From ef5a0a9f9f621e0550dc05ebddbae3c3eac8f352 Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Sat, 18 Jan 2020 19:47:54 +0530 Subject: Publish deploy 2020-01-18 19:47 --- .../index.html | 30 ++++++++++++++++------ 1 file changed, 22 insertions(+), 8 deletions(-) (limited to 'posts/2019-12-10-TensorFlow-Model-Prediction') diff --git a/posts/2019-12-10-TensorFlow-Model-Prediction/index.html b/posts/2019-12-10-TensorFlow-Model-Prediction/index.html index aa51948..24da573 100644 --- a/posts/2019-12-10-TensorFlow-Model-Prediction/index.html +++ b/posts/2019-12-10-TensorFlow-Model-Prediction/index.html @@ -1,9 +1,23 @@ -Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan
Navan Chauhan
🕑 1 minute read.

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))])
+Making Predictions using Image Classifier (TensorFlow) | Navan Chauhan
Navan Chauhan
🕑 1 minute read.
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
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