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Consolidate the BaseCamera, CameraStub, and CameraProtocol, move StreamingPiCamera2 to this repo

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This commit is contained in:
Julian Stirling 2025-05-21 20:31:51 +01:00
parent 628fd145f3
commit b5606984ae
11 changed files with 1617 additions and 143 deletions
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32
hardware-specific-tests/picamera2/test_acquisition.py Normal file
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@ -0,0 +1,32 @@
from labthings_picamera2 import StreamingPiCamera2
from labthings_fastapi.server import ThingServer
from labthings_fastapi.client import ThingClient
from fastapi.testclient import TestClient
from PIL import Image
import numpy as np
from pytest import fixture
@fixture(scope="module")
def client():
server = ThingServer()
server.add_thing(StreamingPiCamera2(), "/camera/")
with TestClient(server.app) as test_client:
client = ThingClient.from_url("/camera/", client=test_client)
yield client
def test_calibration(client):
client.full_auto_calibrate()
def test_jpeg_and_array(client):
blob = client.grab_jpeg()
mjpeg_frame = Image.open(blob.open())
assert mjpeg_frame
blob = client.capture_jpeg(resolution="main")
jpeg_capture = Image.open(blob.open())
assert jpeg_capture
arrlist = client.capture_array(stream_name="main")
array_main = np.array(arrlist)
assert mjpeg_frame.size == jpeg_capture.size
assert array_main.shape[1::-1] == jpeg_capture.size

32
hardware-specific-tests/picamera2/test_exposure_time_drift.py Normal file
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@ -0,0 +1,32 @@
import logging
import time
from fastapi.testclient import TestClient
from labthings_fastapi.server import ThingServer
from labthings_fastapi.client import ThingClient
from labthings_picamera2.thing import StreamingPiCamera2
logging.basicConfig(level=logging.DEBUG)
def test_exposure_time_drift():
cam = StreamingPiCamera2()
server = ThingServer()
server.add_thing(cam, "/camera/")
with TestClient(server.app) as test_client:
client = ThingClient.from_url("/camera/", client=test_client)
client.exposure_time = 50000
time.sleep(0.1)
initial_et = client.exposure_time
print(f"Before capture, et is {client.exposure_time}")
for i in range(10):
client.capture_jpeg(resolution="full")
print(f"After capture, et is {client.exposure_time}")
final_et = client.exposure_time
assert initial_et == final_et
if __name__ == "__main__":
test_exposure_time_drift()

27
hardware-specific-tests/picamera2/test_sensor_mode.py Normal file
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@ -0,0 +1,27 @@
import logging
from fastapi.testclient import TestClient
import numpy as np
from labthings_fastapi.server import ThingServer
from labthings_fastapi.client import ThingClient
from labthings_picamera2.thing import StreamingPiCamera2
logging.basicConfig(level=logging.DEBUG)
def test_sensor_mode():
cam = StreamingPiCamera2()
server = ThingServer()
server.add_thing(cam, "/camera/")
with TestClient(server.app) as test_client:
client = ThingClient.from_url("/camera/", client=test_client)
for size in [(3280, 2464), (1640, 1232)]:
client.sensor_mode = {"output_size": size, "bit_depth": 10}
arr = np.array(client.capture_array(stream_name="raw"))
assert arr.shape[0] == size[1]
if __name__ == "__main__":
test_sensor_mode()

81
hardware-specific-tests/picamera2/test_tuning.py Normal file
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@ -0,0 +1,81 @@
import os
from picamera2 import Picamera2
from labthings_picamera2 import recalibrate_utils
import pytest
MODEL = Picamera2.global_camera_info()[0]['Model']
def check_camera_available():
assert len(Picamera2.global_camera_info()) >= 1
def load_default_tuning():
fname = f"{MODEL}.json"
return Picamera2.load_tuning_file(fname)
def generate_bad_tuning():
default_tuning = load_default_tuning()
bad_tuning = default_tuning.copy()
bad_tuning["version"] = 999
return bad_tuning
def print_tuning(read_file=False):
key = "LIBCAMERA_RPI_TUNING_FILE"
if key in os.environ:
print(f"Tuning file environment variable: {os.environ[key]}")
if read_file:
with open(os.environ[key], "r") as f:
print(f.read())
else:
print("Tuning file environment variable not set")
def _test_bad_tuning_after_good_tuning(configure):
bad_tuning = generate_bad_tuning()
default_tuning = load_default_tuning()
print_tuning()
print("opening camera with explicitly specified tuning")
with Picamera2(tuning=default_tuning) as cam:
print_tuning()
if configure:
cam.configure(cam.create_preview_configuration())
del cam
recalibrate_utils.recreate_camera_manager()
print(f"Opening camera with tuning['version'] = {bad_tuning['version']}")
with pytest.raises(IndexError):
# The bad version should cause a problem
cam = Picamera2(tuning=bad_tuning)
print_tuning()
print("Success (not expected)!")
del cam
recalibrate_utils.recreate_camera_manager()
with Picamera2(tuning=default_tuning) as cam:
# Reload the camera with working tuning, or it will stop responding
# and fail future tests
pass
del cam
@pytest.mark.filterwarnings("ignore: Exception ignored")
def test_bad_tuning_after_good_tuning_noconfigure():
_test_bad_tuning_after_good_tuning(False)
@pytest.mark.filterwarnings("ignore: Exception ignored")
def test_bad_tuning_after_good_tuning_configure():
_test_bad_tuning_after_good_tuning(True)
@pytest.mark.filterwarnings("ignore: Exception ignored")
def test_bad_tuning_after_good_tuning_noconfigure2():
_test_bad_tuning_after_good_tuning(False)
@pytest.mark.filterwarnings("ignore: Exception ignored")
def test_bad_tuning_after_good_tuning_configure2():
_test_bad_tuning_after_good_tuning(True)

2
ofm_config_full.json
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@ -1,6 +1,6 @@
{
"things": {
"/camera/": "labthings_picamera2.thing:StreamingPiCamera2",
"/camera/": "openflexure_microscope_server.things.camera.picamera:StreamingPiCamera2",
"/stage/": "labthings_sangaboard:SangaboardThing",
"/auto_recentre_stage/": "openflexure_microscope_server.things.auto_recentre_stage:RecentringThing",
"/autofocus/": "openflexure_microscope_server.things.autofocus:AutofocusThing",

2
pyproject.toml
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@ -40,7 +40,7 @@ dev = [
"matplotlib~=3.10"
]
pi = [
"labthings-picamera2 == 0.0.2",
"picamera2~=0.3.12",
]
[project.scripts]

141
src/openflexure_microscope_server/things/camera/__init__.py
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@ -10,6 +10,8 @@ from __future__ import annotations
import logging
from typing import Literal, Protocol, runtime_checkable, Optional
from pydantic import RootModel
from labthings_fastapi.thing import Thing
from labthings_fastapi.decorators import thing_action, thing_property
from labthings_fastapi.dependencies.metadata import GetThingStates
@ -24,129 +26,14 @@ from labthings_fastapi.types.numpy import NDArray
class JPEGBlob(Blob):
media_type: str = "image/jpeg"
class PNGBlob(Blob):
media_type: str = "image/png"
@runtime_checkable
class CameraProtocol(Protocol):
"""A Thing representing a camera"""
def __enter__(self) -> None: ...
def __exit__(self, _exc_type, _exc_value, _traceback) -> None: ...
@property
def stream_active(self) -> bool:
"Whether the MJPEG stream is active"
...
def snap_image(self) -> NDArray:
"""Acquire one image from the camera."""
