generative_impaint(raw_image, image=step_1)
+        step_2_alt = quick_impaint(raw_image, image=step_1)
     else:
-        step_2 = impaint(raw_image, image=step_1)
+        step_2 = quick_impaint(raw_image, image=step_1)
+        step_2_alt = do_generative_impaint(raw_image, image=step_1)
     write_image(step_2, "step_2")
+    write_image(step_2_alt, "step_2_alt")
 
     step_3 = pixelate_regions(raw_image, image=step_2)
     write_image(step_3, "step_3")

secure_pixelation/simple_lama_bindings.py

@@ -0,0 +1,77 @@
+import os
+from typing import Tuple
+
+import torch
+import cv2
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# https://github.com/okaris/simple-lama/blob/main/src/simple_lama/simple_lama.py
+
+
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+def prepare_img_and_mask(image: np.ndarray, mask: np.ndarray, device: torch.device, pad_out_to_modulo: int = 8, scale_factor: float = 1) -> Tuple[torch.Tensor, torch.Tensor]:
+    def get_image(img: np.ndarray):
+        img = img.copy()
+        
+        if img.ndim == 3:
+            img = np.transpose(img, (2, 0, 1))  # chw
+        elif img.ndim == 2:
+            img = img[np.newaxis, ...]
+        
+        return img.astype(np.float32) / 255
+
+    def scale_image(img: np.ndarray, factor: float, interpolation=cv2.INTER_AREA) -> np.ndarray:
+        if img.shape[0] == 1:
+            img = img[0]
+        else:
+            img = np.transpose(img, (1, 2, 0))
+        
+        img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation)
+        return img[None, ...] if img.ndim == 2 else np.transpose(img, (2, 0, 1))
+
+    def pad_img_to_modulo(img, mod):
+        channels, height, width = img.shape
+        out_height = height if height % mod == 0 else ((height // mod + 1) * mod)
+        out_width = width if width % mod == 0 else ((width // mod + 1) * mod)
+        return np.pad(img, ((0, 0), (0, out_height - height), (0, out_width - width)), mode="symmetric")
+
+    out_image = get_image(image)
+    out_mask = get_image(mask)
+
+    if scale_factor != 1:
+        out_image = scale_image(out_image, scale_factor)
+        out_mask = scale_image(out_mask, scale_factor, interpolation=cv2.INTER_NEAREST)
+
+    if pad_out_to_modulo > 1:
+        out_image = pad_img_to_modulo(out_image, pad_out_to_modulo)
+        out_mask = pad_img_to_modulo(out_mask, pad_out_to_modulo)
+
+    out_image = torch.from_numpy(out_image).unsqueeze(0).to(device)
+    out_mask = torch.from_numpy(out_mask).unsqueeze(0).to(device)
+
+    return out_image, (out_mask > 0) * 1
+
+
+class SimpleLama:
+    """
+    lama = SimpleLama()
+    result = lama(image, mask)
+    """
+
+    def __init__(self, device=None):
+        self.device = device or torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Using device: {self.device}")
+        
+        model_path = hf_hub_download("okaris/simple-lama", "big-lama.pt")
+        print(f"using model at {model_path}")
+        self.model = torch.jit.load(model_path, map_location=self.device).eval()
+
+    def __call__(self, image: np.ndarray, mask: np.ndarray) -> np.ndarray:
+        image, mask = prepare_img_and_mask(image, mask, self.device)
+
+        with torch.inference_mode():
+            inpainted = self.model(image, mask)
+            cur_res = inpainted[0].permute(1, 2, 0).detach().cpu().numpy()
+            return np.clip(cur_res * 255, 0, 255).astype(np.uint8)