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import tensorflow as tf | |
import numpy as np | |
from flowers_train import class_names | |
#Loader Parameters | |
batch_size = 32 | |
img_height = 180 | |
img_width = 180 | |
TF_MODEL_FILE_PATH = 'model.tflite' | |
def flower_classification(img): | |
interpreter = tf.lite.Interpreter(model_path = TF_MODEL_FILE_PATH) | |
#sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg" | |
#sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url) | |
img_array = tf.keras.utils.img_to_array(img) | |
img_array = tf.expand_dims(img_array, 0) | |
classify_lite = interpreter.get_signature_runner('serving_default') | |
predictions_lite = classify_lite(rescaling_1_input = img_array)['dense_1'] | |
score_lite = tf.nn.softmax(predictions_lite) | |
return_msg = "This image most likely belongs to {} with a {:.2f} percent confidence.".format(class_names[np.argmax(score_lite)], 100 * np.max(score_lite)) | |
return return_msg | |
interpreter = tf.lite.Interpreter(model_path = TF_MODEL_FILE_PATH) | |
sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg" | |
sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url) | |
sunflower_img = tf.keras.utils.load_img( | |
sunflower_path, target_size=(img_height, img_width) | |
) | |
img_array = tf.keras.utils.img_to_array(sunflower_img) | |
img_array = tf.expand_dims(img_array, 0) | |
print(interpreter.get_signature_list()) | |
classify_lite = interpreter.get_signature_runner('serving_default') | |
predictions_lite = classify_lite(rescaling_1_input = img_array)['dense_1'] | |
score_lite = tf.nn.softmax(predictions_lite) | |
print( | |
"This image most likely belongs to {} with a {:.2f} percent confidence." | |
.format(class_names[np.argmax(score_lite)], 100 * np.max(score_lite)) | |
) |