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processor.py
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processor.py
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import cv2
import numpy as np
from annotator.util import HWC3
from typing import Callable, Tuple
def pad64(x):
return int(np.ceil(float(x) / 64.0) * 64 - x)
def safer_memory(x):
# Fix many MAC/AMD problems
return np.ascontiguousarray(x.copy()).copy()
def resize_image_with_pad(input_image, resolution, skip_hwc3=False):
if skip_hwc3:
img = input_image
else:
img = HWC3(input_image)
H_raw, W_raw, _ = img.shape
k = float(resolution) / float(min(H_raw, W_raw))
interpolation = cv2.INTER_CUBIC if k > 1 else cv2.INTER_AREA
H_target = int(np.round(float(H_raw) * k))
W_target = int(np.round(float(W_raw) * k))
img = cv2.resize(img, (W_target, H_target), interpolation=interpolation)
H_pad, W_pad = pad64(H_target), pad64(W_target)
img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode='edge')
def remove_pad(x):
return safer_memory(x[:H_target, :W_target])
return safer_memory(img_padded), remove_pad
model_canny = None
def canny(img, res=512, thr_a=100, thr_b=200, **kwargs):
l, h = thr_a, thr_b
img, remove_pad = resize_image_with_pad(img, res)
global model_canny
if model_canny is None:
from annotator.canny import apply_canny
model_canny = apply_canny
result = model_canny(img, l, h)
return remove_pad(result), True
def scribble_thr(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
result = np.zeros_like(img, dtype=np.uint8)
result[np.min(img, axis=2) < 127] = 255
return remove_pad(result), True
def scribble_xdog(img, res=512, thr_a=32, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
g1 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 0.5)
g2 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 5.0)
dog = (255 - np.min(g2 - g1, axis=2)).clip(0, 255).astype(np.uint8)
result = np.zeros_like(img, dtype=np.uint8)
result[2 * (255 - dog) > thr_a] = 255
return remove_pad(result), True
def tile_resample(img, res=512, thr_a=1.0, **kwargs):
img = HWC3(img)
if thr_a < 1.1:
return img, True
H, W, C = img.shape
H = int(float(H) / float(thr_a))
W = int(float(W) / float(thr_a))
img = cv2.resize(img, (W, H), interpolation=cv2.INTER_AREA)
return img, True
def threshold(img, res=512, thr_a=127, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
result = np.zeros_like(img, dtype=np.uint8)
result[np.min(img, axis=2) > thr_a] = 255
return remove_pad(result), True
def identity(img, **kwargs):
return img, True
def invert(img, res=512, **kwargs):
return 255 - HWC3(img), True
model_hed = None
def hed(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_hed
if model_hed is None:
from annotator.hed import apply_hed
model_hed = apply_hed
result = model_hed(img)
return remove_pad(result), True
def hed_safe(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_hed
if model_hed is None:
from annotator.hed import apply_hed
model_hed = apply_hed
result = model_hed(img, is_safe=True)
return remove_pad(result), True
def unload_hed():
global model_hed
if model_hed is not None:
from annotator.hed import unload_hed_model
unload_hed_model()
def scribble_hed(img, res=512, **kwargs):
result, _ = hed(img, res)
import cv2
from annotator.util import nms
result = nms(result, 127, 3.0)
result = cv2.GaussianBlur(result, (0, 0), 3.0)
result[result > 4] = 255
result[result < 255] = 0
return result, True
model_mediapipe_face = None
def mediapipe_face(img, res=512, thr_a: int = 10, thr_b: float = 0.5, **kwargs):
max_faces = int(thr_a)
min_confidence = thr_b
img, remove_pad = resize_image_with_pad(img, res)
global model_mediapipe_face
if model_mediapipe_face is None:
from annotator.mediapipe_face import apply_mediapipe_face
model_mediapipe_face = apply_mediapipe_face
result = model_mediapipe_face(img, max_faces=max_faces, min_confidence=min_confidence)
return remove_pad(result), True
model_mlsd = None
def mlsd(img, res=512, thr_a=0.1, thr_b=0.1, **kwargs):
thr_v, thr_d = thr_a, thr_b
img, remove_pad = resize_image_with_pad(img, res)
global model_mlsd
if model_mlsd is None:
from annotator.mlsd import apply_mlsd
model_mlsd = apply_mlsd
result = model_mlsd(img, thr_v, thr_d)
return remove_pad(result), True
def unload_mlsd():
global model_mlsd
if model_mlsd is not None:
from annotator.mlsd import unload_mlsd_model
unload_mlsd_model()
model_midas = None
def midas(img, res=512, a=np.pi * 2.0, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_midas
