Add helper scripts

convert_images.py

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+import subprocess
+import os
+from pathlib import Path
+from PIL import Image
+import numpy as np
+
+input_folder = Path("./original_images/")
+output_folder = Path("./data/train/")
+output_folder.mkdir(parents=True, exist_ok=True)
+folders = os.listdir(input_folder)
+for folder in folders:
+    folder_path = input_folder.joinpath(folder)
+    images = os.listdir(folder_path)
+    for image in images:
+        output = output_folder.joinpath(f'{folder}')
+        output.mkdir(parents=True, exist_ok=True)
+
+        output = output.joinpath(f'{folder}_{image}')
+        output = str(output.absolute())
+
+        input = folder_path.joinpath(image)
+        input = str(input.absolute())
+
+        if os.path.isfile(input) == False or '.json' in image:
+            continue
+
+        image_input = Image.open(input)
+
+        background_color = (23, 35, 35, 255)
+        new_color = (0, 0, 0, 255)
+        data = np.array(image_input)
+        data[(data == background_color).all(axis=-1)] = new_color
+        image_input = Image.fromarray(data, 'RGBA')
+
+        new_image = Image.new('RGB', image_input.size)
+        new_image.paste(image_input, mask=image_input.split()[3])
+
+        bbox = new_image.getbbox()
+        new_image = new_image.crop(bbox)
+        new_image.save(output)

create_dataset.py

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+from datasets import load_dataset
+
+dataset = load_dataset('.', data_dir='data', split='train')
+dataset.push_to_hub('frutiemax/rct_dataset')

create_json.py

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+import os
+import pandas
+from pathlib import Path
+import string
+import shutil
+
+# read all the folders in data/train
+data_folder = Path("./data/train")
+folders = os.listdir(data_folder)
+
+entries = []
+
+image_index = 0
+for folder in folders:
+    images_path = data_folder.joinpath(folder)
+    images = os.listdir(images_path)
+
+    view = 0
+    for image in images:
+        object_description = folder.replace('_', ' ')
+        entry = {'file_name' : image, 'object_type' : 'small_scenery', 'object_description' : object_description, 'view': view, 'color1' : 'none', 'color2' : 'none', 'color3' : 'none'}
+        entries.append(entry)
+        view = view + 1
+        
+#put the entries into the metadata.csv
+dataframe = pandas.DataFrame(entries)
+
+# dont overwrite the old metadata.csv
+if os.path.exists('metadata.csv'):
+    shutil.copyfile('metadata.csv', 'metadata_backup.csv')
+dataframe.to_csv('metadata.csv')