Wolfram Image | Things to Try

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Image Processing, Analysis & Computation. For beginners or experts, Wolfram Image provides a comprehensive and efficient set of image processing, recognition and analysis functions, tightly integrated with highly automated machine learning, statistics, visualization and more.

Import, Process and Export an Image

Import an image file:
Run
In[]:=
flower=Import["ExampleData/coneflower.jpg"]
Apply an image effect:
Run
sepiaflower=ImageEffect[flower,"Sepia"]
Export the result in the format of your choice:
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Export["flowerexample.png",sepiaflower]

Enhance and Restore Images

Sharpen an image:
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In[]:=
Sharpen

Automatically enhance contrast, brightness and gamma of an image:
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ImageAdjust

Filter an image to remove noise:
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MedianFilter
,1

Color Processing and Image Effects

Create a new version of a product image by changing colors:
Run
ImageRecolor
,ColorsNear
,0.1
,ColorsNear
,0.02
,_

Simulate natural lighting by automatically balancing color:
Run
ColorBalance

Restyle one image with the graphical style of a second:
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ImageRestyle
,


Align and Stitch Images

Combine overlapping images into a coherent whole:
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ImageStitch
,
,

Align a collection of misaligned images with different focus:
Run
aligned=ImageAlign
,
,
,
;
Make the boundaries uniform with padding, and then combine the aligned images into a well-focused image:
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ImageFocusCombine[ImagePad[#,-9]&/@aligned]

Perform Object Detection

Use pre-trained networks to identify objects in an image:
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In[]:=
ImageContents

Highlight detected objects:
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HighlightImage
,ImageBoundingBoxes

Classify Images and Image Features

Classify the subject of an image:
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In[]:=
ImageIdentify
,
,

Identify facial features such as emotion:
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FacialFeatures
,
,
,"Emotion"

Run a Neural Network with Images

Import a specific, pre-trained model:
Run
In[]:=
net=NetModel["ResNet-101 Trained on YFCC100m Geotagged Data"]
Apply the model on images of famous landmarks to identify the geolocation of images:
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In[]:=
locations=net
,
,

Plot the resulting positions:
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GeoGraphics[GeoMarker[locations],GeoRangePadding->Scaled[0.1]]

Perform Segmentation and Segment Analysis

Create a binary image showing connected components. In this case, an upper threshold for detection is specified:
Run
MorphologicalBinarize
,.9
Colorize each component:
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ColorizeMorphologicalComponents
,.9,Method->"Nested"
Import and use a semantic segmentation network from the Wolfram Neural Net Repository:
Run
cityNet=NetModel["Dilated ResNet-22 Trained on Cityscapes Data"]
Use the imported network to perform semantic segmentation and colorize the results:
Run
ColorizecityNet


Process 3D Images