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Monitoring Lake Mead drought using the new Amazon SageMaker geospatial capabilities

AWS Machine Learning

png') filenames.sort() for filename in filenames: frames.append(imageio.imread(filename)) imageio.mimsave('lake_mead.gif', frames, duration=1) HTML(' ') We can also extract the lake’s boundaries and superimpose them over the satellite images to better visualize the changes in lake’s shoreline. resize(image, (1830, 1830), interpolation=cv2.INTER_LINEAR)

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Image augmentation pipeline for Amazon Lookout for Vision

AWS Machine Learning

png') noise=iaa.AdditiveGaussianNoise(10,40) input_noise=noise.augment_image(input_img) contrast=iaa.GammaContrast((0.5, png","s10-label-ref":"s3://pcbtest22/label/s10-label/annotations/consolidated-annotation/output/0_2022-09-08T18:01:51.334016.png","s10-label-ref-metadata":{"internal-color-map":{"0":{"class-name":"BACKGROUND","hex-color":"#ffffff","confidence":0},"1":{"class-name":"IC","hex-color":"#2ca02c","confidence":0},"2":{"class-name":"resistor_1","hex-color":"#1f77b

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Semantic segmentation data labeling and model training using Amazon SageMaker

AWS Machine Learning

Annotations are expected to be uncompressed PNG images. png | - image2.png png |- validation_annotation | - image3.png png | - image4.png png |- label_map | - train_label_map.json | - validation_label_map.json. The annotations are single-channel PNG images. png | -0_2022-02-10T17:41:04.530266.png