Add Camera peripheral

.github/workflows/calculate-size-delta.yml

@@ -5,6 +5,7 @@ on:
 
 permissions:
   contents: read
+  pull-requests: read
 
 jobs:
   build:

containers/ei-models-runner/Dockerfile

@@ -2,7 +2,7 @@
 #
 # SPDX-License-Identifier: MPL-2.0
 
-FROM public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:4d7979284677b6bdb557abe8948fa1395dc89a63
+FROM public.ecr.aws/z9b3d4t5/inference-container-qc-adreno-702:a7ec92c6c9b2b9c94255baed18328cf4e2fda6d0
 
 # Create the user and group needed to run the container as non-root
 RUN set -ex; \

pyproject.toml

@@ -31,6 +31,7 @@ dev = [
     "setuptools",
     "build",
     "pytest",
+    "pytest-asyncio",
     "websocket-client",
     "ruff",
     "docstring_parser>=0.16",

src/arduino/app_bricks/camera_code_detection/README.md

@@ -6,8 +6,8 @@ This Brick enables real-time barcode and QR code scanning from a camera video st
 
 The Camera Code Detection Brick allows you to:
 
-- Capture frames from a USB camera.
-- Configure camera settings (resolution and frame rate).
+- Capture frames from a Camera (see Camera peripheral for supported cameras).
+- Configure Camera settings (resolution and frame rate).
 - Define the type of code to detect: barcodes and/or QR codes.
 - Process detections with customizable callbacks.
 
@@ -22,7 +22,7 @@ The Camera Code Detection Brick allows you to:
 
 ## Prerequisites
 
-To use this Brick you should have a USB camera connected to your board.
+To use this Brick you can choose to plug a camera to your board or use a network-connected camera.
 
 **Tip**: Use a USB-C® Hub with USB-A connectors to support commercial web cameras.
 
@@ -37,9 +37,25 @@ def render_frame(frame):
 def handle_detected_code(frame, detection):
     ...
 
-# Select the camera you want to use, its resolution and the max fps
-detection = CameraCodeDetection(camera=0, resolution=(640, 360), fps=10)
+detection = CameraCodeDetection()
 detection.on_frame(render_frame)
 detection.on_detection(handle_detected_code)
-detection.start()
+
+App.run()
 ```
+
+You can also select a specific camera to use:
+
+```python
+from arduino.app_bricks.camera_code_detection import CameraCodeDetection
+
+def handle_detected_code(frame, detection):
+    ...
+
+# Select the camera you want to use, its resolution and the max fps
+camera = Camera(camera="rtsp://...", resolution=(640, 360), fps=10)
+detection = CameraCodeDetection(camera)
+detection.on_detection(handle_detected_code)
+
+App.run()
+```

src/arduino/app_bricks/camera_code_detection/__init__.py

@@ -2,7 +2,6 @@
 #
 # SPDX-License-Identifier: MPL-2.0
 
-from .detection import Detection, CameraCodeDetection
-from .utils import draw_bounding_boxes, draw_bounding_box
+from .detection import CameraCodeDetection, Detection
 
-__all__ = ["CameraCodeDetection", "Detection", "draw_bounding_boxes", "draw_bounding_box"]
+__all__ = ["CameraCodeDetection", "Detection"]

src/arduino/app_bricks/camera_code_detection/detection.py

@@ -6,12 +6,12 @@
 import threading
 from typing import Callable
 
-import cv2
 from pyzbar.pyzbar import decode, ZBarSymbol, PyZbarError
 import numpy as np
-from PIL.Image import Image
+from PIL.Image import Image, fromarray
 
-from arduino.app_peripherals.usb_camera import USBCamera
+from arduino.app_peripherals.camera import Camera, BaseCamera
+from arduino.app_utils.image import greyscale
 from arduino.app_utils import brick, Logger
 
 logger = Logger("CameraCodeDetection")
@@ -44,7 +44,7 @@ class CameraCodeDetection:
     """Scans a camera video feed for QR codes and/or barcodes.
 
     Args:
-        camera (USBCamera): The USB camera instance. If None, a default camera will be initialized.
+        camera (BaseCamera): The camera instance to use for capturing video. If None, a default camera will be initialized.
         detect_qr (bool): Whether to detect QR codes. Defaults to True.
         detect_barcode (bool): Whether to detect barcodes. Defaults to True.
 
@@ -55,18 +55,20 @@ class CameraCodeDetection:
 
     def __init__(
         self,
-        camera: USBCamera = None,
+        camera: BaseCamera = None,
         detect_qr: bool = True,
         detect_barcode: bool = True,
     ):
         """Initialize the CameraCodeDetection brick."""
         if detect_qr is False and detect_barcode is False:
             raise ValueError("At least one of 'detect_qr' or 'detect_barcode' must be True.")
 
+        self._camera = camera if camera else Camera()
+
         self._detect_qr = detect_qr
         self._detect_barcode = detect_barcode
 
-        # These callbacks do not require locks as long as we're running on CPython
+        # These callbacks don't require locking as long as we're running on CPython
         self._on_frame_cb = None
         self._on_error_cb = None
 
@@ -76,8 +78,6 @@ def __init__(
 
         self.already_seen_codes = set()
 
-        self._camera = camera if camera else USBCamera()
-
     def start(self):
         """Start the detector and begin scanning for codes."""
         self._camera.start()
@@ -154,13 +154,13 @@ def loop(self):
             self._on_error(e)
             return
 
-        # Use grayscale for barcode/QR code detection
-        gs_frame = cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2GRAY)
-
-        self._on_frame(frame)
+        pil_frame = fromarray(frame)
+        self._on_frame(pil_frame)
 
+        # Use grayscale for barcode/QR code detection
+        gs_frame = greyscale(frame)
         detections = self._scan_frame(gs_frame)
-        self._on_detect(frame, detections)
+        self._on_detect(pil_frame, detections)
 
     def _on_frame(self, frame: Image):
         if self._on_frame_cb:
@@ -170,7 +170,7 @@ def _on_frame(self, frame: Image):
                 logger.error(f"Failed to run on_frame callback: {e}")
                 self._on_error(e)
 
-    def _scan_frame(self, frame: cv2.typing.MatLike) -> list[Detection]:
+    def _scan_frame(self, frame: np.ndarray) -> list[Detection]:
         """Scan the frame for a single barcode or QR code."""
         detections = []
 

src/arduino/app_bricks/camera_code_detection/examples/2_detection_list.py

@@ -19,4 +19,4 @@ def on_codes_detected(frame: Image, detections: list[Detection]):
 detector = CameraCodeDetection()
 detector.on_detect(on_codes_detected)
 
-App.run()  # This will block until the app is stopped
+App.run()

src/arduino/app_bricks/camera_code_detection/examples/3_detection_with_overrides.py

@@ -6,7 +6,7 @@
 # EXAMPLE_REQUIRES = "Requires an USB webcam connected to the Arduino board."
 from PIL.Image import Image
 from arduino.app_utils.app import App
-from arduino.app_peripherals.usb_camera import USBCamera
+from arduino.app_peripherals.usb_camera import Camera
 from arduino.app_bricks.camera_code_detection import CameraCodeDetection, Detection
 
 
@@ -17,7 +17,7 @@ def on_code_detected(frame: Image, detection: Detection):
     # e.g., draw a bounding box, save it to a database or log it.
 
 
-camera = USBCamera(camera=0, resolution=(640, 360), fps=10)
+camera = Camera(camera=2, resolution=(640, 360), fps=10)
 detector = CameraCodeDetection(camera)
 detector.on_detect(on_code_detected)
 

src/arduino/app_bricks/object_detection/README.md

@@ -23,23 +23,24 @@ The Object Detection Brick allows you to:
 ```python
 import os
 from arduino.app_bricks.object_detection import ObjectDetection
+from arduino.app_utils.image import draw_bounding_boxes
 
