<jupyter_start><jupyter_text>Images as Numpy Array (H, W, 3)<jupyter_code>classes = ['vegetable', 'biscuit', 'fruit']
foods = [cv2.imread('./foods/'+f) for f in foodfns]
targets = [0] * len(foodfns)

foods[0].shape
w2 = Innotater(
        ImageInnotation(foods, name='Food'), 
        MultiClassInnotation(targets, name='FoodType', classes=classes, desc='Food Type')
)
display(w2)<jupyter_output><empty_output><jupyter_text>Greyscale Images as Numpy array (H,W)<jupyter_code># Flatten to greyscale
foods_flat = [f.mean(axis=2).astype('int') for f in foods]
foods_flat[0].shape
foods[0].max()
w2f = Innotater(
        ImageInnotation(foods_flat, name='Food'), 
        MultiClassInnotation(targets, name='FoodType', classes=classes, desc='Food Type')
)
display(w2f)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15869017632241814, 'fraction_numerical': 0.013853904282115869, 'mean_word_length': 6.293577981651376, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '152004', 'license': 'MIT', 'n_tokens_mistral': 317, 'n_tokens_neox': 292, 'n_words': 56, 'path': 'Example/FlatImages.ipynb', 'repo_name': 'ideonate/innotater', 'size': '794'}