Tidy core

Author: robmarkcole

README.md

@@ -2,4 +2,4 @@
 [![PyPI Version](https://img.shields.io/pypi/v/deepstack-python.svg)](https://pypi.org/project/deepstack-python/)
 
 # deepstack-python
-Unofficial python API for DeepStack
+Unofficial python API for DeepStack. Provides class for making requests to the object detection endpoint, and functions for processing the result. See the `usage.ipynb` notebook for usage.

deepstack/core.py

@@ -7,8 +7,6 @@
 
 ## Const
 HTTP_OK = 200
-HTTP_BAD_REQUEST = 400
-HTTP_UNAUTHORIZED = 401
 DEFAULT_TIMEOUT = 10  # seconds
 
 
@@ -26,12 +24,12 @@ def get_confidences_above_threshold(
     return [val for val in confidences if val >= confidence_threshold]
 
 
-def get_object_labels(predictions: List[Dict]) -> Set[str]:
+def get_object_labels(predictions: List[Dict]) -> List[str]:
     """
     Get a list of the unique object labels predicted.
     """
     labels = [pred["label"] for pred in predictions]
-    return set(labels)
+    return list(set(labels))
 
 
 def get_label_confidences(predictions: List[Dict], target_label: str):

usage.ipynb

@@ -4,7 +4,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This package provides convenience classes and functions for working with deepstack object detection API"
+    "This package provides convenience classes and functions for working with deepstack object detection API. \n",
+    "Pull the latest image for noavx:\n",
+    "```\n",
+    "docker pull deepquestai/deepstack:noavx\n",
+    "```\n",
+    "\n",
+    "Run deepstack with:\n",
+    "```\n",
+    "docker run -e VISION-DETECTION=True -e VISION-FACE=True -e MODE=High -d \\\n",
+    "      -v localstorage:/datastore -p 5000:5000 \\\n",
+    "      -e API-KEY=\"Mysecretkey\" \\\n",
+    "       --name deepstack deepquestai/deepstack:noavx\n",
+    "```\n",
+    "\n",
+    "Note that by default, the minimum confidence for detected objects is 0.45"
    ]
   },
   {
@@ -22,20 +36,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "IP_ADDRESS = 'localhost'\n",
     "PORT = '5000'\n",
     "API_KEY = \"Mysecretkey\"\n",
-    "# API_KEY = \"BadKey\"\n",
     "TIMEOUT = 8"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -104,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -130,7 +143,7 @@
        "  'x_max': 825}]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -149,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,16 +178,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'dog', 'person'}"
+       "['person', 'dog']"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,12 +200,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Get a summary of the number of occurances of labels, and confidence level"
+    "Get a summary of the number of occurances of labels"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -201,13 +214,34 @@
        "{'person': 2, 'dog': 1}"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary = ds.get_objects_summary(dsobject.predictions)\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['person', 'dog']"
+      ]
+     },
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ds.get_objects_summary(dsobject.predictions)"
+    "list(summary.keys())"
    ]
   },
   {
@@ -219,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -228,7 +262,7 @@
        "[0.9998661, 0.9996547]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,17 +281,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "CONFIDENCE_THRESHOLD = 0.9997\n",
     "print(len(ds.get_confidences_above_threshold(confidences, CONFIDENCE_THRESHOLD)))"