stuff

deblur/symetric_kernel.py

@@ -1,5 +1,31 @@
+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):
     """
@@ -15,7 +41,6 @@ def generate_kernel(radius, sigma=None):
     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
@@ -30,6 +55,197 @@ def generate_kernel(radius, sigma=None):
     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__":
-    kernel = generate_kernel(radius=10, sigma=1)
-    print(kernel)
+    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_())