testing on gaussian blur, not good

Remove_Noise.py

-from audioop import mul
-from matplotlib.image import composite_images
 from WorkingPyDemo import *
 from scipy.ndimage.filters import gaussian_filter
 import paramiko
 from PIL import ImageFilter
+from scipy.signal import convolve2d
+from scipy.stats import multivariate_normal
+
+from skimage.restoration import wiener
 def setup_remote_sftpclient():
     client = paramiko.SSHClient()
     client.load_system_host_keys()
@@ -175,14 +177,14 @@ def save_new_gauss():
     creates gaussian kernel with side length `l` and a sigma of `sig`
     """
     # x,y = np.mgrid[-1:1:.003125, -1:1:.003125]
-    x,y = np.mgrid[-1:1:.003911, -1:1:.003911]
-    # print(x.shape)
+    x,y = np.mgrid[-1:1:.44, -1:1:.44]
+    print(x.shape)
     pos = np.dstack((x,y))
     # grid = np.zeros((l,l))
     # gauss = np.exp(-0.5 * np.square(ax) / np.square(sig))
-    from scipy.stats import multivariate_normal
+    
     # normal_grid = multivariate_normal.pdf(grid, mean = [0]*l, cov = [5]*l)
-    normal_grid = multivariate_normal([0, 0], [[2.0, 0.3], [0.3, 0.5]]).pdf(pos)
+    normal_grid = multivariate_normal([0, 0], [[1.1, 5], [2, 1]]).pdf(pos)
     normal_grid = normal_grid/np.sum(normal_grid)
     end_image = Image.fromarray(normal_grid)
     # fig2 = plt.figure()
@@ -191,43 +193,18 @@ def save_new_gauss():
     # plt.show()
     # print(np.sum(np.array(end_image)))
     end_image.save("gaussian_kernel.tiff")
-def little_inverter(initial_matrix):
-    n = initial_matrix.shape[0]
-    initial_matrix = np.hstack((initial_matrix,np.zeros_like(initial_matrix)))
-    print(initial_matrix.shape)
-    initial_matrix = initial_matrix.tolist()
-    for i in range(n):        
-        for j in range(n):
-            if i == j:
-                initial_matrix[i][j+n] = 1
-
-    # Applying Guass Jordan Elimination
-    for i in range(n):
-        if initial_matrix[i][i] == 0.0:
-            sys.exit('Divide by zero detected!')
 
-        for j in range(n):
-            if i != j:
-                ratio = initial_matrix[j][i]/initial_matrix[i][i]
-
-                for k in range(2*n):
-                    initial_matrix[j][k] = initial_matrix[j][k] - ratio * initial_matrix[i][k]
-
-    # Row operation to make principal diagonal element to 1
-    for i in range(n):
-        divisor = initial_matrix[i][i]
-        for j in range(2*n):
-            initial_matrix[i][j] = initial_matrix[i][j]/divisor
-    return np.array(initial_matrix)[:,n:]
 if __name__ == "__main__":
     # save_new_average(350,"11")
-    save_new_gauss()
+    # save_new_gauss()
+    
     gaussian_kernel = np.array(Image.open("gaussian_kernel.tiff"))
+    # gaussian_kernel = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]])
     scenes = remote_file_extractor("/media/elphel/NVME/lwir16-proc/te0607/scenes/")
     images = remote_image_extractor(scenes)
     images = find_only_in_channel(images, "11")
     # average_image = np.array(Image.open("Average_On_Channel(" + "11" + ").tiff"))
-    # create_testable_images(images,"11",6)
+    # create_testable_images(images,"11",3)
     # plt.imshow(color_adjust(average_image),cmap='gray',vmin = 0, vmax=1)
     # plt.show()
     
@@ -238,15 +215,32 @@ if __name__ == "__main__":
     sftp_client = setup_remote_sftpclient()
     # print(len(images))
     # print(images[10000])
-    test_image = sftp_client.open(images[4700])
+    test_image = sftp_client.open(images[10000])
     test_image = Image.open(test_image)
     test_image = np.array(test_image)[1:]
     # print(test_image)
+    plt.subplot(121)
     plt.imshow(color_adjust(adjust_to_original(test_image,average_image)),cmap='gray',vmin = 0, vmax=1)
-    plt.show()
-    little_more_blurred = gaussian_kernel@adjust_to_original(test_image,average_image)
-    print(little_inverter(gaussian_kernel)@gaussian_kernel)
-    plt.imshow(color_adjust(little_inverter(gaussian_kernel)@little_more_blurred),cmap='gray',vmin = 0, vmax=1)
+    plt.subplot(122)
+    # plt.show()
+    # print(gaussian_kernel)
+    little_more_blurred = convolve2d(np.pad(test_image,2, mode = 'edge'),gaussian_kernel, "valid")
+    # print(little_more_blurred)
+    print(little_more_blurred.shape)
+    # print(little_inverter(gaussian_kernel)@gaussian_kernel)
+    altered_image = Image.fromarray(little_more_blurred.astype(np.uint16))
+
+    altered_image.save("averaged_images(" + "11" + ")/innerfolder" + "/testable.tiff")
+    plt.imshow(color_adjust(little_more_blurred[3:-3,3:-3]),cmap='gray',vmin = 0, vmax=1)
+    # plt.show()
+    # renewed_array = 
+    # H = fft(kernel)
+	# deconvolved = np.real(ifft(fft(signal)*np.conj(H)/(H*np.conj(H) + lambd**2)))
+    back_to_normal = wiener(little_more_blurred,gaussian_kernel,.02,clip=False)
+    # print(back_to_normal)
+    # print(back_to_normal.shape)
+    
+    # plt.imshow(color_adjust(back_to_normal), cmap='gray',vmin = 0, vmax=1)
     plt.show()
     # newimage = Image.fromarray(test_image - average_image)
     # newimage.save("NoInterference.tiff")

WorkingPyDemo.py

@@ -11,6 +11,7 @@ import numpy.linalg as la
 from time import time
 from time import sleep
 import tifffile as tiff
+
 folder_name = "images"
 outputlocation = ""
 
@@ -514,7 +515,7 @@ def text_to_tiff(filename, list_dic, bins):
 
 if __name__ == "__main__":
     
-    scenes = file_extractor("original_images(11)")
+    scenes = file_extractor("averaged_images(11)")
     images = image_extractor(scenes)
     newnamesforlater = []
     list_dic, bins = make_dictionary(images, 4, False)