Update durhamtrees.py

durhamtrees.py

@@ -34,7 +34,7 @@ _URLS = {
     "first_domain1": {
         "csv_file": "https://drive.google.com/uc?export=download&id=18HmgMbtbntWsvAySoZr4nV1KNu-i7GCy",
         "geojson_file": "https://drive.google.com/uc?export=download&id=1cbn7EY7RofXN7c6Ph0eIGFIZowPZ5lKE",
-        "parquet_file": "https://drive.google.com/uc?export=download&id=1RNDLJLoSSV9RJptVyfWFhPra0nh-i_CN",
+        
     },
     "first_domain2": {
         "csv_file2": "https://drive.google.com/uc?export=download&id=1RVdaI5dSTPStjhOHO40ypDv2cAQZpi_Y",
@@ -63,8 +63,6 @@ class DurhamTrees(GeneratorBasedBuilder):
                 "OBJECTID": Value("int64"),
                 "X": Value("float64"),
                 "Y": Value("float64"),
-                "image": Value("binary"),
-                "label": Value("int64"),
                 "feature1_from_class2": Value("string"),
                 "streetaddress": Value("string"),
                 "city": Value("string"),
@@ -139,7 +137,6 @@ class DurhamTrees(GeneratorBasedBuilder):
                       "class1_data_file": downloaded_files["first_domain1"]["csv_file"],
                       "class1_geojson_file": downloaded_files["first_domain1"]["geojson_file"],
                       "class2_data_file": downloaded_files["first_domain2"]["csv_file2"],
-                      "parquet_file": downloaded_files["first_domain1"]["parquet_file"],
                       "split": Split.TRAIN,
                   },
               ),
@@ -152,9 +149,6 @@ class DurhamTrees(GeneratorBasedBuilder):
         class1_examples = list(self._generate_examples_from_class1(class1_data_file, class1_geojson_file))
         class2_examples = list(self._generate_examples_from_class2(class2_data_file))
 
-        # Load Parquet file
-        parquet_data = pq.read_table(parquet_file).to_pandas()
-        class1_examples += list(self._generate_examples_from_parquet(parquet_data))
 
         examples = class1_examples + class2_examples
         df = pd.DataFrame(examples)
@@ -209,25 +203,6 @@ class DurhamTrees(GeneratorBasedBuilder):
             yield id_, example
 
 
-    def _generate_examples_from_parquet(self, parquet_data):
-        for id_, row in parquet_data.iterrows():
-            # Check if the "image" column is present and not empty
-            if "image" in row and "bytes" in row["image"]:
-                # Decode the base64-encoded image bytes
-                image_data = base64.b64decode(row["image"]["bytes"])
-                example = {"image": image_data, "label": row["label"]}
-
-                # Display the image
-                image_bytes = example.get('image', None)
-                if image_bytes:
-                    img = mpimg.imread(io.BytesIO(image_bytes), format='PNG')  # Use 'PNG' instead of 'JPG'
-                    plt.imshow(img)
-                    plt.show()
-
-                yield id_, example
-            else:
-                print(f"Skipping example {id_} as it has missing or invalid image data")
-
 
 
     def _correlation_analysis(self, df):
@@ -250,14 +225,6 @@ durham_trees_dataset.download_and_prepare()
 # Access the dataset
 dataset = durham_trees_dataset.as_dataset()
 
-# Iterate through the dataset and display images
-for example in dataset['train']:
-    if "image" in example and example["image"] is not None and "bytes" in example["image"]:
-        # Display the image
-        image_data = base64.b64decode(example["image"]["bytes"])
-        img = mpimg.imread(io.BytesIO(image_data), format='PNG')
-        plt.imshow(img)
-        plt.show()
 
 # Create an instance of the DurhamTrees class for another configuration
 durham_trees_dataset_another = DurhamTrees(name='class2_domain1')
@@ -267,12 +234,3 @@ durham_trees_dataset_another.download_and_prepare()
 
 # Access the dataset for the new instance
 dataset_another = durham_trees_dataset_another.as_dataset()
-
-# Iterate through the dataset for the new instance and display images
-for example in dataset_another['train']:
-    if "image" in example and example["image"] is not None and "bytes" in example["image"]:
-        # Display the image
-        image_data = base64.b64decode(example["image"]["bytes"])
-        img = mpimg.imread(io.BytesIO(image_data), format='PNG')
-        plt.imshow(img)
-        plt.show()