在训练图像分割之前无法预处理数据

Question

我正在尝试为 ocr 做一些图像分割，我的蒙版图像是 3 类图像，像这样

我的原始图像是这样的灰色图像

但是当我尝试拟合模型时出现此错误

无法将输入数组从形状 (128,128,3) 广播到形状 (128,128)

这是我用来创建数据集的代码

    img_size = (128, 128)
    batch_size = 32
    input_img_paths = sorted(
        [  os.path.join(input_dir, fname)
            for fname in os.listdir(input_dir)
            if fname.endswith(".jpg") ] )
    target_img_paths = sorted(
        [   os.path.join(target_dir, fname)
            for fname in os.listdir(target_dir)
            if fname.endswith(".jpg") and not fname.startswith(".") ])

class OxfordPets(keras.utils.Sequence):
    """Helper to iterate over the data (as Numpy arrays)."""

    def __init__(self, batch_size, img_size, input_img_paths, target_img_paths):
        self.batch_size = batch_size
        self.img_size = img_size
        self.input_img_paths = input_img_paths
        self.target_img_paths = target_img_paths

    def __len__(self):
        return len(self.target_img_paths) // self.batch_size

    def __getitem__(self, idx):
        """Returns tuple (input, target) correspond to batch #idx."""
        i = idx * self.batch_size
        batch_input_img_paths = self.input_img_paths[i : i + self.batch_size]
        batch_target_img_paths = self.target_img_paths[i : i + self.batch_size]
        x = np.zeros((batch_size,) + self.img_size, dtype="float32")
        for j, path in enumerate(batch_input_img_paths):
            img = load_img(path, target_size=self.img_size)

            x[j] = img
        y = np.zeros((batch_size,) + self.img_size, dtype="float32")
        for j, path in enumerate(batch_target_img_paths):
            img = load_img(path, target_size=self.img_size, color_mode="rgb")
            y[j] = img
        return x, y



val_samples = 150
random.Random(1337).shuffle(input_img_paths)
random.Random(1337).shuffle(target_img_paths)
train_input_img_paths = input_img_paths[:-val_samples]
train_target_img_paths = target_img_paths[:-val_samples]
val_input_img_paths = input_img_paths[-val_samples:]
val_target_img_paths = target_img_paths[-val_samples:]

# Instantiate data Sequences for each split
train_gen = OxfordPets(
    batch_size, img_size, train_input_img_paths, train_target_img_paths
)
val_gen = OxfordPets(batch_size, img_size, val_input_img_paths, val_target_img_paths)

但是当我试图适应这个

model_history = model.fit(train_gen, epochs=30,
                          steps_per_epoch=50,
                          validation_steps=25,
                          validation_data=val_gen)

我收到错误消息，我正在尝试调整此解决方案 https://keras.io/examples/vision/oxford_pets_image_segmentation/?fbclid=IwAR2wFYju-N0X7FUaWkhvOVaAAaVqLdOryBwg7xDC0Rji9LQ5F2jYOkeNnns 来自 keras

进入tensorflow页面的例子 https://www.tensorflow.org/tutorials/images/segmentation

我的印象是问题与原始图像是灰度的事实有关，我该如何解决这个错误？任何建议都会很棒！

Answer 1

您应该先将图像转换为 RGB。您的图像是灰度图像，只有 1 个通道。它的形状是 (128,128,1)。他们将诸如 opencv:backtorgb = cv2.cvtColor(gray,cv2.COLOR_GRAY2RGB) 之类的应用到数据中的每个图像，一切都会好起来的

Answer 2

您的蒙版是 RGB 并且有 3 个通道。但你的图像是灰度的，只有一个通道。 RGB图像转灰度图见This question