Terjemahan disediakan oleh mesin penerjemah. Jika konten terjemahan yang diberikan bertentangan dengan versi bahasa Inggris aslinya, utamakan versi bahasa Inggris.
import boto3 import sagemaker import sagemaker_geospatial_map import json ## SageMaker Geospatial is currently only avaialable in US-WEST-2 session = boto3.Session(region_name='us-west-2') execution_role = sagemaker.get_execution_role() ## Creates a SageMaker Geospatial client instance geospatial_client = session.client(service_name="sagemaker-geospatial") # Creates a resusable Paginator for the list_raster_data_collections API operation paginator = geospatial_client.get_paginator("list_raster_data_collections") # Create a PageIterator from the paginator class page_iterator = paginator.paginate() # Use the iterator to iterate throught the results of list_raster_data_collections results = [] for page in page_iterator: results.append(page['RasterDataCollectionSummaries']) print(results)
{ "Arn": "arn:aws:sagemaker-geospatial:us-west-2:378778860802:raster-data-collection/public/nmqj48dcu3g7ayw8", "Description": "Sentinel-2a and Sentinel-2b imagery, processed to Level 2A (Surface Reflectance) and converted to Cloud-Optimized GeoTIFFs", "DescriptionPageUrl": "https://registry.opendata.aws/sentinel-2-l2a-cogs", "Name": "Sentinel 2 L2A COGs", "SupportedFilters": [ { "Maximum": 100, "Minimum": 0, "Name": "EoCloudCover", "Type": "number" }, { "Maximum": 90, "Minimum": 0, "Name": "ViewOffNadir", "Type": "number" }, { "Name": "Platform", "Type": "string" } ], "Tags": {}, "Type": "PUBLIC" }
search_rdc_query = { "AreaOfInterest": { "AreaOfInterestGeometry": { "PolygonGeometry": { "Coordinates": [ [ # coordinates are input as longitute followed by latitude[-114.529, 36.142],[-114.373, 36.142],[-114.373, 36.411],[-114.529, 36.411],[-114.529, 36.142], ] ] } } }, "TimeRangeFilter": { "StartTime":"2022-01-01T00:00:00Z", "EndTime":"2022-07-10T23:59:59Z"}, "PropertyFilters": { "Properties": [ { "Property": { "EoCloudCover": { "LowerBound": 0, "UpperBound": 1 } } } ], "LogicalOperator": "AND" }, "BandFilter": ["visual"] }
search_rdc_response = sm_geo_client.search_raster_data_collection( Arn='arn:aws:sagemaker-geospatial:us-west-2:378778860802:raster-data-collection/public/nmqj48dcu3g7ayw8', RasterDataCollectionQuery=search_rdc_query ) ## items_list is the response from the API request. items_list = [] ## Use the python .get() method to check that the 'NextToken' exists, if null returns None breaking the while loop while search_rdc_response.get('NextToken'): items_list.extend(search_rdc_response['Items']) search_rdc_response = sm_geo_client.search_raster_data_collection( Arn='arn:aws:sagemaker-geospatial:us-west-2:378778860802:raster-data-collection/public/nmqj48dcu3g7ayw8', RasterDataCollectionQuery=search_rdc_query, NextToken=search_rdc_response['NextToken'] ) ## Print the number of observation return based on the query print (len(items_list))
{ 'Assets': { 'visual': { 'Href': 'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/15/T/UH/2022/6/S2A_15TUH_20220623_0_L2A/TCI.tif' } }, 'DateTime': datetime.datetime(2022, 6, 23, 17, 22, 5, 926000, tzinfo = tzlocal()), 'Geometry': { 'Coordinates': [ [[-114.529, 36.142],[-114.373, 36.142],[-114.373, 36.411],[-114.529, 36.411],[-114.529, 36.142], ] ], 'Type': 'Polygon' }, 'Id': 'S2A_15TUH_20220623_0_L2A', 'Properties': { 'EoCloudCover': 0.046519, 'Platform': 'sentinel-2a' } }
