Commit 2aa38181 authored by Kelly Chang's avatar Kelly Chang
Browse files

some changes

parent 2838647d

compress_start.py

+19 −3
Original line number Original line Diff line number Diff line
@@ -72,7 +72,7 @@ def plot_hist(tiff_list): 
    As it stands it needs some work in order to function again. We will

    As it stands it needs some work in order to function again. We will

    fix this later. 1/25/22

    fix this later. 1/25/22

    """

    """

    jj = 0

    '''jj = 0

    fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(15,12))

    fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(15,12))

    for cam, ax in zip(cameras, axs.ravel()):

    for cam, ax in zip(cameras, axs.ravel()):

        diff = []

        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 = 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

            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

            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, 

            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

                                                                            #this is now a random pixel selection within the image
@@ -96,7 +95,24 @@ def plot_hist(tiff_list): 
        jj += 1

        jj += 1

    plt.tight_layout()

    plt.tight_layout()

    plt.show()

    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__':

if __name__ == '__main__':