update

compress_start.py

@@ -101,26 +101,15 @@ def plot_hist(tiff_list):
     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(col)       # keep the first row from the image
-    diff[:,0] = np.zeros(row)
-    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
-    predict[-1,:] = image[-1,:]       # keep the first row from the image
-    predict[:,-1] = image[:,-1]       # keep the first columen from the image
-    diff = np.empty([row,col])
-    diff[0,:] = np.zeros(col)       # keep the first row from the image
-    diff[:,0] = np.zeros(row)
-    diff[-1,:] = np.zeros(col)       # keep the first row from the image
-    diff[:,-1] = np.zeros(row)
+    predict = np.empty([row-1,col-1])     # create a empty matrix to update prediction
+    temp = image.copy
+    diff = np.empty([row-1,col-1])
+    
     for r in range(1,row-1):                  # loop through the rth row
         for c in range(1,col-1):              # loop through the cth column
-            surrounding = np.array([predict[r-1,c-1], predict[r-1,c], predict[r-1,c+1], predict[r,c-1]])
+            surrounding = np.array([temp[r-1,c-1], temp[r-1,c], temp[r-1,c+1], temp[r,c-1]])
             predict[r,c] = np.mean(surrounding)       # take the mean of the previous 4 pixels
+            temp[r,c] = np.mean(surrounding)
             diff[r,c] = (np.max(surrounding)-np.min(surrounding))
 
     predict = np.ravel(predict)