# Visualize an example image. import os from urllib import request import tifffile import matplotlib.pyplot as plt image_dir = "./images/lake_mead" os.makedirs(image_dir, exist_ok=True) image_dir = "./images/lake_mead" os.makedirs(image_dir, exist_ok=True) image_url = items_list[0]["Assets"]["visual"]["Href"] img_id = image_url.split("/")[-2] path_to_image = image_dir + "/" + img_id + "_TCI.tif" response = request.urlretrieve(image_url, path_to_image) print("Downloaded image: " + img_id) tci = tifffile.imread(path_to_image) plt.figure(figsize=(6, 6)) plt.imshow(tci) plt.show()
{ 0: "No_data", 1: "Saturated_or_defective", 2: "Dark_area_pixels", 3: "Cloud_shadows", 4: "Vegetation", 5: "Not_vegetated", 6: "Water", 7: "Unclassified", 8: "Cloud_medium_probability", 9: "Cloud_high_probability", 10: "Thin_cirrus", 11: "Snow_ice" }
-
Contoh ini menggunakan
LandCoverSegmentationConfig.
# Perform land cover segmentation on images returned from the Sentinel-2 dataset. eoj_input_config = { "RasterDataCollectionQuery": { "RasterDataCollectionArn": "arn:aws:sagemaker-geospatial:us-west-2:378778860802:raster-data-collection/public/nmqj48dcu3g7ayw8", "AreaOfInterest": { "AreaOfInterestGeometry": { "PolygonGeometry": { "Coordinates":[ [[-114.529, 36.142],[-114.373, 36.142],[-114.373, 36.411],[-114.529, 36.411],[-114.529, 36.142], ] ] } } }, "TimeRangeFilter": { "StartTime":"2021-01-01T00:00:00Z", "EndTime":"2022-07-10T23:59:59Z", }, "PropertyFilters": { "Properties": [{"Property": {"EoCloudCover": {"LowerBound": 0, "UpperBound": 1}}}], "LogicalOperator": "AND", }, } }
eoj_config = {"LandCoverSegmentationConfig": {}}
# Gets the execution role arn associated with current notebook instance execution_role_arn = sagemaker.get_execution_role() # Starts an earth observation job response = sm_geo_client.start_earth_observation_job( Name="lake-mead-landcover", InputConfig=eoj_input_config, JobConfig=eoj_config, ExecutionRoleArn=execution_role_arn, ) print(response)
# List all jobs in the account sg_client.list_earth_observation_jobs()["EarthObservationJobSummaries"]
{ 'Arn': 'arn:aws:sagemaker-geospatial:us-west-2:111122223333:earth-observation-job/futg3vuq935t', 'CreationTime': datetime.datetime(2023, 10, 19, 4, 33, 54, 21481, tzinfo = tzlocal()), 'DurationInSeconds': 3493, 'Name':'lake-mead-landcover', 'OperationType': 'LAND_COVER_SEGMENTATION', 'Status': 'COMPLETED', 'Tags': {} }, { 'Arn': 'arn:aws:sagemaker-geospatial:us-west-2:111122223333:earth-observation-job/wu8j9x42zw3d', 'CreationTime': datetime.datetime(2023, 10, 20, 0, 3, 27, 270920, tzinfo = tzlocal()), 'DurationInSeconds': 1, 'Name':'mt-shasta-landcover', 'OperationType': 'LAND_COVER_SEGMENTATION', 'Status': 'INITIALIZING', 'Tags': {} }
sagemaker_session = sagemaker.Session() s3_bucket_name = sagemaker_session.default_bucket() # Replace with your own bucket if needed s3_bucket = session.resource("s3").Bucket(s3_bucket_name) prefix ="export-lake-mead-eoj"# Replace with the S3 prefix desired export_bucket_and_key = f"s3://{s3_bucket_name}/{prefix}/" eoj_output_config = {"S3Data": {"S3Uri": export_bucket_and_key}} export_response = sm_geo_client.export_earth_observation_job( Arn="arn:aws:sagemaker-geospatial:us-west-2:111122223333:earth-observation-job/7xgwzijebynp", ExecutionRoleArn=execution_role_arn, OutputConfig=eoj_output_config, ExportSourceImages=False, )
