34 lines
1.0 KiB
Python
34 lines
1.0 KiB
Python
import tensorflow as tf
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import numpy as np
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import keras.models
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def serve_unet_model():
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TFLITE_MODEL = "../app/UNet_25_Crack.tflite"
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tflite_interpreter = tf.lite.Interpreter(model_path=TFLITE_MODEL)
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input_details = tflite_interpreter.get_input_details()
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output_details = tflite_interpreter.get_output_details()
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tflite_interpreter.allocate_tensors()
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height = input_details[0]['shape'][1]
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width = input_details[0]['shape'][2]
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return tflite_interpreter, height, width, input_details, output_details
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def serve_rcnn_model():
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detection_graph = tf.Graph()
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with detection_graph.as_default():
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od_graph_def = tf.compat.v1.GraphDef()
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with tf.compat.v1.gfile.GFile("../app/frozen_inference_graph.pb", 'rb') as fid:
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serialized_graph = fid.read()
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od_graph_def.ParseFromString(serialized_graph)
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tf.import_graph_def(od_graph_def, name='')
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return detection_graph
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def serve_bridge_model():
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mp = "../app/crack_model.h5"
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model = keras.models.load_model(mp)
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return model
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