rahix/openflexure-microscope-server
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Include colour correction matrix and gamma in saved images

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I've replicated more of the camera pipeline in the images
saved during scans - in particular, I've added the colour
correction matrix (increases saturation) and the contrast
agorithm (implements gamma correction).

This slows down saving to ~0.5-2 seconds, but that's still less time than it takes to move.
This commit is contained in:
Richard Bowman 2024-02-15 16:38:15 +00:00
parent a16c54d2db
commit 82e2a250fc
1 changed files with 10 additions and 1 deletions
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11
src/openflexure_microscope_server/things/smart_scan.py
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@ -12,6 +12,7 @@ from PIL import Image
from pydantic import BaseModel from pydantic import BaseModel
from scipy.stats import norm from scipy.stats import norm
from scipy.ndimage import zoom from scipy.ndimage import zoom
from scipy.interpolate import interp1d
from copy import deepcopy from copy import deepcopy
from datetime import datetime from datetime import datetime
from subprocess import CompletedProcess, Popen, PIPE, SubprocessError, run from subprocess import CompletedProcess, Popen, PIPE, SubprocessError, run
@ -554,6 +555,14 @@ class SmartScanThing(Thing):
white_norm_lores = np.stack([R, G, B], axis=2) white_norm_lores = np.stack([R, G, B], axis=2)
zoom_factors = [i/n for i, n in zip(rgb[...,:3].shape, white_norm_lores.shape)] zoom_factors = [i/n for i, n in zip(rgb[...,:3].shape, white_norm_lores.shape)]
white_norm = zoom(white_norm_lores, zoom_factors, order=1)[:rgb.shape[0], :rgb.shape[1], :] # Could use some work white_norm = zoom(white_norm_lores, zoom_factors, order=1)[:rgb.shape[0], :rgb.shape[1], :] # Could use some work
colour_correction_matrix = np.array(cam.colour_correction_matrix).reshape((3,3))
contrast_algorithm = cam.tuning["algorithms"][9]["rpi.contrast"]
gamma = np.array(contrast_algorithm["gamma_curve"]).reshape((-1,2))
gamma_8bit = interp1d(gamma[:, 0]/255, gamma[:, 1]/255)
def process_raw_image(img):
normed = img/white_norm
corrected = np.dot(colour_correction_matrix, normed.reshape((-1, 3)).T).T.reshape(normed.shape)
return gamma_8bit(corrected)
logger.info( logger.info(
f"Generated normalisation image with shape {white_norm.shape}, " f"Generated normalisation image with shape {white_norm.shape}, "
f"max {white_norm.max(axis=(0,1))}, min {white_norm.min(axis=(0,1))}" f"max {white_norm.max(axis=(0,1))}, min {white_norm.min(axis=(0,1))}"
@ -579,7 +588,7 @@ class SmartScanThing(Thing):
# Save the raw image # Save the raw image
np.savez(os.path.join(raw_images_folder, name + ".npz"), raw_image=raw_image, **norm_inputs) np.savez(os.path.join(raw_images_folder, name + ".npz"), raw_image=raw_image, **norm_inputs)
# Process it into 8 bit RGB # Process it into 8 bit RGB
processed = rggb2rgb(raw2rggb(raw_image)) / white_norm processed = process_raw_image(rggb2rgb(raw2rggb(raw_image)))
processed[processed > 255] = 255 processed[processed > 255] = 255
processed[processed < 0] = 0 processed[processed < 0] = 0
img = Image.fromarray(processed.astype(np.uint8), mode="RGB") img = Image.fromarray(processed.astype(np.uint8), mode="RGB")