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434b0b0 | 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 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # Multi-HMR
# Copyright (c) 2024-present NAVER Corp.
# CC BY-NC-SA 4.0 license
import torch
import numpy as np
from PIL import Image, ImageOps
import torch.nn.functional as F
import cv2
import time
IMG_NORM_MEAN = [0.485, 0.456, 0.406]
IMG_NORM_STD = [0.229, 0.224, 0.225]
def normalize_rgb_tensor(img, imgenet_normalization=True):
img = img / 255.0
if imgenet_normalization:
img = (
img - torch.tensor(IMG_NORM_MEAN, device=img.device).view(1, 3, 1, 1)
) / torch.tensor(IMG_NORM_STD, device=img.device).view(1, 3, 1, 1)
return img
def normalize_rgb(img, imagenet_normalization=True):
"""
Args:
- img: np.array - (W,H,3) - np.uint8 - 0/255
Return:
- img: np.array - (3,W,H) - np.float - -3/3
"""
img = img.astype(np.float32) / 255.0
img = np.transpose(img, (2, 0, 1))
if imagenet_normalization:
img = (img - np.asarray(IMG_NORM_MEAN).reshape(3, 1, 1)) / np.asarray(
IMG_NORM_STD
).reshape(3, 1, 1)
img = img.astype(np.float32)
return img
def denormalize_rgb(img, imagenet_normalization=True):
"""
Args:
- img: np.array - (3,W,H) - np.float - -3/3
Return:
- img: np.array - (W,H,3) - np.uint8 - 0/255
"""
if imagenet_normalization:
img = (img * np.asarray(IMG_NORM_STD).reshape(3, 1, 1)) + np.asarray(
IMG_NORM_MEAN
).reshape(3, 1, 1)
img = np.transpose(img, (1, 2, 0)) * 255.0
img = img.astype(np.uint8)
return img
def unpatch(data, patch_size=14, c=3, img_size=224):
# c = 3
if len(data.shape) == 2:
c = 1
data = data[:, :, None].repeat([1, 1, patch_size**2])
B, N, HWC = data.shape
HW = patch_size**2
c = int(HWC / HW)
h = w = int(N**0.5)
p = q = int(HW**0.5)
data = data.reshape([B, h, w, p, q, c])
data = torch.einsum("nhwpqc->nchpwq", data)
return data.reshape([B, c, img_size, img_size])
def image_pad(img, img_size, device=torch.device("cuda")):
img_pil = ImageOps.contain(img, (img_size, img_size))
img_pil_bis = ImageOps.pad(
img_pil.copy(), size=(img_size, img_size), color=(255, 255, 255)
)
img_pil = ImageOps.pad(
img_pil, size=(img_size, img_size)
) # pad with zero on the smallest side
# Go to numpy
resize_img = np.asarray(img_pil)
# Normalize and go to torch.
resize_img = normalize_rgb(resize_img)
x = torch.from_numpy(resize_img).unsqueeze(0).to(device)
return x, img_pil_bis
def image_pad_cuda(img, img_size, rot=0, device=torch.device("cuda"), vis=False):
img = torch.Tensor(img).to(device)
img = torch.flip(img, dims=[2]).unsqueeze(0).permute(0, 3, 1, 2)
if rot != 0:
img = torch.rot90(img, rot, [2, 3])
if vis:
image = img.clone()[0].permute(1, 2, 0).cpu().numpy()
if image.dtype != np.uint8:
image = image.astype(np.uint8)
cv2.imshow("k4a", image[..., ::-1])
cv2.waitKey(1)
_, _, h, w = img.shape
scale_factor = min(img_size / w, img_size / h)
img = F.interpolate(img, scale_factor=scale_factor, mode="bilinear")
_, _, h, w = img.shape
pad_w = (img_size - w) // 2
pad_h = (img_size - h) // 2
img = F.pad(img, (pad_w, pad_w, pad_h, pad_h), mode="constant", value=255)
# Normalize and go to torch.
resize_img = normalize_rgb_tensor(img)
return resize_img, img
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