我正在尝试使用 SLIC 变体进行语义分割，并希望根据可用的基于点的注释为每个片段着色（根据其类）为原始图像创建一个掩码。如果该段中没有基于点的注释，则 leave 为 0。
我目前有一个图像的 x、y 点及其相关标签，以及一个查找和着色所需片段的（慢）方法。我熟悉矢量化或“pythonic”是做事，但我似乎无法加快最后一个 for 循环，并且希望得到一些关于优化的建议或参考。谢谢。
# Point-based annotations
annotation = pd.read_csv("a_dataframe.csv") # [X, Y, Label]
color_label = {'class 1' : 25, 'class 2' : 50, 'class 3' : 75}
# Uses CPU to create single segmented image with current params
slic = SlicAvx2(num_components = n_segments, compactness = n_compactness)
segmented_image = slic.iterate(cv2.cvtColor(each_image, cv2.COLOR_RGB2LAB))
# Finds the segments of interest and records their ID
X = np.array(each_annotation.iloc[:, 0], dtype = 'uint8')
Y = np.array(each_annotation.iloc[:, 1], dtype = 'uint8')
L = np.array(each_annotation.iloc[:, 2], dtype = 'str') # Labels
DS = segmented_image[X, Y] # Desired Segments
# Empty mask, marks the segments of interest with the classes of the point in them
mask = np.zeros(each_image.shape[:2], dtype = "uint8")
# Would ideally like to find a more quickly way of doing this
for (index, segVal) in enumerate(DS):
mask[segmented_image == segVal] = color_label.get(L[index])
我基本上有我想用这里替换那个循环的东西：
[mask[segmented_image == s] for i, s in enumerate(DS)]
但我无法在mask. 我认为这将是类似的东西：
[mask[segmented_image == s] for i, s in enumerate(DS)] = color_label.get(L[i])
但似乎我正在尝试为我正在生成的列表分配一个颜色值......