MarsImagesAnalysis

Python Dependencies: numpy, keras, tensorflow, pil, matplotlib

• Objective 1: Find subjective quality for images in the qualitydata folder
• Objective 2: Classify images in the classdata folder
• Objective 3: 3D projections for images in the 2D folder

Run the following command:

• > python EvaluateQuality.py

Once the code executed you will see the following menu:

------------------------------ MENU ------------------------------

1. Option 1: Image aesthetic quality
2. Option 2: Image classification
3. Option 3: 2D to 3D approximate projection
4. Exit

For demonstration purposes, please select the number 1 to determine the quality of the images in your dataset:

Enter your choice [1-4]: 1

Menu 1 (Image aesthetic quality) has been selected Enter a threshold between 1 and 10. suggested - 5: 5

• Finding Image aesthetic quality ... ... with threshold: 5.0 ... ... 1/1
• [==============================] - 2s 2s/step 1/1
• [==============================] - 1s 1s/step 1/1
• :
• [==============================] - 1s 1s/step 1/1
• [==============================] - 1s 1s/step 1/1
• [==============================] - 1s 1s/step

Number of images above threshold 60 Number of images below threshold 10

Once the images with acceptable quality are identified, now it is the time to select option 2 which will separate the images automatically based on various environmental conditions:

------------------------------ MENU ------------------------------

1. Option 1: Image aesthetic quality
2. Option 2: Image classification
3. Option 3: 2D to 3D approximate projection
4. Exit

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Enter your choice [1-4]: 2

Menu 2 (Image classification) has been selected

 Classifying Images ...  ...

... ...

• Precision for Aeolian: 0.86               Recall for Aeolian: 0.72
• Precision for Dry: 1.00                     Recall for Dry: 0.88
• Precision for Glacial: 0.73                 Recall for Glacial: 0.78
• Precision for Volcanic: 0.73              Recall for Volcanic: 1.00

... ...
After classifying images into environmental conditions classes, the researcher could also convert these images into 3D images for better exploration by selecting option 3:

~~~ Finished writing to the file named imagesclassification.csv ~~~

------------------------------ MENU ------------------------------

1. Option 1 : Image aesthetic quality
2. Option 2 : Image classification
3. Option 3 : 2D to 3D approximate projection
4. Exit

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Enter your choice [1-4]: 3

Menu 3 (2D to 3D projection) has been selected

 Converting Images ...  ...

... ...

Please check the samples of D2 and 3D images in the corresponding folders.

------------------------------ MENU ------------------------------

1. Option 1: Image aesthetic quality
2. Option 2: Image classification
3. Option 3: 2D to 3D approximate projection
4. Exit

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Enter your choice [1-4]: 4 (to exit the demo)