Commit be3f26c2 authored by Nathaniel Callens's avatar Nathaniel Callens
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update

parents c14a08b2 62b8cccf

.ipynb_checkpoints/compression_jupyter-checkpoint.ipynb

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.ipynb_checkpoints/prediction_MSE-checkpoint.ipynb

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1640843156_846487_14.tiff

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compress_start.py

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Original line number Original line Diff line number Diff line
@@ -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)

compression_jupyter.ipynb

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