...
def capture_array(
self,
stream_name: Literal["main", "lores", "raw", "full"] = "main",
wait: Optional[float] = 5,
) -> NDArray: ...
def capture_jpeg(
self,
metadata_getter: GetThingStates,
resolution: Literal["lores", "main", "full"] = "main",
wait: Optional[float] = 5,
) -> JPEGBlob:
"""Acquire one image from the camera and return as a JPEG blob"""
...
def grab_jpeg(
self,
portal: BlockingPortal,
stream_name: Literal["main", "lores"] = "main",
) -> JPEGBlob:
"""Acquire one image from the preview stream and return as an array
This differs from `capture_jpeg` in that it does not pause the MJPEG
preview stream. Instead, we simply return the next frame from that
stream (either "main" for the preview stream, or "lores" for the low
resolution preview). No metadata is returned.
"""
...
def grab_jpeg_size(
self,
portal: BlockingPortal,
stream_name: Literal["main", "lores"] = "main",
) -> int:
"""Acquire one image from the preview stream and return its size"""
...
def start_streaming(self, main_resolution, buffer_count) -> None:
"""Start (or stop and restart) the camera with the given resolution
for the main stream, and buffer_count number of images in the buffer"""
...
def capture_image(self, stream_name, wait):
"""Capture a PIL image from stream stream_name with timeout wait"""
...
class ArrayModel(RootModel):
"""A model for an array"""
root: NDArray
class BaseCamera(Thing):
"""A Thing representing a camera
This is a concrete base class for `Thing`s implementing the `CameraProtocol`.
It provides the stream descriptors and actions to grab from the stream.
"""
mjpeg_stream = MJPEGStreamDescriptor()
lores_mjpeg_stream = MJPEGStreamDescriptor()
@thing_action
def snap_image(self) -> NDArray:
"""Acquire one image from the camera.
This action cannot run if the camera is in use by a background thread, for
example if a preview stream is running.
"""
return self.capture_array()
@thing_action
def grab_jpeg(
self,
portal: BlockingPortal,
stream_name: Literal["main", "lores"] = "main",
) -> JPEGBlob:
"""Acquire one image from the preview stream and return as an array
This differs from `capture_jpeg` in that it does not pause the MJPEG
preview stream. Instead, we simply return the next frame from that
stream (either "main" for the preview stream, or "lores" for the low
resolution preview). No metadata is returned.
"""
logging.info(
f"StreamingPiCamera2.grab_jpeg(stream_name={stream_name}) starting"
)
stream = (
self.lores_mjpeg_stream if stream_name == "lores" else self.mjpeg_stream
)
frame = portal.call(stream.grab_frame)
logging.info(
f"StreamingPiCamera2.grab_jpeg(stream_name={stream_name}) got frame"
)
return JPEGBlob.from_bytes(frame)
@thing_action
def grab_jpeg_size(
self,
portal: BlockingPortal,
stream_name: Literal["main", "lores"] = "main",
) -> int:
"""Acquire one image from the preview stream and return its size"""
stream = (
self.lores_mjpeg_stream if stream_name == "lores" else self.mjpeg_stream
)
return portal.call(stream.next_frame_size)
class CameraStub(BaseCamera):
"""A stub for a camera, to allow dependencies
@ -158,8 +45,11 @@ class CameraStub(BaseCamera):
This stub class should be used for dependencies on the CameraProtocol.
"""
mjpeg_stream = MJPEGStreamDescriptor()
lores_mjpeg_stream = MJPEGStreamDescriptor()
def __enter__(self) -> None:
raise NotImplementedError("Cameras must not inherit from CameraStub")
raise NotImplementedError("CameraThings must define their own __enter__ method")
def __exit__(self, _exc_type, _exc_value, _traceback) -> None:
raise NotImplementedError("Cameras must not inherit from CameraStub")
@ -169,11 +59,6 @@ class CameraStub(BaseCamera):
"Whether the MJPEG stream is active"
raise NotImplementedError("Cameras must not inherit from CameraStub")
@thing_action
def snap_image(self) -> NDArray:
"""Acquire one image from the camera."""
raise NotImplementedError("Cameras must not inherit from CameraStub")
@thing_action
def capture_array(
self,
@ -204,5 +89,5 @@ class CameraStub(BaseCamera):
raise NotImplementedError("Cameras must not inherit from CameraStub")
CameraDependency = direct_thing_client_dependency(CameraStub, "/camera/")
RawCameraDependency = raw_thing_dependency(CameraProtocol)
CameraDependency = direct_thing_client_dependency(BaseCamera, "/camera/")
RawCameraDependency = raw_thing_dependency(BaseCamera)

3
src/openflexure_microscope_server/things/camera/opencv.py
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@ -53,9 +53,6 @@ class OpenCVCamera(BaseCamera):
return self._capture_thread.is_alive()
return False
mjpeg_stream = MJPEGStreamDescriptor()
lores_mjpeg_stream = MJPEGStreamDescriptor()
def _capture_frames(self):
portal = get_blocking_portal(self)
while self._capture_enabled:

868
src/openflexure_microscope_server/things/camera/picamera.py Normal file
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@ -0,0 +1,868 @@
from __future__ import annotations
from dataclasses import dataclass
from datetime import datetime
import io
import json
import logging
import os
import tempfile
import time
from tempfile import TemporaryDirectory
from pydantic import BaseModel, BeforeValidator
from labthings_fastapi.descriptors.property import PropertyDescriptor
from labthings_fastapi.thing import Thing
from labthings_fastapi.decorators import thing_action, thing_property
from labthings_fastapi.outputs.mjpeg_stream import MJPEGStream
from labthings_fastapi.utilities import get_blocking_portal
from labthings_fastapi.types.numpy import NDArray
from labthings_fastapi.dependencies.metadata import GetThingStates
from labthings_fastapi.dependencies.blocking_portal import BlockingPortal
from typing import Annotated, Any, Iterator, Literal, Mapping, Optional, Self
from contextlib import contextmanager
import piexif
from scipy.ndimage import zoom
from scipy.interpolate import interp1d
from PIL import Image
from threading import RLock
import picamera2
from picamera2 import Picamera2
from picamera2.encoders import MJPEGEncoder
from picamera2.outputs import Output
import numpy as np
from . import picamera_recalibrate_utils as recalibrate_utils
from . import BaseCamera, JPEGBlob, PNGBlob, ArrayModel
class PicameraControl(PropertyDescriptor):
def __init__(
self, control_name: str, model: type = float, description: Optional[str] = None
):
"""A property descriptor controlling a picamera control"""
PropertyDescriptor.__init__(
self, model, observable=False, description=description
)
self.control_name = control_name
def _getter(self, obj: StreamingPiCamera2):
with obj.picamera() as cam:
ret = cam.capture_metadata()[self.control_name]
return ret
def _setter(self, obj: StreamingPiCamera2, value: Any):
with obj.picamera() as cam:
cam.set_controls({self.control_name: value})
class PicameraStreamOutput(Output):
"""An Output class that sends frames to a stream"""
def __init__(self, stream: MJPEGStream, portal: BlockingPortal):
"""Create an output that puts frames in an MJPEGStream
We need to pass the stream object, and also the blocking portal, because
new frame notifications happen in the anyio event loop and frames are
sent from a thread. The blocking portal enables thread-to-async
communication.
