<jupyter_start><jupyter_text>Bonus!<jupyter_code># Extract mutiple plate license in one image 

multiple_plates_image = "Plate_examples/multiple_plates.png"
LpImg,cor = get_plate(multiple_plates_image)

print("Detect %i plate(s) in"%len(LpImg),splitext(basename(multiple_plates_image))[0])

# Visualize the original image
plt.figure(figsize=(10,5))
plt.axis(False)
plt.imshow(preprocess_image(multiple_plates_image))

# Visualize the obtained plates
plt.figure(figsize=(10,5))
plt.subplot(1,2,1)
plt.axis(False)
plt.imshow(LpImg[1])
plt.subplot(1,2,2)
plt.axis(False)
plt.imshow(LpImg[0])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1423948220064725, 'fraction_numerical': 0.024271844660194174, 'mean_word_length': 10.254545454545454, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '314589', 'license': 'MIT', 'n_tokens_mistral': 268, 'n_tokens_neox': 246, 'n_words': 34, 'path': '[Part 1]License_plate_detection.ipynb', 'repo_name': 'kylenannig/Plate_detect_and_recognize', 'size': '618'}