Added draft PiCamera LST extension

openflexure_microscope/api/default_extensions/__init__.py

@@ -1,3 +1,4 @@
 from .autofocus import autofocus_extension_v2
 from .scan import scan_extension_v2
 from .zip_builder import zip_extension_v2
+from .picamera_autocalibrate import lst_extension_v2

openflexure_microscope/api/default_extensions/picamera_autocalibrate/__init__.py

@@ -0,0 +1 @@
+from .extension import lst_extension_v2

openflexure_microscope/api/default_extensions/picamera_autocalibrate/extension.py

@@ -0,0 +1,67 @@
+from openflexure_microscope.common.flask_labthings.view import View
+from openflexure_microscope.common.flask_labthings.find import find_component
+from openflexure_microscope.common.flask_labthings.extensions import BaseExtension
+from openflexure_microscope.common.flask_labthings.decorators import (
+    marshal_task,
+    ThingAction,
+)
+
+from openflexure_microscope.common.labthings_core.tasks import taskify
+
+from flask import abort
+
+import logging
+
+from .recalibrate_utils import recalibrate_camera, auto_expose_and_freeze_settings
+
+
+def recalibrate(microscope):
+    """Reset the camera's settings.
+
+    This generates new gains, exposure time, and lens shading
+    table such that the background is as uniform as possible
+    with a gray level of 230.  It takes a little while to run.
+    """
+    scamera = microscope.camera
+    with scamera.lock:
+        assert not scamera.status["record_active"], "Can't recalibrate while recording!"
+        streaming = scamera.status["stream_active"]
+        if streaming:
+            logging.info("Stopping stream before recalibration")
+            scamera.stop_stream_recording(resolution=(640, 480))
+        old_resolution = scamera.camera.resolution
+        try:
+            scamera.camera.resolution = (640, 480)
+            auto_expose_and_freeze_settings(scamera.camera)
+            recalibrate_camera(scamera.camera)
+        finally:
+            scamera.camera.resolution = old_resolution
+            microscope.save_settings()
+            if streaming:
+                logging.info("Restarting stream after recalibration")
+                scamera.start_stream_recording()
+
+
+@ThingAction
+class RecalibrateView(View):
+    @marshal_task
+    def post(self):
+        microscope = find_component("org.openflexure.microscope")
+
+        if not microscope:
+            abort(503, "No microscope connected. Unable to recalibrate.")
+
+        logging.info("Starting microscope recalibration...")
+
+        return taskify(recalibrate)(microscope)
+
+
+lst_extension_v2 = BaseExtension(
+    "org.openflexure.calibration.picamera", version="2.0.0-beta.1"
+)
+
+lst_extension_v2.add_method(
+    recalibrate, "org.openflexure.calibration.picamera.recalibrate"
+)
+
+lst_extension_v2.add_view(RecalibrateView, "/recalibrate")

openflexure_microscope/api/default_extensions/picamera_autocalibrate/recalibrate_utils.py

