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5dc4333 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | 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))
) |