add aws_auto_checkin_app

doc/jsk_perception/nodes/aws_auto_checkin_app.rst

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+aws_auto_cehckin_app.py
+=========================
+
+What is this?
+-------------
+
+.. image:: https://d1.awsstatic.com/Solutions/Solutions%20Category%20Template%20Draft/Solution%20Architecture%20Diagrams/auto-check-in-app-architecture.8baa84b79c2294d035c7b9cee323d7c9ba53a43a.png
+
+Face recognition using Amazon Rekognition, see
+https://aws.amazon.com/solutions/implementations/auto-check-in-app/
+for more info.
+
+Subscribing Topic
+-----------------
+
+
+* ``~image`` (``sensor_msgs/Image``)
+
+  Raw image.
+
+* ``~face_roi`` (``opencv_apps/FaceArrayStamped``)
+
+  Rectangles on the face of input image. Use ROI value.
+  ```
+        msg.faces[].face.x      : X coordinates of the center of the face image in the ~image input
+        msg.faces[].face.y      : Y coordinates of the center of the face image in the ~image input
+        msg.faces[].face.width  : Width of the face image
+        msg.faces[].face.height : Height of the face iamge
+  ```
+
+Publishing Topic
+----------------
+
+* ``~face_name`` (``opencv_apps/FaceArrayStamped``)
+
+  Publish recognized face name as well as face image. The face.{x,y,width,height} corresponds to input `face_roi`, that means x, y is the center of face rectangle.
+
+Parameters
+----------
+
+* ``~use_window`` (Bool, default: ``False``)
+
+  Show input image on the window, if it is true.
+
+* ``~env_path`` (String, default: ``env.json``)
+
+  Json file for environment variables to run aws auto-checkin app. You
+  can find how to generate this file on
+  https://aws.amazon.com/jp/builders-flash/202004/auto-checkin-app/.
+  In addition to that, you need to add "UserName" and "UserPassword"
+  ```
+  {
+    "Region": "%%REGION%%",
+    "ApiEndpoint" : "%%REST_API_ID%%.execute-api.%%REGION%%.amazonaws.com/prod/rekognize_face",
+    "CognitoUserPoolId": "%%COGNITO_USER_POOL_ID%%",
+    "CognitoUserPoolClientId": "%%COGNITO_USER_POOL_CLIENT_ID%%",
+    "FaceAreaThreshold": 1e4,
+    "FaceMarginRatio": 0.2,
+    "FaceSimilarityThreshold": 90,
+    "CroppedImageWidth": 540,
+    "CroppedImageHeight": 540,
+    "NameTtlSec": 10,
+    "UseDeepLeaningForDetector": true,
+    "UserName": "%%YOUR_USER_NAME%%",
+    "UserPassword": "%%YOUR_PASSWORD%%"
+  }
+  ```
+
+Sample
+------
+
+.. code-block:: bash
+
+  roslaunch jsk_perception sample_aws_auto_checkin_app.launch use_window:=true
+
+
+For JSK user, Download `env.json` file from
+[Gdrive](https://drive.google.com/file/d/1Wl-yzRD8LNipqcE4jQfOOcJQZJZbdcMW/view?usp=sharing)
+and put this under `/tmp` directory to run sample code.
+
+To add new people to face database, add face image file to [Amazon
+S3](https://console.aws.amazon.com/s3),
+`auto-check-in-gapp-register...` buckets