if model_midas is None:
from annotator.midas import apply_midas
model_midas = apply_midas
result, _ = model_midas(img, a)
return remove_pad(result), True
def midas_normal(img, res=512, a=np.pi * 2.0, thr_a=0.4, **kwargs): # bg_th -> thr_a
bg_th = thr_a
img, remove_pad = resize_image_with_pad(img, res)
global model_midas
if model_midas is None:
from annotator.midas import apply_midas
model_midas = apply_midas
_, result = model_midas(img, a, bg_th)
return remove_pad(result), True
def unload_midas():
global model_midas
if model_midas is not None:
from annotator.midas import unload_midas_model
unload_midas_model()
model_leres = None
def leres(img, res=512, a=np.pi * 2.0, thr_a=0, thr_b=0, boost=False, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_leres
if model_leres is None:
from annotator.leres import apply_leres
model_leres = apply_leres
result = model_leres(img, thr_a, thr_b, boost=boost)
return remove_pad(result), True
def unload_leres():
global model_leres
if model_leres is not None:
from annotator.leres import unload_leres_model
unload_leres_model()
class OpenposeModel(object):
def __init__(self) -> None:
self.model_openpose = None
def run_model(
self,
img: np.ndarray,
include_body: bool,
include_hand: bool,
include_face: bool,
json_pose_callback: Callable[[str], None] = None,
res: int = 512,
**kwargs # Ignore rest of kwargs
) -> Tuple[np.ndarray, bool]:
"""Run the openpose model. Returns a tuple of
- result image
- is_image flag
The JSON format pose string is passed to `json_pose_callback`.
"""
if json_pose_callback is None:
json_pose_callback = lambda x: None
img, remove_pad = resize_image_with_pad(img, res)
if self.model_openpose is None:
from annotator.openpose import OpenposeDetector
self.model_openpose = OpenposeDetector()
return remove_pad(self.model_openpose(
img,
include_body=include_body,
include_hand=include_hand,
include_face=include_face,
json_pose_callback=json_pose_callback
)), True
def unload(self):
if self.model_openpose is not None:
self.model_openpose.unload_model()
g_openpose_model = OpenposeModel()
model_uniformer = None
def uniformer(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_uniformer
if model_uniformer is None:
from annotator.uniformer import apply_uniformer
model_uniformer = apply_uniformer
result = model_uniformer(img)
return remove_pad(result), True
def unload_uniformer():
global model_uniformer
if model_uniformer is not None:
from annotator.uniformer import unload_uniformer_model
unload_uniformer_model()
model_pidinet = None
def pidinet(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_pidinet
if model_pidinet is None:
from annotator.pidinet import apply_pidinet
model_pidinet = apply_pidinet
result = model_pidinet(img)
return remove_pad(result), True
def pidinet_ts(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_pidinet
if model_pidinet is None:
from annotator.pidinet import apply_pidinet
model_pidinet = apply_pidinet
result = model_pidinet(img, apply_fliter=True)
return remove_pad(result), True
def pidinet_safe(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_pidinet
if model_pidinet is None:
from annotator.pidinet import apply_pidinet
model_pidinet = apply_pidinet
result = model_pidinet(img, is_safe=True)
return remove_pad(result), True
def scribble_pidinet(img, res=512, **kwargs):
result, _ = pidinet(img, res)
import cv2
from annotator.util import nms
result = nms(result, 127, 3.0)
result = cv2.GaussianBlur(result, (0, 0), 3.0)
result[result > 4] = 255
result[result < 255] = 0
return result, True
def unload_pidinet():
global model_pidinet
if model_pidinet is not None:
from annotator.pidinet import unload_pid_model
unload_pid_model()
clip_encoder = None
def clip(img, res=512, **kwargs):
img = HWC3(img)
global clip_encoder
if clip_encoder is None:
from annotator.clip import apply_clip
clip_encoder = apply_clip
result = clip_encoder(img)
return result, False
def clip_vision_visualization(x):
x = x.detach().cpu().numpy()[0]
x = np.ascontiguousarray(x).copy()
return np.ndarray((x.shape[0] * 4, x.shape[1]), dtype="uint8", buffer=x.tobytes())
def unload_clip():
global clip_encoder
if clip_encoder is not None:
from annotator.clip import unload_clip_model
unload_clip_model()
model_color = None
def color(img, res=512, **kwargs):
img = HWC3(img)
global model_color
if model_color is None:
from annotator.color import apply_color
model_color = apply_color
result = model_color(img, res=res)
return result, True
