Question

My task is cephalometric landmark localization. I have images path with coordinates show in this data frame.

filename	X1	Y1
/Images_data/binary0006.png	89	80
/Images_data/binary0008.png	37	70
/Images_data/binary0007.png	50	76
/Images_data/binary0003.png	55	92
/Images_data/binary0005.png	91	64
/Images_data/binary0004.png	100	76

How to prepare dataset for training in model.fit ? I try to create images dataset for training with ImageDataGenerator.

  train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  label_mode=None,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

但现在我 st,因为我不知道如何与图像相匹配。

Answer 1

使用<代码>流程_从_dataframe方法中获取。

Load your DataFrame containing the file paths and coordinates.

import pandas as pd
data_dir = "images/"
df = pd.read_csv("dataframe.csv")
df = df.sample(frac=1, random_state=9)
print(df)

Create ImageDataGenerator

from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(
    rescale=1./255,
)

Flow from DataFrame

from tensorflow.keras.preprocessing.image import ImageDataGenerator
img_height, img_width = 100, 100
batch_size = 32
train_generator = datagen.flow_from_dataframe(
    dataframe=df,
    directory=data_dir,
    x_col="x_col",
    y_col=["X1", "Y1"],
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode="raw",
    shuffle=True,
    seed=9,
    subset="training"
)

此处,x_col被定为“x_col”,以具体说明含有文档途径的栏目, y_col被定为[“X1”、“Y1”],以具体说明含有坐标的栏目。等级为“raw”,以表明标签是连续的价值观。

Now, you can use train_generator in the model.fit function:

model.fit(train_generator, epochs=num_epochs, ...)

友情链接