export_job_details = sm_geo_client.get_earth_observation_job(Arn=export_response["Arn"])
import os from glob import glob import cv2 import numpy as np import tifffile import matplotlib.pyplot as plt from urllib.parse import urlparse from botocore import UNSIGNED from botocore.config import Config # Download land cover masks mask_dir = "./masks/lake_mead" os.makedirs(mask_dir, exist_ok=True) image_paths = [] for s3_object in s3_bucket.objects.filter(Prefix=prefix).all(): path, filename = os.path.split(s3_object.key) if "output" in path: mask_name = mask_dir + "/" + filename s3_bucket.download_file(s3_object.key, mask_name) print("Downloaded mask: " + mask_name) # Download source images for visualization for tci_url in tci_urls: url_parts = urlparse(tci_url) img_id = url_parts.path.split("/")[-2] tci_download_path = image_dir + "/" + img_id + "_TCI.tif" cogs_bucket = session.resource( "s3", config=Config(signature_version=UNSIGNED, region_name="us-west-2") ).Bucket(url_parts.hostname.split(".")[0]) cogs_bucket.download_file(url_parts.path[1:], tci_download_path) print("Downloaded image: " + img_id) print("Downloads complete.") image_files = glob("images/lake_mead/*.tif") mask_files = glob("masks/lake_mead/*.tif") image_files.sort(key=lambda x: x.split("SQA_")[1]) mask_files.sort(key=lambda x: x.split("SQA_")[1]) overlay_dir = "./masks/lake_mead_overlay" os.makedirs(overlay_dir, exist_ok=True) lake_areas = [] mask_dates = [] for image_file, mask_file in zip(image_files, mask_files): image_id = image_file.split("/")[-1].split("_TCI")[0] mask_id = mask_file.split("/")[-1].split(".tif")[0] mask_date = mask_id.split("_")[2] mask_dates.append(mask_date) assert image_id == mask_id image = tifffile.imread(image_file) image_ds = cv2.resize(image, (1830, 1830), interpolation=cv2.INTER_LINEAR) mask = tifffile.imread(mask_file) water_mask = np.isin(mask, [6]).astype(np.uint8) # water has a class index 6 lake_mask = water_mask[1000:, :1100] lake_area = lake_mask.sum() * 60 * 60 / (1000 * 1000) # calculate the surface area lake_areas.append(lake_area) contour, _ = cv2.findContours(water_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) combined = cv2.drawContours(image_ds, contour, -1, (255, 0, 0), 4) lake_crop = combined[1000:, :1100] cv2.putText(lake_crop, f"{mask_date}", (10,50), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 0), 3, cv2.LINE_AA) cv2.putText(lake_crop, f"{lake_area} [sq km]", (10,100), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 0), 3, cv2.LINE_AA) overlay_file = overlay_dir + '/' + mask_date + '.png' cv2.imwrite(overlay_file, cv2.cvtColor(lake_crop, cv2.COLOR_RGB2BGR)) # Plot water surface area vs. time. plt.figure(figsize=(20,10)) plt.title('Lake Mead surface area for the 2021.02 - 2022.07 period.', fontsize=20) plt.xticks(rotation=45) plt.ylabel('Water surface area [sq km]', fontsize=14) plt.plot(mask_dates, lake_areas, marker='o') plt.grid('on') plt.ylim(240, 320) for i, v in enumerate(lake_areas): plt.text(i, v+2, "%d" %v, ha='center') plt.show()
