连续生成cloudwatch日志的Lambda（与Kinesis数据流连接）函数

Question

在我当前的项目中，我的目标是从帧流中检测不同的对象。视频帧是用与覆盆子PI连接的摄像机拍摄的。

体系结构设计如下：

>

video_cap.py代码正在raspberry PI上运行。此代码将图像流发送到AWS中的Kinesis数据流（称为framestream)。

# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
# Licensed under the Amazon Software License (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at
#     http://aws.amazon.com/asl/
# or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, express or implied. See the License for the specific language governing permissions and limitations under the License.

import sys
import cPickle
import datetime
import cv2
import boto3
import time
import cPickle
from multiprocessing import Pool
import pytz

kinesis_client = boto3.client("kinesis")
rekog_client = boto3.client("rekognition")

camera_index = 0 # 0 is usually the built-in webcam
capture_rate = 30 # Frame capture rate.. every X frames. Positive integer.
rekog_max_labels = 123
rekog_min_conf = 50.0

#Send frame to Kinesis stream
def encode_and_send_frame(frame, frame_count, enable_kinesis=True, enable_rekog=False, write_file=False):
    try:
        #convert opencv Mat to jpg image
        #print "----FRAME---"
        retval, buff = cv2.imencode(".jpg", frame)

        img_bytes = bytearray(buff)

        utc_dt = pytz.utc.localize(datetime.datetime.now())
        now_ts_utc = (utc_dt - datetime.datetime(1970, 1, 1, tzinfo=pytz.utc)).total_seconds()

        frame_package = {
            'ApproximateCaptureTime' : now_ts_utc,
            'FrameCount' : frame_count,
            'ImageBytes' : img_bytes
        }

        if write_file:
            print("Writing file img_{}.jpg".format(frame_count))
            target = open("img_{}.jpg".format(frame_count), 'w')
            target.write(img_bytes)
            target.close()

        #put encoded image in kinesis stream
        if enable_kinesis:
            print "Sending image to Kinesis"
            response = kinesis_client.put_record(
                StreamName="FrameStream",
                Data=cPickle.dumps(frame_package),
                PartitionKey="partitionkey"
            )
            print response

        if enable_rekog:
            response = rekog_client.detect_labels(
                Image={
                    'Bytes': img_bytes
                },
                MaxLabels=rekog_max_labels,
                MinConfidence=rekog_min_conf
            )
            print response

    except Exception as e:
        print e


def main():

    argv_len = len(sys.argv)

    if argv_len > 1 and sys.argv[1].isdigit():
        capture_rate = int(sys.argv[1])

    cap = cv2.VideoCapture(0) #Use 0 for built-in camera. Use 1, 2, etc. for attached cameras.
    pool = Pool(processes=3)

    frame_count = 0
    while True:
        # Capture frame-by-frame
        ret, frame = cap.read()
        #cv2.resize(frame, (640, 360));

        if ret is False:
            break

        if frame_count % capture_rate == 0:
            result = pool.apply_async(encode_and_send_frame, (frame, frame_count, True, False, False,))

        frame_count += 1

        # Display the resulting frame
        cv2.imshow('frame', frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    # When everything done, release the capture
    cap.release()
    cv2.destroyAllWindows()
    return

if __name__ == '__main__':
    main()

Lambda函数(lambda_function.py)

from __future__ import print_function

import base64
import json
import logging
import _pickle as cPickle
#import time
from datetime import datetime
import decimal
import uuid
import boto3
from copy import deepcopy

logger = logging.getLogger()
logger.setLevel(logging.INFO) 
rekog_client = boto3.client('rekognition')

# S3 Configuration
s3_client = boto3.client('s3')
s3_bucket = "bucket-name-XXXXXXXXXXXXX"
s3_key_frames_root = "frames/"

# SNS Configuration
sns_client = boto3.client('sns')
label_watch_sns_topic_arn = "SNS-ARN-XXXXXXXXXXXXXXXX" 

#Iterate on rekognition labels. Enrich and prep them for storage in DynamoDB
labels_on_watch_list = []
labels_on_watch_list_set = []
text_list_set = []

