Rename object helper functions

Author: robmarkcole

deepstack/core.py

@@ -12,6 +12,8 @@
 ## API urls
 URL_OBJECT_DETECTION = "http://{}:{}/v1/vision/detection"
 URL_FACE_DETECTION = "http://{}:{}/v1/vision/face"
+URL_FACE_REGISTRATION = "http://{}:{}/v1/vision/face/register"
+URL_FACE_RECOGNITION = "http://{}:{}/v1/vision/face/recognize"
 
 
 def format_confidence(confidence: Union[str, float]) -> float:
@@ -28,20 +30,20 @@ def get_confidences_above_threshold(
     return [val for val in confidences if val >= confidence_threshold]
 
 
-def get_object_labels(predictions: List[Dict]) -> List[str]:
+def get_objects(predictions: List[Dict]) -> List[str]:
     """
-    Get a list of the unique object labels predicted.
+    Get a list of the unique objects predicted.
     """
     labels = [pred["label"] for pred in predictions]
     return list(set(labels))
 
 
-def get_label_confidences(predictions: List[Dict], target_label: str):
+def get_object_confidences(predictions: List[Dict], target_object: str):
     """
     Return the list of confidences of instances of target label.
     """
     confidences = [
-        pred["confidence"] for pred in predictions if pred["label"] == target_label
+        pred["confidence"] for pred in predictions if pred["label"] == target_object
     ]
     return confidences
 
@@ -50,18 +52,19 @@ def get_objects_summary(predictions: List[Dict]):
     """
     Get a summary of the objects detected.
     """
-    labels = get_object_labels(predictions)
+    objects = get_objects(predictions)
     return {
-        label: len(get_label_confidences(predictions, target_label=label))
-        for label in labels
+        target_object: len(get_object_confidences(predictions, target_object))
+        for target_object in objects
     }
 
 
-def post_image(url: str, image: bytes, api_key: str, timeout: int):
+def post_image(url: str, image: bytes, api_key: str, timeout: int, data: dict = {}):
     """Post an image to Deepstack."""
     try:
+        data["api_key"] = api_key
         response = requests.post(
-            url, files={"image": image}, data={"api_key": api_key}, timeout=timeout
+            url, files={"image": image}, data=data, timeout=timeout
         )
         return response
     except requests.exceptions.Timeout:
@@ -99,7 +102,7 @@ def process_file(self, file_path: str):
             self.process_image_bytes(image_bytes)
 
     def process_image_bytes(self, image_bytes: bytes):
-        """Process an image."""
+        """Process an image, performing detection."""
         self._predictions = []
         url = self._url_detection.format(self._ip_address, self._port)
 
@@ -132,6 +135,7 @@ def __init__(
             ip_address, port, api_key, timeout, url_detection=URL_OBJECT_DETECTION
         )
 
+
 class DeepstackFace(Deepstack):
     """Work with objects"""
 
@@ -145,3 +149,23 @@ def __init__(
         super().__init__(
             ip_address, port, api_key, timeout, url_detection=URL_FACE_DETECTION
         )
+
+    def register_face(self, name: str, file_path: str):
+        """
+        Register a face name to a file.
+        """
+
+        with open(file_path, "rb") as image:
+            response = post_image(
+                url=URL_FACE_REGISTRATION.format(self._ip_address, self._port),
+                image=image,
+                api_key=self._api_key,
+                timeout=self._timeout,
+                data={"userid": name},
+            )
+
+        if response.status_code == 200 and response.json()["success"] == True:
+            print("Taught face {} using file {}".format(name, file_path))
+        elif response.status_code == 200 and response.json()["success"] == False:
+            error = response.json()["error"]
+            print("Error: {}".format(error))

tests/test_deepstack.py

@@ -13,7 +13,7 @@
 MOCK_API_KEY = "mock_api_key"
 MOCK_TIMEOUT = 8
 
-MOCK_RESPONSE = {
+MOCK_OBJECT_DETECTION_RESPONSE = {
     "success": True,
     "predictions": [
         {
@@ -43,19 +43,19 @@
     ],
 }
 
