47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
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import torch
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import PIL
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import cv2
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from PIL import Image
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import numpy as np
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def make_canny_control_image(image: np.ndarray) -> Image:
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canny_image = cv2.Canny(image, 100, 200)
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canny_image = canny_image[:, :, None]
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canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
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canny_image = PIL.Image.fromarray(canny_image)
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control_image = canny_image
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return control_image
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def make_openpose_control_image(image: np.ndarray) -> Image:
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from controlnet_aux import OpenposeDetector
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processor = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
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control_image = processor(image, hand_and_face=True)
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return control_image
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def make_depth_control_image(image: np.ndarray) -> Image:
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from transformers import pipeline
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depth_estimator = pipeline("depth-estimation")
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depth_image = depth_estimator(PIL.Image.fromarray(image))["depth"]
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depth_image = np.array(depth_image)
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depth_image = depth_image[:, :, None]
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depth_image = np.concatenate([depth_image, depth_image, depth_image], axis=2)
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control_image = PIL.Image.fromarray(depth_image)
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return control_image
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def make_inpaint_control_image(image: np.ndarray, mask: np.ndarray) -> torch.Tensor:
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"""
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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"""
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image = image.astype(np.float32) / 255.0
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image[mask[:, :, -1] > 128] = -1.0 # set as masked pixel
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image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return image
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