diff --git a/openflexure_microscope/api/default_extensions/__init__.py b/openflexure_microscope/api/default_extensions/__init__.py index 9ad73b3c..b3bd9b69 100644 --- a/openflexure_microscope/api/default_extensions/__init__.py +++ b/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 diff --git a/openflexure_microscope/api/default_extensions/picamera_autocalibrate/__init__.py b/openflexure_microscope/api/default_extensions/picamera_autocalibrate/__init__.py new file mode 100644 index 00000000..49d03097 --- /dev/null +++ b/openflexure_microscope/api/default_extensions/picamera_autocalibrate/__init__.py @@ -0,0 +1 @@ +from .extension import lst_extension_v2 diff --git a/openflexure_microscope/api/default_extensions/picamera_autocalibrate/extension.py b/openflexure_microscope/api/default_extensions/picamera_autocalibrate/extension.py new file mode 100644 index 00000000..fb0d71d7 --- /dev/null +++ b/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") diff --git a/openflexure_microscope/api/default_extensions/picamera_autocalibrate/recalibrate_utils.py b/openflexure_microscope/api/default_extensions/picamera_autocalibrate/recalibrate_utils.py new file mode 100644 index 00000000..5dae166d --- /dev/null +++ b/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 .""" + 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)