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Upload img_classification.py
Browse files- img_classification.py +125 -0
img_classification.py
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import tensorflow as tf
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import numpy as np
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import matplotlib.pyplot as plt
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import warnings
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warnings.filterwarnings("ignore")
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class_names = ['apple_pie',
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'baby_back_ribs',
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'baklava',
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'beef_carpaccio',
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'beef_tartare',
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'beet_salad',
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'beignets',
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'bibimbap',
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'bread_pudding',
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'breakfast_burrito',
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'bruschetta',
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'caesar_salad',
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'cannoli',
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'caprese_salad',
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'carrot_cake',
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'ceviche',
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'cheesecake',
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'cheese_plate',
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'chicken_curry',
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'chicken_quesadilla',
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'chicken_wings',
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'chocolate_cake',
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'chocolate_mousse',
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'churros',
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'clam_chowder',
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'club_sandwich',
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'crab_cakes',
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'creme_brulee',
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'croque_madame',
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'cup_cakes',
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'deviled_eggs',
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'donuts',
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'dumplings',
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'edamame',
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'eggs_benedict',
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'escargots',
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'falafel',
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'filet_mignon',
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'fish_and_chips',
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'foie_gras',
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'french_fries',
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'french_onion_soup',
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'french_toast',
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'fried_calamari',
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'fried_rice',
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'frozen_yogurt',
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'garlic_bread',
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'gnocchi',
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'greek_salad',
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'grilled_cheese_sandwich',
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'grilled_salmon',
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'guacamole',
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'gyoza',
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'hamburger',
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'hot_and_sour_soup',
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'hot_dog',
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'huevos_rancheros',
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'hummus',
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'ice_cream',
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'lasagna',
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'lobster_bisque',
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'lobster_roll_sandwich',
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'macaroni_and_cheese',
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'macarons',
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'miso_soup',
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'mussels',
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'nachos',
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'omelette',
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'onion_rings',
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'oysters',
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'pad_thai',
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'paella',
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'pancakes',
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'panna_cotta',
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'peking_duck',
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'pho',
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'pizza',
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'pork_chop',
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'poutine',
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'prime_rib',
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'pulled_pork_sandwich',
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'ramen',
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'ravioli',
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'red_velvet_cake',
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'risotto',
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'samosa',
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'sashimi',
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'scallops',
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'seaweed_salad',
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'shrimp_and_grits',
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'spaghetti_bolognese',
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'spaghetti_carbonara',
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'spring_rolls',
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'steak',
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'strawberry_shortcake',
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'sushi',
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'tacos',
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'takoyaki',
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'tiramisu',
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'tuna_tartare',
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'waffles']
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def load_and_prep_image(filename, img_shape=224, scale = True):
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img = tf.io.read_file(filename)
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img = tf.io.decode_image(img)
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img = tf.image.resize(img, [img_shape, img_shape])
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if scale:
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return img/255.
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else:
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return img
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model = tf.keras.models.load_model('converted_model.h5')
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def classify(img):
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pred_prob = model.predict(tf.expand_dims(img, axis=0))
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pred_class = class_names[pred_prob.argmax()]
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return pred_class
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