"""
Output.__init__(self)
self.stream = stream
self.portal = portal
def outputframe(
self, frame, _keyframe=True, _timestamp=None, _packet=None, _audio=False
):
"""Add a frame to the stream's ringbuffer"""
self.stream.add_frame(frame, self.portal)
class SensorMode(BaseModel):
unpacked: str
bit_depth: int
size: tuple[int, int]
fps: float
crop_limits: tuple[int, int, int, int]
exposure_limits: tuple[Optional[int], Optional[int], Optional[int]]
format: Annotated[str, BeforeValidator(repr)]
class SensorModeSelector(BaseModel):
output_size: tuple[int, int]
bit_depth: int
class LensShading(BaseModel):
luminance: list[list[float]]
Cr: list[list[float]]
Cb: list[list[float]]
class StreamingPiCamera2(BaseCamera):
"""A Thing that represents an OpenCV camera"""
def __init__(self, camera_num: int = 0):
self.camera_num = camera_num
self.camera_configs: dict[str, dict] = {}
# NB persistent controls will be updated with settings, in __enter__.
self.persistent_controls = {
"AeEnable": False,
"AnalogueGain": 1.0,
"AwbEnable": False,
"Brightness": 0,
"ColourGains": (1, 1),
"Contrast": 1,
"ExposureTime": 0,
"Saturation": 1,
"Sharpness": 1,
}
self.persistent_control_tolerances = {
"ExposureTime": 30,
}
def update_persistent_controls(self, discard_frames: int = 1):
"""Update the persistent controls dict from the camera
Query the camera and update the value of `persistent_controls` to
match the current state of the camera.
There is a work-around here, that will suppress small updates. There
appears to be a bug in the camera code that causes a slight drift in
`ExposureTime` each time the camera is reinitialised: this can
add up over time, particularly if the camera is reconfigured many
times. To get around this, we look in `self.persistent_control_tolerances`
and only update `self.persistent_controls` if the change is greater than
this tolerance.
"""
with self.picamera() as cam:
for i in range(discard_frames):
# Discard frames, so we know our data is fresh
cam.capture_metadata()
for k, v in cam.capture_metadata().items():
if k in self.persistent_controls:
if k in self.persistent_control_tolerances:
if (
np.abs(self.persistent_controls[k] - v)
< self.persistent_control_tolerances[k]
):
logging.debug(
f"Ignoring a small change in persistent control {k}"
f"from {self.persistent_controls[k]} to {v}"
"while updating persistent controls."
)
continue # Ignore small changes, to avoid drift
self.persistent_controls[k] = v
self.thing_settings.update(
self.persistent_controls
) # TODO: Is this saving to the wrong place?
def settings_to_persistent_controls(self):
"""Update the persistent controls dict from the settings dict
NB this must be called **after** self.thing_settings is initialised,
i.e. during or after `__enter__`.
"""
try:
pc = self.thing_settings["persistent_controls"]
except KeyError:
return # If there are no saved settings, use defaults
for k in self.persistent_controls:
try:
self.persistent_controls[k] = pc[k]
except KeyError:
pass # If controls are missing, leave at default
stream_resolution = PropertyDescriptor(
tuple[int, int],
initial_value=(820, 616),
description="Resolution to use for the MJPEG stream",
)
mjpeg_bitrate = PropertyDescriptor(
Optional[int],
initial_value=100000000,
description="Bitrate for MJPEG stream (None for default)",
)
@mjpeg_bitrate.setter
def mjpeg_bitrate(self, value: Optional[int]):
"""Restart the stream when we set the bitrate"""
with self.picamera(pause_stream=True):
pass # just pausing and restarting the stream is enough.
stream_active = PropertyDescriptor(
bool,
initial_value=False,
description="Whether the MJPEG stream is active",
observable=True,
readonly=True,
)
analogue_gain = PicameraControl("AnalogueGain", float)
colour_gains = PicameraControl("ColourGains", tuple[float, float])
exposure_time = PicameraControl(
"ExposureTime", int, description="The exposure time in microseconds"
)
_sensor_modes = None
@thing_property
def sensor_modes(self) -> list[SensorMode]:
"""All the available modes the current sensor supports"""
if not self._sensor_modes:
with self.picamera() as cam:
self._sensor_modes = cam.sensor_modes
return self._sensor_modes
@thing_property
def sensor_mode(self) -> Optional[SensorModeSelector]:
"""The intended sensor mode of the camera"""
return self.thing_settings["sensor_mode"]
@sensor_mode.setter
def sensor_mode(self, new_mode: Optional[SensorModeSelector]):
"""Change the sensor mode used"""
if isinstance(new_mode, SensorModeSelector):
new_mode = new_mode.model_dump()
with self.picamera(pause_stream=True):
self.thing_settings["sensor_mode"] = new_mode
@thing_property
def sensor_resolution(self) -> tuple[int, int]:
"""The native resolution of the camera's sensor"""
with self.picamera() as cam:
return cam.sensor_resolution
tuning = PropertyDescriptor(Optional[dict], None, readonly=True)
def settings_to_properties(self):
"""Set the values of properties based on the settings dict"""
try:
props = self.thing_settings["properties"]
except KeyError:
return
for k, v in props.items():
setattr(self, k, v)
def properties_to_settings(self):
"""Save certain properties to the settings dictionary"""
props = {}
for k in ["mjpeg_bitrate", "stream_resolution"]:
props[k] = getattr(self, k)
self.thing_settings["properties"] = props
def initialise_tuning(self):
"""Read the tuning from the settings, or load default tuning
NB this relies on `self.thing_settings` and `self.default_tuning`
so will fail if it's run before those are populated in `__enter__`.
"""
if "tuning" in self.thing_settings:
# TODO: should this be a separate file?
self.tuning = self.thing_settings["tuning"].dict
else:
logging.info("Did not find tuning in settings, reading from camera...")
self.tuning = self.default_tuning
def initialise_picamera(self):
"""Acquire the picamera device and store it as `self._picamera`"""
if hasattr(self, "_picamera_lock"):
# Don't close the camera if it's in use
self._picamera_lock.acquire()
with tempfile.NamedTemporaryFile("w") as tuning_file:
# This duplicates logic in `Picamera2.__init__` to provide a tuning file
# that will be read when the camera system initialises.
# This is a necessary work-around until `picamera2` better supports
# reinitialisation of the camera with new tuning.
json.dump(self.tuning, tuning_file)
tuning_file.flush() # but leave it open as closing it will delete it
os.environ["LIBCAMERA_RPI_TUNING_FILE"] = tuning_file.name
# NB even though we've put the tuning file in the environment, we will
# need to specify the filename in the `Picamera2` initialiser as otherwise
# it will be overwritten with None.
if hasattr(self, "_picamera") and self._picamera:
print("Closing picamera object for reinitialisation")
logging.info(
"Camera object already exists, closing for reinitialisation"
)
self._picamera.close()
print("closed, deleting picamera")
del self._picamera
recalibrate_utils.recreate_camera_manager()
print("[re]creating Picamera2 object")
self._picamera = picamera2.Picamera2(
camera_num=self.camera_num,
tuning=self.tuning,
)
self._picamera_lock = RLock()
def __enter__(self):
self.populate_default_tuning()
self.initialise_tuning()
self.initialise_picamera()
self.sensor_modes
self.settings_to_persistent_controls()
self.settings_to_properties()
self.start_streaming()
return self
@contextmanager
def picamera(self, pause_stream=False) -> Iterator[Picamera2]:
"""Return the underlying `Picamera2` instance, optionally pausing the stream.
If pause_stream is True (default is False), we will stop the MJPEG stream
before yielding control of the camera, and restart afterwards. If you make
changes to the camera settings, these may be ignored when the stream is
restarted: you may nened to call `update_persistent_controls()` to ensure
your changes persist after the stream restarts.
"""
already_streaming = self.stream_active
with self._picamera_lock:
if pause_stream and already_streaming:
self.update_persistent_controls()
self.stop_streaming(stop_web_stream=False)
try:
yield self._picamera
finally:
if pause_stream and already_streaming:
self.start_streaming()
def populate_default_tuning(self):
"""Sensor modes are enumerated and stored, once, on start-up (`__enter__`).
This opens and closes the camera - must be run before the camera is
initialised.
