convert_play.py 文件源码

def crop_image(fname,target_size):
    print('Processing image: %s' % fname)

    #otsu thresholding
    img = Image.open(fname)
    blurred = img.filter(ImageFilter.BLUR)
    ba = np.array(blurred)
    gray_image = cv2.cvtColor(ba, cv2.COLOR_BGR2GRAY)
    retval2, threshold2 = cv2.threshold(gray_image, 125, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    #storing white pixel in  each row and column in two arrays
    #these arrays are later used to find boundaries for cropping image
    row_white_pixel_count=np.count_nonzero(threshold2,axis=1)
    col_white_pixel_count=np.count_nonzero(threshold2,axis=0)

    #find x,y,w,h for cropping image
    y=find_boundary(row_white_pixel_count,col_white_pixel_count.size)
    h=find_boundary_reverse(row_white_pixel_count,col_white_pixel_count.size)
    x=find_boundary(col_white_pixel_count,row_white_pixel_count.size)
    w=find_boundary_reverse(col_white_pixel_count,row_white_pixel_count.size)
    crop_array = ba[y:h, x:w]

    #resize the image
    crop_img=Image.fromarray(crop_array)
    resized = crop_img.resize([target_size, target_size])


    #uncomment below line to see histogram of both white pixel vs rows and white pixel vs columns
    subplots(threshold2, row_white_pixel_count, col_white_pixel_count, crop_img)
    return resized