Added draft PiCamera LST extension
This commit is contained in:
Joel Collins
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4 changed files with 260 additions and 0 deletions
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from .autofocus import autofocus_extension_v2
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from .scan import scan_extension_v2
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from .zip_builder import zip_extension_v2
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from .picamera_autocalibrate import lst_extension_v2
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from .extension import lst_extension_v2
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from openflexure_microscope.common.flask_labthings.view import View
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from openflexure_microscope.common.flask_labthings.find import find_component
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from openflexure_microscope.common.flask_labthings.extensions import BaseExtension
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from openflexure_microscope.common.flask_labthings.decorators import (
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marshal_task,
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ThingAction,
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)
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from openflexure_microscope.common.labthings_core.tasks import taskify
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from flask import abort
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import logging
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from .recalibrate_utils import recalibrate_camera, auto_expose_and_freeze_settings
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def recalibrate(microscope):
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"""Reset the camera's settings.
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This generates new gains, exposure time, and lens shading
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table such that the background is as uniform as possible
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with a gray level of 230. It takes a little while to run.
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"""
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scamera = microscope.camera
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with scamera.lock:
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assert not scamera.status["record_active"], "Can't recalibrate while recording!"
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streaming = scamera.status["stream_active"]
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if streaming:
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logging.info("Stopping stream before recalibration")
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scamera.stop_stream_recording(resolution=(640, 480))
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old_resolution = scamera.camera.resolution
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try:
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scamera.camera.resolution = (640, 480)
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auto_expose_and_freeze_settings(scamera.camera)
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recalibrate_camera(scamera.camera)
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finally:
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scamera.camera.resolution = old_resolution
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microscope.save_settings()
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if streaming:
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logging.info("Restarting stream after recalibration")
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scamera.start_stream_recording()
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@ThingAction
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class RecalibrateView(View):
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@marshal_task
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def post(self):
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microscope = find_component("org.openflexure.microscope")
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if not microscope:
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abort(503, "No microscope connected. Unable to recalibrate.")
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logging.info("Starting microscope recalibration...")
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return taskify(recalibrate)(microscope)
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lst_extension_v2 = BaseExtension(
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"org.openflexure.calibration.picamera", version="2.0.0-beta.1"
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)
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lst_extension_v2.add_method(
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recalibrate, "org.openflexure.calibration.picamera.recalibrate"
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)
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lst_extension_v2.add_view(RecalibrateView, "/recalibrate")
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import numpy as np
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import time
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from picamera import PiCamera
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from picamera.array import PiRGBArray, PiBayerArray
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def rgb_image(camera, resize=None, **kwargs):
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"""Capture an image and return an RGB numpy array"""
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with PiRGBArray(camera, size=resize) as output:
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camera.capture(output, format="rgb", resize=resize, **kwargs)
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return output.array
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def flat_lens_shading_table(camera):
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"""Return a flat (i.e. unity gain) lens shading table.
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This is mostly useful because it makes it easy to get the size
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of the array correct. NB if you are not using the forked picamera
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library (with lens shading table support) it will raise an error.
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"""
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if not hasattr(PiCamera, "lens_shading_table"):
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raise ImportError(
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"This program requires the forked picamera library with lens shading support"
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)
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return np.zeros(camera._lens_shading_table_shape(), dtype=np.uint8) + 32
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def adjust_exposure_to_setpoint(camera, setpoint):
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"""Adjust the camera's exposure time until the maximum pixel value is <setpoint>."""
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print("Adjusting shutter speed to hit setpoint {}".format(setpoint), end="")
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for i in range(3):
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print(".", end="")
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camera.shutter_speed = int(
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camera.shutter_speed * setpoint / np.max(rgb_image(camera))
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)
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time.sleep(1)
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print("done")
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def auto_expose_and_freeze_settings(camera):
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"""Freeze the settings after auto-exposing to white illumination"""
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print("Allowing the camera to auto-expose")
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camera.awb_mode = "auto"
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camera.exposure_mode = "auto"
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camera.iso = (
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0
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) # This is important, if it's on a fixed ISO, gain might not set properly.
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for i in range(6):
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print(".", end="")
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time.sleep(0.5)
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print("done")
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print("Freezing the camera settings...")
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camera.shutter_speed = camera.exposure_speed
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print("Shutter speed = {}".format(camera.shutter_speed))
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camera.exposure_mode = "off"
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print("Auto exposure disabled")
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g = camera.awb_gains
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camera.awb_mode = "off"
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camera.awb_gains = g
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print("Auto white balance disabled, gains are {}".format(g))
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print(
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"Analogue gain: {}, Digital gain: {}".format(
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camera.analog_gain, camera.digital_gain
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)
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)
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adjust_exposure_to_setpoint(camera, 215)
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def channels_from_bayer_array(bayer_array):
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"""Given the 'array' from a PiBayerArray, return the 4 channels."""
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bayer_pattern = [(i // 2, i % 2) for i in range(4)]
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channels = np.zeros(
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(4, bayer_array.shape[0] // 2, bayer_array.shape[1] // 2),
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dtype=bayer_array.dtype,
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)
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for i, offset in enumerate(bayer_pattern):
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# We simplify life by dealing with only one channel at a time.
