使用PIL置換二進制掩碼中的像素

我完全被這個問題困住了——我需要隨機排列 2D 影像上一個圓圈內的像素。我需要這個用于我正在設計的實驗，在這個實驗中，搜索這個小圓圈將作為任務的低級感知部分。重要的是像素來自影像本身而不是隨機值，以保持修改后的影像和未修改的影像之間的影像強度均勻。

我不太了解Python，所以到目前為止我的代碼非常hacky - 我首先打開影像，并確保它是RGB。然后我制作一個深拷貝（因為我將使用蒙版修改原始檔案，但我仍然需要實際的原始像素值）。然后我畫了一個鮮紅色的圓圈（255,0,0），并將pix_mask定義為紅色為255的任何地方（我們的刺激是自然場景和物體，所以影像中不應該有任何其他地方） ）。然后我使用創建一個新的蒙版影像pix_mask，并將蒙版乘以復制的影像資料以獲取圓圈內的資料。聽起來很亂，但結果如下：

這是我用來執行此操作的代碼：

def create_pixel_mask_images(image_fname: str):
    # Open the image which requires a scrambled dot. Add an alpha channel and make a copy of the data 
    image: JpegImage          = Image.open(image_fname)
    image                     = image.convert("RGB")
    image_copy                = image.copy()

    # Create the drawer and draw a filled red circle on the original image (255, 0, 0)
    image_draw                = ImageDraw.Draw(image)
    image_draw.ellipse((150, 150, 250, 250), fill = "red")
    image.save("TEST_DOT.png")

    # Read the data from the copy and the now-modified original image into arrays
    image_arr: NumpyArray      = numpy.array(image)
    image_copy_arr: NumpyArray = numpy.array(image_copy)

    # Create a binary mask by testing for the red circle we drew on the original image (255 red)
    pix_mask: NumpyArray       = image_arr[:, :, 0] == 255

    # Invert the mask (i.e., everything outside of the dot is now white, and the dot black)
    # Write them out to file to error-check
    mask_image: PILImage         = Image.fromarray((pix_mask * 255).astype(numpy.uint8))
    image.convert("RGB")
    mask_image_inverse: PILImage = ImageOps.invert(mask_image)
    image.convert("RGB")
    pix_mask_inverse: NumpyArray = numpy.array(mask_image_inverse)

    # Read the inverted pixel mask as a numpy array. Multiply the unmodified image by both masks
    # in order to get the data both inside and outside of the mask
    inner_data = image_copy_arr * pix_mask[..., None]
    outer_data = image_copy_arr * pix_mask_inverse[..., None]

    # Create new images from the masked data
    inner_image: PILImage = Image.fromarray(inner_data.astype(numpy.uint8))
    outer_image: PILImage = Image.fromarray(outer_data.astype(numpy.uint8))
    outer_image           = ImageOps.invert(outer_image)

    # AS A CHECK multiply the inner- and outer-masked images together. We should get the original image back
    image_data = image.load()

        for y in range(image.size[1]):
            for x in range(image.size[0]):
                if image_data[x, y] == (255, 0, 0):
                    image_data[x, y] = (255, 255, 255)
    recombined_image = ImageChops.multiply(inner_image, outer_image)
    
    inner_image.save('INNER_DATA.png')
    
    mask_image.save('MASK.jpg')
    mask_image_inverse.save('MASK_INVERSE.jpg')
    recombined_image.save('RECOMBINATION_TEST.jpg')

You can see at the bottom I create a recombined_image by multiplying the inner_image, which is the data within the circle mask, and the outer_image, which is the rest of the data outside of the circle mask, together; however, I just end up the same image as the second picture here (just the data inside the circle).

I use the nested for-loop here to set any red pixels in the original image to white as if I left the region black I would lose the data there (checked the docs for ImageChops.multiply() -

標籤：python python-3.x python-imaging-library

上一篇：Python：使用嵌套字典創建嵌套資料類的類

下一篇：`==`和`and`之間的區別