Merge pull request #4 from robmarkcole/adds_teach_service

Adds teach service

README.md

@@ -11,7 +11,9 @@ sudo docker pull deepquestai/deepstack
 Place the `custom_components` folder in your configuration directory (or add its contents to an existing `custom_components` folder). Then configure face recognition and/or object detection. Note that at we use `scan_interval` to (optionally) limit computation, [as described here](https://www.home-assistant.io/components/image_processing/#scan_interval-and-optimising-resources).
 
 ## Face recognition
-Deepstack [face recognition](https://deepstackpython.readthedocs.io/en/latest/facerecognition.html) counts faces and will recognise them if you have trained your Deepstack. On you machine with docker, run Deepstack with the face recognition service active on port `5000`:
+Deepstack [face recognition](https://deepstackpython.readthedocs.io/en/latest/facerecognition.html) counts faces and will recognise them if you have trained your Deepstack using the `deepstack_teach_face` service.
+
+On you machine with docker, run Deepstack with the face recognition service active on port `5000`:
 ```
 sudo docker run -e VISION-FACE=True -v localstorage:/datastore -p 5000:5000 deepquestai/deepstack
 ```
@@ -36,6 +38,18 @@ Configuration variables:
 - **source**: Must be a camera.
 - **name**: (Optional) A custom name for the the entity.
 
+#### Service `deepstack_teach_face`
+This service is for teaching (or [registering](https://deepstackpython.readthedocs.io/en/latest/facerecognition.html#face-registeration)) faces with deepstack, so that they can be recognised.
+
+Example valid service data:
+```
+{
+  "name": "superman",
+  "file_path": "/Users/robincole/.homeassistant/images/superman_1.jpeg"
+}
+```
+
+
 <p align="center">
 <img src="https://github.com/robmarkcole/HASS-Deepstack/blob/master/docs/face_usage.png" width="500">
 </p>

custom_components/image_processing/deepstack_face.py

@@ -25,19 +25,32 @@
 _LOGGER = logging.getLogger(__name__)
 
 CLASSIFIER = 'deepstack_face'
+DATA_DEEPSTACK = 'deepstack_classifiers'
+FILE_PATH = 'file_path'
+SERVICE_TEACH_FACE = 'deepstack_teach_face'
 
 
 PLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({
     vol.Required(CONF_IP_ADDRESS): cv.string,
     vol.Required(CONF_PORT): cv.port,
 })
 
+SERVICE_TEACH_SCHEMA = vol.Schema({
+    vol.Optional(ATTR_ENTITY_ID): cv.entity_ids,
+    vol.Required(ATTR_NAME): cv.string,
+    vol.Required(FILE_PATH): cv.string,
+})
 
 def get_matched_faces(predictions):
     """
     Get the predicted faces and their confidence.
     """
-    return {face['userid']: round(face['confidence']*100, 1) for face in predictions if not face['userid'] == 'unknown'}
+    try:
+        matched_faces = {face['userid']: round(face['confidence']*100, 1) 
+            for face in predictions if not face['userid'] == 'unknown'}
+        return matched_faces
+    except:
+        return {}
 
 
 def post_image(url, image):
@@ -53,18 +66,75 @@ def post_image(url, image):
         return None
 
 
+def register_face(url, name, file_path):
+    """
+    Register a name to a file.
+    """
+    try:
+        with open(file_path, "rb") as image:
+            response = requests.post(url,
+                                     files={"image": image.read()},
+                                     data={"userid": name})
+
+        if response.status_code == 200 and response.json()['success'] == True:
+            _LOGGER.info(
+                "%s taught face %s using file %s", CLASSIFIER, name, file_path)
+        elif response.status_code == 200 and response.json()['success'] == False:
+            error = response.json()['error']
+            _LOGGER.warning(
+                "%s taught error: %s", CLASSIFIER, error)
+
+        else:
+            _LOGGER.error("%s error : %s", CLASSIFIER, response.json())
+
+    except Exception as exc:
+        _LOGGER.warning("%s error : %s", CLASSIFIER, exc)
+
+
+def valid_file_path(file_path):
+    """Check that a file_path points to a valid file."""
+    try:
+        cv.isfile(file_path)
+        return True
+    except vol.Invalid:
+        _LOGGER.error(
+            "%s error: Invalid file path: %s", CLASSIFIER, file_path)
+        return False
+
+
 def setup_platform(hass, config, add_devices, discovery_info=None):
     """Set up the classifier."""
+    if DATA_DEEPSTACK not in hass.data:
+            hass.data[DATA_DEEPSTACK] = []
+
     ip_address = config[CONF_IP_ADDRESS]
     port = config[CONF_PORT]
     entities = []
     for camera in config[CONF_SOURCE]:
         face_entity = FaceClassifyEntity(
             ip_address, port, camera[CONF_ENTITY_ID], camera.get(CONF_NAME))
         entities.append(face_entity)
+        hass.data[DATA_DEEPSTACK].append(face_entity)
 
     add_devices(entities)
 
