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Updating models

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Sean
2024-10-26 12:20:02 -04:00
parent 4c05f955b3
commit f7011b5554
10 changed files with 88 additions and 298 deletions
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32
Models/model_host.py
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@@ -35,7 +35,9 @@
# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
import keras
from keras.preprocessing.image import ImageDataGenerator
#from keras.preprocessing.image import ImageDataGenerator
from keras.src.legacy.preprocessing.image import ImageDataGenerator
from flask import Flask, render_template, send_file, request
import numpy as np
import tensorflow
@@ -124,6 +126,24 @@ def predict_resnet50():
response=str(predictions)+'-->'+str(threshold_output)
return response
# New model updated in October 2024
@app.route('/predict_resnet50_20241024_270', methods=['GET','POST'])
def predict_resnet50_20241024_270():
print('/predict_resnet50_20241024_270')
test_image=request.get_data()
test_image = PIL.Image.open(io.BytesIO(test_image))
test_image = test_image.convert('L')
test_array=keras.preprocessing.image.img_to_array(test_image)
batch_test_array=np.array([test_array])
predictions=resnet50_20241024_270_model.predict(batch_test_array)
if type(predictions) == list:
average_prediction = sum(predictions)/len(predictions)
threshold_output = np.where(average_prediction > 0.5, 1, 0)
else :
threshold_output = np.where(predictions > 0.5, 1, 0)
response=str(predictions)+'-->'+str(threshold_output)
return response
# This version expects the image to be of the form (x,x,3).
@app.route('/predict_resnet50B', methods=['GET','POST'])
def predict_resnet50B():
@@ -175,15 +195,23 @@ def process_image():
if __name__ == '__main__':
resnet50_model_name='../Weights/resnet50.h5'
resnet50_model = keras.models.load_model(resnet50_model_name)
print('Loading {model_name}'.format(model_name=resnet50_model_name))
resnet50_model = keras.models.load_model(resnet50_model_name)
resnet50_20241024_270_model_name='../Weights/resnet50_20241024_270.h5.keras'
print('Loading {model_name}'.format(model_name=resnet50_20241024_270_model_name))
resnet50_20241024_270_model = keras.models.load_model(resnet50_20241024_270_model_name)
resnet50b_model_name='../Weights/resnet50B.h5'
print('Loading {model_name}'.format(model_name=resnet50b_model_name))
resnet50b_model = keras.models.load_model(resnet50b_model_name)
vgg16_model_name='../Weights/vggnet16.h5'
print('Loading {model_name}'.format(model_name=vgg16_model_name))
vgg16_model=keras.models.load_model(vgg16_model_name)
lenet_model_name='../Weights/lenet5.h5'
print('Loading {model_name}'.format(model_name=lenet_model_name))
lenet_model=keras.models.load_model(lenet_model_name)
port = int(os.environ.get('PORT',5000))