概要
Pose検出するマイクロサービスを作成しようとしたところ、意外と苦戦したのでメモ。
Edgeの処理を軽くするため、サーバーサイド（Lambda）でMediaPipeを使用したPose検出を行うマイクロサービスを作成して呼び出そうとした。
Dockerイメージの作成
Zipの制限に収まらないので、DockerイメージでLambda関数を作成する。
Dockerfile
# AWS提供ベースイメージ
FROM public.ecr.aws/lambda/python:3.8
COPY app.py ${LAMBDA_TASK_ROOT}
RUN yum -y install mesa-libGL.x86_64 make gcc curl
COPY requirements.txt .
RUN pip3 install -r requirements.txt --target "${LAMBDA_TASK_ROOT}"
# モデルをダウンロード
RUN mkdir -p //var/task/mediapipe/modules/pose_landmark/ \
&& curl -SL https://github.com/google/mediapipe/blob/master/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite?raw=true > /var/task/mediapipe/modules/pose_landmark/pose_landmark_heavy.tflite
# pthread problem workaround
ADD https://raw.githubusercontent.com/mitchellharper12/lambda-pthread-nameshim/master/Makefile .
ADD https://raw.githubusercontent.com/mitchellharper12/lambda-pthread-nameshim/master/pthread_shim.c .
RUN make pthread_shim.so && cp pthread_shim.so /opt
# Set the CMD to your handler (could also be done as a parameter override outside of the Dockerfile)
CMD [ "app.handler" ]
app.py
import logging
import sys
import time
import json
import base64
import cv2
import mediapipe as mp
import numpy as np
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_pose = mp.solutions.pose
def handler(event, context):
print(event)
with mp_pose.Pose(
static_image_mode=True,
model_complexity=2,
enable_segmentation=True,
min_detection_confidence=0.5) as pose:
img_data = base64.b64decode(event['body'])
img_np = np.fromstring(img_data, np.uint8)
image = cv2.imdecode(img_np, cv2.IMREAD_ANYCOLOR)
image_height, image_width, _ = image.shape
results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
if not results.pose_landmarks:
return '{"message": "no result"}'
arr = []
for index in range(len(mp_pose.PoseLandmark)):
arr.append({'id':index, 'name':mp_pose.PoseLandmark(index).name, 'x':results.pose_landmarks.landmark[mp_pose.PoseLandmark(index)].x, 'y':results.pose_landmarks.landmark[mp_pose.PoseLandmark(index)].y})
return json.dumps({'results': arr, 'height': image_height, 'width': image_width})
requirements.txt
opencv-python
mediapipe
ビルドする。
docker build -t test-pose .
テストのため実行する。
docker run -p=8080:8080 test-pose:latest
うまくビルドできたか試してみる
curl -X POST "http://localhost:8080/2015-03-31/functions/function/invocations" -d '{"body":""}'
画像データが空なのでエラーが出力される。
{"errorMessage": "OpenCV(4.5.5) /io/opencv/modules/imgcodecs/src/loadsave.cpp:816: error: (-215:Assertion failed) !buf.empty() in function 'imdecode_'\n", "errorType": "error", "stackTrace": [" File \"/var/task/app.py\", line 26, in handler\n image = cv2.imdecode(img_np, cv2.IMREAD_ANYCOLOR)\n"]}
Lambda作成の準備
作成したイメージをECRに登録する。最初にタグ付けする。
この例では xxxxxxxxxxxx はアカウントID、リージョンは ａｐ－ｎｏｒｔｈｅａｓｔ－１ を使用。
docker tag test-pose:latest xxxxxxxxxxxx.dkr.ecr.ap-northeast-1.amazonaws.com/test-pose:latest
ECRにログイン
aws ecr get-login-password | docker login --username AWS --password-stdin xxxxxxxxxxxx.dkr.ecr.ap-northeast-1.amazonaws.com
プッシュする
aws ecr create-repository --repository-name test-pose --image-scanning-configuration scanOnPush=true --image-tag-mutability MUTABLE
