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Added extra type hints

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This commit is contained in:
Joel Collins 2020-12-04 15:23:03 +00:00
parent 1de6b2a0ab
commit 7ba3f447e1
1 changed files with 14 additions and 8 deletions
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22
openflexure_microscope/api/default_extensions/picamera_autocalibrate/recalibrate_utils.py
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@ -8,14 +8,16 @@ from picamerax import PiCamera
from picamerax.array import PiBayerArray, PiRGBArray
def rgb_image(camera: PiCamera, resize: Optional[Tuple[int, int]] = None, **kwargs):
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):
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
@ -107,9 +109,13 @@ def lst_from_channels(channels: np.ndarray) -> np.ndarray:
[channels.shape[0]] + lst_resolution, dtype=np.float
)
for i in range(lens_shading.shape[0]):
image_channel = channels[i, :, :]
image_channel: np.ndarray = channels[i, :, :]
iw: int
ih: int
iw, ih = image_channel.shape
ls_channel = lens_shading[i, :, :]
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
@ -118,7 +124,7 @@ def lst_from_channels(channels: np.ndarray) -> np.ndarray:
# 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(
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(
@ -131,7 +137,7 @@ def lst_from_channels(channels: np.ndarray) -> np.ndarray:
)
# 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.
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:
@ -152,10 +158,10 @@ def lst_from_channels(channels: np.ndarray) -> np.ndarray:
# 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: 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 = gains.astype(np.uint8)
lens_shading_table: np.ndarray = gains.astype(np.uint8)
return lens_shading_table[::-1, :, :].copy()