ct_array_min = np.percentile(ct_array, 0.1)
时间: 2023-10-24 11:07:20 浏览: 40
这行代码是用来计算 CT 图像数组 ct_array 中的最小值的 0.1 分位数(即排在最小值的前 0.1% 的数值)。这个值可以用来作为 CT 图像的阈值,用于将图像分割成不同的区域。比如可以将所有小于该值的像素点设为黑色(背景),将大于等于该值的像素点设为白色(前景),从而实现对 CT 图像的二值化处理。
相关问题
sp = data_train.target data_train.target1 =np.power(1.5,sp) print(data_train.target1.describe())
This code calculates the target1 variable for the data_train dataset, which is the target variable (sp) raised to the power of 1.5. Then, it uses the describe() method to get descriptive statistics of the target1 variable.
The describe() method provides information such as the count, mean, standard deviation, minimum value, 25th percentile, median (50th percentile), 75th percentile, and maximum value of the target1 variable.
It is important to note that the interpretation of these descriptive statistics depends on the specific dataset and problem being analyzed.
attention_map_normalized_opt = tf.zeros_like(attention_map) for i in range(attention_map.shape[3]): channel = attention_map[:, :, :, i] # channel = np.reshape(attention_map[:, :, i],14,14) # for j in range(attention_map.shape[0]): #14*14 # max_value = np.max(channel) threshold = tf.contrib.distributions.percentile(channel,60)# yu zhi wei 60%,she qu xia yu 60%de bu fen threshold1 = np.full((14,14),threshold) print(channel,type(channel)) print(threshold1,type(threshold1)) normalized_channel = tf.where(channel >= threshold1, channel, 0) attention_map_normalized_opt = tf.concat([attention_map_normalized_opt[:, :, :i], tf.expend_dims(normalized_channel, axis=-1), attention_map_normalized_opt[:, :, i+1:]], axis=-1)有哪些错误,帮我改过来
在这段代码中,有几个错误:
1. `tf.expend_dims` 应该是 `tf.expand_dims`,这是一个打错字的错误。
2. `tf.concat` 中的 `tf.expend_dims(normalized_channel, axis=-1)` 应该修改为 `tf.expand_dims(normalized_channel, axis=-1)`。
3. `threshold1` 应该被转换为 `tf.constant`,以与 `channel` 和其他张量的类型匹配。
4. `attention_map_normalized_opt = tf.concat([attention_map_normalized_opt[:, :, :i], tf.expend_dims(normalized_channel, axis=-1), attention_map_normalized_opt[:, :, i+1:]], axis=-1)` 中的 `tf.expend_dims` 应该改为 `tf.expand_dims`。
下面是修改后的代码:
```
attention_map_normalized_opt = tf.zeros_like(attention_map)
for i in range(attention_map.shape[3]):
channel = attention_map[:, :, :, i]
threshold = tf.contrib.distributions.percentile(channel, 60)
threshold1 = tf.constant(np.full((14, 14), threshold))
normalized_channel = tf.where(channel >= threshold1, channel, 0)
attention_map_normalized_opt = tf.concat([attention_map_normalized_opt[:, :, :i], tf.expand_dims(normalized_channel, axis=-1), attention_map_normalized_opt[:, :, i+1:]], axis=-1)
```
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