"""
logging.info("Starting & reconfiguring camera to populate sensor_modes.")
with Picamera2(camera_num=self.camera_num) as cam:
self.default_tuning = recalibrate_utils.load_default_tuning(cam)
logging.info("Done reading sensor modes & default tuning.")
def __exit__(self, exc_type, exc_value, traceback):
# Allow key controls to persist across restarts
self.update_persistent_controls()
self.thing_settings["persistent_controls"] = self.persistent_controls
self.thing_settings["tuning"] = self.tuning
self.properties_to_settings()
self.thing_settings.write_to_file()
# Shut down the camera
self.stop_streaming()
with self.picamera() as cam:
cam.close()
del self._picamera
@thing_action
def start_streaming(
self, main_resolution: tuple[int, int] = (820, 616), buffer_count: int = 6
) -> None:
"""
Start the MJPEG stream
Sets the camera resolutions based on input parameters, and sets the low-res
resolution to (320, 240). Note: (320, 240) is a standard from the Pi Camera
manual.
Create two streams:
- `lores_mjpeg_stream` for autofocus at low-res resolution
- `mjpeg_stream` for preview. This is the `main_resolution` if this is less
than (1280, 960), or the low-res resolution if above. This allows for
high resolution capture without streaming high resolution video.
main_resolution: the resolution for the main configuration. Defaults to
(820, 616), 1/4 sensor size.
buffer_count: the number of frames to hold in the buffer. Higher uses more memory,
lower may cause dropped frames. Defaults to 6.
"""
with self.picamera() as picam:
# TODO: Filip: can we use the lores output to keep preview stream going
# while recording? According to picamera2 docs 4.2.1.6 this should work
try:
if picam.started:
picam.stop()
picam.stop_encoder() # make sure there are no other encoders going
stream_config = picam.create_video_configuration(
main={"size": main_resolution},
lores={"size": (320, 240), "format": "YUV420"},
sensor=self.thing_settings.get("sensor_mode", None),
controls=self.persistent_controls,
)
# Set buffer count - can't be negative
stream_config["buffer_count"] = buffer_count
picam.configure(stream_config)
logging.info("Starting picamera MJPEG stream...")
stream_name = "lores" if main_resolution[0] > 1280 else "main"
picam.start_recording(
MJPEGEncoder(self.mjpeg_bitrate),
PicameraStreamOutput(
self.mjpeg_stream,
get_blocking_portal(self),
),
name=stream_name,
)
picam.start_encoder(
MJPEGEncoder(100000000),
PicameraStreamOutput(
self.lores_mjpeg_stream,
get_blocking_portal(self),
),
name="lores",
)
except Exception as e:
logging.exception("Error while starting preview: {e}")
logging.exception(e)
else:
self.stream_active = True
logging.debug(
"Started MJPEG stream at %s on port %s", self.stream_resolution, 1
)
@thing_action
def stop_streaming(self, stop_web_stream=True) -> None:
"""
Stop the MJPEG stream
"""
with self.picamera() as picam:
try:
picam.stop_recording() # This should also stop the extra lores encoder
except Exception as e:
logging.info("Stopping recording failed")
logging.exception(e)
else:
self.stream_active = False
if stop_web_stream:
self.mjpeg_stream.stop()
self.lores_mjpeg_stream.stop()
logging.info("Stopped MJPEG stream.")
# Increase the resolution for taking an image
time.sleep(
0.2
) # Sprinkled a sleep to prevent camera getting confused by rapid commands
@thing_action
def capture_image(
self,
stream_name: Literal["main", "lores", "raw", "full"] = "main",
wait: Optional[float] = 0.9,
):
"""Acquire one image from the camera.
Return it as a PIL Image
stream_name: (Optional) The PiCamera2 stream to use, should be one of ["main", "lores", "raw", "full"]. Default = "main"
wait: (Optional, float) Set a timeout in seconds.
A TimeoutError is raised if this time is exceeded during capture.
Default = 0.9s, lower than the 1s timeout default in picamera yaml settings
"""
with self.picamera() as cam:
return cam.capture_image(stream_name, wait=wait)
@thing_action
def capture_array(
self,
stream_name: Literal["main", "lores", "raw", "full"] = "main",
wait: Optional[float] = 0.9,
) -> ArrayModel:
"""Acquire one image from the camera and return as an array
This function will produce a nested list containing an uncompressed RGB image.
It's likely to be highly inefficient - raw and/or uncompressed captures using
binary image formats will be added in due course.
stream_name: (Optional) The PiCamera2 stream to use, should be one of ["main", "lores", "raw", "full"]. Default = "main"
wait: (Optional, float) Set a timeout in seconds.
A TimeoutError is raised if this time is exceeded during capture.
Default = 0.9s, lower than the 1s timeout default in picamera yaml settings
"""
# This was slower than capture_image for our use case, but directly returning
# an image as an array is still a useful feature
if stream_name == "full":
with self.picamera(pause_stream=True) as picam2:
capture_config = picam2.create_still_configuration()
return picam2.switch_mode_and_capture_array(capture_config, wait=wait)
with self.picamera() as cam:
return cam.capture_array(stream_name, wait=wait)
@thing_property
def camera_configuration(self) -> Mapping:
"""The "configuration" dictionary of the picamera2 object
The "configuration" sets the resolution and format of the camera's streams.
Together with the "tuning" it determines how the sensor is configured and
how the data is processed.
Note that the configuration may be modified when taking still images, and
this property refers to whatever configuration is currently in force -
usually the one used for the preview stream.
"""
with self.picamera() as cam:
return cam.camera_configuration()
@thing_action
def capture_jpeg(
self,
metadata_getter: GetThingStates,
resolution: Literal["lores", "main", "full"] = "main",
wait: Optional[float] = 0.9,
) -> JPEGBlob:
"""Acquire one image from the camera as a JPEG
The JPEG will be acquired using `Picamera2.capture_file`. If the
`resolution` parameter is `main` or `lores`, it will be captured
from the main preview stream, or the low-res preview stream,
respectively. This means the camera won't be reconfigured, and
the stream will not pause (though it may miss one frame).
If `full` resolution is requested, we will briefly pause the
MJPEG stream and reconfigure the camera to capture a full
resolution image.
wait: (Optional, float) Set a timeout in seconds.
A TimeoutError is raised if this time is exceeded during capture.
Default = 0.9s, lower than the 1s timeout default in picamera yaml settings
Note that this always uses the image processing pipeline - to
bypass this, you must use a raw capture.
"""
fname = datetime.now().strftime("%Y-%m-%d-%H%M%S.jpeg")
folder = TemporaryDirectory()
path = os.path.join(folder.name, fname)
config = self.camera_configuration
# Low-res and main streams are running already - so we don't need
# to reconfigure for these
if resolution in ("lores", "main") and config[resolution]:
with self.picamera() as cam:
cam.capture_file(path, name=resolution, format="jpeg", wait=wait)
else:
if resolution != "full":
logging.warning(
f"There was no {resolution} stream, capturing full resolution"
)
with self.picamera(pause_stream=True) as cam:
logging.info("Reconfiguring camera for full resolution capture")
cam.configure(cam.create_still_configuration())
cam.start()
cam.options["quality"] = 95
logging.info("capturing")
cam.capture_file(path, name="main", format="jpeg", wait=wait)
logging.info("done")
# After the file is written, add metadata about the current Things
exif_dict = piexif.load(path)
exif_dict["Exif"][piexif.ExifIFD.UserComment] = json.dumps(
metadata_getter()
).encode("utf-8")
piexif.insert(piexif.dump(exif_dict), path)
return JPEGBlob.from_temporary_directory(folder, fname)
@thing_action
def grab_jpeg(
self,
portal: BlockingPortal,
stream_name: Literal["main", "lores"] = "main",
) -> JPEGBlob:
"""Acquire one image from the preview stream and return as an array
This differs from `capture_jpeg` in that it does not pause the MJPEG
preview stream. Instead, we simply return the next frame from that
stream (either "main" for the preview stream, or "lores" for the low
resolution preview). No metadata is returned.