+    def service_handle(service):
+        """Handle for services."""
+        entity_ids = service.data.get('entity_id')
+
+        classifiers = hass.data[DATA_DEEPSTACK]
+        if entity_ids:
+            classifiers = [c for c in classifiers if c.entity_id in entity_ids]
+
+        for classifier in classifiers:
+            name = service.data.get(ATTR_NAME)
+            file_path = service.data.get(FILE_PATH)
+            classifier.teach(name, file_path)
+
+    hass.services.register(
+        DOMAIN, SERVICE_TEACH_FACE, service_handle,
+        schema=SERVICE_TEACH_SCHEMA)
+
 
 class FaceClassifyEntity(ImageProcessingFaceEntity):
     """Perform a face classification."""
@@ -74,6 +144,8 @@ def __init__(self, ip_address, port, camera_entity, name=None):
         super().__init__()
         self._url_check = "http://{}:{}/v1/vision/face/recognize".format(
             ip_address, port)
+        self._url_register = "http://{}:{}/v1/vision/face/register".format(
+            ip_address, port)
         self._camera = camera_entity
         if name:
             self._name = name
@@ -99,6 +171,14 @@ def process_image(self, image):
             self.total_faces = None
             self._matched = {}
 
+    def teach(self, name, file_path):
+        """Teach classifier a face name."""
+        if (not self.hass.config.is_allowed_path(file_path)
+                or not valid_file_path(file_path)):
+            return
+        register_face(
+            self._url_register, name, file_path)
+
     @property
     def camera_entity(self):
         """Return camera entity id from process pictures."""

development/.ipynb_checkpoints/Deepstack face dev-checkpoint.ipynb

@@ -40,7 +40,7 @@
     "    \"\"\"\n",
     "    Get the predicted faces and their confidence.\n",
     "    \"\"\"\n",
-    "    return {face['userid']: round(face['confidence']*100, 1) for face in predictions if face['userid'] is not 'unknown'}"
+    "    return {face['userid']: round(face['confidence']*100, 1) for face in predictions if not face['userid'] == 'unknown'}"
    ]
   },
   {
@@ -183,7 +183,7 @@
     {
      "data": {
       "text/plain": [
-       "{'Adele': 50.1, 'unknown': 0}"
+       "{'Adele': 50.1}"
       ]
      },
      "execution_count": 9,
@@ -226,25 +226,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = requests.post(f\"http://{IP}:{PORT}/v1/vision/face/register\",files={\"image\":open(\"adele_train.jpg\",\"rb\").read()}, data={\"userid\":\"Adele\"})"
+    "response = requests.post(f\"http://{IP}:{PORT}/v1/vision/face/register\",files={\"image\":open(\"adele_train.jpg\",\"rb\").read()}, data={\"userid\":\"\"})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'success': True, 'message': 'face updated'}"
+       "200"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response.status_code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'success': False, 'error': 'userid not specified'}"
+      ]
+     },
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -253,6 +273,77 @@
     "response.json()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'message'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-27-c92a3c225643>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'message'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'message'"
+     ]
+    }
+   ],
+   "source": [
+    "response.json()['message']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def register_face(url, name, file_path):\n",
+    "    \"\"\"\n",
+    "    Register a name to a file.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        with open(file_path,\"rb\") as image:\n",
+    "            response = requests.post(url, \n",
+    "                                     files={\"image\":image.read()}, \n",
+    "                                     data={\"userid\":name})\n",
+    "            \n",
+    "        if response.status_code == 200 and response.json()['success'] == True:\n",
+    "            print(f\"Deepstack taught face {name} using file {file_path}\")\n",
+    "        elif response.status_code == 200 and response.json()['success'] == False:\n",
+    "            error = response.json()['error']\n",
+    "            print(f\"Deepstack error: {error}\")\n",
+    "\n",
+    "        else:\n",
+    "            print(response.json())\n",
+    "        \n",
+    "    except Exception as exc:\n",
+    "        print(exc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deepstack taught face Adele using file adele_train.jpg\n"
+     ]
+    }
+   ],
+   "source": [
+    "url = f\"http://{IP}:{PORT}/v1/vision/face/register\"\n",
+    "name = \"Adele\"\n",
+    "file_path = \"adele_train.jpg\"\n",
+    "\n",
+    "register_face(url, name, file_path)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},