# List for detected text
text_list = []

def process_image(event, context):

    # Start of for Loop
    for record in event['Records']:
        frame_package_b64 = record['kinesis']['data']
        frame_package = cPickle.loads(base64.b64decode(frame_package_b64))

        img_bytes = frame_package["ImageBytes"]

        approx_capture_ts = frame_package["ApproximateCaptureTime"]
        frame_count = frame_package["FrameCount"]

        now_ts = datetime.now()

        frame_id = str(uuid.uuid4())
        approx_capture_timestamp = decimal.Decimal(approx_capture_ts)

        year = now_ts.strftime("%Y")
        mon = now_ts.strftime("%m")
        day = now_ts.strftime("%d")
        hour = now_ts.strftime("%H")

        #=== Object Detection from an Image =====

        # AWS Rekognition - Label detection from an image
        rekog_response = rekog_client.detect_labels(
            Image={
                'Bytes': img_bytes
            },
            MaxLabels=10,
            MinConfidence= 90.0
        )

        logger.info("Rekognition Response" + str(rekog_response) )

        for label in rekog_response['Labels']:
            lbl = label['Name']
            conf = label['Confidence']
            labels_on_watch_list.append(deepcopy(lbl)) 

        labels_on_watch_list_set = set(labels_on_watch_list)

        #print(labels_on_watch_list)
        logger.info("Labels on watch list ==>" + str(labels_on_watch_list_set) )

            # Vehicle Detection
            #if (lbl.upper() in (label.upper() for label in ["Transportation", "Vehicle", "Van" , "Ambulance" , "Bus"]) and conf >= 50.00):
                #labels_on_watch_list.append(deepcopy(label))

        #=== Detecting text from a detected Object
        # Detect text from the detected vehicle using detect_text()
        response=rekog_client.detect_text( Image={ 'Bytes': img_bytes })
        textDetections=response['TextDetections']
        for text in textDetections:
            text_list.append(text['DetectedText']) 

        text_list_set = set(text_list)   
        logger.info("Text Detected ==>" + str(text_list_set))

    # End of for Loop

    # SNS Notification
    if len(labels_on_watch_list_set) > 0 :
        logger.info("I am in SNS Now......")
        notification_txt = 'On {} Vehicle was spotted with {}% confidence'.format(now_ts.strftime('%x, %-I:%M %p %Z'), round(label['Confidence'], 2))
        resp = sns_client.publish(TopicArn=label_watch_sns_topic_arn,
            Message=json.dumps( 
                {
                    "message": notification_txt + " Detected Object Categories " + str(labels_on_watch_list_set) + " " + " Detect text on the Object " + " " + str(text_list_set)
                }
            ))

    #Store frame image in S3
    s3_key = (s3_key_frames_root + '{}/{}/{}/{}/{}.jpg').format(year, mon, day, hour, frame_id)
    s3_client.put_object(
        Bucket=s3_bucket,
        Key=s3_key,
        Body=img_bytes
    )

    print ("Successfully processed  records.")
    return {
        'statusCode': 200,
        'body': json.dumps('Successfully processed  records.')
    }

def lambda_handler(event, context):
   logger.info("Received event from Kinesis ......" )
   logger.info("Received event ===>" + str(event))
   return process_image(event, context)

以下是Kinesis数据流日志（日期为2019年8月17日-IST下午1:54）。最后一次，2019年8月16日通过覆盆子PI摄取的数据-下午6:45）

Answer 1

看起来流中大约有117K条记录，但每次处理1条记录的速度很慢。lambda处理一条记录需要多长时间？我会得到lambda运行的时间，将python put代码更新为lambda运行的Hibernate时间长一点（开始时长20%)，然后用空队列重新启动，并实时查看统计数据。