def lineart_standard(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
x = img.astype(np.float32)
g = cv2.GaussianBlur(x, (0, 0), 6.0)
intensity = np.min(g - x, axis=2).clip(0, 255)
intensity /= max(16, np.median(intensity[intensity > 8]))
intensity *= 127
result = intensity.clip(0, 255).astype(np.uint8)
return remove_pad(result), True
model_lineart = None
def lineart(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_lineart
if model_lineart is None:
from annotator.lineart import LineartDetector
model_lineart = LineartDetector(LineartDetector.model_default)
# applied auto inversion
result = 255 - model_lineart(img)
return remove_pad(result), True
def unload_lineart():
global model_lineart
if model_lineart is not None:
model_lineart.unload_model()
model_lineart_coarse = None
def lineart_coarse(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_lineart_coarse
if model_lineart_coarse is None:
from annotator.lineart import LineartDetector
model_lineart_coarse = LineartDetector(LineartDetector.model_coarse)
# applied auto inversion
result = 255 - model_lineart_coarse(img)
return remove_pad(result), True
def unload_lineart_coarse():
global model_lineart_coarse
if model_lineart_coarse is not None:
model_lineart_coarse.unload_model()
model_lineart_anime = None
def lineart_anime(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_lineart_anime
if model_lineart_anime is None:
from annotator.lineart_anime import LineartAnimeDetector
model_lineart_anime = LineartAnimeDetector()
# applied auto inversion
result = 255 - model_lineart_anime(img)
return remove_pad(result), True
def unload_lineart_anime():
global model_lineart_anime
if model_lineart_anime is not None:
model_lineart_anime.unload_model()
model_manga_line = None
def lineart_anime_denoise(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_manga_line
if model_manga_line is None:
from annotator.manga_line import MangaLineExtration
model_manga_line = MangaLineExtration()
# applied auto inversion
result = model_manga_line(img)
return remove_pad(result), True
def unload_lineart_anime_denoise():
global model_manga_line
if model_manga_line is not None:
model_manga_line.unload_model()
model_lama = None
def lama_inpaint(img, res=512, **kwargs):
H, W, C = img.shape
raw_color = img[:, :, 0:3].copy()
raw_mask = img[:, :, 3:4].copy()
res = 256 # Always use 256 since lama is trained on 256
img_res, remove_pad = resize_image_with_pad(img, res, skip_hwc3=True)
global model_lama
if model_lama is None:
from annotator.lama import LamaInpainting
model_lama = LamaInpainting()
# applied auto inversion
prd_color = model_lama(img_res)
prd_color = remove_pad(prd_color)
prd_color = cv2.resize(prd_color, (W, H))
alpha = raw_mask.astype(np.float32) / 255.0
fin_color = prd_color.astype(np.float32) * alpha + raw_color.astype(np.float32) * (1 - alpha)
fin_color = fin_color.clip(0, 255).astype(np.uint8)
result = np.concatenate([fin_color, raw_mask], axis=2)
return result, True
def unload_lama_inpaint():
global model_lama
if model_lama is not None:
model_lama.unload_model()
model_zoe_depth = None
def zoe_depth(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_zoe_depth
if model_zoe_depth is None:
from annotator.zoe import ZoeDetector
model_zoe_depth = ZoeDetector()
result = model_zoe_depth(img)
return remove_pad(result), True
def unload_zoe_depth():
global model_zoe_depth
if model_zoe_depth is not None:
model_zoe_depth.unload_model()
model_normal_bae = None
def normal_bae(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_normal_bae
if model_normal_bae is None:
from annotator.normalbae import NormalBaeDetector
model_normal_bae = NormalBaeDetector()
result = model_normal_bae(img)
return remove_pad(result), True
def unload_normal_bae():
global model_normal_bae
if model_normal_bae is not None:
model_normal_bae.unload_model()
model_oneformer_coco = None
def oneformer_coco(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_oneformer_coco
if model_oneformer_coco is None:
from annotator.oneformer import OneformerDetector
model_oneformer_coco = OneformerDetector(OneformerDetector.configs["coco"])
result = model_oneformer_coco(img)
return remove_pad(result), True
def unload_oneformer_coco():
global model_oneformer_coco
if model_oneformer_coco is not None:
model_oneformer_coco.unload_model()
model_oneformer_ade20k = None
def oneformer_ade20k(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
global model_oneformer_ade20k
if model_oneformer_ade20k is None:
