Loading script update

Signed-off-by: Jiri Podivin <[email protected]>

plantorgans.py

@@ -1,6 +1,7 @@
 import datasets
-import os
-import json
+import pandas as pd
+import glob
+from pathlib import Path
 
 _DESCRIPTION = """Photos of various plants with their major, above ground organs labeled. Includes labels for stem, leafs, fruits and flowers."""
 
@@ -20,9 +21,11 @@ _NAMES = [
 _BASE_URL = "https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/"
 _TRAIN_URLS = [_BASE_URL + f"sourcedata_labeled.tar.{i:02}" for i in range(0, 8)]
 _TEST_URLS = [_BASE_URL + f"sourcedata_labeled.tar.{i:02}" for i in range(8, 12)]
+_MASKS_URLS = [_BASE_URL + f"masks.tar.0{i}" for i in range(0, 2)]
+
 _METADATA_URLS = {
-    'train': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/labels_train.csv',
-    'test': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/labels_test.csv'
+    'train': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/metadata_train.csv',
+    'test': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/metadata_test.csv'
 }
 
 
@@ -61,7 +64,7 @@ class PlantOrgans(datasets.GeneratorBasedBuilder):
         features=datasets.Features(
             {
                 "image": datasets.Image(),
-                "annotation": datasets.ClassLabel(names=_NAMES),
+                "mask": datasets.Image(),
             }
         ),
         supervised_keys=("image", "annotation"),
@@ -72,29 +75,68 @@ class PlantOrgans(datasets.GeneratorBasedBuilder):
 
 
     def _split_generators(self, dl_manager):
-        train_archive_path = dl_manager.download_and_extract(_TRAIN_URLS)
-        test_archive_path = dl_manager.download_and_extract(_TEST_URLS)
-
+        
+        train_archives_paths = dl_manager.download_and_extract(_TRAIN_URLS)
+        test_archives_paths = dl_manager.download_and_extract(_TEST_URLS)
+      
+        train_paths = []
+        test_paths = []
+        
+        for p in train_archives_paths:
+            train_paths.extend(glob.glob(str(p)+'/sourcedata/labeled/**.jpg'))
+        for p in test_archives_paths:
+            test_paths.extend(glob.glob(str(p)+'/sourcedata/labeled/**.jpg'))
         split_metadata_paths = dl_manager.download(_METADATA_URLS)
+
+        mask_archives_paths = dl_manager.download_and_extract(_MASKS_URLS)
+            
+        mask_paths = []
+        for p in mask_archives_paths:
+            mask_paths.extend(glob.glob(str(p)+'/masks/**.png'))
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(os.path.join(train_archive_path, 'sourcedata/labeled')),
+                    "images": train_paths,
                     "metadata_path": split_metadata_paths["train"],
+                    "masks_path": mask_paths,
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "images": dl_manager.iter_archive(os.path.join(test_archive_path, 'sourcedata/labeled')),
+                    "images": test_paths,
                     "metadata_path": split_metadata_paths["test"],
+                    "masks_path": mask_paths,
                 },
             ),
         ]
-    def _generate_examples(self, images, metadata_path):
+
+
+    def _generate_examples(self, images, metadata_path, masks_path):
+        """
+        images: path to image directory
+        metadata_path: path to metadata csv
+        """
+
+        # Get local image paths
+        image_paths = pd.DataFrame(
+            [(str(Path(*Path(e).parts[-3:])), e) for e in images], columns=['image', 'image_path'])
+
+        # Get local mask paths
+        masks_paths = pd.DataFrame(
+            [(str(Path(*Path(e).parts[-2:])), e) for e in masks_path], columns=['mask', 'mask_path'])
         
-        with open(metadata_path, 'w', encoding='utf-8') as fp:
-            metadata = json.load(fp)
-            images = metadata['image']
-            annotations = metadata['annotations']
+        # Get all common about images and masks from csv
+        metadata = pd.read_csv(metadata_path)
+
+        # Merge dataframes
+        metadata = metadata.merge(masks_paths, on='mask', how='inner')
+        metadata = metadata.merge(image_paths, on='image', how='inner')
+
+        # Make examples and yield
+        for i, r in metadata.iterrows():
+            
+            # Each example must contain path to image and list of annotations under object key
+            yield i, {'mask': r['mask_path'], 'image': r['image_path']}