File size: 3,776 Bytes
e3e5f9e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
# Open Source Model Licensed under the Apache License Version 2.0 and Other Licenses of the Third-Party Components therein:
# 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.
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
# The below software and/or models in this distribution may have been
# modified by THL A29 Limited ("Tencent Modifications").
# All Tencent Modifications are Copyright (C) THL A29 Limited.
# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import numpy as np
from PIL import Image
def to_rgb_image(maybe_rgba: Image.Image):
'''
convert a PIL.Image to rgb mode with white background
maybe_rgba: PIL.Image
return: PIL.Image
'''
if maybe_rgba.mode == 'RGB':
return maybe_rgba
elif maybe_rgba.mode == 'RGBA':
rgba = maybe_rgba
img = np.random.randint(255, 256, size=[rgba.size[1], rgba.size[0], 3], dtype=np.uint8)
img = Image.fromarray(img, 'RGB')
img.paste(rgba, mask=rgba.getchannel('A'))
return img
else:
raise ValueError("Unsupported image type.", maybe_rgba.mode)
def white_out_background(pil_img, is_gray_fg=True):
data = pil_img.getdata()
new_data = []
# convert fore-ground white to gray
for r, g, b, a in data:
if a < 16:
new_data.append((255, 255, 255, 0)) # back-ground to be black
else:
is_white = is_gray_fg and (r>235) and (g>235) and (b>235)
new_r = 235 if is_white else r
new_g = 235 if is_white else g
new_b = 235 if is_white else b
new_data.append((new_r, new_g, new_b, a))
pil_img.putdata(new_data)
return pil_img
def recenter_img(img, size=512, color=(255,255,255)):
img = white_out_background(img)
mask = np.array(img)[..., 3]
image = np.array(img)[..., :3]
H, W, C = image.shape
coords = np.nonzero(mask)
x_min, x_max = coords[0].min(), coords[0].max()
y_min, y_max = coords[1].min(), coords[1].max()
h = x_max - x_min
w = y_max - y_min
if h == 0 or w == 0: raise ValueError
roi = image[x_min:x_max, y_min:y_max]
border_ratio = 0.15 # 0.2
pad_h = int(h * border_ratio)
pad_w = int(w * border_ratio)
result_tmp = np.full((h + pad_h, w + pad_w, C), color, dtype=np.uint8)
result_tmp[pad_h // 2: pad_h // 2 + h, pad_w // 2: pad_w // 2 + w] = roi
cur_h, cur_w = result_tmp.shape[:2]
side = max(cur_h, cur_w)
result = np.full((side, side, C), color, dtype=np.uint8)
result[(side-cur_h)//2:(side-cur_h)//2+cur_h, (side-cur_w)//2:(side - cur_w)//2+cur_w,:] = result_tmp
result = Image.fromarray(result)
return result.resize((size, size), Image.LANCZOS) if size else result
|