from annotator.oneformer import OneformerDetector
model_oneformer_ade20k = OneformerDetector(OneformerDetector.configs["ade20k"])
result = model_oneformer_ade20k(img)
return remove_pad(result), True
def unload_oneformer_ade20k():
global model_oneformer_ade20k
if model_oneformer_ade20k is not None:
model_oneformer_ade20k.unload_model()
model_shuffle = None
def shuffle(img, res=512, **kwargs):
img, remove_pad = resize_image_with_pad(img, res)
img = remove_pad(img)
global model_shuffle
if model_shuffle is None:
from annotator.shuffle import ContentShuffleDetector
model_shuffle = ContentShuffleDetector()
result = model_shuffle(img)
return result, True
model_free_preprocessors = [
"reference_only",
"reference_adain",
"reference_adain+attn"
]
flag_preprocessor_resolution = "רזולוציית טרום-מעבד"
preprocessor_sliders_config = {
"none": [],
"inpaint": [],
"inpaint_only": [],
"canny": [
{
"name": flag_preprocessor_resolution,
"value": 512,
"min": 64,
"max": 2048
},
{
"name": "Canny סף נמוך של",
"value": 100,
"min": 1,
"max": 255
},
{
"name": "Canny סף גבוה של",
"value": 200,
"min": 1,
"max": 255
},
],
"mlsd": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
},
{
"name": "MLSD סף ערך של",
"min": 0.01,
"max": 2.0,
"value": 0.1,
"step": 0.01
},
{
"name": "MLSD סף מרחק של",
"min": 0.01,
"max": 20.0,
"value": 0.1,
"step": 0.01
}
],
"hed": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
}
],
"scribble_hed": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
}
],
"hed_safe": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
}
],
"openpose": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
}
],
"openpose_full": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
}
],
"segmentation": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
}
],
"depth": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
}
],
"depth_leres": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
},
{
"name": "Remove Near %",
"min": 0,
"max": 100,
"value": 0,
"step": 0.1,
},
{
"name": "Remove Background %",
"min": 0,
"max": 100,
"value": 0,
"step": 0.1,
}
],
"depth_leres++": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
},
{
"name": "Remove Near %",
"min": 0,
"max": 100,
"value": 0,
"step": 0.1,
},
{
"name": "Remove Background %",
"min": 0,
"max": 100,
"value": 0,
"step": 0.1,
}
],
"normal_map": [
{
"name": flag_preprocessor_resolution,
"min": 64,
"max": 2048,
"value": 512
},
{
"name": "Normal Background Threshold",
"min": 0.0,
"max": 1.0,
"value": 0.4,
"step": 0.01
}
],
"threshold": [
{
"name": flag_preprocessor_resolution,
"value": 512,
"min": 64,
"max": 2048
},
{
"name": "Binarization Threshold",
"min": 0,
"max": 255,
"value": 127
}
],
"scribble_xdog": [
{
"name": flag_preprocessor_resolution,
"value": 512,
"min": 64,
"max": 2048
},
{
"name": "XDoG Threshold",
"min": 1,
"max": 64,
"value": 32,
}
],
"tile_resample": [
None,
{
"name": "ירידה בקצב הדגימה",
"value": 1.0,
"min": 1.0,
"max": 8.0,
"step": 0.01
}
],
"tile_colorfix": [
None,
{
"name": "Variation",
"value": 8.0,
"min": 3.0,
"max": 32.0,
"step": 1.0
}
],
"tile_colorfix+sharp": [
None,
{
"name": "Variation",
"value": 8.0,
"min": 3.0,
"max": 32.0,
"step": 1.0
},
{
"name": "Sharpness",
"value": 1.0,
"min": 0.0,
"max": 2.0,
"step": 0.01
}
],
"reference_only": [
None,
{
"name": r'("נאמנות לסגנון (רק עבור מצב "מאוזן',
"value": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"reference_adain": [
None,
{
"name": r'Style Fidelity (only for "Balanced" mode)',
"value": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"reference_adain+attn": [
None,
{
"name": r'Style Fidelity (only for "Balanced" mode)',
"value": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"inpaint_only+lama": [],
"color": [
{
"name": flag_preprocessor_resolution,
"value": 512,
"min": 64,
"max": 2048,
}
],
"mediapipe_face": [
{
"name": flag_preprocessor_resolution,
"value": 512,
"min": 64,
"max": 2048,
},
{
"name": "Max Faces",
"value": 1,
"min": 1,
"max": 10,
"step": 1
},
{
"name": "Min Face Confidence",
"value": 0.5,
"min": 0.01,
"max": 1.0,
"step": 0.01
}
],
}
preprocessor_filters = {
"All": "none",
"Canny": "canny",
"Depth": "depth_midas",
"Normal": "normal_bae",
"OpenPose": "openpose_full",
"MLSD": "mlsd",
"Lineart": "lineart_standard (from white bg & black line)",
"SoftEdge": "softedge_pidinet",
"Scribble": "scribble_pidinet",
"Seg": "seg_ofade20k",
"Shuffle": "shuffle",
"Tile": "tile_resample",
"Inpaint": "inpaint_only",
"IP2P": "none",
"Reference": "reference_only",
"T2IA": "none",
}