1 minute readCreated on December 10, 2019Last modified on January 18, 2020

Making Predictions using Image Classifier (TensorFlow)

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

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

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

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

import cv2
+import os
+
+
+

Then we read the file using OpenCV.

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.

image_from_array = Image.fromarray(image, 'RGB')
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+
+

Then we resize the image

size_image = image_from_array.resize((50,50))
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+
+

After this we create a batch consisting of only one image

p = np.expand_dims(size_image, 0)
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+
+

We then convert this uint8 datatype to a float32 datatype

img = tf.cast(p, tf.float32)
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+
+

Finally we make the prediction

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

Infected

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Making Predictions using Image Classifier (TensorFlow)

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

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

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

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

import cv2 +import os + + +

Then we read the file using OpenCV.

image=cv2.imread(imagePath) + + +

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

image_from_array = Image.fromarray(image, 'RGB') + + +

Then we resize the image

size_image = image_from_array.resize((50,50)) + + +

After this we create a batch consisting of only one image

p = np.expand_dims(size_image, 0) + + +

We then convert this uint8 datatype to a float32 datatype

img = tf.cast(p, tf.float32) + + +

Finally we make the prediction

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

Infected