source/operators/rekognition/start_label_detection.py [60:126]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - return response['JobId'] except Exception as e: output_object.update_workflow_status("Error") output_object.add_workflow_metadata(LabelDetectionError=str(e)) raise MasExecutionError(output_object.return_output_object()) # Lambda function entrypoint: def lambda_handler(event, context): print("We got the following event:\n", event) try: if "ProxyEncode" in event["Input"]["Media"]: s3bucket = event["Input"]["Media"]["ProxyEncode"]["S3Bucket"] s3key = event["Input"]["Media"]["ProxyEncode"]["S3Key"] elif "Video" in event["Input"]["Media"]: s3bucket = event["Input"]["Media"]["Video"]["S3Bucket"] s3key = event["Input"]["Media"]["Video"]["S3Key"] elif "Image" in event["Input"]["Media"]: s3bucket = event["Input"]["Media"]["Image"]["S3Bucket"] s3key = event["Input"]["Media"]["Image"]["S3Key"] workflow_id = str(event["WorkflowExecutionId"]) asset_id = event['AssetId'] except Exception: output_object.update_workflow_status("Error") output_object.add_workflow_metadata(LabelDetectionError="No valid inputs") raise MasExecutionError(output_object.return_output_object()) print("Processing s3://"+s3bucket+"/"+s3key) valid_video_types = [".avi", ".mp4", ".mov"] valid_image_types = [".png", ".jpg", ".jpeg"] file_type = os.path.splitext(s3key)[1].lower() if file_type in valid_image_types: # Image processing is synchronous. response = detect_labels(s3bucket, urllib.parse.unquote_plus(s3key)) output_object.add_workflow_metadata(AssetId=asset_id,WorkflowExecutionId=workflow_id) dataplane = DataPlane() metadata_upload = dataplane.store_asset_metadata(asset_id, operator_name, workflow_id, response) if "Status" not in metadata_upload: output_object.update_workflow_status("Error") output_object.add_workflow_metadata( LabelDetectionError="Unable to upload metadata for asset: {asset}".format(asset=asset_id)) raise MasExecutionError(output_object.return_output_object()) else: if metadata_upload["Status"] == "Success": print("Uploaded metadata for asset: {asset}".format(asset=asset_id)) output_object.update_workflow_status("Complete") return output_object.return_output_object() elif metadata_upload["Status"] == "Failed": output_object.update_workflow_status("Error") output_object.add_workflow_metadata( LabelDetectionError="Unable to upload metadata for asset: {asset}".format(asset=asset_id)) raise MasExecutionError(output_object.return_output_object()) else: output_object.update_workflow_status("Error") output_object.add_workflow_metadata( LabelDetectionError="Unable to upload metadata for asset: {asset}".format(asset=asset_id)) raise MasExecutionError(output_object.return_output_object()) elif file_type in valid_video_types: # Video processing is asynchronous. job_id = start_label_detection(s3bucket, urllib.parse.unquote_plus(s3key)) output_object.update_workflow_status("Executing") output_object.add_workflow_metadata(JobId=job_id, AssetId=asset_id, WorkflowExecutionId=workflow_id) return output_object.return_output_object() else: print("ERROR: invalid file type") output_object.update_workflow_status("Error") output_object.add_workflow_metadata(LabelDetectionError="Not a valid file type") raise MasExecutionError(output_object.return_output_object()) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - source/operators/rekognition/start_shot_detection.py [61:127]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - return response['JobId'] except Exception as e: output_object.update_workflow_status("Error") output_object.add_workflow_metadata(LabelDetectionError=str(e)) raise MasExecutionError(output_object.return_output_object()) # Lambda function entrypoint: def lambda_handler(event, context): print("We got the following event:\n", event) try: if "ProxyEncode" in event["Input"]["Media"]: s3bucket = event["Input"]["Media"]["ProxyEncode"]["S3Bucket"] s3key = event["Input"]["Media"]["ProxyEncode"]["S3Key"] elif "Video" in event["Input"]["Media"]: s3bucket = event["Input"]["Media"]["Video"]["S3Bucket"] s3key = event["Input"]["Media"]["Video"]["S3Key"] elif "Image" in event["Input"]["Media"]: s3bucket = event["Input"]["Media"]["Image"]["S3Bucket"] s3key = event["Input"]["Media"]["Image"]["S3Key"] workflow_id = str(event["WorkflowExecutionId"]) asset_id = event['AssetId'] except Exception: output_object.update_workflow_status("Error") output_object.add_workflow_metadata(LabelDetectionError="No valid inputs") raise MasExecutionError(output_object.return_output_object()) print("Processing s3://"+s3bucket+"/"+s3key) valid_video_types = [".avi", ".mp4", ".mov"] valid_image_types = [".png", ".jpg", ".jpeg"] file_type = os.path.splitext(s3key)[1].lower() if file_type in valid_image_types: # Image processing is synchronous. response = detect_labels(s3bucket, urllib.parse.unquote_plus(s3key)) output_object.add_workflow_metadata(AssetId=asset_id,WorkflowExecutionId=workflow_id) dataplane = DataPlane() metadata_upload = dataplane.store_asset_metadata(asset_id, operator_name, workflow_id, response) if "Status" not in metadata_upload: output_object.update_workflow_status("Error") output_object.add_workflow_metadata( LabelDetectionError="Unable to upload metadata for asset: {asset}".format(asset=asset_id)) raise MasExecutionError(output_object.return_output_object()) else: if metadata_upload["Status"] == "Success": print("Uploaded metadata for asset: {asset}".format(asset=asset_id)) output_object.update_workflow_status("Complete") return output_object.return_output_object() elif metadata_upload["Status"] == "Failed": output_object.update_workflow_status("Error") output_object.add_workflow_metadata( LabelDetectionError="Unable to upload metadata for asset: {asset}".format(asset=asset_id)) raise MasExecutionError(output_object.return_output_object()) else: output_object.update_workflow_status("Error") output_object.add_workflow_metadata( LabelDetectionError="Unable to upload metadata for asset: {asset}".format(asset=asset_id)) raise MasExecutionError(output_object.return_output_object()) elif file_type in valid_video_types: # Video processing is asynchronous. job_id = start_label_detection(s3bucket, urllib.parse.unquote_plus(s3key)) output_object.update_workflow_status("Executing") output_object.add_workflow_metadata(JobId=job_id, AssetId=asset_id, WorkflowExecutionId=workflow_id) return output_object.return_output_object() else: print("ERROR: invalid file type") output_object.update_workflow_status("Error") output_object.add_workflow_metadata(LabelDetectionError="Not a valid file type") raise MasExecutionError(output_object.return_output_object()) - 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