86 lines
3.7 KiB
Python
86 lines
3.7 KiB
Python
# Open Source Model Licensed under the Apache License Version 2.0 and Other Licenses of the Third-Party Components therein:
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# The below Model in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
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# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
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# The below software and/or models in this distribution may have been
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# modified by THL A29 Limited ("Tencent Modifications").
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# All Tencent Modifications are Copyright (C) THL A29 Limited.
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# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
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# except for the third-party components listed below.
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# Hunyuan 3D does not impose any additional limitations beyond what is outlined
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# in the repsective licenses of these third-party components.
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# Users must comply with all terms and conditions of original licenses of these third-party
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# components and must ensure that the usage of the third party components adheres to
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# all relevant laws and regulations.
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# For avoidance of doubts, Hunyuan 3D means the large language models and
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# their software and algorithms, including trained model weights, parameters (including
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# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
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# fine-tuning enabling code and other elements of the foregoing made publicly available
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# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
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import numpy as np
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from PIL import Image
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def to_rgb_image(maybe_rgba: Image.Image):
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'''
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convert a PIL.Image to rgb mode with white background
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maybe_rgba: PIL.Image
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return: PIL.Image
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'''
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if maybe_rgba.mode == 'RGB':
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return maybe_rgba
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elif maybe_rgba.mode == 'RGBA':
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rgba = maybe_rgba
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img = np.random.randint(255, 256, size=[rgba.size[1], rgba.size[0], 3], dtype=np.uint8)
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img = Image.fromarray(img, 'RGB')
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img.paste(rgba, mask=rgba.getchannel('A'))
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return img
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else:
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raise ValueError("Unsupported image type.", maybe_rgba.mode)
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def white_out_background(pil_img, is_gray_fg=True):
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data = pil_img.getdata()
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new_data = []
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# convert fore-ground white to gray
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for r, g, b, a in data:
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if a < 16:
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new_data.append((255, 255, 255, 0)) # back-ground to be black
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else:
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is_white = is_gray_fg and (r>235) and (g>235) and (b>235)
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new_r = 235 if is_white else r
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new_g = 235 if is_white else g
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new_b = 235 if is_white else b
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new_data.append((new_r, new_g, new_b, a))
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pil_img.putdata(new_data)
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return pil_img
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def recenter_img(img, size=512, color=(255,255,255)):
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img = white_out_background(img)
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mask = np.array(img)[..., 3]
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image = np.array(img)[..., :3]
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H, W, C = image.shape
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coords = np.nonzero(mask)
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x_min, x_max = coords[0].min(), coords[0].max()
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y_min, y_max = coords[1].min(), coords[1].max()
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h = x_max - x_min
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w = y_max - y_min
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if h == 0 or w == 0: raise ValueError
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roi = image[x_min:x_max, y_min:y_max]
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border_ratio = 0.15 # 0.2
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pad_h = int(h * border_ratio)
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pad_w = int(w * border_ratio)
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result_tmp = np.full((h + pad_h, w + pad_w, C), color, dtype=np.uint8)
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result_tmp[pad_h // 2: pad_h // 2 + h, pad_w // 2: pad_w // 2 + w] = roi
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cur_h, cur_w = result_tmp.shape[:2]
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side = max(cur_h, cur_w)
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result = np.full((side, side, C), color, dtype=np.uint8)
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result[(side-cur_h)//2:(side-cur_h)//2+cur_h, (side-cur_w)//2:(side - cur_w)//2+cur_w,:] = result_tmp
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result = Image.fromarray(result)
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return result.resize((size, size), Image.LANCZOS) if size else result
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