92 lines
4.4 KiB
Python
92 lines
4.4 KiB
Python
#!/usr/bin/env python
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import numpy as np
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from PIL import Image # import Python Imaging Library (PIL)
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def convert_image_to_array(file_name,imin,imax):
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# file_name: the name of the input image (a string)
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# imin: (left) boundary that defines the output array
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# imax: (right) boundary that defines the output array
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# Note: assume a grayscale image (whose R, G, and B values are the same)
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# Return an numpy array with RGB values that has the same size as the wanted micromodel
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im = Image.open(file_name).convert('RGB')
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im_array = np.array(im) # 'im_array' has dimension (height, width, 3)
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# Grayscale input image is assumed, thus its R, G, and B values are the same
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im_array = im_array[:,:,0] # 2D array with dimension (height, width)
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im_array = im_array[:,imin:imax] # Chop the original image to a desirable size
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#im_array = np.flipud(im_array[:,imin:imax]) # You might want to flip the Z-axis
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return im_array
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#end def
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def read_input_parameters(input_file_name):
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# input_file_name is a string
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# The *.in file has the following lines
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# line 0: the base name of the experimental images
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# line 1: imin imax
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# line 2: imin_b imax_b
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# line 3: top_layer bottom_layer DEPTH
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# line 4: threshold_nw threshold_s
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# Note: 'threshold_nw' means: NW phase: RGB values > threshold_nw
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# 'threshold_s' means: solid: RGB values < threshold_s
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f = open(input_file_name,'r') # read-only
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lines = f.readlines()
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output_file = {'base_name':lines[0].splitlines()[0]}
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line1_array = np.fromstring(lines[1].splitlines()[0],dtype=np.int32,sep=' ')
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line2_array = np.fromstring(lines[2].splitlines()[0],dtype=np.int32,sep=' ')
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line3_array = np.fromstring(lines[3].splitlines()[0],dtype=np.int32,sep=' ')
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line4_array = np.fromstring(lines[4].splitlines()[0],dtype=np.int32,sep=' ')
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output_file['imin']=line1_array[0]
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output_file['imax']=line1_array[1]
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output_file['imin_b']=line2_array[0]
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output_file['imax_b']=line2_array[1]
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output_file['top_layer']=line3_array[0]
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output_file['bot_layer']=line3_array[1]
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output_file['DEPTH']=line3_array[2]
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output_file['threshold_nw']=line4_array[0]
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output_file['threshold_s'] =line4_array[1]
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f.close()
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return output_file
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#end def
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def get_initial_config(image_array,Para,NX,NY,NZ):
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# This function will give the same fluid configuration shown in the *.Tiff image
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# Function input:
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# 'image_array' is a numpy array of RGB values, with the same size as micromodel
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# 'Para' is a dictionary of input parameters
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# Initialize the segmented image 'ID'
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# NOTE: 'ID' has the dimension (height, width, DEPTH) or (NZ,NY,NX)
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ID = np.zeros((NZ,NY,NX),dtype=np.uint8)
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# Map the pixels to the 3D geometry
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# Rules: NW phase: RGB values > threshold_nw
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# Solid: RGB values < threshold_s
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# 1. Identify the non-wetting phase
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ID[image_array>Para['threshold_nw'],Para['top_layer']:Para['top_layer']+Para['DEPTH']] = 1
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# 2. Identify the wetting phase
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ID[np.logical_and(image_array>=Para['threshold_s'],image_array<=Para['threshold_nw']),\
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Para['top_layer']:Para['top_layer']+Para['DEPTH']] = 2
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# 3. Post boundary retreatment along y-axis. Note z-axis is always the flow direction
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ID[:,:Para['imin_b']-Para['imin'],Para['top_layer']:Para['top_layer']+Para['DEPTH']]=0
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ID[:,ID.shape[1]-(Para['imax']-Para['imax_b']):,Para['top_layer']:Para['top_layer']+Para['DEPTH']]=0
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return ID
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#end def
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def get_porous_medium(image_array,Para,NX,NY,NZ):
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# This function only gives the domain (0->solid,1->fluid) of the *.Tiff image
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# Function input:
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# 'image_array' is a numpy array of RGB values, with the same size as micromodel
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# 'Para' is a dictionary of input parameters
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domain = np.zeros((NZ,NY,NX),dtype=np.uint8)
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# Map the pixels to the 3D geometry
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# Rules: NW phase: RGB values > threshold_nw
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# Solid: RGB values < threshold_s
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# 1. Identify fluid phase
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domain[image_array>=Para['threshold_s'],Para['top_layer']:Para['top_layer']+Para['DEPTH']]=1
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# 2. Post boundary retreatment along y-axis. Note z-axis is always the flow direction
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domain[:,:Para['imin_b']-Para['imin'],Para['top_layer']:Para['top_layer']+Para['DEPTH']]=0
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domain[:,domain.shape[1]-(Para['imax']-Para['imax_b']):,Para['top_layer']:Para['top_layer']+Para['DEPTH']]=0
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return domain
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#end def
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