init commit, add code

run.py

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+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import os
+import gzip
+
+def split_fft(image):
+    fshift_channels = []
+    magnitude_spectrums = []
+    for i in range(3):  # Process B, G, R channels separately
+        channel = image[:, :, i]
+        f = np.fft.fft2(channel)
+        fshift = np.fft.fftshift(f)
+        magnitude_spectrum = 20 * np.log(np.abs(fshift) + 1)
+        fshift_channels.append(fshift)
+        magnitude_spectrums.append(magnitude_spectrum)
+    return fshift_channels, magnitude_spectrums
+
+def save_fft_parts_compressed(path_prefix, fshift_channels):
+    for i, fshift in enumerate(fshift_channels):
+        file_path = f"{path_prefix}_channel_{i}.npz.gz"
+        with gzip.GzipFile(file_path, 'w') as f:
+            np.save(f, fshift)
+        print(f"Saved compressed FFT part: {file_path}")
+
+def save_magnitude_spectrums_as_images(path_prefix, magnitude_spectrums):
+    for i, mag in enumerate(magnitude_spectrums):
+        normalized = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
+        img = normalized.astype(np.uint8)
+        file_path = f"{path_prefix}_magnitude_{i}.png"
+        cv2.imwrite(file_path, img)
+        print(f"Saved magnitude spectrum image: {file_path}")
+
+def load_fft_parts_compressed(path_prefix):
+    fshift_channels = []
+    for i in range(3):
+        file_path = f"{path_prefix}_channel_{i}.npz.gz"
+        with gzip.GzipFile(file_path, 'r') as f:
+            part = np.load(f)
+        fshift_channels.append(part)
+        print(f"Loaded compressed FFT part: {file_path}")
+    return fshift_channels
+
+def merge_fft(fshift_channels):
+    reconstructed_channels = []
+    for fshift in fshift_channels:
+        f_ishift = np.fft.ifftshift(fshift)
+        img_back = np.fft.ifft2(f_ishift)
+        img_back = np.abs(img_back)
+        img_back = np.clip(img_back, 0, 255).astype(np.uint8)
+        reconstructed_channels.append(img_back)
+    reconstructed_image = cv2.merge(reconstructed_channels)
+    return reconstructed_image
+
+def load_image(path):
+    if not os.path.exists(path):
+        raise FileNotFoundError(f"Image file '{path}' not found")
+    image = cv2.imread(path)
+    if image is None:
+        raise ValueError(f"Failed to load image from '{path}'")
+    return image
+
+def plot_images(original=None, magnitude_spectrums=None, reconstructed=None):
+    if original.any():
+        plt.figure(figsize=(12, 6))
+        plt.subplot(1, 3, 1)
+        plt.imshow(cv2.cvtColor(original, cv2.COLOR_BGR2RGB))
+        plt.title('Original Image')
+        plt.axis('off')
+
+    if magnitude_spectrums:
+        plt.subplot(1, 3, 2)
+        combined_mag = np.stack(magnitude_spectrums, axis=2)
+        combined_mag = np.clip(combined_mag / np.max(combined_mag), 0, 1)
+        plt.imshow(combined_mag)
+        plt.title('Magnitude Spectrum (BGR)')
+        plt.axis('off')
+
+    if reconstructed.any():
+        plt.subplot(1, 3, 3)
+        plt.imshow(cv2.cvtColor(reconstructed, cv2.COLOR_BGR2RGB))
+        plt.title('Reconstructed Image')
+        plt.axis('off')
+
+    plt.show()
+
+def main(image_path, fft_prefix):
+    image = load_image(image_path)
+    fshift_channels, magnitude_spectrums = split_fft(image)
+    save_fft_parts_compressed(fft_prefix, fshift_channels)
+    save_magnitude_spectrums_as_images(fft_prefix, magnitude_spectrums)
+
+    # To test loading and reconstructing:
+    loaded_fshift_channels = load_fft_parts_compressed(fft_prefix)
+    reconstructed_image = merge_fft(loaded_fshift_channels)
+
+    plot_images(image, magnitude_spectrums, reconstructed_image)
+    #plot_images(None, None, reconstructed_image)
+
+if __name__ == "__main__":
+    input_image_path = 'your_image.jpg'        # Replace with your image path
+    fft_parts_prefix = 'fft_parts/your_image'  # Directory + prefix for saving FFT parts
+    os.makedirs(os.path.dirname(fft_parts_prefix), exist_ok=True)
+    main(input_image_path, fft_parts_prefix)