jsk_perception/node_scripts/aws_auto_checkin_app.py

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+#!/usr/bin/env python
+
+##
+## Modified from awslabs/auto-check-in-app
+##
+## https://github.com/awslabs/auto-check-in-app/blob/master/source/frontend/controller.py
+##
+##
+## Author: Yuli-disney <yulikamiya@gmail.com>
+##         Kei Okada <kei.okada@gmail.com>
+
+import rospy
+
+import message_filters
+from sensor_msgs.msg import CompressedImage
+from opencv_apps.msg import FaceArrayStamped, Face, Rect
+
+import numpy as np
+
+import boto3
+import cv2
+import datetime
+import json
+import requests
+import math
+
+class AutoCheckIn(object):
+
+    def _get_id_token_by_cognito(self, username, password):
+        client = boto3.client('cognito-idp', self.REGION)
+        rospy.loginfo("Initiate User Auth for {}".format(username))
+        response = client.initiate_auth(
+            ClientId=self.COGNITO_USERPOOL_CLIENT_ID,
+            AuthFlow='USER_PASSWORD_AUTH',
+            AuthParameters={
+                'USERNAME': username,
+                'PASSWORD': password
+            }
+        )
+        return response['AuthenticationResult']['IdToken']
+
+    def __init__(self):
+        rospy.init_node('aws_auto_checkin_service')
+        rospy.loginfo("ROS node initialized as {}".format(rospy.get_name()))
+
+        env_path = rospy.get_param('~env_path', 'env.json')
+        rospy.loginfo("Loading AutoCheckin env variables from {}".format(env_path))
+        try:
+            with open(env_path) as env_json:
+                env = json.load(env_json)
+        except IOError:
+            rospy.logerr('Cannot open "{}".\nCopy "default.env.json" file as a new file called "env.json" and edit parameters in it.'.format(env_path))
+            raise
+
+        try:
+            self.API_ENDPOINT = env['ApiEndpoint']
+            self.FACE_AREA_THRESHOLD = env['FaceAreaThreshold']
+            self.NAME_TTL_SEC = env['NameTtlSec']
+            self.FACE_SIMILARITY_THRESHOLD = env['FaceSimilarityThreshold']
+            self.COGNITO_USERPOOL_ID = env['CognitoUserPoolId']
+            self.COGNITO_USERPOOL_CLIENT_ID = env['CognitoUserPoolClientId']
+            self.REGION = env['Region']
+        except KeyError:
+            print('Invalid config file')
+            raise
+
+        self.id_token = self._get_id_token_by_cognito(env['UserName'], env['UserPassword'])
+
+        self.use_window = rospy.get_param('~use_window', False)
+        rospy.loginfo("Launch image window : {}".format(self.use_window))
+
+        self.name_pub = rospy.Publisher('face_name', FaceArrayStamped, queue_size=1)
+        self.image_sub = message_filters.Subscriber('{}/compressed'.format(rospy.resolve_name('image')), CompressedImage)
+        # we wan to use RegionOfInterest, but it message_filters requires
+        # header information, so use CameraInfo
+        self.roi_sub = message_filters.Subscriber('face_roi', FaceArrayStamped)
+        self.ts = message_filters.ApproximateTimeSynchronizer([self.image_sub, self.roi_sub], 10, 1, allow_headerless = True)
+        self.ts.registerCallback(self.callback)
+        rospy.loginfo("Waiting for {} and {}".format(self.image_sub.name, self.roi_sub.name))
+
+    def findface(self, face_image):
+        area = face_image.shape[0] * face_image.shape[1]
+        if area < self.FACE_AREA_THRESHOLD / 10:
+            return None
+        if area > self.FACE_AREA_THRESHOLD * 2:
+            # resize
+            ratio = math.sqrt(area / (self.FACE_AREA_THRESHOLD * 2))
+            face_image = cv2.resize(face_image, (int(
+                face_image.shape[1] / ratio), int(face_image.shape[0] / ratio)))
+
+        _, encoded_face_image = cv2.imencode('.jpg', face_image)
+
+        # Call API
+        try:
+            endpoint = 'https://' + self.API_ENDPOINT
+            t = datetime.datetime.utcnow()
+            amz_date = t.strftime('%Y%m%dT%H%M%SZ')
+            headers = {
+                'Content-Type': 'image/jpg',
+                'X-Amz-Date':amz_date,
+                'Authorization': self.id_token
+            }
+            request_parameters = encoded_face_image.tostring()
+            res = requests.post(endpoint, data=request_parameters, headers=headers).json()
+            rospy.loginfo("responce : {}".format(res))
+            # renponse samples:
+            #      {'result': 'OK', 'name': 'hoge', 'similarity': 95.15}
+            #      {'result': 'NO_MATCH', 'name': '', 'similarity': 0}
+            #      {'result': 'INVALID', 'name': '', 'similarity': 0}
+
+            result = res['result']
+        except Exception as e:
+            print(e)
+
+        else:
+            if result == 'OK':
+                name = res['name']
+                similarity = res['similarity']
+                if similarity > self.FACE_SIMILARITY_THRESHOLD:
+                    return res
+
+        return None
+
+    def callback(self, image, roi):
+        # decode compressed image
+        np_arr = np.fromstring(image.data, np.uint8)
+        img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
+        if image.format != "rgb8; jpeg compressed bgr8":
+            img = img[:, :, ::-1]
+
+        if self.use_window:
+            img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+            img_gray = cv2.cvtColor(img_gray, cv2.COLOR_GRAY2BGR)
+
+        faces = FaceArrayStamped()
+        faces.header = image.header
+        faces.faces = []
+        for face in roi.faces:
+            try:
+                cx = int(face.face.x)
+                cy = int(face.face.y)
+                w =  int(face.face.width)
+                h =  int(face.face.height)
+            except Exception as e:
+                rospy.logerr(e)
+                return
+
+            ret = self.findface(img[cy-h/2:cy+h/2,cx-w/2:cx+w/2])
+            if ret != None:
+                faces.faces.append(Face(face=Rect(cx, cy, w, h),
+                                        label=ret['name'],
+                                        confidence=ret['similarity']))
+
+            if self.use_window: # copy colored face rectangle to img_gray
+                img_gray[cy-h/2:cy+h/2,cx-h/2:cx+w/2] = img[cy-h/2:cy+h/2,cx-w/2:cx+w/2]
+
+        self.name_pub.publish(faces)
+
+        if self.use_window:
+            cv2.imshow(image._connection_header['topic'], img_gray)
+            cv2.waitKey(1)
+
+
+if __name__ == '__main__':
+    auto = AutoCheckIn()
+    rospy.spin()
+

jsk_perception/sample/sample_aws_auto_checkin_app.launch

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+<launch>
+  <arg name="use_window" default="true" />
+
+  <include file="$(find jsk_perception)/sample/include/play_rosbag_people.xml" />
+
+  <!-- face_detection.cpp -->
+  <include file="$(find opencv_apps)/launch/face_detection.launch" >
+    <arg name="debug_view" value="$(arg use_window)" />
+    <arg name="image" value="/camera/rgb/image_raw" />
+  </include>
+
+  <node name="aws_auto_checkin_app"
+        pkg="jsk_perception" type="aws_auto_checkin_app.py"
+        output="screen"
+        clear_params="true" >
+    <remap from="image" to="/camera/rgb/image_raw" />
+    <remap from="face_roi" to="/face_detection/faces" />
+    <rosparam subst_value="true">
+      use_window: $(arg use_window)
+      env_path: /tmp/env.json
+    </rosparam>
+  </node>
+
+</launch>