Commit be3f26c2 authored by Nathaniel Callens's avatar Nathaniel Callens

update

parents c14a08b2 62b8cccf
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...@@ -101,26 +101,15 @@ def plot_hist(tiff_list): ...@@ -101,26 +101,15 @@ def plot_hist(tiff_list):
image = Image.open(image) #Open the image and read it as an Image object 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 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 row, col = image.shape
predict = np.empty((row,col)) # create a empty matrix to update prediction predict = np.empty([row-1,col-1]) # create a empty matrix to update prediction
predict[0,:] = image[0,:] # keep the first row from the image temp = image.copy
predict[:,0] = image[:,0] # keep the first columen from the image diff = np.empty([row-1,col-1])
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 r in range(1,row-1): # loop through the rth row
for c in range(1,col-1): # loop through the cth column 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 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)) diff[r,c] = (np.max(surrounding)-np.min(surrounding))
predict = np.ravel(predict) predict = np.ravel(predict)
......
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