import numpy as np from scipy import fftpack, stats from PIL import Image import os def load_and_prepare_image(file_path): img = Image.open(file_path).convert('L') return np.array(img) def compute_fft(image): f = np.fft.fft2(image) fshift = np.fft.fftshift(f) magnitude_spectrum = np.abs(fshift) return magnitude_spectrum def radial_profile(data, center): y, x = np.indices(data.shape) r = np.sqrt((x - center[0])**2 + (y - center[1])**2) r = r.astype(int) tbin = np.bincount(r.ravel(), data.ravel()) nr = np.bincount(r.ravel()) radialprofile = np.divide(tbin, nr, where=nr!=0) return radialprofile def compute_spectral_slope(fft_image): center = tuple(map(lambda x: x // 2, fft_image.shape)) radial_prof = radial_profile(fft_image, center) frequencies = np.fft.fftfreq(len(radial_prof))[1:len(radial_prof)//2] power = radial_prof[1:len(radial_prof)//2] mask = np.isfinite(power) & (power > 0) frequencies, power = frequencies[mask], power[mask] log_freq = np.log(frequencies) log_power = np.log(power) slope, _ = np.polyfit(log_freq, log_power, 1) return slope def compute_entropy(image): histogram = np.histogram(image, bins=256)[0] histogram = histogram / np.sum(histogram) return -np.sum(histogram * np.log2(histogram + 1e-10)) def compute_kurtosis(image): return stats.kurtosis(image.flatten()) def compute_moments(fft_image): # Compute the first 4 moments of the power spectrum power_spectrum = np.abs(fft_image)**2 moments = [np.mean(power_spectrum)] for i in range(2, 5): moments.append(np.mean((power_spectrum - moments[0])**i)) return moments def process_image(file_path): image = load_and_prepare_image(file_path) fft_image = compute_fft(image) slope = compute_spectral_slope(fft_image) entropy = compute_entropy(image) kurtosis = compute_kurtosis(image) moments = compute_moments(fft_image) # Save FFT image fft_filename = os.path.splitext(file_path)[0] + "_fft.png" Image.fromarray((np.log(fft_image + 1) * 255 / np.log(1 + np.max(fft_image))).astype(np.uint8)).save(fft_filename) return fft_filename, slope, entropy, kurtosis, moments def generate_html(results): html_content = """ """ for original_path, fft_path, slope, entropy, kurtosis, moments in results: html_content += f""" """ html_content += """

Original Image	2D FFT	Spectral Slope	Entropy	Kurtosis	Moments
		{slope:.4f}	{entropy:.4f}	{kurtosis:.4f}	{', '.join([f'{m:.2e}' for m in moments])}

""" with open("powerSpectra.html", "w") as f: f.write(html_content) # List of image file paths image_files = [ '1722307680.png', '1722307715.png', '1722307729.png', '1722307742.png', 'forest.jpg', 'streetviewKingStreet.jpeg', 'streetviewNorfolkStreet.jpeg' ] # Process each image results = [] for file_path in image_files: fft_path, slope, entropy, kurtosis, moments = process_image(file_path) results.append((file_path, fft_path, slope, entropy, kurtosis, moments)) print(f"Processed {file_path}") # Generate HTML generate_html(results) print("powerSpectra.html has been generated.")