"""
logging.debug(
f"StreamingPiCamera2.grab_jpeg(stream_name={stream_name}) starting"
)
stream = (
self.lores_mjpeg_stream if stream_name == "lores" else self.mjpeg_stream
)
frame = portal.call(stream.grab_frame)
logging.debug(
f"StreamingPiCamera2.grab_jpeg(stream_name={stream_name}) got frame"
)
return JPEGBlob.from_bytes(frame)
@thing_action
def grab_jpeg_size(
self,
portal: BlockingPortal,
stream_name: Literal["main", "lores"] = "main",
) -> int:
"""Acquire one image from the preview stream and return its size"""
stream = (
self.lores_mjpeg_stream if stream_name == "lores" else self.mjpeg_stream
)
return portal.call(stream.next_frame_size)
@thing_property
def exposure(self) -> float:
"""An alias for `exposure_time` to fit the micromanager API"""
return self.exposure_time
@exposure.setter # type: ignore
def exposure(self, value):
self.exposure_time = value
@thing_property
def capture_metadata(self) -> dict:
"""Return the metadata from the camera"""
with self.picamera() as cam:
return cam.capture_metadata()
@thing_action
def auto_expose_from_minimum(
self,
target_white_level: int = 700,
percentile: float = 99.9,
):
"""Adjust exposure to hit the target white level
Starting from the minimum exposure, we gradually increase exposure until
we hit the specified white level. We use a percentile rather than the
maximum, in order to be robust to a small number of noisy/bright pixels.
"""
with self.picamera(pause_stream=True) as cam:
recalibrate_utils.adjust_shutter_and_gain_from_raw(
cam,
target_white_level=target_white_level,
percentile=percentile,
)
self.update_persistent_controls()
@thing_action
def calibrate_white_balance(
self,
method: Literal["percentile", "centre"] = "centre",
luminance_power: float = 1.0,
):
"""Correct the white balance of the image
This calibration requires a neutral image, such that the 99th centile
of each colour channel should correspond to white. We calculate the
centiles and use this to set the colour gains. This is done on the raw
image with the lens shading correction applied, which should mean
that the image is uniform, rather than weighted towards the centre.
If `method` is `"centre"`, we will correct the mean of the central 10%
of the image.
"""
with self.picamera(pause_stream=True) as cam:
if self.lens_shading_is_static:
lst: LensShading = self.lens_shading_tables
recalibrate_utils.adjust_white_balance_from_raw(
cam,
percentile=99,
luminance=lst.luminance,
Cr=lst.Cr,
Cb=lst.Cb,
luminance_power=luminance_power,
method=method,
)
else:
recalibrate_utils.adjust_white_balance_from_raw(
cam, percentile=99, method=method
)
self.update_persistent_controls()
@thing_action
def calibrate_lens_shading(self):
"""Take an image and use it for flat-field correction.
This method requires an empty (i.e. bright) field of view. It will take
a raw image and effectively divide every subsequent image by the current
one. This uses the camera's "tuning" file to correct the preview and
the processed images. It should not affect raw images.
"""
with self.picamera(pause_stream=True) as cam:
L, Cr, Cb = recalibrate_utils.lst_from_camera(cam)
recalibrate_utils.set_static_lst(self.tuning, L, Cr, Cb)
self.initialise_picamera()
@thing_property
def colour_correction_matrix(
self,
) -> tuple[float, float, float, float, float, float, float, float, float]:
"""An alias for `colour_correction_matrix` to fit the micromanager API"""
return self.thing_settings.get(
"colour_correction_matrix",
tuple(recalibrate_utils.get_static_ccm(self.tuning)[0]["ccm"]),
)
@colour_correction_matrix.setter # type: ignore
def colour_correction_matrix(self, value):
self.thing_settings["colour_correction_matrix"] = value
self.calibrate_colour_correction(value)
@thing_action
def reset_ccm(self):
"""Overwrite the colour correction matrix in camera tuning with default values from the documentation"""
c = [
1.80439,
-0.73699,
-0.06739,
-0.36073,
1.83327,
-0.47255,
-0.08378,
-0.56403,
1.64781,
]
self.colour_correction_matrix = c
@thing_action
def calibrate_colour_correction(self, c: tuple):
"""Overwrite the colour correction matrix in camera tuning"""
with self.picamera(pause_stream=True):
recalibrate_utils.set_static_ccm(self.tuning, c)
self.initialise_picamera()
@thing_action
def set_static_green_equalisation(self, offset: int = 65535):
"""Set the green equalisation to a static value.
Green equalisation avoids the debayering algorithm becoming confused
by the two green channels having different values, which is a problem
when the chief ray angle isn't what the sensor was designed for, and
that's the case in e.g. a microscope using camera module v2.
A value of 0 here does nothing, a value of 65535 is maximum correction.
"""
with self.picamera(pause_stream=True):
recalibrate_utils.set_static_geq(self.tuning, offset)
self.initialise_picamera()
@thing_action
def full_auto_calibrate(self):
"""Perform a full auto-calibration
This function will call the other calibration actions in sequence:
* `flat_lens_shading` to disable flat-field
* `auto_expose_from_minimum`
* `set_static_green_equalisation` to set geq offset to max
* `calibrate_lens_shading`
* `calibrate_white_balance`
"""
self.flat_lens_shading()
self.auto_expose_from_minimum()
self.set_static_green_equalisation()
self.calibrate_lens_shading()
self.calibrate_white_balance()
@thing_action
def flat_lens_shading(self):
"""Disable flat-field correction
This method will set a completely flat lens shading table. It is not the
same as the default behaviour, which is to use an adaptive lens shading
table.
"""
with self.picamera(pause_stream=True):
f = np.ones((12, 16))
recalibrate_utils.set_static_lst(self.tuning, f, f, f)
self.initialise_picamera()
@thing_property
def lens_shading_tables(self) -> Optional[LensShading]:
"""The current lens shading (i.e. flat-field correction)
This returns the current lens shading correction, as three 2D lists
each with dimensions 16x12. This assumes that we are using a static
lens shading table - if adaptive control is enabled, or if there
are multiple LSTs in use for different colour temperatures,
we return a null value to avoid confusion.
"""
if not self.lens_shading_is_static:
return None
alsc = Picamera2.find_tuning_algo(self.tuning, "rpi.alsc")
if any(len(alsc[f"calibrations_C{c}"]) != 1 for c in ("r", "b")):
return None
def reshape_lst(lin: list[float]) -> list[list[float]]:
w, h = 16, 12
return [lin[w * i : w * (i + 1)] for i in range(h)]
return LensShading(
luminance=reshape_lst(alsc["luminance_lut"]),
Cr=reshape_lst(alsc["calibrations_Cr"][0]["table"]),
Cb=reshape_lst(alsc["calibrations_Cb"][0]["table"]),
)
@lens_shading_tables.setter
def lens_shading_tables(self, lst: LensShading) -> None:
"""Set the lens shading tables"""
with self.picamera(pause_stream=True):
recalibrate_utils.set_static_lst(
self.tuning,
luminance=lst.luminance,
cr=lst.Cr,
cb=lst.Cb,
)
self.initialise_picamera()
def correct_colour_gains_for_lens_shading(
self, colour_gains: tuple[float, float]
) -> tuple[float, float]:
"""Correct white balance gains for the effect of lens shading
The white balance algorithm we use assumes the brightest pixels
should be white, and that the only thing affecting the colour of
said pixels is the `colour_gains`.
The lens shading correction is normalised such that the *minimum*
gain in the `Cr` and `Cb` channels is 1. The white balance
assumption above requires that the gain for the brightest pixels
is 1. The solution might be that, when calibrating, we note which
pixels are brightest (usually the centre) and explicitly use
the LST values for there. However, for now I will assume that we
need to normalise by the **maximum** of the `Cr` and `Cb`
channels, which is correct the majority of the time.
