317 lines
13 KiB
Python
317 lines
13 KiB
Python
import logging
|
|
import time
|
|
from typing import List, Optional, Tuple
|
|
|
|
import numpy as np
|
|
from picamerax import PiCamera
|
|
from picamerax.array import PiBayerArray, PiRGBArray
|
|
|
|
# I have used f strings throughout, for readability.
|
|
# The performance implication of using them in logging statements
|
|
# really doesn't matter here - also, loglevel is usually set to
|
|
# INFO to report progress via the LabThings action API
|
|
# pylint: disable=logging-fstring-interpolation
|
|
|
|
|
|
def rgb_image(
|
|
camera: PiCamera, resize: Optional[Tuple[int, int]] = None, **kwargs
|
|
) -> PiRGBArray:
|
|
"""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: PiCamera) -> np.ndarray:
|
|
"""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"
|
|
)
|
|
# pylint: disable=protected-access
|
|
return np.zeros(camera._lens_shading_table_shape(), dtype=np.uint8) + 32
|
|
|
|
|
|
def adjust_exposure_to_setpoint(camera: PiCamera, setpoint: int):
|
|
"""Adjust the camera's exposure time until the maximum pixel value is <setpoint>."""
|
|
logging.info(f"Adjusting shutter speed to hit setpoint {setpoint}")
|
|
for _ in range(3):
|
|
camera.shutter_speed = int(
|
|
camera.shutter_speed * setpoint / np.max(rgb_image(camera))
|
|
)
|
|
time.sleep(1)
|
|
|
|
|
|
def adjust_shutter_and_gain_from_raw(
|
|
camera: PiCamera,
|
|
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 1023, 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()``.
|
|
"""
|
|
# Start by setting fully manual exposure.
|
|
camera.exposure_mode = "off"
|
|
camera.iso = 0 # We must set ISO=0 (auto) or we can't set gain
|
|
camera.analog_gain = 1
|
|
camera.digital_gain = 1
|
|
camera.shutter_speed = 1 # This is not valid - we'll get the minimum
|
|
time.sleep(0.5)
|
|
|
|
# We start with very low exposure settings and work up in coarse steps
|
|
# until either the brightness is high enough, or we can't increase the
|
|
# shutter speed any more.
|
|
iterations = 0
|
|
shutter_speed_increments = [10, 2, None]
|
|
shutter_speed_increment = shutter_speed_increments.pop(0)
|
|
while iterations < max_iterations:
|
|
iterations += 1
|
|
max_brightness = np.max(get_channel_percentiles(camera, percentile))
|
|
tested_shutter_speed = camera.shutter_speed
|
|
tested_analog_gain = camera.analog_gain
|
|
logging.info(
|
|
f"Brightness: {max_brightness: >5.0f}, "
|
|
f"Target: {target_white_level: >5.0f}, "
|
|
f"Gain: {float(tested_analog_gain): >4.1f}, "
|
|
f"Shutter: {float(tested_shutter_speed): >7.0f}"
|
|
)
|
|
|
|
if abs(max_brightness - target_white_level) < target_white_level * tolerance:
|
|
logging.info(
|
|
f"Brightness has converged to within {tolerance * 100 :.0f}% "
|
|
f"after {iterations} iterations."
|
|
)
|
|
break
|
|
|
|
# if not shutter_speed_maximised: # Start by adjusting shutter speed
|
|
if max_brightness > target_white_level // 2:
|
|
shutter_speed_increment = (
|
|
None
|
|
) # If we're within a factor of 2, trigger fine-tuning
|
|
if shutter_speed_increment is None:
|
|
logging.info("Fine-tuning shutter speed")
|
|
new_shutter_speed = int(
|
|
tested_shutter_speed * target_white_level / max_brightness
|
|
)
|
|
camera.shutter_speed = new_shutter_speed
|
|
else:
|
|
if max_brightness > target_white_level:
|
|
logging.info("Reducing shutter speed")
|
|
camera.shutter_speed = int(
|
|
tested_shutter_speed / shutter_speed_increment
|
|
)
|
|
else:
|
|
logging.info("Increasing shutter speed")
|
|
camera.shutter_speed = tested_shutter_speed * shutter_speed_increment
|
|
time.sleep(0.5)
|
|
|
|
# Check whether the shutter speed is still going up - if not, we've hit a maximum
|
|
current_shutter_speed = camera.shutter_speed
|
|
if current_shutter_speed == tested_shutter_speed:
|
|
camera.analog_gain *= 2
|
|
if camera.analog_gain == tested_analog_gain:
|
|
logging.info("Shutter speed and gain are both maxed out. Giving up!")
