Merge pull request #30 from pyronear/poses-occlusion-masks

adapt the sending of alerts to the concept of poses

Author: fe51

containers/init_script/init_script.py

@@ -1,4 +1,4 @@
-###### TODO REFACTOR : use the init scripts which are in the test_update_pi directory
+#  TODO REFACTOR : use the init scripts which are in the test_update_pi directory
 
 #!/usr/bin/env python
 
@@ -72,6 +72,7 @@ def write_json_file(file_path, data):
 organizations = pd.read_csv(f"data/csv/API_DATA{sub_path} - organizations.csv")
 cameras = pd.read_csv(f"data/csv/API_DATA{sub_path} - cameras.csv")
 cameras = cameras.fillna("")
+poses = pd.read_csv(f"data/csv/API_DATA{sub_path} - poses.csv")
 
 for orga in organizations.itertuples(index=False):
     logging.info(f"saving orga : {orga.name}")
@@ -131,6 +132,16 @@ def write_json_file(file_path, data):
 write_json_file(credentials_path, data)
 write_json_file(credentials_path_etl, data_wildfire)
 
+logging.info("creating poses")
+for pose in poses.itertuples(index=False):
+    payload = {
+        "camera_id": pose.camera_id,
+        "azimuth": pose.azimuth,
+        "patrol_id": pose.patrol_id,
+    }
+    api_request("post", f"{api_url}/poses/", superuser_auth, payload)
+
+
 # Load environment variables from .env file
 # load_dotenv()
 

containers/notebooks/app/send_real_alerts.ipynb

@@ -139,6 +139,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "send_alert_from_cam_ids = [2, 4, 5, 14]  # select cameras\n",
+    "\n",
     "sequances_folders = glob.glob(f\"{SAMPLE_PATH}/*\")\n",
     "\n",
     "for camera_id in send_alert_from_cam_ids:\n",
@@ -151,17 +153,20 @@
     "    imgs.sort()\n",
     "    preds = glob.glob(f\"{sequances_folder}/labels_predictions/*\")\n",
     "    preds.sort()\n",
-    "    \n",
-    "    cam_center_azimuth = random.randint(0,360)\n",
-    "    print(f\"Sending alerts from camera {camera_id} at azimuth {cam_center_azimuth}\")\n",
+    "\n",
+    "    cam_poses = camera_client.get_current_poses().json()\n",
+    "    # pick a random pose id\n",
+    "    pose_id = random.choice(cam_poses)['id']\n",
+    "\n",
+    "    print(f\"Sending alerts from camera {camera_id} at (pose_id={pose_id})\")\n",
     "    for img_file, pred_file in zip(imgs, preds):\n",
     "    \n",
     "        stream = io.BytesIO()\n",
     "        im = Image.open(img_file)\n",
     "        im.save(stream, format=\"JPEG\", quality=80)\n",
     "\n",
     "        bboxes = read_pred_file(pred_file)\n",
-    "        response = camera_client.create_detection(stream.getvalue(), cam_center_azimuth, bboxes)\n",
+    "        response = camera_client.create_detection(stream.getvalue(), bboxes, pose_id=pose_id)\n",
     "        # Force a KeyError if the request failed\n",
     "        \n",
     "        response.json()[\"id\"]"
@@ -193,7 +198,7 @@
    "source": [
     "SEQUENCES_DIR_PATH = \"../data/alert_samples/single_sequences/*\"\n",
     "sequences_directories = glob.glob(SEQUENCES_DIR_PATH)\n",
-    "CAM_MAPPING = {22:13, 42:7, 59:5, 14:11, 43:8, 79:14, 13:10, 11:15, 59:1, 60:2, 15:12, 12:9, 10:16, 41:2, 65:8}\n",
+    "CAM_MAPPING = {22: 13, 42: 7, 59: 5, 14: 11, 43: 8, 79: 14, 13: 10, 11: 15, 60: 2, 15: 12, 12: 9, 10: 16, 41: 2, 65: 8}\n",
     "\n",
     "for seq_path in sequences_directories:\n",
     "    \n",
@@ -211,9 +216,12 @@
     "        preds = glob.glob(f\"{seq_path}/labels_predictions/*\")\n",
     "        preds.sort()\n",
     "\n",
-    "        cam_center_azimuth = random.randint(0,360)\n",
+    "        cam_poses = camera_client.get_current_poses().json()\n",
+    "        # pick a random pose id\n",
+    "        pose_id = random.choice(cam_poses)['id']\n",
+    "\n",
     "        \n",
-    "        print(f\"Sending alerts from camera {camera_id} at azimuth {cam_center_azimuth}\")\n",
+    "        print(f\"Sending alerts from camera {camera_id} at (pose_id={pose_id})\")\n",
     "        for img_file, pred_file in zip(imgs, preds):\n",
     "        \n",
     "            stream = io.BytesIO()\n",
@@ -223,9 +231,9 @@
     "            #bboxes = read_pred_file(pred_file)\n",
     "            with open(pred_file, 'r', encoding='utf-8') as file:\n",
     "                bboxes =  ast.literal_eval(file.read())\n",
-    "    \n",
     "            #bboxes = np.loadtxt(pred_file, ndmin=2)\n",
-    "            response = camera_client.create_detection(stream.getvalue(), cam_center_azimuth, bboxes)\n",
+    "            response = camera_client.create_detection(stream.getvalue(), bboxes, pose_id=pose_id)\n",
+    "\n",
     "            # Force a KeyError if the request failed\n",
     "            time.sleep(0.5)\n",
     "            response.json()[\"id\"]"
@@ -352,7 +360,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.7"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,