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Zero
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# 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 cv2
import numpy as np
import torch
from PIL import Image
from einops import repeat, rearrange
def array_to_tensor(np_array):
image_pt = torch.tensor(np_array).float()
image_pt = image_pt / 255 * 2 - 1
image_pt = rearrange(image_pt, "h w c -> c h w")
image_pts = repeat(image_pt, "c h w -> b c h w", b=1)
return image_pts
class ImageProcessorV2:
def __init__(self, size=512, border_ratio=None):
self.size = size
self.border_ratio = border_ratio
@staticmethod
def recenter(image, border_ratio: float = 0.2):
""" recenter an image to leave some empty space at the image border.
Args:
image (ndarray): input image, float/uint8 [H, W, 3/4]
mask (ndarray): alpha mask, bool [H, W]
border_ratio (float, optional): border ratio, image will be resized to (1 - border_ratio). Defaults to 0.2.
Returns:
ndarray: output image, float/uint8 [H, W, 3/4]
"""
if image.shape[-1] == 4:
mask = image[..., 3]
else:
mask = np.ones_like(image[..., 0:1]) * 255
image = np.concatenate([image, mask], axis=-1)
mask = mask[..., 0]
H, W, C = image.shape
size = max(H, W)
result = np.zeros((size, size, C), dtype=np.uint8)
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('input image is empty')
desired_size = int(size * (1 - border_ratio))
scale = desired_size / max(h, w)
h2 = int(h * scale)
w2 = int(w * scale)
x2_min = (size - h2) // 2
x2_max = x2_min + h2
y2_min = (size - w2) // 2
y2_max = y2_min + w2
result[x2_min:x2_max, y2_min:y2_max] = cv2.resize(image[x_min:x_max, y_min:y_max], (w2, h2),
interpolation=cv2.INTER_AREA)
bg = np.ones((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255
# bg = np.zeros((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255
mask = result[..., 3:].astype(np.float32) / 255
result = result[..., :3] * mask + bg * (1 - mask)
mask = mask * 255
result = result.clip(0, 255).astype(np.uint8)
mask = mask.clip(0, 255).astype(np.uint8)
return result, mask
def __call__(self, image, border_ratio=0.15, to_tensor=True, return_mask=False, **kwargs):
if self.border_ratio is not None:
border_ratio = self.border_ratio
print(f"Using border_ratio from init: {border_ratio}")
if isinstance(image, str):
image = cv2.imread(image, cv2.IMREAD_UNCHANGED)
image, mask = self.recenter(image, border_ratio=border_ratio)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
elif isinstance(image, Image.Image):
image = np.asarray(image)
image, mask = self.recenter(image, border_ratio=border_ratio)
image = cv2.resize(image, (self.size, self.size), interpolation=cv2.INTER_CUBIC)
mask = cv2.resize(mask, (self.size, self.size), interpolation=cv2.INTER_NEAREST)
mask = mask[..., np.newaxis]
if to_tensor:
image = array_to_tensor(image)
mask = array_to_tensor(mask)
if return_mask:
return image, mask
return image
IMAGE_PROCESSORS = {
"v2": ImageProcessorV2,
}
DEFAULT_IMAGEPROCESSOR = 'v2'
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