-MOCK_PREDICTIONS = MOCK_RESPONSE["predictions"]
-MOCK_CONFIDENCES = [0.6998661, 0.7996547]
+MOCK_OBJECT_PREDICTIONS = MOCK_OBJECT_DETECTION_RESPONSE["predictions"]
+MOCK_OBJECT_CONFIDENCES = [0.6998661, 0.7996547]
 CONFIDENCE_THRESHOLD = 0.7
 
 
 def test_DeepstackObject_process_image_bytes():
     """Test a good response from server."""
     with requests_mock.Mocker() as mock_req:
-        mock_req.post(MOCK_URL, status_code=ds.HTTP_OK, json=MOCK_RESPONSE)
+        mock_req.post(MOCK_URL, status_code=ds.HTTP_OK, json=MOCK_OBJECT_DETECTION_RESPONSE)
 
         dsobject = ds.DeepstackObject(MOCK_IP_ADDRESS, MOCK_PORT)
         dsobject.process_image_bytes(MOCK_BYTES)
-        assert dsobject.predictions == MOCK_PREDICTIONS
+        assert dsobject.predictions == MOCK_OBJECT_PREDICTIONS
 
 
 def test_DeepstackObject_process_image_bytes_timeout():
@@ -69,27 +69,27 @@ def test_DeepstackObject_process_image_bytes_timeout():
             assert "SHOULD FAIL" in str(excinfo.value)
 
 
-def test_get_object_labels():
+def test_get_objects():
     """Cant always be sure order of returned list items."""
-    object_labels = ds.get_object_labels(MOCK_PREDICTIONS)
-    assert type(object_labels) is list
-    assert "dog" in object_labels
-    assert "person" in object_labels
-    assert len(object_labels) == 2
+    objects = ds.get_objects(MOCK_OBJECT_PREDICTIONS)
+    assert type(objects) is list
+    assert "dog" in objects
+    assert "person" in objects
+    assert len(objects) == 2
 
 
 def test_get_objects_summary():
-    objects_summary = ds.get_objects_summary(MOCK_PREDICTIONS)
+    objects_summary = ds.get_objects_summary(MOCK_OBJECT_PREDICTIONS)
     assert objects_summary == {"dog": 1, "person": 2}
 
 
-def test_get_label_confidences():
-    label_confidences = ds.get_label_confidences(MOCK_PREDICTIONS, "person")
-    assert label_confidences == MOCK_CONFIDENCES
+def test_get_object_confidences():
+    object_confidences = ds.get_object_confidences(MOCK_OBJECT_PREDICTIONS, "person")
+    assert object_confidences == MOCK_OBJECT_CONFIDENCES
 
 
 def test_get_confidences_above_threshold():
     assert (
-        len(ds.get_confidences_above_threshold(MOCK_CONFIDENCES, CONFIDENCE_THRESHOLD))
+        len(ds.get_confidences_above_threshold(MOCK_OBJECT_CONFIDENCES, CONFIDENCE_THRESHOLD))
         == 1
     )

usage-face-recognition.ipynb

@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -118,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -136,7 +136,7 @@
        "  'x_max': 1199}]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -156,7 +156,7 @@
        "2"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -165,6 +165,52 @@
     "len(dsface.predictions)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Register a face"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Taught face idris using file tests/images/couple.jpg\n"
+     ]
+    }
+   ],
+   "source": [
+    "dsface.register_face(name=\"idris\", file_path = 'tests/images/couple.jpg')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'tests/images/couplesdf.jpg'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-ab14dae0eb4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdsface\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_face\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"idris\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfile_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'tests/images/couplesdf.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Github/deepstack-python/deepstack/core.py\u001b[0m in \u001b[0;36mregister_face\u001b[0;34m(self, name, file_path)\u001b[0m\n\u001b[1;32m    155\u001b[0m         \"\"\"\n\u001b[1;32m    156\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 157\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    158\u001b[0m             response = post_image(\n\u001b[1;32m    159\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mURL_FACE_REGISTRATION\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ip_address\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_port\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'tests/images/couplesdf.jpg'"
+     ]
+    }
+   ],
+   "source": [
+    "dsface.register_face(name=\"idris\", file_path = 'tests/images/couplesdf.jpg')"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -173,6 +219,17 @@
     "The package provides helper functions for extracting info out of deepstack predictions"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name=\"idris\"\n",
+    "file_path=\"foo.jpg\"\n",
+    "print(\"Taught face {} using file {}\".format(name, file_path))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

usage-object-detection.ipynb

@@ -106,7 +106,6 @@
     "#%%time\n",
     "try:\n",
     "    dsobject.process_file(image_path)\n",
-    "    print(dsobject.predictions)\n",
     "except ds.DeepstackException as exc:\n",
     "    print(exc)"
    ]