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channels[i, :, :] = np.sum(
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bayer_array[offset[0] :: 2, offset[1] :: 2, :], axis=2
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)
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return channels
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def lst_from_channels(channels):
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"""Given the 4 Bayer colour channels from a white image, generate a LST."""
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full_resolution = np.array(channels.shape[1:]) * 2 # channels have been binned
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# lst_resolution = list(np.ceil(full_resolution / 64.0).astype(int))
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lst_resolution = [(r // 64) + 1 for r in full_resolution]
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# NB the size of the LST is 1/64th of the image, but rounded UP.
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print("Generating a lens shading table at {}x{}".format(*lst_resolution))
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lens_shading = np.zeros([channels.shape[0]] + lst_resolution, dtype=np.float)
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for i in range(lens_shading.shape[0]):
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image_channel = channels[i, :, :]
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iw, ih = image_channel.shape
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ls_channel = lens_shading[i, :, :]
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lw, lh = ls_channel.shape
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# The lens shading table is rounded **up** in size to 1/64th of the size of
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# the image. Rather than handle edge images separately, I'm just going to
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# pad the image by copying edge pixels, so that it is exactly 32 times the
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# size of the lens shading table (NB 32 not 64 because each channel is only
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# half the size of the full image - remember the Bayer pattern... This
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# should give results very close to 6by9's solution, albeit considerably
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# less computationally efficient!
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padded_image_channel = np.pad(
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image_channel, [(0, lw * 32 - iw), (0, lh * 32 - ih)], mode="edge"
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) # Pad image to the right and bottom
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print(
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"Channel shape: {}x{}, shading table shape: {}x{}, after padding {}".format(
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iw, ih, lw * 32, lh * 32, padded_image_channel.shape
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)
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)
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# Next, fill the shading table (except edge pixels). Please excuse the
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# for loop - I know it's not fast but this code needn't be!
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box = 3 # We average together a square of this side length for each pixel.
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# NB this isn't quite what 6by9's program does - it averages 3 pixels
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# horizontally, but not vertically.
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for dx in np.arange(box) - box // 2:
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for dy in np.arange(box) - box // 2:
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ls_channel[:, :] += (
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padded_image_channel[16 + dx :: 32, 16 + dy :: 32] - 64
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)
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ls_channel /= box ** 2
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# The original C code written by 6by9 normalises to the central 64 pixels in each channel.
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# ls_channel /= np.mean(image_channel[iw//2-4:iw//2+4, ih//2-4:ih//2+4])
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# I have had better results just normalising to the maximum:
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ls_channel /= np.max(ls_channel)
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# NB the central pixel should now be *approximately* 1.0 (may not be exactly
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# due to different averaging widths between the normalisation & shading table)
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# For most sensible lenses I'd expect that 1.0 is the maximum value.
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# NB ls_channel should be a "view" of the whole lens shading array, so we don't
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# need to update the big array here.
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# What we actually want to calculate is the gains needed to compensate for the
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# lens shading - that's 1/lens_shading_table_float as we currently have it.
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gains = 32.0 / lens_shading # 32 is unity gain
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gains[gains > 255] = 255 # clip at 255, maximum gain is 255/32
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gains[gains < 32] = 32 # clip at 32, minimum gain is 1 (is this necessary?)
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lens_shading_table = gains.astype(np.uint8)
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return lens_shading_table[::-1, :, :].copy()
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def recalibrate_camera(camera):
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"""Reset the lens shading table and exposure settings.
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This method first resets to a flat lens shading table, then auto-exposes,
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then generates a new lens shading table to make the current view uniform.
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It should be run when the camera is looking at a uniform white scene.
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NB the only parameter ``camera`` is a ``PiCamera`` instance and **not** a
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``StreamingCamera``.
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"""
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camera.lens_shading_table = flat_lens_shading_table(camera)
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_ = rgb_image(camera) # for some reason the camera won't work unless I do this!
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with PiBayerArray(camera) as a:
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camera.capture(a, format="jpeg", bayer=True)
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raw_image = a.array.copy()
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# Now we need to calculate a lens shading table that would make this flat.
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# raw_image is a 3D array, with full resolution and 3 colour channels. No
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# de-mosaicing has been done, so 2/3 of the values are zero (3/4 for R and B
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# channels, 1/2 for green because there's twice as many green pixels).
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channels = channels_from_bayer_array(raw_image)
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lens_shading_table = lst_from_channels(channels)
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camera.lens_shading_table = lens_shading_table
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_ = rgb_image(camera)
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# Fix the AWB gains so the image is neutral
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channel_means = np.mean(np.mean(rgb_image(camera), axis=0, dtype=np.float), axis=0)
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old_gains = camera.awb_gains
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camera.awb_gains = (
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channel_means[1] / channel_means[0] * old_gains[0],
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channel_means[1] / channel_means[2] * old_gains[1],
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)
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time.sleep(1)
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# Ensure the background is bright but not saturated
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adjust_exposure_to_setpoint(camera, 230)
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if __name__ == "__main__":
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with PiCamera() as camera:
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camera.start_preview()
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time.sleep(3)
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print("Recalibrating...")
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recalibrate_camera(camera)
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print("Done.")
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time.sleep(2)
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