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| import tensorflow as tf | |
| import numpy as np | |
| import cv2 | |
| def outputs(y): | |
| return { | |
| "Achaemenid architecture": y[0], | |
| "American craftsman style": y[1], | |
| "American Foursquare architecture": y[2], | |
| "Ancient Egyptian architecture": y[3], | |
| "Art Deco architecture": y[4], | |
| "Art Nouveau architecture": y[5], | |
| "Baroque architecture": y[6], | |
| "Bauhaus architecture": y[7], | |
| "Beaux Arts architecture": y[8], | |
| "Byzantine architecture": y[9], | |
| "Chicago school_architecture": y[10], | |
| "Colonial architecture": y[11], | |
| "Deconstructivism": y[12], | |
| "Edwardian architecture": y[13], | |
| "Georgian architecture": y[14], | |
| "Gothic architecture": y[15], | |
| "Greek Revival architecture": y[16], | |
| "International style": y[17], | |
| "Novelty architecture": y[18], | |
| "Palladian architecture": y[19], | |
| "Postmodern architecture": y[20], | |
| "Queen Anne architecture": y[21], | |
| "Romanesque architecture": y[22], | |
| "Russian Revival_architecture": y[23], | |
| "Tudor Revival architecture": y[24], | |
| } | |
| def efficientnetv2b0_25_arch_styles_Classifier(image): | |
| resized_image = cv2.resize(image, dsize=( | |
| 224, 224), interpolation=cv2.INTER_CUBIC) | |
| img = tf.expand_dims(resized_image, 0) | |
| efficientnetv2b0 = tf.keras.models.load_model( | |
| "EfficientNetV2B0.h5") | |
| y = efficientnetv2b0.predict(img).reshape(-1) | |
| y = (np.round(y, 3)).tolist() | |
| return outputs(y) | |