initial commit

pohi.py

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+'''deconvolving 2D images'''
+
+import numpy as np
+import h5py
+import argparse
+from scipy.signal import convolve
+from scipy.ndimage import gaussian_filter
+from deconvolve_func import DeconvolveWithBead as DWB
+
+parser = argparse.ArgumentParser()
+parser.add_argument('bead', type = str, help = "original file")
+parser.add_argument('--output', type = str, required = True, help = "file name for the output of images")
+parser.add_argument('-pd', '--plot_data', type = str, required = True, help = "data storage file name, iterations added automatically")
+parser.add_argument('-it', '--iterations', type = int, required = True, help = "nr of iterations")
+parser.add_argument('-in', '--intensity', type = float, nargs = '+', required = True, help = "image intensity; division of the signal by intensity")
+parser.add_argument('-k', '--kernel', type = float, nargs = '+', required = True, help = "kernel g sigma values")
+parser.add_argument('--vox', type = float, nargs = '+', default = [1.0, 1.0, 1.0], help = "voxel values in micrometers, default [1.0, 1.0, 1.0]")
+
+args = parser.parse_args()
+
+
+#--------- importing the image, that will become the GROUND TRUTH ------
+with h5py.File(args.bead, 'r') as hdf5_file:
+    original = hdf5_file['t0/channel0'][:]
+
+print("algse pildi integraal", np.sum(original))
+
+#--------- creating the 2D gaussian PSF----------------------------------
+points = np.max(original)
+point_sources = np.zeros(original.shape)
+
+center_x = original.shape[0] // 2
+center_y = original.shape[1] // 2
+
+point_sources[center_x, center_y] = points
+#point_sources[90, 110] = points
+psf = gaussian_filter(point_sources, sigma = 2.4)
+
+print("psf integraal", np.sum(psf))
+if not np.isclose(np.sum(psf), 1.0, atol=1e-6):
+    psf /= np.sum(psf) # normaliseerin, et piksli väärtused oleksid samas suurusjärgus
+print("psf-integraal-uuesti", np.sum(psf))
+
+#-------------- DECONVOLUTION--------------------------------------------------------------
+with h5py.File(args.output, 'w') as hdf5_file:
+    'erinevad mürad ### signal intensity #################################################'
+    intensity = args.intensity
+
+    for i in intensity:
+        scaled_original = original / i if i!=0 else original.copy()
+        scaled_original = scaled_original.astype(np.float64)
+        image =  gaussian_filter(scaled_original, sigma = 2.4)#convolve(scaled_original, psf, mode='same')
+        image[image <= 0] = 0
+
+        "rakendan pildile Poissoni müra #################################################"
+        if i == 0:
+            noisy_image = image.copy()          
+        else:
+            noisy_image = np.random.poisson(lam = image, size = image.shape).astype(np.float64)
+
+        hdf5_file.create_dataset(f"noise_level_{i:08.3f}", data = noisy_image)
+        hdf5_file.create_dataset(f"scaled_original_{i:08.3f}", data = scaled_original)
+
+        '''kernelid erinevate sigmadega - pildi taastamine ##################'''
+        g_sigma = args.kernel
+        vox = np.array(args.vox)
+        mses = []
+        mssim = []
+        
+        for j in g_sigma:
+            image_kerneled = gaussian_filter(noisy_image, sigma = j)
+            psf_kerneled = gaussian_filter(psf, sigma = j)
+            print(np.sum(image_kerneled),np.sum(noisy_image), np.sum(psf_kerneled), np.sum(psf))
+            'dekonvolveerin - taastan pilti ######################################'
+            deconv = DWB(image = image_kerneled, voxel_size = vox, bead = scaled_original)
+
+            dec = deconv.deconvolve_rl(n_iterations = args.iterations,
+                bead = scaled_original,
+                psf = psf_kerneled,
+                im = image_kerneled)
+
+            hdf5_file.create_dataset(f'intensity_{i:08.3f}/SIGMA_{j:03.1f}', data = dec[0])
+            hdf5_file.create_dataset(f"intensity_{i:08.3f}/minimum_image_{j:03.1f}", data = dec[4])
+
+            mses.append(dec[1])
+            mssim.append(dec[2])
+
+
+        print("---------------lõpp-pildi integraal", np.sum(dec[0]))       
+        data = np.column_stack((np.column_stack(mses),np.column_stack(mssim)))
+        np.save(f"{args.plot_data}_{args.iterations}_{i:08.3f}.npy", np.column_stack((dec[3], data)))
+
+
+
+