Automated image quality with cleanvision

What aspects are automated?

dark, light, odd_aspect_ratio, low_information, exact_duplicates, near_duplicates, blurry, grayscale images 

	#https://github.com/cleanlab/cleanvision/
	!pip install cleanvision
	from google.colab import drive
	drive.mount('/content/drive')
	#dark, light, odd_aspect_ratio, low_information, exact_duplicates, near_duplicates, blurry, grayscale images
	from cleanvision.imagelab import Imagelab
	# Specify path to folder containing the image files in your dataset
	imagelab = Imagelab(data_path="/content/drive/MyDrive/SampleImages/")
	# Automatically check for a predefined list of issues within your dataset
	imagelab.find_issues()
	# Produce a neat report of the issues found in your dataset
	imagelab.report()
	#https://github.com/cleanlab/cleanvision/blob/main/examples/run.py
	imagelab.save("./results")
	#https://github.com/cleanlab/cleanvision-examples/blob/main/tutorial.ipynb
	imagelab.issue_summary
	imagelab.issues.head()
	type(imagelab.issues)
	imagelab.issues
	#blurry_images = imagelab.issues[imagelab.issues["is_blurry_issue"] == True].sort_values(by=['blurry_score'])
	#blurry_image_files = blurry_images.index.tolist()
	imagelab.info.keys()
	imagelab.info['statistics'].keys()
	imagelab.info['statistics']['entropy']
	imagelab.info['exact_duplicates']['num_sets']

view raw cleanvisionexample.py hosted with ❤ by GitHub

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