some changes

compress_start.py

@@ -72,7 +72,7 @@ def plot_hist(tiff_list):
     As it stands it needs some work in order to function again. We will
     fix this later. 1/25/22
     """
-    jj = 0
+    '''jj = 0
     fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(15,12))
     for cam, ax in zip(cameras, axs.ravel()):
         diff = []
@@ -80,7 +80,6 @@ def plot_hist(tiff_list):
             image = Image.open(cam[ii])    #Open the image and read it as an Image object
             image = np.array(image)[1:,:]    #Convert to an array, leaving out the first row because the first row is just housekeeping data
             ar1, ar2 = image.shape
-           
             ind1, ind2 = np.random.randint(1,ar1-1), np.random.randint(1,ar2-1) #ind1 randomly selects a row, ind2 randomly selects a column, 
                                                                             #this is now a random pixel selection within the image
             
@@ -96,7 +95,24 @@ def plot_hist(tiff_list):
         jj += 1
     plt.tight_layout()
     plt.show()
-    return 
+    return '''
+
+    image = tiff_list[0]
+    image = Image.open(image)    #Open the image and read it as an Image object
+    image = np.array(image)[1:,:]    #Convert to an array, leaving out the first row because the first row is just housekeeping data
+    row, col = image.shape
+    predict = np.empty(row,col)     # create a empty matrix to update prediction
+    predict[0,:] = image[0,:]       # keep the first row from the image
+    predict[:,0] = image[:,0]       # keep the first columen from the image
+    diff = np.empty(row,col)
+    diff[0,:] = np.zeros(row)       # keep the first row from the image
+    diff[:,0] = np.zeros(col)
+    for r in range(1,row):                  # loop through the rth row
+        for c in range(1,col):              # loop through the cth column
+            surrounding = anp.array([predict[r-1,c-1], predict[r-1,c], predict[r-1,c+1], predict[r1,c-1]])
+            predict[r,c] = np.mean(surrounding)       # take the mean of the previous 4 pixels
+            diff[r,c] = (np.max(surrounding)-np.min(surrounding))
+    
 
 if __name__ == '__main__':