controlnet_aux/shuffle/__init__.py

import warnings

import cv2
import numpy as np
from PIL import Image

from ..util import HWC3, img2mask, make_noise_disk, resize_image


class ContentShuffleDetector:
 def __call__(self, input_image, h=None, w=None, f=None, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
 if "return_pil" in kwargs:
 warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
 output_type = "pil" if kwargs["return_pil"] else "np"
 if type(output_type) is bool:
 warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
 if output_type:
 output_type = "pil"

 if not isinstance(input_image, np.ndarray):
 input_image = np.array(input_image, dtype=np.uint8)

 input_image = HWC3(input_image)
 input_image = resize_image(input_image, detect_resolution)

 H, W, C = input_image.shape
 if h is None:
 h = H
 if w is None:
 w = W
 if f is None:
 f = 256
 x = make_noise_disk(h, w, 1, f) * float(W - 1)
 y = make_noise_disk(h, w, 1, f) * float(H - 1)
 flow = np.concatenate([x, y], axis=2).astype(np.float32)
 detected_map = cv2.remap(input_image, flow, None, cv2.INTER_LINEAR)

 img = resize_image(input_image, image_resolution)
 H, W, C = img.shape

 detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)

 if output_type == "pil":
 detected_map = Image.fromarray(detected_map)

 return detected_map


class ColorShuffleDetector:
 def __call__(self, img):
 H, W, C = img.shape
 F = np.random.randint(64, 384)
 A = make_noise_disk(H, W, 3, F)
 B = make_noise_disk(H, W, 3, F)
 C = (A + B) / 2.0
 A = (C + (A - C) * 3.0).clip(0, 1)
 B = (C + (B - C) * 3.0).clip(0, 1)
 L = img.astype(np.float32) / 255.0
 Y = A * L + B * (1 - L)
 Y -= np.min(Y, axis=(0, 1), keepdims=True)
 Y /= np.maximum(np.max(Y, axis=(0, 1), keepdims=True), 1e-5)
 Y *= 255.0
 return Y.clip(0, 255).astype(np.uint8)


class GrayDetector:
 def __call__(self, img):
 eps = 1e-5
 X = img.astype(np.float32)
 r, g, b = X[:, :, 0], X[:, :, 1], X[:, :, 2]
 kr, kg, kb = [random.random() + eps for _ in range(3)]
 ks = kr + kg + kb
 kr /= ks
 kg /= ks
 kb /= ks
 Y = r * kr + g * kg + b * kb
 Y = np.stack([Y] * 3, axis=2)
 return Y.clip(0, 255).astype(np.uint8)


class DownSampleDetector:
 def __call__(self, img, level=3, k=16.0):
 h = img.astype(np.float32)
 for _ in range(level):
 h += np.random.normal(loc=0.0, scale=k, size=h.shape)
 h = cv2.pyrDown(h)
 for _ in range(level):
 h = cv2.pyrUp(h)
 h += np.random.normal(loc=0.0, scale=k, size=h.shape)
 return h.clip(0, 255).astype(np.uint8)


class Image2MaskShuffleDetector:
 def __init__(self, resolution=(640, 512)):
 self.H, self.W = resolution

 def __call__(self, img):
 m = img2mask(img, self.H, self.W)
 m *= 255.0
 return m.clip(0, 255).astype(np.uint8)