Display a Selection of Images From Each Class in Jupyter

Python

In this example, a random two images will be displayed from each of the five classes. Each image is resized to 400x300 for displaying.

 1|  from IPython.display import Image, display
 2|  import pandas as pd
 3|  
 4|  PATH = 'data/raw/train_images/'
 5|  LABELS = [0,1,2,3,4]
 6|  IMAGES_PER_CLASS = 2
 7|  
 8|  #Contains image_id field which is the filename of the image
 9|  train = pd.read_csv('data/raw/train.csv')
10|  
11|  def show_images(row):
12|      display(Image(filename=PATH + row['image_id'],width=400,height=300))
13|  
14|  for label in LABELS:
15|      print('Label:',label)
16|      images = train[train['label']==label].sample(frac=1).iloc[0:IMAGES_PER_CLASS,:]
17|      images.apply(show_images, axis=1)
detro
Jupyter | IPython.display
Did you find this snippet useful?

Sign up for free to to add this to your code library

Remove Stop Words from Text in DataFrame Column
Python

Pandas | Nltk | Nlp

133
LightGBM Custom Loss Function
Python

LightGBM | Loss function

121
Tuning XGBoost Hyperparameters with Grid Search
Python

XGBoost | Tuning

117
How to Convert DataFrame Values Into Percentages
Python

Lambda | Percentages | Pipe

109
How to Scale Data Using Standard Scaler But Keep Column Names
Python

Scaler | Standard

79
LightGBM Hyperparameter Tuning with GridSearch
Python

Python | LightGBM | Hyperparameter tuning | Gridsearch

77
Calculating Root Mean Squared Error (RMSE) with Sklearn and Python
Python

Rmse

77
Dynamically Create Columns in Pandas Dataframe
Python

Pandas | Columns

61
How to Train a Catboost Classifier with GridSearch Hyperparameter Tuning
Python

Catboost | Hyperparameter tuning

60
Overwriting a Python Logging File Instead of Appending
Python

Logging

55