使用Keras提取同一图像的多个补丁 [英] Extracting multiple patches of a same image with Keras

问题描述

我正在对包含多种尺寸图像的数据集进行训练/测试ML模型.我知道Keras允许我们使用target_size参数提取固定大小的随机补丁:

I'm training/testing ML models over a dataset containing images of multiple sizes. I know Keras allows us to extract a random patch of fixed size using the target_size parameter:

gen = ImageDataGenerator(width_shift_range=.9, height_shift_range=.9)
data = gen.flow_from_directory('/path/to/dataset/train',
          target_size=(224, 224),
            classes=10,
            batch_size=32,
            seed=0)

for _ in range(data.N // data.batch_size):
    X, y = next(data)

对于每个迭代，X包含32个补丁(每个不同的样本一个).在所有迭代中，我都可以访问数据集中每个样本的一个补丁.

For each iteration, X contains 32 patches (one for each different sample). Across all iterations, I have access to one patch of each sample in the dataset.

问题:提取同一样品的多个贴片的最佳方法是什么?

Question: what is the best way to extract MULTIPLE patches of a same sample?

类似的东西:

data = gen.flow_from_directory(..., nb_patches=10)
X, y = next(data)
# X contains 320 rows (10 patches for each 32 sample in the batch)

我知道我可以编写第二个for循环并在数据集上迭代多次，但这似乎有些混乱.我还想更坚决地保证自己确实在获取样本样品的补丁.

I know I can write a second for loop and iterate multiple times over the dataset, but this seems a little bit messy. I also would like to have a more strong guarantee that I am really fetching patches of a sample sample.

推荐答案

我决定自己实现它.那就是它的结局:

I decided to implement it myself. That's how it ended up:

n_patches = 10
labels = ('class1', 'class2', ...)

for label in labels:
    data_dir = os.path.join('path-to-dir', label)

    for name in os.listdir(data_dir):
        full_name = os.path.join(data_dir, name)
        img = Image.open(full_name).convert('RGB')

        patches = []

        for patch in range(n_patches):
            start = (np.random.rand(2) * (img.width - image_shape[1],
                                img.height -image_shape[0])).astype('int')
            end = start + (image_shape[1], image_shape[0])
            patches.append(img_to_array(img.crop((start[0], start[1],
                                                  end[0], end[1]))))

        X.append(patches)
        y.append(label)

X, y = np.array(X, dtype=np.float), np.array(y, dtype=np.float)

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