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对Lavalindoor数据集crops曝光程度进行分析

发表于 更新于
数据分析

在分析emlight的intensity项loss为何不下降的过程中，我发现有一张全黑的crop，经过tonemapping后仍然为全黑的，但是重新曝光系数ahpla竟然能达到10的6次方数量级。

我觉得这是由于不合理的tonemapping函数设计导致的。参照另一个人的tonemapping函数设计方法，我在检测到全黑crop时直接将re-expose系数设置为1。

修改后函数如下：

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class TonemapHDR(object):
    """
        Tonemap HDR image globally. First, we find alpha that maps the (max(numpy_img) * percentile) to max_mapping.
        Then, we calculate I_out = alpha * I_in ^ (1/gamma)
        input : nd.array batch of images : [H, W, C]
        output : nd.array batch of images : [H, W, C]
    """

    def __init__(self, gamma=2.4, percentile=50, max_mapping=0.5):
        self.gamma = gamma
        self.percentile = percentile
        self.max_mapping = max_mapping  # the value to which alpha will map the (max(numpy_img) * percentile) to

    def __call__(self, numpy_img, clip=True, alpha=None, gamma=True):
        if gamma:
            power_numpy_img = np.power(numpy_img, 1 / self.gamma)
        else:
            power_numpy_img = numpy_img
        non_zero = power_numpy_img > 0
        if non_zero.any():
            r_percentile = np.percentile(power_numpy_img[non_zero], self.percentile)
        else:
            # r_percentile = np.percentile(power_numpy_img, self.percentile)
            # https://github.com/weberhen/Illumination-Estimation-1/blob/9bd3bc4533960afd72b3496ff8601f03b6e799f6/gen_hdr_crops.py#L8
            # 全零输入，直接不变动
            alpha = 1
            
        if alpha is None:
            alpha = self.max_mapping / (r_percentile + 1e-10)
        tonemapped_img = np.multiply(alpha, power_numpy_img)

        if clip:
            tonemapped_img = np.clip(tonemapped_img, 0, 1)

        return tonemapped_img.astype('float32'), alpha

这种修改应该能禁止全黑crop干扰emlight的学习了。为了看看我这次改动ban掉了多少坏crops，我专门写了点代码遍历了一遍数据集，搜寻这种全黑crop。结果只发现了之前发现的那一张。所以说，就一张坏图片，对emlight的影响应该没那么大？

接着我想看看有多少crops的ahpla是大于100或小于0.01的。这种图我觉得算是太亮或者太暗的图。结果一张都没有。数据集还是不错的。

然后缩小范围，用20个线程分布式地检查整个数据集的不同区间，搜一下有多少crops的ahpla是大于50或小于0.02的。下面列出了输出结果，先是被查出的图片名称及其ahpla系数，接着是20个线程各自查出多少张（可以看出“坏数据”在整个数据集上的分布），最后还统计了总的张数。

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AG8A0251-others-240-2    63.29404408421544
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9C4A8673-others-240-2    50.71437620814117
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AG8A1806-others-280-1 AG8A9830-others-00-1    51.21602499759086
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63.6466598767675
MyThread17 :11
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MyThread7AG8A1932-others-240-1    66.94180564168063 :17

MyThread13 :13
MyThread18 :9
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9C4A8673-others-280-2 AG8A0293-others-240-1    61.94711944112786
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MyThread11 :10
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MyThread19 :11
MyThread9 :13
MyThread14 :15
(emlight) jixinlong@ubun:~/Illumination-Estimation-1/RegressionNetwork/representation$ ls tmp|wc -l
191

有191张，且绝大部分是太暗的，需要用50~70的系数重曝光。坏数据在数据集上的分布基本均匀。