docker push xxxxxxxxxxxx.dkr.ecr.ap-northeast-1.amazonaws.com/test-pose:latest
Lambdaの作成
関数の作成
作成後にメモリ、タイムアウトを設定
新機能、関数URLを作成
環境変数の設定
テスト
画像をアップロードする
curl -X POST -H 'Content-Type: image/png' --data-binary @pose.png https://12345678901234567890123456789012.lambda-url.ap-northeast-1.on.aws/
結果
{
"results": [
{
"id": 0,
"name": "NOSE",
"x": 0.5232403874397278,
"y": 0.12018030881881714
},
{
"id": 1,
"name": "LEFT_EYE_INNER",
"x": 0.5452666282653809,
"y": 0.10341935604810715
},
{
"id": 2,
"name": "LEFT_EYE",
"x": 0.5573577880859375,
"y": 0.1040365993976593
},
{
"id": 3,
"name": "LEFT_EYE_OUTER",
"x": 0.5683303475379944,
"y": 0.10525256395339966
},
{
"id": 4,
"name": "RIGHT_EYE_INNER",
"x": 0.504645824432373,
"y": 0.10398414731025696
},
{
"id": 5,
"name": "RIGHT_EYE",
"x": 0.4921768307685852,
"y": 0.10489142686128616
},
{
"id": 6,
"name": "RIGHT_EYE_OUTER",
"x": 0.48190298676490784,
"y": 0.10615649819374084
},
{
"id": 7,
"name": "LEFT_EAR",
"x": 0.5838826894760132,
"y": 0.11600765585899353
},
{
"id": 8,
"name": "RIGHT_EAR",
"x": 0.46655985713005066,
"y": 0.11607295274734497
},
{
"id": 9,
"name": "MOUTH_LEFT",
"x": 0.5485259294509888,
"y": 0.1413123607635498
},
{
"id": 10,
"name": "MOUTH_RIGHT",
"x": 0.50005042552948,
"y": 0.1397590935230255
},
{
"id": 11,
"name": "LEFT_SHOULDER",
"x": 0.6766172051429749,
"y": 0.2485317587852478
},
{
"id": 12,
"name": "RIGHT_SHOULDER",
"x": 0.3809528350830078,
"y": 0.24369803071022034
},
{
"id": 13,
"name": "LEFT_ELBOW",
"x": 0.6916986107826233,
"y": 0.3813675045967102
},
{
"id": 14,
"name": "RIGHT_ELBOW",
"x": 0.34289777278900146,
"y": 0.37505054473876953
},
{
"id": 15,
"name": "LEFT_WRIST",
"x": 0.706161618232727,
"y": 0.49870720505714417
},
{
"id": 16,
"name": "RIGHT_WRIST",
"x": 0.330485463142395,
"y": 0.4918277859687805
},
{
"id": 17,
"name": "LEFT_PINKY",
"x": 0.7168028354644775,
"y": 0.5329775810241699
},
{
"id": 18,
"name": "RIGHT_PINKY",
"x": 0.3190672993659973,
"y": 0.5271245837211609
},
{
"id": 19,
"name": "LEFT_INDEX",
"x": 0.7006966471672058,
"y": 0.5377188324928284
},
{
"id": 20,
"name": "RIGHT_INDEX",
"x": 0.34219032526016235,
"y": 0.5301030874252319
},
{
"id": 21,
"name": "LEFT_THUMB",
"x": 0.6904131174087524,
"y": 0.5276884436607361
},
{
"id": 22,
"name": "RIGHT_THUMB",
"x": 0.35810160636901855,
"y": 0.5179416537284851
},
{
"id": 23,
"name": "LEFT_HIP",
"x": 0.5979184508323669,
"y": 0.5145007967948914
},
{
"id": 24,
"name": "RIGHT_HIP",
"x": 0.43190252780914307,
"y": 0.511218786239624
},
{
"id": 25,
"name": "LEFT_KNEE",
"x": 0.5888926386833191,
"y": 0.704571545124054
},
{
"id": 26,
"name": "RIGHT_KNEE",
"x": 0.4408313035964966,
"y": 0.702862024307251
},
{
"id": 27,
"name": "LEFT_ANKLE",
"x": 0.563112199306488,
"y": 0.877585768699646
},
{
"id": 28,
"name": "RIGHT_ANKLE",
"x": 0.48251473903656006,
"y": 0.8749133348464966
},
{
"id": 29,
"name": "LEFT_HEEL",
"x": 0.5614821910858154,
"y": 0.8919190168380737
},
{
"id": 30,
"name": "RIGHT_HEEL",
"x": 0.49692264199256897,
"y": 0.8878955841064453
},
{
"id": 31,
"name": "LEFT_FOOT_INDEX",
"x": 0.5484899282455444,
"y": 0.9480408430099487
},
{
"id": 32,
"name": "RIGHT_FOOT_INDEX",
"x": 0.4944060742855072,
"y": 0.9436366558074951
}
],
"height": 858,
"width": 482
}