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operators/microphone_op.py

@@ -1,9 +1,6 @@
 # Run this in the consol first :
 
 # pip install sounddevice numpy
-
-# Don't forget to install whisper
-
 import time
 
 import numpy as np
@@ -19,7 +16,7 @@ MAX_DURATION = 5
 
 class Operator:
     """
-    Infering object from images
+    Microphone operator that records the audio
     """
 
     def on_event(

operators/object_detection.py

@@ -1,7 +1,3 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-
 import numpy as np
 import pyarrow as pa
 
@@ -16,7 +12,9 @@ CAMERA_HEIGHT = 540
 
 class Operator:
     """
-    Infering object from images
+    Object Detection: Infering object from images using Deep Learning model YOLOv8.
+    The output sent is bounding box representing the corners of the bounding box
+    as well as their COCO label.
     """
 
     def __init__(self):
@@ -36,16 +34,7 @@ class Operator:
         dora_input,
         send_output,
     ) -> DoraStatus:
-        """Handle image
-        Args:
-            dora_input (dict) containing the "id", value, and "metadata"
-            send_output Callable[[str, bytes | pa.Array, Optional[dict]], None]:
-                Function for sending output to the dataflow:
-                - First argument is the `output_id`
-                - Second argument is the data as either bytes or `pa.Array`
-                - Third argument is dora metadata dict
-                e.g.: `send_output("bbox", pa.array([100], type=pa.uint8()), dora_event["metadata"])`
-        """
+        """Handle image"""
 
         frame = dora_input["value"].to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
         frame = frame[:, :, ::-1]  # OpenCV image (BGR to RGB)
@@ -53,6 +42,7 @@ class Operator:
         # Process results
         boxes = np.array(results[0].boxes.xyxy.cpu())
         conf = np.array(results[0].boxes.conf.cpu())
+        # COCO Label
         label = np.array(results[0].boxes.cls.cpu())
         # concatenate them together
         arrays = np.concatenate((boxes, conf[:, None], label[:, None]), axis=1)

operators/plot.py

@@ -1,6 +1,3 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
 import os
 from typing import Callable, Optional, Union
 
@@ -29,7 +26,7 @@ writer = cv2.VideoWriter(
 
 class Operator:
     """
-    Plot image and bounding box
+    Plot image on window and additional context
     """
 
     def __init__(self):
@@ -54,17 +51,7 @@ class Operator:
         send_output: Callable[[str, Union[bytes, pa.UInt8Array], Optional[dict]], None],
     ) -> DoraStatus:
         """
-        Put image and bounding box on cv2 window.
-
-        Args:
-            dora_input["id"] (str): Id of the dora_input declared in the yaml configuration
-            dora_input["value"] (arrow array): message of the dora_input
-            send_output Callable[[str, bytes | pa.UInt8Array, Optional[dict]], None]:
-                Function for sending output to the dataflow:
-                - First argument is the `output_id`
-                - Second argument is the data as either bytes or `pa.UInt8Array`
-                - Third argument is dora metadata dict
-                e.g.: `send_output("bbox", pa.array([100], type=pa.uint8()), dora_event["metadata"])`
+        Plot image on window and additional context
         """
         if dora_input["id"] == "image":
             frame = (

operators/sentence_transformers_op.py

@@ -12,13 +12,15 @@ SHOULD_NOT_BE_INCLUDED = [
     "utils.py",
     "sentence_transformers_op.py",
     "chatgpt_op.py",
-    "whisper_op.py",
-    "microphone_op.py",
-    "object_detection_op.py",
-    "webcam.py",
+    "mistral_op.py",
 ]
 
-SHOULD_BE_INCLUDED = ["planning_op.py"]
+SHOULD_BE_INCLUDED = [
+    "planning_op.py",
+    "object_detection_op.py",
+    "plot.py",
+    "microphone_op.py",
+]
 
 
 ## Get all python files path in given directory

operators/webcam.py

@@ -14,7 +14,7 @@ CI = os.environ.get("CI")
 
 CAMERA_WIDTH = 960
 CAMERA_HEIGHT = 540
-CAMERA_INDEX = int(os.getenv("CAMERA_INDEX", 0))
+CAMERA_INDEX = int(os.getenv("CAMERA_INDEX", 2))
 
 font = cv2.FONT_HERSHEY_SIMPLEX
 

operators/whisper_op.py

@@ -16,7 +16,7 @@ model = whisper.load_model("base")
 
 class Operator:
     """
-    Infering object from images
+    Transforming Speech to Text using OpenAI Whisper model
     """
 
     def on_event(