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from tensorflow import keras
from PIL import Image, ImageOps
import numpy as np
import io, os
import logging
import keras_metrics
from tensorflow import keras
import utils
## Configs
keras.utils.get_custom_objects()['recall'] = utils.recall
keras.utils.get_custom_objects()['precision'] = utils.precision
keras.utils.get_custom_objects()['f1'] = utils.f1
def teachable_machine_classification(img=None, model=None):
"""Performs inference on image uploaded"""
# Create the array of the right shape to feed into the keras model
data = np.ndarray(shape=(1, 299, 299, 3), dtype=np.float32)
image = img
# image sizing
size = (299, 299)
image = ImageOps.fit(image, size, Image.ANTIALIAS)
# turn the image into a numpy array
image_array = np.asarray(image)
# Normalize the image
normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
# Load the image into the array
data[0] = normalized_image_array
# run the inference
prediction = model.predict(data)
print("Prediction", prediction)
return prediction[0][
1
] # np.argmax(prediction) # return position of the highest probability
def load_model(weights_file=None):
"""Loads trained keras model"""
dependencies = {
"binary_f1_score": keras_metrics.binary_f1_score,
"binary_precision": keras_metrics.binary_precision,
"binary_recall": keras_metrics.binary_recall,
}
try:
assert os.path.exists(weights_file), f"File '{weights_file}' does not exist"
# Load the model
model = keras.models.load_model(
weights_file, custom_objects=dependencies, compile=False
)
return model
except Exception as e:
logging.error("ERROR: ", e)
print("ERROR: ", e, " Failed to load ML model")
return None
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