 object_detection = ObjectDetection()
 
-# Image frame can be as bytes or PIL image
-frame = os.read("path/to/your/image.jpg")
+# Image can be provided as bytes or PIL.Image
+img = os.read("path/to/your/image.jpg")
 
-out = object_detection.detect(frame)
-# is it possible to customize image type, confidence level and box overlap
-# out = object_detection.detect(frame, image_type = "png", confidence = 0.35, overlap = 0.5)
+out = object_detection.detect(img)
+# You can also provide a confidence level
+# out = object_detection.detect(frame, confidence = 0.35)
 if out and "detection" in out:
     for i, obj_det in enumerate(out["detection"]):
-        # For every object detected, get its details
+        # For every object detected, print its details
         detected_object = obj_det.get("class_name", None)
-        bounding_box = obj_det.get("bounding_box_xyxy", None)
         confidence = obj_det.get("confidence", None)
+        bounding_box = obj_det.get("bounding_box_xyxy", None)
 
-# draw the bounding box and key points on the image
-out_image = object_detection.draw_bounding_boxes(frame, out)
+# Draw the bounding boxes
+out_image = draw_bounding_boxes(img, out)
 ```
 

src/arduino/app_bricks/object_detection/__init__.py

@@ -4,7 +4,8 @@
 from typing import Any
 
 from PIL import Image
-from arduino.app_utils import brick, Logger, draw_bounding_boxes, Shape
+from arduino.app_utils import brick, Logger
+from arduino.app_utils.image import draw_bounding_boxes, Shape
 from arduino.app_internal.core import EdgeImpulseRunnerFacade
 
 logger = Logger("ObjectDetection")

src/arduino/app_bricks/object_detection/examples/object_detection_example.py

@@ -3,23 +3,24 @@
 # SPDX-License-Identifier: MPL-2.0
 
 # EXAMPLE_NAME = "Object Detection"
+import os
 from arduino.app_bricks.object_detection import ObjectDetection
+from arduino.app_utils.image import draw_bounding_boxes
 
 object_detection = ObjectDetection()
 
-# Image frame can be as bytes or PIL image
-with open("image.png", "rb") as f:
-    frame = f.read()
+# Image can be provided as bytes or PIL.Image
+img = os.read("path/to/your/image.jpg")
 
-out = object_detection.detect(frame)
-# is it possible to customize image type, confidence level and box overlap
-# out = object_detection.detect(frame, image_type = "png", confidence = 0.35, overlap = 0.5)
+out = object_detection.detect(img)
+# You can also provide a confidence level
+# out = object_detection.detect(frame, confidence = 0.35)
 if out and "detection" in out:
     for i, obj_det in enumerate(out["detection"]):
-        # For every object detected, get its details
+        # For every object detected, print its details
         detected_object = obj_det.get("class_name", None)
-        bounding_box = obj_det.get("bounding_box_xyxy", None)
         confidence = obj_det.get("confidence", None)
+        bounding_box = obj_det.get("bounding_box_xyxy", None)
 
-# draw the bounding box and key points on the image
-out_image = object_detection.draw_bounding_boxes(frame, out)
+# Draw the bounding boxes
+out_image = draw_bounding_boxes(img, out)