merge

compress_start.py

@@ -107,13 +107,40 @@ def plot_hist(tiff_list):
     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)
     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]])
             predict[r,c] = np.mean(surrounding)       # take the mean of the previous 4 pixels
             diff[r,c] = (np.max(surrounding)-np.min(surrounding))
+
+    predict = np.ravel(predict)
+    diff = np.ravel(diff)
+    n = len(predict)
+    fig = plt.figure()
+
+    ax1 = fig.add_subplot(111, projection='3d')
+    z3 = np.zeros(n)
+    
+    dx = np.ones(n)
+    dy = np.ones(n)
+    dz = np.arange(n)
+    
+    ax1.bar3d(predict, diff, z3, dx, dy, dz, color="red")
+    ax1.axis('off')
+    plt.show()
     return image, predict, diff
 
+
 if __name__ == '__main__':
 
     """For boundary cases: Start by grabbing the shape of the images and saving those
@@ -127,6 +154,7 @@ if __name__ == '__main__':
     image, predict, difference = plot_hist(images)
     error = np.abs(image-predict)
     
-    
-    
+
+    plot_hist(images)
+
     
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