@@ -0,0 +1,191 @@
+import numpy as np
+import time
+
+from picamera import PiCamera
+from picamera.array import PiRGBArray, PiBayerArray
+
+
+def rgb_image(camera, resize=None, **kwargs):
+    """Capture an image and return an RGB numpy array"""
+    with PiRGBArray(camera, size=resize) as output:
+        camera.capture(output, format="rgb", resize=resize, **kwargs)
+        return output.array
+
+
+def flat_lens_shading_table(camera):
+    """Return a flat (i.e. unity gain) lens shading table.
+    
+    This is mostly useful because it makes it easy to get the size
+    of the array correct.  NB if you are not using the forked picamera
+    library (with lens shading table support) it will raise an error.
+    """
+    if not hasattr(PiCamera, "lens_shading_table"):
+        raise ImportError(
+            "This program requires the forked picamera library with lens shading support"
+        )
+    return np.zeros(camera._lens_shading_table_shape(), dtype=np.uint8) + 32
+
+
+def adjust_exposure_to_setpoint(camera, setpoint):
+    """Adjust the camera's exposure time until the maximum pixel value is <setpoint>."""
+    print("Adjusting shutter speed to hit setpoint {}".format(setpoint), end="")
+    for i in range(3):
+        print(".", end="")
+        camera.shutter_speed = int(
+            camera.shutter_speed * setpoint / np.max(rgb_image(camera))
+        )
+        time.sleep(1)
+    print("done")
+
+
+def auto_expose_and_freeze_settings(camera):
+    """Freeze the settings after auto-exposing to white illumination"""
+    print("Allowing the camera to auto-expose")
+    camera.awb_mode = "auto"
+    camera.exposure_mode = "auto"
+    camera.iso = (
+        0
+    )  # This is important, if it's on a fixed ISO, gain might not set properly.
+    for i in range(6):
+        print(".", end="")
+        time.sleep(0.5)
+    print("done")
+
+    print("Freezing the camera settings...")
+    camera.shutter_speed = camera.exposure_speed
+    print("Shutter speed = {}".format(camera.shutter_speed))
+    camera.exposure_mode = "off"
+    print("Auto exposure disabled")
+    g = camera.awb_gains
+    camera.awb_mode = "off"
+    camera.awb_gains = g
+    print("Auto white balance disabled, gains are {}".format(g))
+    print(
+        "Analogue gain: {}, Digital gain: {}".format(
+            camera.analog_gain, camera.digital_gain
+        )
+    )
+    adjust_exposure_to_setpoint(camera, 215)
+
+
+def channels_from_bayer_array(bayer_array):
+    """Given the 'array' from a PiBayerArray, return the 4 channels."""
+    bayer_pattern = [(i // 2, i % 2) for i in range(4)]
+    channels = np.zeros(
+        (4, bayer_array.shape[0] // 2, bayer_array.shape[1] // 2),
+        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, :, :] = np.sum(
+            bayer_array[offset[0] :: 2, offset[1] :: 2, :], axis=2
+        )
+
+    return channels
+
+
+def lst_from_channels(channels):
+    """Given the 4 Bayer colour channels from a white image, generate a LST."""
+    full_resolution = np.array(channels.shape[1:]) * 2  # channels have been binned
+    # lst_resolution = list(np.ceil(full_resolution / 64.0).astype(int))
+    lst_resolution = [(r // 64) + 1 for r in full_resolution]
+    # NB the size of the LST is 1/64th of the image, but rounded UP.
+    print("Generating a lens shading table at {}x{}".format(*lst_resolution))
+    lens_shading = np.zeros([channels.shape[0]] + lst_resolution, dtype=np.float)
+    for i in range(lens_shading.shape[0]):
+        image_channel = channels[i, :, :]
+        iw, ih = image_channel.shape
+        ls_channel = lens_shading[i, :, :]
+        lw, lh = ls_channel.shape
+        # The lens shading table is rounded **up** in size to 1/64th of the size of
+        # the image.  Rather than handle edge images separately, I'm just going to
+        # pad the image by copying edge pixels, so that it is exactly 32 times the
+        # size of the lens shading table (NB 32 not 64 because each channel is only
+        # half the size of the full image - remember the Bayer pattern...  This
+        # should give results very close to 6by9's solution, albeit considerably
+        # less computationally efficient!
+        padded_image_channel = np.pad(
+            image_channel, [(0, lw * 32 - iw), (0, lh * 32 - ih)], mode="edge"
+        )  # Pad image to the right and bottom
+        print(
+            "Channel shape: {}x{}, shading table shape: {}x{}, after padding {}".format(
+                iw, ih, lw * 32, lh * 32, padded_image_channel.shape
+            )
+        )
+        # Next, fill the shading table (except edge pixels).  Please excuse the
+        # for loop - I know it's not fast but this code needn't be!
+        box = 3  # We average together a square of this side length for each pixel.
+        # NB this isn't quite what 6by9's program does - it averages 3 pixels
+        # horizontally, but not vertically.
+        for dx in np.arange(box) - box // 2:
+            for dy in np.arange(box) - box // 2:
+                ls_channel[:, :] += (
+                    padded_image_channel[16 + dx :: 32, 16 + dy :: 32] - 64
+                )
+        ls_channel /= box ** 2
+        # The original C code written by 6by9 normalises to the central 64 pixels in each channel.
+        # ls_channel /= np.mean(image_channel[iw//2-4:iw//2+4, ih//2-4:ih//2+4])
+        # I have had better results just normalising to the maximum:
+        ls_channel /= np.max(ls_channel)
+        # NB the central pixel should now be *approximately* 1.0 (may not be exactly
+        # due to different averaging widths between the normalisation & shading table)
+        # For most sensible lenses I'd expect that 1.0 is the maximum value.
+        # NB ls_channel should be a "view" of the whole lens shading array, so we don't
+        # need to update the big array here.
+
+    # 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.
+    gains = 32.0 / lens_shading  # 32 is unity gain
+    gains[gains > 255] = 255  # clip at 255, maximum gain is 255/32
+    gains[gains < 32] = 32  # clip at 32, minimum gain is 1 (is this necessary?)
+    lens_shading_table = gains.astype(np.uint8)
+    return lens_shading_table[::-1, :, :].copy()
+
+
+def recalibrate_camera(camera):
+    """Reset the lens shading table and exposure settings.
+
+    This method first resets to a flat lens shading table, then auto-exposes,
+    then generates a new lens shading table to make the current view uniform.
+    It should be run when the camera is looking at a uniform white scene.
+
+    NB the only parameter ``camera`` is a ``PiCamera`` instance and **not** a
+    ``StreamingCamera``.
+    """
+    camera.lens_shading_table = flat_lens_shading_table(camera)
+    _ = rgb_image(camera)  # for some reason the camera won't work unless I do this!
+
+    with PiBayerArray(camera) as a:
+        camera.capture(a, format="jpeg", bayer=True)
+        raw_image = a.array.copy()
+
+    # 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).
+    channels = channels_from_bayer_array(raw_image)
+    lens_shading_table = lst_from_channels(channels)
+
+    camera.lens_shading_table = lens_shading_table
+    _ = rgb_image(camera)
+
+    # Fix the AWB gains so the image is neutral
+    channel_means = np.mean(np.mean(rgb_image(camera), axis=0, dtype=np.float), axis=0)
+    old_gains = camera.awb_gains
+    camera.awb_gains = (
+        channel_means[1] / channel_means[0] * old_gains[0],
+        channel_means[1] / channel_means[2] * old_gains[1],
+    )
+    time.sleep(1)
+    # Ensure the background is bright but not saturated
+    adjust_exposure_to_setpoint(camera, 230)
+
+
+if __name__ == "__main__":
+    with PiCamera() as camera:
+        camera.start_preview()
+        time.sleep(3)
+        print("Recalibrating...")
+        recalibrate_camera(camera)
+        print("Done.")
+        time.sleep(2)