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