SuihongSong
commented
5 months ago Hi Siyu,
Yes, in this version of dataset, we deliberately sample well facies data to make sure they are not 100% consistent with the ground truth facies map, instead well facies data are sampled with a probability to be consistent with the ground truth meaning that some well facies are consistent while some are not. Such a setting is mainly because in this project, we will still have a channel probability map showing the possible location of channels and if the well facies data are just sampl
| Suihong Song | |
|---|---|
@. | ---- Replied Message ---- | From | @.> | | Date | 03/14/2024 00:53 | | To | SuihongSong/GeoModeling_GANSim-2D_Condition_to_Well_Facies_and_Global_Features @.> | | Cc | Subscribed @.> | | Subject | [SuihongSong/GeoModeling_GANSim-2D_Condition_to_Well_Facies_and_Global_Features] Well facies data does not match the actual facies model when reading the dataset (Issue #2) |
tensorflow2.1 python 3.8
Well facies data does not match the actual facies model when reading the dataset.But the facies model and global conditions match. Here is my code
from tensorflow import keras import tensorflow as tf import os import numpy as np import matplotlib.pyplot as plt
定义 TFRecord 文件路径TestData
tfrecord_path = 'DataSets/TrainingData/TrainingData-1r06.tfrecords' # 分辨率最大64*64 wellfacies_path = 'DataSets/TrainingData/TrainingData-3wellfacies.tfrecords' label_path = 'DataSets/TrainingData/TrainingData-4rxx.labels'
labels = np.load(label_path) label_type = [1,2,3]
标签数据集处理函数
def load_labels(label):
比例,河道宽度,蜿蜒度
# return labels[label_type]
return tf.gather(label,label_type)def parse_function(record): feature_description = { 'shape': tf.io.FixedLenFeature([3], tf.int64), 'data': tf.io.FixedLenFeature([], tf.string), } features = tf.io.parse_single_example(record, feature_description)
# 获取'shape'特征并转换为NumPy数组
# shape_array = np.array(features['shape'])
# print("Shape of 'shape' feature:", shape_array)
data = tf.io.decode_raw(features['data'], tf.uint8)
data = tf.reshape(data, features['shape'])
data = tf.transpose(data, perm=[1,2,0])
return dataimg64_dataset = tf.data.TFRecordDataset(tfrecord_path).map(parse_function) wellfacies_dataset = tf.data.TFRecordDataset(wellfacies_path).map(parse_function) label_dataset = tf.data.Dataset.from_tensor_slices(labels).map(load_labels)
合并数据集
dataset = tf.data.Dataset.zip((img64_dataset, wellfacies_dataset, label_dataset))
def process_labels(labels):
计算方位角
# labels[:,0] = (labels[:,0]/2+0.5)*168-84
# 背景相比例
labels[0] = (labels[0]/2+0.5)*0.8037109375+0.167724609375
# 河道宽度
labels[1] = (labels[1]/2+0.5)*0.8+2.7
# amplitud/wavelength
labels[2] = (labels[2]/2+0.5)*0.4866197183098592+0.06338028169014084
return labels打印示例
num_columns = 2 # 设置子图的列数 fig, axes = plt.subplots(2, num_columns, figsize=(10, 10)) # 创建子图
for i, (image, wellfacie, label) in enumerate(dataset.take(num_columns)): label = label.numpy()
print(label.shape)
label = process_labels(label)
print(f"Image shape: {image.shape}, Label: {label}")
# 在子图中显示图像
axes[0,i].imshow(image.numpy())
axes[0,i].axis('on') # 可以选择是否显示坐标轴
axes[1,i].imshow(wellfacie.numpy())
axes[1,i].axis('on')调整为紧凑布局
fig.tight_layout() plt.show()
Here's the output graph output.png (view on web)
Almost the eighth row of the second chart does not have a river, but it does show a river. May I ask if there is something I am doing incorrectly
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SiyuLuoX
tensorflow2.1 python 3.8
Well facies data does not match the actual facies model when reading the dataset.But the facies model and global conditions match. Here is my code
Here's the output graph
Almost the eighth row of the second chart does not have a river, but it does show a river. May I ask if there is something I am doing incorrectly