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Running
on
Zero
File size: 2,499 Bytes
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import numpy as np
from PIL import Image
from skimage.filters import threshold_multiotsu
def quantization_otsu(img, severity=1):
"""
Color Quantization using OTSU method.
severity=[1, 2, 3, 4, 5] corresponding to num_classes=[15, 11, 8, 5, 3].
severity mainly refer to KADID-10K and Imagecorruptions.
@param img: Input image, H x W x 3, value range [0, 255]
@param severity: Severity of distortion, [1, 5]
@return: Degraded image, H x W x 3, value range [0, 255]
"""
c = [15, 11, 8, 5, 3][severity - 1]
img = np.array(img).astype(np.float32)
for i in range(img.shape[2]):
img_gray = img[:, :, i]
thresholds = threshold_multiotsu(img_gray, classes=c, nbins=30) # modify skimage
v_max = img_gray.max()
v_min = img_gray.min()
img[:, :, i] = np.digitize(img[:, :, i], bins=thresholds) * (v_max - v_min) / c + v_min
img = np.clip(img, 0, 255)
return img
def quantization_median(img, severity=1):
"""
Color Quantization using Histogram Median.
severity=[1, 2, 3, 4, 5] corresponding to num_classes=[20, 15, 10, 6, 3].
severity mainly refer to KADID-10K and Imagecorruptions.
@param img: Input image, H x W x 3, value range [0, 255]
@param severity: Severity of distortion, [1, 5]
@return: Degraded image, H x W x 3, value range [0, 255]
"""
c = [20, 15, 10, 6, 3][severity - 1]
for i in range(img.shape[2]):
img_gray = Image.fromarray(img[:, :, i])
img_gray = img_gray.quantize(colors=c, method=Image.Quantize.MEDIANCUT).convert("L")
img[:, :, i] = np.array(img_gray)
img = np.clip(img, 0, 255)
return img
def quantization_hist(img, severity=1):
"""
Color Quantization using Histogram Equalization.
severity=[1, 2, 3, 4, 5] corresponding to num_classes=[24, 16, 8, 6, 4].
severity mainly refer to KADID-10K and Imagecorruptions.
@param img: Input image, H x W x 3, value range [0, 255]
@param severity: Severity of distortion, [1, 5]
@return: Degraded image, H x W x 3, value range [0, 255]
"""
c = [24, 16, 8, 6, 4][severity - 1]
hist, _ = np.histogram(img.flatten(), bins=c, range=[0, 255])
cdf = hist.cumsum()
cdf_m = np.ma.masked_equal(cdf, 0)
cdf_m = (cdf_m - cdf_m.min()) * 255 / (cdf_m.max() - cdf_m.min())
cdf = np.ma.filled(cdf_m, 0).astype('uint8')
img = np.uint8(np.round(img / 255 * (c - 1)))
img = cdf[img]
img = np.clip(img, 0, 255)
return img
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