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tidy: "fit_image_to_resolution" -> "resize_image_to_resolution"

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This commit is contained in:
psychedelicious 2024-03-21 23:00:29 +11:00 committed by Kent Keirsey
parent 64fb15e117
commit a6283b9fb6
5 changed files with 13 additions and 13 deletions
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6
invokeai/backend/image_util/canny.py
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@ -3,9 +3,9 @@ from PIL import Image
from invokeai.backend.image_util.util import (
cv2_to_pil,
fit_image_to_resolution,
normalize_image_channel_count,
pil_to_cv2,
resize_image_to_resolution,
)
@ -32,10 +32,10 @@ def get_canny_edges(
np_image = pil_to_cv2(image)
np_image = normalize_image_channel_count(np_image)
np_image = fit_image_to_resolution(np_image, detect_resolution)
np_image = resize_image_to_resolution(np_image, detect_resolution)
edge_map = cv2.Canny(np_image, low_threshold, high_threshold)
edge_map = normalize_image_channel_count(edge_map)
edge_map = fit_image_to_resolution(edge_map, image_resolution)
edge_map = resize_image_to_resolution(edge_map, image_resolution)
return cv2_to_pil(edge_map)

6
invokeai/backend/image_util/hed.py
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@ -8,11 +8,11 @@ from huggingface_hub import hf_hub_download
from PIL import Image
from invokeai.backend.image_util.util import (
fit_image_to_resolution,
non_maximum_suppression,
normalize_image_channel_count,
np_to_pil,
pil_to_np,
resize_image_to_resolution,
safe_step,
)
@ -109,7 +109,7 @@ class HEDProcessor:
device = next(iter(self.network.parameters())).device
np_image = pil_to_np(input_image)
np_image = normalize_image_channel_count(np_image)
np_image = fit_image_to_resolution(np_image, detect_resolution)
np_image = resize_image_to_resolution(np_image, detect_resolution)
assert np_image.ndim == 3
height, width, _channels = np_image.shape
@ -128,7 +128,7 @@ class HEDProcessor:
detected_map = edge
detected_map = normalize_image_channel_count(detected_map)
img = fit_image_to_resolution(np_image, image_resolution)
img = resize_image_to_resolution(np_image, image_resolution)
height, width, _channels = img.shape
detected_map = cv2.resize(detected_map, (width, height), interpolation=cv2.INTER_LINEAR)

6
invokeai/backend/image_util/lineart.py
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@ -9,10 +9,10 @@ from huggingface_hub import hf_hub_download
from PIL import Image
from invokeai.backend.image_util.util import (
fit_image_to_resolution,
normalize_image_channel_count,
np_to_pil,
pil_to_np,
resize_image_to_resolution,
)
@ -131,7 +131,7 @@ class LineartProcessor:
np_image = pil_to_np(input_image)
np_image = normalize_image_channel_count(np_image)
np_image = fit_image_to_resolution(np_image, detect_resolution)
np_image = resize_image_to_resolution(np_image, detect_resolution)
model = self.model_coarse if coarse else self.model
assert np_image.ndim == 3
@ -149,7 +149,7 @@ class LineartProcessor:
detected_map = normalize_image_channel_count(detected_map)
img = fit_image_to_resolution(np_image, image_resolution)
img = resize_image_to_resolution(np_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)

6
invokeai/backend/image_util/lineart_anime.py
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@ -12,10 +12,10 @@ from huggingface_hub import hf_hub_download
from PIL import Image
from invokeai.backend.image_util.util import (
fit_image_to_resolution,
normalize_image_channel_count,
np_to_pil,
pil_to_np,
resize_image_to_resolution,
)
@ -173,7 +173,7 @@ class LineartAnimeProcessor:
np_image = pil_to_np(input_image)
np_image = normalize_image_channel_count(np_image)
np_image = fit_image_to_resolution(np_image, detect_resolution)
np_image = resize_image_to_resolution(np_image, detect_resolution)
H, W, C = np_image.shape
Hn = 256 * int(np.ceil(float(H) / 256.0))
@ -194,7 +194,7 @@ class LineartAnimeProcessor:
detected_map = normalize_image_channel_count(detected_map)
img = fit_image_to_resolution(np_image, image_resolution)
img = resize_image_to_resolution(np_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)

2
invokeai/backend/image_util/util.py
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@ -128,7 +128,7 @@ def normalize_image_channel_count(image: np.ndarray) -> np.ndarray:
raise ValueError("Invalid number of channels.")
def fit_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.ndarray:
def resize_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.ndarray:
"""Resizes an image, fitting it to the given resolution.
Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license).