From dfc509b95ff03d0c9027ee74d31d7b171f867bf1 Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Wed, 26 May 2021 23:59:17 +0530 Subject: generated website --- .../2019-12-10-TensorFlow-Model-Prediction.html | 76 ++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 docs/posts/2019-12-10-TensorFlow-Model-Prediction.html (limited to 'docs/posts/2019-12-10-TensorFlow-Model-Prediction.html') diff --git a/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html b/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html new file mode 100644 index 0000000..46eb777 --- /dev/null +++ b/docs/posts/2019-12-10-TensorFlow-Model-Prediction.html @@ -0,0 +1,76 @@ + + + + + + + + + Hey - Post + + + + + +
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Making Predictions using Image Classifier (TensorFlow)

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This was tested on TF 2.x and works as of 2019-12-10

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If you want to understand how to make your own custom image classifier, please refer to my previous post.

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If you followed my last post, then you created a model which took an image of dimensions 50x50 as an input.

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First we import the following if we have not imported these before

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import cv2
+import os
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Then we read the file using OpenCV.

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image=cv2.imread(imagePath)
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The cv2. imread() function returns a NumPy array representing the image. Therefore, we need to convert it before we can use it.

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image_from_array = Image.fromarray(image, 'RGB')
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Then we resize the image

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size_image = image_from_array.resize((50,50))
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After this we create a batch consisting of only one image

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p = np.expand_dims(size_image, 0)
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We then convert this uint8 datatype to a float32 datatype

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img = tf.cast(p, tf.float32)
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Finally we make the prediction

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print(['Infected','Uninfected'][np.argmax(model.predict(img))])
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Infected

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