Untitled

from modules.utils.create_folder_not_exists  import create_folder_not_exists

def get_segmented_lungs(imagePath, num, save=False, plot=False, show_on_window=False, crop_percentage=0.05):
    #This funtion segments the lungs from the given 2D slice.
    im = cv.imread(imagePath, cv.IMREAD_GRAYSCALE)
    im = cv.resize(im, (528, 528))
    crop = im.copy()
    path_save=os.path.dirname(imagePath)
    if show_on_window:
        height,width=im.shape[:2]
        start_row,start_col=int(height*crop_percentage),int(width*crop_percentage)
        end_row,end_col=int(height*(1-crop_percentage)),int(width*(1-crop_percentage))
        crop=crop[start_row:end_row,start_col:end_col]
    else:
        if num == 161 or (num >= 173 and num <= 174) or (num == 758):
            height,width=im.shape[:2]
            start_row,start_col=int(height*0.20),int(width*0.20)
            end_row,end_col=int(height*0.80),int(width*0.80)
            crop=crop[start_row:end_row,start_col:end_col]
        elif num >= 756 and num <= 767:
            #Step 1: Crop the image 
            height,width=im.shape[:2]
            start_row,start_col=int(height*0),int(width*0)
            end_row,end_col=int(height*1),int(width*1)
            crop=crop[start_row:end_row,start_col:end_col]
        elif num == 1320 or num == 1219 or (num >= 712 and num <= 767) or (num >= 779 and num <= 799) or (num >= 688 and num <= 699) or (num >= 648 and num <= 664) or (num >= 225 and num <= 234):
            #Step 1: Crop the image 
            height,width=im.shape[:2]
            start_row,start_col=int(height*0.03),int(width*0.03)
            end_row,end_col=int(height*0.97),int(width*0.97)
            crop=crop[start_row:end_row,start_col:end_col]
        else:
            #Step 1: Crop the image 
            height,width=im.shape[:2]
            start_row,start_col=int(height*0.12),int(width*0.12)
            end_row,end_col=int(height*0.88),int(width*0.88)
            crop=crop[start_row:end_row,start_col:end_col]
        
    #Step 2: Convert into a binary image. 
    ret,binary = cv.threshold(crop,140,255,cv.THRESH_BINARY_INV)
    
    #Step 3: Remove the blobs connected to the border of the image.
    cleared = clear_border(binary) 
    
    #Step 4: Closure operation with a disk of radius 10. This operation is 
    #to keep nodules attached to the lung wall.
    selem = disk(2)
    closing = binary_closing(cleared, selem)
        
    #Step 5: Label the image.
    label_image = label(closing)
    
    #Step 6: Keep the labels with 2 largest areas.
    areas = [r.area for r in regionprops(label_image)]
    areas.sort()
    if len(areas) > 2:
        for region in regionprops(label_image):
            if region.area < areas[-2]:
                for coordinates in region.coords:                
                       label_image[coordinates[0], coordinates[1]] = 0
    segmented_area = label_image > 0
        
    #Step 7: Erosion operation with a disk of radius 2. This operation is 
    #seperate the lung nodules attached to the blood vessels.
    selem = disk(2)
    erosion = binary_erosion(segmented_area, selem) 
        
    #     Step 4: Closure operation with a disk of radius 10. This operation is 
    #     to keep nodules attached to the lung wall.
    selem = disk(10)
    closing2 = binary_closing(erosion, selem)    
    
    #Step 8: Fill in the small holes inside the binary mask of lungs.
    edges = roberts(closing2)
    fill_holes = ndi.binary_fill_holes(edges)
        
    superimpose = crop.copy()
    #Step 9: Superimpose叠加 the binary mask on the input image.
    get_high_vals = fill_holes == 0
    superimpose[get_high_vals] = 0

    superimpose = cv.resize(superimpose, (528, 528))    
    
    if show_on_window:
        directory1 = path_save+'/'
        directory2 = '.jpg'
        images = [im, crop, binary, cleared, closing, segmented_area, erosion, closing2, fill_holes, superimpose]
        titles = ['0_original_image', '1_cropped_image', '2_binary_image', '3_remove_blobs', '4_closure', '5_roi', '6_erosion', '7_closure', '8_fill_hole', '9_result']
        for i, title in enumerate(titles):
            filename = directory1 + title + directory2
            create_folder_not_exists(filename)
            try:
                cv.imwrite(filename, images[i])
            except:
                indices = images[i].astype(np.uint8)  #convert to an unsigned byte
                indices*=255
                cv.imwrite(filename, indices)
    else:
        #flip vertically
        directory1 = 'preprocessing/pre1/'
        directory2 = '.jpg'
        images = [crop, binary, cleared, label_image, superimpose]
        titles = ['cropped_image', 'binary_image', 'remove_blobs', 'label', 'result']

        if save:
            for y in range(5):
                filename = directory1 + str(y+1) + titles[y] + '/' + titles[y] + str(num+1) + directory2
                create_folder_not_exists(filename)
                cv.imwrite(filename, images[y])

    images = [im, crop, binary, cleared, closing, label_image, segmented_area, erosion, closing2, fill_holes, superimpose]
    
    if plot:
        titles = ['Original Image', 
                 'Step 1: Cropped Image', 
                 'Step 2: Binary image', 
                 'Step 3: Remove blobs', 
                 'Step 4: Closure', 
                 'Step 5: Label', 
                 'Step 6: Region On Interest',
                 'Step 7: Erosion',
                 'Step 8: Closure', 
                 'Step 9: Fill Holes',
                 'Step 10: Result']
        plot_img(images, titles, camp=plt.cm.bone, rows = 3, cols = 4, fontsize= 50)
    

    
    return superimpose