Python:去除图像中水平黑线的快速方法

Question

我想去除图像上的水平黑线:

为此，我对每列像素的 RGB 值进行插值。

黑线消失了，但我认为可以优化这个功能:

def fillMissingValue(img_in):
    img_out = np.copy(img_in)

    #Proceed column by column
    for i in tqdm(range(img_in.shape[1])):
        col = img_in[:,i]

        col_r = col[:,0]
        col_g = col[:,1]
        col_b = col[:,2]
        r = interpolate(col_r)
        g = interpolate(col_g)
        b = interpolate(col_b)
        img_out[:,i,0] = r
        img_out[:,i,1] = g
        img_out[:,i,2] = b

    return img_out

def interpolate(y):
    x = np.arange(len(y))
    idx = np.nonzero(y)
    interp = interp1d(x[idx],y[idx],fill_value="extrapolate" )

    return interp(x)

if __name__ == "__main__":
    img = cv2.imread("lena.png")
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    img = cv2.resize(img, (1024,1024))


    start = time.time()
    img2 = fillMissingValue(img)
    end = time.time()

    print("Process time: {}".format(np.round(end-start,3)))

你有什么想法吗？我想通过识别黑线的位置来做一个预处理步骤。因此仅内插相邻像素。但我不认为它更快

当前结果:

Answer 1

interp1d 效率不高。

正如@ChristophRackwitz 在评论中提出的，您可以检测线条的位置并使用 OpenCV 提供的修复方法:

img = cv2.imread('lena.jpg')

# Locate the relatively black lines
threshold = 25
lineIdx = np.where(np.mean(img, axis=(1,2)) < threshold)

# Perform inpainting on the located lines
mask = np.zeros(img.shape[:2], dtype=np.uint8)
mask[lineIdx] = 255

# Actual inpainting.
# Note: using 2 or 1 instead of 3 make the computation 
# respectively ~2 and ~4 time faster on my machine but 
# the result is not as beautiful with 3.
img2 = cv2.inpaint(img, mask, 3, cv2.INPAINT_NS)

计算部分在我的机器上需要 87 毫秒，而您的代码需要 342 毫秒。请注意，由于 JPEG 压缩，结果不是很好。您可以修复相邻的线(例如 lineIdx-1 和 lineIdx+1)，以便以较慢的计算速度为代价获得更好的结果(大约慢 2.5我的机器)。

---

另一种解决方案是在 Numpy 中自己执行插值:

%%time
# Locate the relatively black lines
threshold = 25
lineIdx = np.where(np.mean(img, axis=(1,2)) < threshold)[0]
lineIdxSet = set(lineIdx)

img2 = img.copy()
start, end = None, None
interpolate = False
for i in range(img.shape[0]+1):
    if i in lineIdxSet:
        if start is None:
            start = i
        end = i
    else:
        if not (start is None):
            assert not (end is None)
            # The first lines are black
            if start <= 0:
                i0, i1 = end+1, end+1
            # The last lines are black
            elif end >= img.shape[0]-1:
                i0, i1 = start-1, start-1
            # usual case
            else:
                i0, i1 = start-1, end+1
            # The full image is black
            if i0 < 0 or i1 >= img.shape[0]:
                continue
            end = min(end, img.shape[0]-1)
            # Actual linear interpolation (of a bloc of lines)
            coef = np.linspace(0, 1, end-start+3)[1:-1].reshape(-1, 1)
            l0 = img[i0].reshape(-1)
            l1 = img[i1].reshape(-1)
            img2[start:end+1] = (coef * l0 + (1.0-coef) * l1).reshape(-1, img.shape[1], 3)
        start, end = None, None

这段代码在我的机器上只需要 5 毫秒。结果应该与您的原始代码相似，除了它是逐行工作而不是逐列工作，并且每个颜色 channel 的检测不是独立的。请注意，当大块线条为黑色时，修复方法会给出更漂亮的结果。