"""
if not self.lens_shading_is_static:
return colour_gains
lst = self.lens_shading_tables
# The Cr and Cb corrections are normalised to have a minimum of 1,
# but the white balance algorithm normalises the brightest pixels
# to be white, assuming the brightest pixels have equal gain from
# the LST.
gain_r, gain_b = colour_gains
return (
float(gain_r / np.max(lst.Cr)),
float(gain_b / np.max(lst.Cb)),
)
@thing_action
def flat_lens_shading_chrominance(self):
"""Disable flat-field correction
This method will set the chrominance of the lens shading table to be
flat, i.e. we'll correct vignetting of intensity, but not any change in
colour across the image.
"""
with self.picamera(pause_stream=True):
alsc = Picamera2.find_tuning_algo(self.tuning, "rpi.alsc")
luminance = alsc["luminance_lut"]
flat = np.ones((12, 16))
recalibrate_utils.set_static_lst(self.tuning, luminance, flat, flat)
self.initialise_picamera()
@thing_action
def reset_lens_shading(self):
"""Revert to default lens shading settings
This method will restore the default "adaptive" lens shading method used
by the Raspberry Pi camera.
"""
with self.picamera(pause_stream=True):
recalibrate_utils.copy_alsc_section(self.default_tuning, self.tuning)
self.initialise_picamera()
@thing_property
def lens_shading_is_static(self) -> bool:
"""Whether the lens shading is static
This property is true if the lens shading correction has been set to use
a static table (i.e. the number of automatic correction iterations is zero).
The default LST is not static, but all the calibration controls will set it
to be static (except "reset")
"""
return recalibrate_utils.lst_is_static(self.tuning)

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src/openflexure_microscope_server/things/camera/picamera_recalibrate_utils.py Normal file
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@ -0,0 +1,561 @@
"""
Functions to set up a Raspberry Pi Camera v2 for scientific use
This module provides slower, simpler functions to set the
gain, exposure, and white balance of a Raspberry Pi camera, using
the `picamera2` Python library. It's mostly used by the OpenFlexure
Microscope, though it deliberately has no hard dependencies on
said software, so that it's useful on its own.
There are three main calibration steps:
* Setting exposure time and gain to get a reasonably bright
image.
* Fixing the white balance to get a neutral image
* Taking a uniform white image and using it to calibrate
the Lens Shading Table
The most reliable way to do this, avoiding any issues relating
to "memory" or nonlinearities in the camera's image processing
pipeline, is to use raw images. This is quite slow, but very
reliable. The three steps above can be accomplished by:
```
picamera = picamera2.Picamera2()
adjust_shutter_and_gain_from_raw(picamera)
adjust_white_balance_from_raw(picamera)
lst = lst_from_camera(picamera)
picamera.lens_shading_table = lst
```
"""
from __future__ import annotations
import gc
import logging
import time
from typing import List, Literal, Optional, Tuple
from pydantic import BaseModel
import numpy as np
from scipy.ndimage import zoom
from picamera2 import Picamera2
import picamera2
def load_default_tuning(cam: Picamera2) -> dict:
"""Load the default tuning file for the camera
This will open and close the camera to determine its model. If you are
using a model that's supported by `picamera2` it should have a tuning
file built in. If not, this will probably crash with an error.
Error handling for unsupported cameras is not something we are likely
to test in the short term.
"""
cp = cam.camera_properties
fname = f"{cp['Model']}.json"
try:
return cam.load_tuning_file(fname)
except RuntimeError:
dir = "/usr/share/libcamera/ipa/raspberrypi" # from picamera2 v0.3.9
# The directory above has been removed from the search path, which I
# find odd - as that's where the files currently are on a default
# Raspbian image. This may need updating if the files have moved
# in future updates to the system libcamera package
return cam.load_tuning_file(fname, dir=dir)
def set_minimum_exposure(camera: Picamera2):
"""Enable manual exposure, with low gain and shutter speed
We set exposure mode to manual, analog and digital gain
to 1, and shutter speed to the minimum (8us for Pi Camera v2)
NB ISO is left at auto, because this is needed for the gains
to be set correctly.
"""
camera.set_controls({"AeEnable": False, "AnalogueGain": 1, "ExposureTime": 1})
# camera.iso = 0 # We must set ISO=0 (auto) or we can't set gain
# camera.analog_gain = 1
# camera.digital_gain = 1 (not configurable)
# Setting the shutter speed to 1us will result in it being set
# to the minimum possible, which is probably 8us for PiCamera v2
# camera.shutter_speed = 1
time.sleep(0.5)
class ExposureTest(BaseModel):
"""Record the results of testing the camera's current exposure settings"""
level: int
exposure_time: int
analog_gain: float
def test_exposure_settings(camera: Picamera2, percentile: float) -> ExposureTest:
"""Evaluate current exposure settings using a raw image
CAMERA SHOULD BE STARTED!
We will acquire a raw image and calculate the given percentile
of the pixel values. We return a dictionary containing the
percentile (which will be compared to the target), as well as
the camera's shutter and gain values.
"""
camera.capture_array("raw") # controls might not be updated for the first frame?
max_brightness = np.percentile(
channels_from_bayer_array(camera.capture_array("raw")),
percentile,
)
# The reported brightness can, theoretically, be negative or zero
# because of black level compensation. The line below forces a
# minimum value of 1 which will keep things well-behaved!
if max_brightness < 1:
logging.warning(
f"Measured brightness of {max_brightness}. "
"This should normally be >= 1, and may indicate the "
"camera's black level compensation has gone wrong."
)
max_brightness = 1
metadata = camera.capture_metadata()
result = ExposureTest(
level=max_brightness,
exposure_time=int(metadata["ExposureTime"]),
analog_gain=float(metadata["AnalogueGain"]),
)
logging.info(f"{result.model_dump()}")
return result
def check_convergence(test: ExposureTest, target: int, tolerance: float):
"""Check whether the brightness is within the specified target range"""
converged = abs(test.level - target) < target * tolerance
return converged
def adjust_shutter_and_gain_from_raw(
camera: Picamera2,
target_white_level: int = 700,
max_iterations: int = 20,
tolerance: float = 0.05,
percentile: float = 99.9,
) -> float:
"""Adjust exposure and analog gain based on raw images.
This routine is slow but effective. It uses raw images, so we
are not affected by white balance or digital gain.
Arguments:
target_white_level:
The raw, 10-bit value we aim for. The brightest pixels
should be approximately this bright. Maximum possible
is about 900, 700 is reasonable.
max_iterations:
We will terminate once we perform this many iterations,
whether or not we converge. More than 10 shouldn't happen.
tolerance:
How close to the target value we consider "done". Expressed
as a fraction of the ``target_white_level`` so 0.05 means
+/- 5%
percentile:
Rather then use the maximum value for each channel, we
calculate a percentile. This makes us robust to single
pixels that are bright/noisy. 99.9% still picks the top
of the brightness range, but seems much more reliable
than just ``np.max()``.
"""
# TODO: read black level and bit depth from camera?
if target_white_level * (tolerance + 1) >= 959:
raise ValueError(
"The target level is too high - a saturated image would be "
"considered successful. target_white_level * (tolerance + 1) "
"must be less than 959."
