From 429c1862546a2cbda044f459865e6cee7d9aa314 Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Sun, 24 May 2020 18:57:49 +0530 Subject: Publish deploy 2020-05-24 18:57 --- .../index.html | 28 ++++++---------------- 1 file changed, 7 insertions(+), 21 deletions(-) (limited to 'posts/2019-12-10-TensorFlow-Model-Prediction/index.html') diff --git a/posts/2019-12-10-TensorFlow-Model-Prediction/index.html b/posts/2019-12-10-TensorFlow-Model-Prediction/index.html index ebd6f4a..da7cae5 100644 --- a/posts/2019-12-10-TensorFlow-Model-Prediction/index.html +++ b/posts/2019-12-10-TensorFlow-Model-Prediction/index.html @@ -1,23 +1,9 @@ Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan

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
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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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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')
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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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