Update README.md

README.md

@@ -49,6 +49,8 @@ Image with caption:
 -->
 
 # Dataset Card for Fish-Visual Trait Analysis (Fish-Vista)
+* Note that the '**</Use this dataset>**' option will only load the CSV files. To download the entire dataset, including all images and segmentation annotations, refer to [Instructions for downloading dataset and images](https://huggingface.co/datasets/imageomics/fish-vista#instructions-for-downloading-dataset-and-images).
+* See [Example Code to Use the Segmentation Dataset])(https://huggingface.co/datasets/imageomics/fish-vista#example-code-to-use-the-segmentation-dataset)
 |![Figure 1](https://huggingface.co/datasets/imageomics/fish-vista/resolve/main/metadata/figures/FishVista.png)|
 |:--|
 |**Figure 1.** A schematic representation of the different tasks in Fish-Vista Dataset. |
@@ -73,6 +75,10 @@ mv AllImages Images
 
   * You should now have all the images in the *Images* directory
 
+* Install requirements.txt
+```bash
+pip install -r requirements.txt
+```
 * Run the following commands to download and process copyrighted images 
 ```bash
 python download_and_process_nd_images.py --save_dir Images
@@ -180,6 +186,47 @@ CSV Columns are as follows:
 
 For each task (or subset), the split is indicated by the CSV name (e.g., `classification_<split>.csv`). More information is provided in [Data Instances](#data-instances), above.
 
+## Example Code to Use the Segmentation Dataset
+
+We provide an example code to use the FV-1200 segmentation dataset for convenience of users. Please install *pillow*, *numpy*, *pandas* and *matplotlib* before trying the code:
+
+```python
+from PIL import Image
+import os 
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import json
+
+# Set the the fish_vista_repo_dir to the path of your cloned fish-vista HF repository. This code assumes you are running from within the fish-vista directory
+fish_vista_repo_dir = '.'
+
+# segmentation_masks/images contains the annotated segmentation maps for the traits. 
+# If image filename is <image_filename>.jpg, the corresponding annotation is contained in segmentation_masks/images/<image_filename>.png 
+seg_mask_path = os.path.join(fish_vista_repo_dir, 'segmentation_masks/images')
+
+# seg_id_trait_map.json maps the annotation id to the corresponding trait name.
+# For example, pixels annotated with 1 correspond to the trait: 'Head'
+id_trait_map_file = os.path.join(fish_vista_repo_dir, 'segmentation_masks/seg_id_trait_map.json')
+with open(id_trait_map_file, 'r') as f:
+    id_trait_map = json.load(f)
+
+# Read a segmentation csv file
+train_path = os.path.join(fish_vista_repo_dir, 'segmentation_train.csv')
+train_df = pd.read_csv(train_path)
+
+# Get image and segmentation mask of image at index 'idx'
+idx = 0
+img_filename = train_df.iloc[idx].filename
+img_mask_filename = os.path.splitext(img_filename)[0]+'.png'
+# Load and view the mask 
+img_mask = Image.open(os.path.join(seg_mask_path, img_mask_filename))
+plt.imshow(img_mask)
+
+# List the traits that are present in this image
+img_mask_arr = np.asarray(img_mask)
+print([id_trait_map[str(trait_id)] for trait_id in np.unique(img_mask_arr)])
+```
 
 ## Dataset Details