|
|
break
|
|
logging.info("Shutter speed has maxed out, doubled gain to compensate.")
|
|
return max_brightness
|
|
|
|
|
|
def adjust_white_balance_from_raw(
|
|
camera: PiCamera, percentile: float = 99
|
|
) -> 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...
|
|
"""
|
|
blue, g1, g2, red = get_channel_percentiles(camera, percentile)
|
|
green = (g1 + g2) / 2.0
|
|
new_awb_gains = (green / red, green / blue)
|
|
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.awb_mode = "off"
|
|
camera.awb_gains = new_awb_gains
|
|
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)]
|
|
channels_shape: Tuple[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, :, :] = np.sum(
|
|
bayer_array[offset[0] :: 2, offset[1] :: 2, :], axis=2
|
|
)
|
|
|
|
return channels
|
|
|
|
|
|
def get_channel_percentiles(camera: PiCamera, percentile: float) -> np.ndarray:
|
|
"""Calculate the brightness percentile of the pixels in each channel"""
|
|
with PiBayerArray(camera) as output:
|
|
camera.capture(output, format="jpeg", bayer=True)
|
|
channels = channels_from_bayer_array(output.array)
|
|
return np.percentile(channels, percentile, axis=(1, 2))
|
|
|
|
|
|
def lst_from_channels(channels: np.ndarray) -> np.ndarray:
|
|
"""Given the 4 Bayer colour channels from a white image, generate a LST."""
|
|
full_resolution: np.ndarray = np.array(
|
|
channels.shape[1:]
|
|
) * 2 # channels have been binned
|
|
|
|
# NOTE: the size of the LST is 1/64th of the image, but rounded UP.
|
|
lst_resolution: List[int] = [(r // 64) + 1 for r in full_resolution]
|
|
|
|
logging.info("Generating a lens shading table at %sx%s", *lst_resolution)
|
|
lens_shading: np.ndarray = np.zeros(
|
|
[channels.shape[0]] + lst_resolution, dtype=float
|
|
)
|
|
for i in range(lens_shading.shape[0]):
|
|
image_channel: np.ndarray = channels[i, :, :]
|
|
iw: int
|
|
ih: int
|
|
iw, ih = image_channel.shape
|
|
ls_channel: np.ndarray = lens_shading[i, :, :]
|
|
lw: int
|
|
lh: int
|
|
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.ndarray = np.pad(
|
|
image_channel, [(0, lw * 32 - iw), (0, lh * 32 - ih)], mode="edge"
|
|
) # Pad image to the right and bottom
|
|
logging.info(
|
|
"Channel shape: %sx%s, shading table shape: %sx%s, after padding %s",
|
|
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: int = 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: np.ndarray = 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: np.ndarray = gains.astype(np.uint8)
|
|
return lens_shading_table[::-1, :, :].copy()
|
|
|
|
|
|
def lst_from_camera(camera: PiCamera) -> np.ndarray:
|
|
"""Acquire a raw image and use it to calculate a lens shading table."""
|
|
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)
|
|
return lst_from_channels(channels)
|
|
|
|
|
|
def recalibrate_camera(camera: PiCamera):
|
|
"""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!
|
|
|
|
lens_shading_table = lst_from_camera(camera)
|
|
|
|
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=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 main_camera:
|
|
main_camera.start_preview()
|
|
time.sleep(3)
|
|
logging.info("Recalibrating...")
|
|
recalibrate_camera(main_camera)
|
|
logging.info("Done.")
|
|
time.sleep(2)
|