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import torch
import PIL
import cv2
from PIL import Image
import numpy as np
from iopaint.helper import pad_img_to_modulo
def make_canny_control_image(image: np.ndarray) -> Image:
canny_image = cv2.Canny(image, 100, 200)
canny_image = canny_image[:, :, None]
canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
canny_image = PIL.Image.fromarray(canny_image)
control_image = canny_image
return control_image
def make_openpose_control_image(image: np.ndarray) -> Image:
from controlnet_aux import OpenposeDetector
processor = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
control_image = processor(image, hand_and_face=True)
return control_image
def resize_image(input_image, resolution):
H, W, C = input_image.shape
H = float(H)
W = float(W)
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(np.round(H / 64.0)) * 64
W = int(np.round(W / 64.0)) * 64
img = cv2.resize(
input_image,
(W, H),
interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA,
)
return img
def make_depth_control_image(image: np.ndarray) -> Image:
from controlnet_aux import MidasDetector
midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
origin_height, origin_width = image.shape[:2]
pad_image = pad_img_to_modulo(image, mod=64, square=False, min_size=512)
depth_image = midas(pad_image)
depth_image = depth_image[0:origin_height, 0:origin_width]
depth_image = depth_image[:, :, None]
depth_image = np.concatenate([depth_image, depth_image, depth_image], axis=2)
control_image = PIL.Image.fromarray(depth_image)
return control_image
def make_inpaint_control_image(image: np.ndarray, mask: np.ndarray) -> torch.Tensor:
"""
image: [H, W, C] RGB
mask: [H, W, 1] 255 means area to repaint
"""
image = image.astype(np.float32) / 255.0
image[mask[:, :, -1] > 128] = -1.0 # set as masked pixel
image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
return image
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