https://github.com/nikitbobba/lens-smear-detection

An application that can detect a smear/dirt on camera lens given a set of images taken by the camera.
https://github.com/nikitbobba/lens-smear-detection

Last synced: 8 days ago
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An application that can detect a smear/dirt on camera lens given a set of images taken by the camera.

• Host: GitHub
• URL: https://github.com/nikitbobba/lens-smear-detection
• Owner: nikitbobba
• Created: 2018-04-23T21:53:06.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-04-26T03:45:13.000Z (over 6 years ago)
• Last Synced: 2024-08-02T15:33:45.836Z (3 months ago)
• Language: Python
• Homepage:
• Size: 9.18 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Lens Smear Detection

An application that will detect any smear on camera lens given a set of images taken by the camera.

## Setup

In order to run this script, you must have `Python 2.7` installed. Additionally, you will need to have Open CV and Numpy installed as well. 

We recommend creating a `conda` environment and installing these two required packages.

## Running the project

In order to run our project, a directory must be specified that locates the image sets. Open smear-detection.py. At the top of the script, there is a variable called `path`. Enter the path to your directory here. The directory should specify the folder that contains all the subfolders with the images from each camera.

Example:

sample_drive contains 4 folders. Ensure that the `path` variable contains the path to the sample drive directory. The function 'start-detection' will then be able to automatically identify all the sub-directories in the path (i.e. cam_0, cam_1 etc) and create final images for each sub-directory.

```

_sample_drive

___cam_0

___cam_1

___cam_2

___cam_3

___cam_5

```

Once 'path' has been updated in smear-detection.py, you can run the script by calling the following command:

`python smear-detection.py`

## Final Results

After running smear-detection.py, you will receive 3 results for each camera:

1. cam_number_mean_image.jpg

2. cam_number_intermediate_mask.jpg

3. cam_number_final_mask.jpg

where `cam_number` is the name of the sub-directory