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This commit is contained in:
Martin Kristiansen
2023-07-27 10:54:01 -04:00
parent 2183dba5c5
commit 218b6d0546
148 changed files with 5486 additions and 6296 deletions
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52
invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
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@ -261,9 +261,7 @@ def srmd_degradation(x, k, sf=3):
year={2018}
}
"""
x = ndimage.filters.convolve(
x, np.expand_dims(k, axis=2), mode="wrap"
) # 'nearest' | 'mirror'
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap") # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
@ -389,21 +387,15 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
np.float32
)
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(
0, noise_level / 255.0, (*img.shape[:2], 1)
).astype(np.float32)
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal(
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
).astype(np.float32)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@ -413,21 +405,15 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(
np.float32
)
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(
0, noise_level / 255.0, (*img.shape[:2], 1)
).astype(np.float32)
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal(
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
).astype(np.float32)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@ -440,9 +426,7 @@ def add_Poisson_noise(img):
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
noise_gray = (
np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
)
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
@ -451,9 +435,7 @@ def add_Poisson_noise(img):
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode(
".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
)
result, encimg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
@ -540,9 +522,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(
img, np.expand_dims(k_shifted, axis=2), mode="mirror"
)
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode="mirror")
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
@ -646,9 +626,7 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(
image, np.expand_dims(k_shifted, axis=2), mode="mirror"
)
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode="mirror")
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
@ -796,9 +774,7 @@ if __name__ == "__main__":
print(i)
img_lq = deg_fn(img)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(
max_size=h, interpolation=cv2.INTER_CUBIC
)(image=img)["image"]
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
@ -812,7 +788,5 @@ if __name__ == "__main__":
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
img_concat = np.concatenate(
[lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + ".png")