)
config = camera.create_still_configuration(raw={"format": "SBGGR10"})
camera.configure(config)
camera.start()
set_minimum_exposure(camera)
# We start with very low exposure settings and work up
# until either the brightness is high enough, or we can't increase the
# shutter speed any more.
iterations = 0
while iterations < max_iterations:
test = test_exposure_settings(camera, percentile)
if check_convergence(test, target_white_level, tolerance):
break
iterations += 1
# Adjust shutter speed so that the brightness approximates the target
# NB we put a maximum of 8 on this, to stop it increasing too quickly.
new_time = int(test.exposure_time * min(target_white_level / test.level, 8))
camera.controls.ExposureTime = new_time
camera.controls.AeEnable = False
time.sleep(0.5)
# Check whether the shutter speed is still going up - if not, we've hit a maximum
if camera.capture_metadata()["ExposureTime"] == test.exposure_time:
logging.info(f"Shutter speed has maxed out at {test.exposure_time}")
break
# Now, if we've not converged, increase gain until we converge or run out of options.
while iterations < max_iterations:
test = test_exposure_settings(camera, percentile)
if check_convergence(test, target_white_level, tolerance):
break
iterations += 1
# Adjust gain to make the white level hit the target, again with a maximum
camera.controls.AnalogueGain = test.analog_gain * min(
target_white_level / test.level, 2
)
time.sleep(0.5)
# Check the gain is still changing - if not, we have probably hit the maximum
if camera.capture_metadata()["AnalogueGain"] == test.analog_gain:
logging.info(f"Gain has maxed out. at {test.analog_gain}")
break
if check_convergence(test, target_white_level, tolerance):
logging.info(f"Brightness has converged to within {tolerance * 100 :.0f}%.")
else:
logging.warning(
f"Failed to reach target brightness of {target_white_level}."
f"Brightness reached {test.level} after {iterations} iterations."
)
return test.level
def adjust_white_balance_from_raw(
camera: Picamera2,
percentile: float = 99,
luminance: Optional[np.ndarray] = None,
Cr: Optional[np.ndarray] = None,
Cb: Optional[np.ndarray] = None,
luminance_power: float = 1.0,
method: Literal["percentile", "centre"] = "centre",
) -> Tuple[float, float]:
"""Adjust the white balance in a single shot, based on the raw image.
NB if ``channels_from_raw_image`` is broken, this will go haywire.
We should probably have better logic to verify the channels really
are BGGR...
"""
config = camera.create_still_configuration(raw={"format": "SBGGR10"})
camera.configure(config)
camera.start()
channels = channels_from_bayer_array(camera.capture_array("raw"))
# logging.info(f"White balance: channels were retrieved with shape {channels.shape}.")
if luminance is not None and Cr is not None and Cb is not None:
# Reconstruct a low-resolution image from the lens shading tables
# and use it to normalise the raw image, to compensate for
# the brightest pixels in each channel not coinciding.
grids = grids_from_lst(np.array(luminance) ** luminance_power, Cr, Cb)
channel_gains = 1 / grids
if channel_gains.shape[1:] != channels.shape[1:]:
channel_gains = upsample_channels(channel_gains, channels.shape[1:])
logging.info(f"Before gains, channel maxima are {np.max(channels, axis=(1,2))}")
channels = channels * channel_gains
logging.info(f"After gains, channel maxima are {np.max(channels, axis=(1,2))}")
if method == "centre":
_, h, w = channels.shape
blue, g1, g2, red = (
np.mean(
channels[:, 9 * h // 20 : 11 * h // 20, 9 * w // 20 : 11 * w // 20],
axis=(1, 2),
)
- 64
)
else:
# TODO: read black level from camera rather than hard-coding 64
blue, g1, g2, red = np.percentile(channels, percentile, axis=(1, 2)) - 64
green = (g1 + g2) / 2.0
new_awb_gains = (green / red, green / blue)
if Cr is not None and Cb is not None:
# The LST algorithm normalises Cr and Cb by their minimum.
# The lens shading correction only ever boosts the red and blue values.
# Here, we decrease the gains by the minimum value of Cr and Cb.
new_awb_gains = (green / red * np.min(Cr), green / blue * np.min(Cb))
logging.info(
f"Raw white point is R: {red} G: {green} B: {blue}, "
f"setting AWB gains to ({new_awb_gains[0]:.2f}, "
f"{new_awb_gains[1]:.2f})."
)
camera.controls.AwbEnable = False
camera.controls.ColourGains = new_awb_gains
time.sleep(0.2)
m = camera.capture_metadata()
print(f"Camera confirms gains are now {m['ColourGains']}")
return new_awb_gains
def channels_from_bayer_array(bayer_array: np.ndarray) -> np.ndarray:
"""Given the 'array' from a PiBayerArray, return the 4 channels."""
bayer_pattern: List[Tuple[int, int]] = [(0, 0), (0, 1), (1, 0), (1, 1)]
bayer_array = bayer_array.view(np.uint16)
channels_shape: Tuple[int, int, int] = (
4,
bayer_array.shape[0] // 2,
bayer_array.shape[1] // 2,
)
channels: np.ndarray = np.zeros(channels_shape, dtype=bayer_array.dtype)
for i, offset in enumerate(bayer_pattern):
# We simplify life by dealing with only one channel at a time.
channels[i, :, :] = bayer_array[offset[0] :: 2, offset[1] :: 2]
return channels
LensShadingTables = tuple[np.ndarray, np.ndarray, np.ndarray]
def get_16x12_grid(chan: np.ndarray, dx: int, dy: int):
"""Compresses channel down to a 16x12 grid - from libcamera
This is taken from https://git.linuxtv.org/libcamera.git/tree/utils/raspberrypi/ctt/ctt_alsc.py
for consistency.
"""
grid = []
"""
since left and bottom border will not necessarily have rectangles of
dimension dx x dy, the 32nd iteration has to be handled separately.
"""
for i in range(11):
for j in range(15):
grid.append(np.mean(chan[dy * i : dy * (1 + i), dx * j : dx * (1 + j)]))
grid.append(np.mean(chan[dy * i : dy * (1 + i), 15 * dx :]))
for j in range(15):
grid.append(np.mean(chan[11 * dy :, dx * j : dx * (1 + j)]))
grid.append(np.mean(chan[11 * dy :, 15 * dx :]))
"""
return as np.array, ready for further manipulation
"""
return np.reshape(np.array(grid), (12, 16))
def upsample_channels(grids: np.ndarray, shape: tuple[int]):
"""Zoom an image in the last two dimensions
This is effectively the inverse operation of `get_16x12_grid`
"""
zoom_factors = [
1,
] + list(np.ceil(np.array(shape) / np.array(grids.shape[1:])))
return zoom(grids, zoom_factors, order=1)[:, : shape[0], : shape[1]]
def downsampled_channels(channels: np.ndarray, blacklevel=64) -> list[np.ndarray]:
"""Generate a downsampled, un-normalised image from which to calculate the LST
TODO: blacklevel probably ought to be determined from the camera...
"""
channel_shape = np.array(channels.shape[1:])
lst_shape = np.array([12, 16])
step = np.ceil(channel_shape / lst_shape).astype(int)
return np.stack(
[
get_16x12_grid(
channels[i, ...].astype(float) - blacklevel, step[1], step[0]
)
for i in range(channels.shape[0])
],
axis=0,
)
def lst_from_channels(channels: np.ndarray) -> LensShadingTables:
"""Given the 4 Bayer colour channels from a white image, generate a LST.
Internally, is just calls `downsampled_channels` and `lst_from_grids`.
"""
grids = downsampled_channels(channels)
return lst_from_grids(grids)
def lst_from_grids(grids: np.ndarray) -> LensShadingTables:
"""Given 4 downsampled grids, generate the luminance and chrominance tables
The LST format has changed with `picamera2` and now uses a fixed resolution,
and is in luminance, Cr, Cb format. This function returns three ndarrays of
luminance, Cr, Cb, each with shape (12, 16).
# TODO: make consistent with
https://git.linuxtv.org/libcamera.git/tree/utils/raspberrypi/ctt/ctt_alsc.py
"""
r: np.ndarray = grids[3, ...]
g: np.ndarray = np.mean(grids[1:3, ...], axis=0)
b: np.ndarray = grids[0, ...]
