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feat(api): add stage for local standard deviation denoising for XL
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Original file line number | Diff line number | Diff line change |
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from logging import getLogger | ||
from typing import Optional | ||
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import numpy as np | ||
from PIL import Image | ||
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from ..params import ImageParams, SizeChart, StageParams | ||
from ..server import ServerContext | ||
from ..worker import ProgressCallback, WorkerContext | ||
from .base import BaseStage | ||
from .result import StageResult | ||
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logger = getLogger(__name__) | ||
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class BlendDenoiseLocalStdStage(BaseStage): | ||
max_tile = SizeChart.max | ||
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def run( | ||
self, | ||
_worker: WorkerContext, | ||
_server: ServerContext, | ||
_stage: StageParams, | ||
_params: ImageParams, | ||
sources: StageResult, | ||
*, | ||
strength: int = 3, | ||
stage_source: Optional[Image.Image] = None, | ||
callback: Optional[ProgressCallback] = None, | ||
**kwargs, | ||
) -> StageResult: | ||
logger.info("denoising source images") | ||
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results = [] | ||
for source in sources.as_numpy(): | ||
results.append(remove_noise(source)) | ||
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return StageResult(arrays=results) | ||
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def downscale_image(image): | ||
result_image = np.zeros((image.shape[0] // 2, image.shape[1] // 2), dtype=np.uint8) | ||
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for i in range(0, image.shape[0] - 1, 2): | ||
for j in range(0, image.shape[1] - 1, 2): | ||
# Average the four neighboring pixels | ||
pixel_average = np.mean(image[i : i + 2, j : j + 2], axis=(0, 1)) | ||
result_image[i // 2, j // 2] = pixel_average.astype(np.uint8) | ||
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return result_image | ||
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def replace_noise(region, threshold): | ||
# Identify stray pixels (brightness significantly deviates from surrounding pixels) | ||
central_pixel = region[1, 1] | ||
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region_median = np.median(region) | ||
region_deviation = np.std(region) | ||
diff = np.abs(central_pixel - region_median) | ||
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# If the whole region is fairly consistent but the central pixel deviates significantly, | ||
if diff > region_deviation and diff > threshold: | ||
surrounding_pixels = region[region != central_pixel] | ||
surrounding_median = np.median(surrounding_pixels) | ||
# replace it with the median of surrounding pixels | ||
region[1, 1] = surrounding_median | ||
return True | ||
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return False | ||
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def remove_noise(image, region_size=(6, 6), threshold=10): | ||
# Assuming 'image' is a 3D numpy array representing the RGB image | ||
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# Create a copy of the original image to store the result | ||
result_image = np.copy(image) | ||
# result_mask = np.ones_like(image) * 255 | ||
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# Iterate over regions in each channel | ||
i_inc = region_size[0] // 2 | ||
j_inc = region_size[1] // 2 | ||
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for i in range(i_inc, image.shape[0] - i_inc, 1): | ||
for j in range(j_inc, image.shape[1] - j_inc, 1): | ||
i_min = i - (region_size[0] // 2) | ||
i_max = i + (region_size[0] // 2) | ||
j_min = j - (region_size[1] // 2) | ||
j_max = j + (region_size[1] // 2) | ||
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# Extract region from each channel | ||
region_red = downscale_image(image[i_min:i_max, j_min:j_max, 0]) | ||
region_green = downscale_image(image[i_min:i_max, j_min:j_max, 1]) | ||
region_blue = downscale_image(image[i_min:i_max, j_min:j_max, 2]) | ||
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replaced = any( | ||
[ | ||
replace_noise(region_red, threshold), | ||
replace_noise(region_green, threshold), | ||
] | ||
) | ||
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# Apply the noise removal function to each channel | ||
if replaced: | ||
# Assign the processed region back to the result image | ||
result_image[i - 1 : i + 1, j - 1 : j + 1, 0] = region_red[1, 1] | ||
result_image[i - 1 : i + 1, j - 1 : j + 1, 1] = region_green[1, 1] | ||
result_image[i - 1 : i + 1, j - 1 : j + 1, 2] = region_blue[1, 1] | ||
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# result_mask[i-1:i+1, j-1:j+1, 0] = 0 | ||
# result_mask[i-1:i+1, j-1:j+1, 1] = 0 | ||
# result_mask[i-1:i+1, j-1:j+1, 2] = 0 | ||
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return result_image # , result_mask) |
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