# What we actually want to calculate is the gains needed to compensate for the
# lens shading - that's 1/lens_shading_table_float as we currently have it.
luminance_gains: np.ndarray = np.max(g) / g # Minimum luminance gain is 1
cr_gains: np.ndarray = g / r
# cr_gains /= cr_gains[5, 7] # Normalise so the central colour doesn't change
cb_gains: np.ndarray = g / b
# cb_gains /= cb_gains[5, 7]
return luminance_gains, cr_gains, cb_gains
def grids_from_lst(lum: np.ndarray, Cr: np.ndarray, Cb: np.ndarray) -> np.ndarray:
"""Convert form luminance/chrominance dict to four RGGB channels
Note that these will be normalised - the maximum green value is always 1.
Also, note that the channels are BGGR, to be consistent with the
`channels_from_raw_image` function. This should probably change in the
future.
"""
G = 1 / np.array(lum)
R = G / np.array(Cr)
B = G / np.array(Cb)
return np.stack([B, G, G, R], axis=0)
def set_static_lst(
tuning: dict,
luminance: np.ndarray,
cr: np.ndarray,
cb: np.ndarray,
) -> None:
"""Update the `rpi.alsc` section of a camera tuning dict to use a static correcton.
`tuning` will be updated in-place to set its shading to static, and disable any
adaptive tweaking by the algorithm.
"""
for table in luminance, cr, cb:
assert np.array(table).shape == (12, 16), "Lens shading tables must be 12x16!"
alsc = Picamera2.find_tuning_algo(tuning, "rpi.alsc")
alsc["n_iter"] = 0 # disable the adaptive part
alsc["luminance_strength"] = 1.0
alsc["calibrations_Cr"] = [
{"ct": 4500, "table": np.reshape(cr, (-1)).round(3).tolist()}
]
alsc["calibrations_Cb"] = [
{"ct": 4500, "table": np.reshape(cb, (-1)).round(3).tolist()}
]
alsc["luminance_lut"] = np.reshape(luminance, (-1)).round(3).tolist()
def set_static_ccm(tuning: dict, c: list) -> None:
"""Update the `rpi.alsc` section of a camera tuning dict to use a static correcton.
`tuning` will be updated in-place to set its shading to static, and disable any
adaptive tweaking by the algorithm.
"""
ccm = Picamera2.find_tuning_algo(tuning, "rpi.ccm")
ccm["ccms"] = [{"ct": 2860, "ccm": c}]
def get_static_ccm(tuning: dict) -> None:
"""Get the `rpi.ccm` section of a camera tuning dict"""
ccm = Picamera2.find_tuning_algo(tuning, "rpi.ccm")
return ccm["ccms"]
def lst_is_static(tuning: dict) -> bool:
"""Whether the lens shading table is set to static"""
alsc = Picamera2.find_tuning_algo(tuning, "rpi.alsc")
return alsc["n_iter"] == 0
def set_static_geq(
tuning: dict,
offset: int = 65535,
) -> None:
"""Update the `rpi.geq` section of a camera tuning dict to always use green
equalisation that averages the green pixels in the red and blue rows.
`tuning` will be updated in-place to set the geq offest to the given value.
The default 65535 is the maximum allowed value. This means
the brightness will always be below the threshold where averaging is used.
"""
geq = Picamera2.find_tuning_algo(tuning, "rpi.geq")
geq["offset"] = offset # max out offset to disable the adaptive green equalisation
def _geq_is_static(tuning: dict) -> bool:
"""Whether the green equalisation is set to static"""
geq = Picamera2.find_tuning_algo(tuning, "rpi.geq")
return geq["offset"] == 65535
def index_of_algorithm(algorithms: list[dict], algorithm: str):
"""Find the index of an algorithm's section in the tuning file"""
for i, a in enumerate(algorithms):
if algorithm in a:
return i
def copy_alsc_section(from_tuning: dict, to_tuning: dict):
"""Copy the `rpi.alsc` algorithm from one tuning to another.
This is done in-place, i.e. modifying to_tuning.
"""
from_i = index_of_algorithm(from_tuning["algorithms"], "rpi.alsc")
to_i = index_of_algorithm(to_tuning["algorithms"], "rpi.alsc")
# Please excuse the clumsy update-and-delete - this lets us use
# the nice Picamera2 function to find the relevant sub-dict.
to_tuning["algorithms"][to_i] = from_tuning["algorithms"][from_i]
def lst_from_camera(camera: Picamera2) -> LensShadingTables:
"""Acquire a raw image and use it to calculate a lens shading table."""
channels = raw_channels_from_camera(camera)
return lst_from_channels(channels)
def raw_channels_from_camera(camera: Picamera2) -> LensShadingTables:
"""Acquire a raw image and return a 4xNxM array of the colour channels."""
if camera.started:
camera.stop_recording()
# We will acquire a raw image with unpacked pixels, which is what the
# format below requests. Bit depth and Bayer order may be overwritten.
# TODO: don't assume 10-bit - the high quality camera uses 12.
# TODO: what's the best mode to use here?
config = camera.create_still_configuration(raw={"format": "SBGGR10"})
camera.configure(config)
camera.start()
raw_image = camera.capture_array("raw")
camera.stop()
# Now we need to calculate a lens shading table that would make this flat.
# raw_image is a 3D array, with full resolution and 3 colour channels. No
# de-mosaicing has been done, so 2/3 of the values are zero (3/4 for R and B
# channels, 1/2 for green because there's twice as many green pixels).
format = camera.camera_configuration()["raw"]["format"]
print(f"Acquired a raw image in format {format}")
return channels_from_bayer_array(raw_image)
def recreate_camera_manager():
"""Delete and recreate the camera manager.
This is necessary to ensure the tuning file is re-read.
"""
del Picamera2._cm
gc.collect()
Picamera2._cm = picamera2.picamera2.CameraManager()
if __name__ == "__main__":
"""This block is untested but has been updated."""
with Picamera2() as cam:
tuning = load_default_tuning(cam)
f = np.ones((12, 16))
set_static_lst(tuning, f, f, f)
set_static_geq(tuning)
with Picamera2(tuning=tuning) as cam:
cam.start_preview()
time.sleep(3)
logging.info("Recalibrating...")
adjust_shutter_and_gain_from_raw(cam)
adjust_white_balance_from_raw(cam)
lst = lst_from_camera(cam)
set_static_lst(tuning, *lst)
logging.info("Done.")
with Picamera2(tuning=tuning) as cam:
cam.start_preview()
time.sleep(2)

11
src/openflexure_microscope_server/things/camera/simulation.py
View file

@ -22,12 +22,11 @@ from scipy.ndimage import gaussian_filter
from labthings_fastapi.utilities import get_blocking_portal
from labthings_fastapi.decorators import thing_action, thing_property
from labthings_fastapi.dependencies.metadata import GetThingStates
from labthings_fastapi.outputs.mjpeg_stream import MJPEGStreamDescriptor
from labthings_fastapi.types.numpy import NDArray
from labthings_fastapi.server import ThingServer
from pydantic import RootModel
from . import BaseCamera, JPEGBlob
from . import BaseCamera, JPEGBlob, ArrayModel
from ..stage import StageProtocol as Stage
# The ratio between "motor" steps and pixels
@ -35,11 +34,6 @@ from ..stage import StageProtocol as Stage
RATIO = 0.2
class ArrayModel(RootModel):
"""A model for an array"""
root: NDArray
class SimulatedCamera(BaseCamera):
"""A Thing representing an OpenCV camera"""
@ -160,9 +154,6 @@ class SimulatedCamera(BaseCamera):
return self._capture_thread.is_alive()
return False
mjpeg_stream = MJPEGStreamDescriptor()
lores_mjpeg_stream = MJPEGStreamDescriptor()
def _capture_frames(self):
portal = get_blocking_portal(self)
while self._capture_enabled: