File size: 6,453 Bytes
2568013 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# utilitary functions about images (loading/converting...)
# --------------------------------------------------------
import os
import numpy as np
import PIL.Image
import torch
import torchvision.transforms as tvf
from PIL.ImageOps import exif_transpose
from PIL import Image
import torchvision
os.environ["OPENCV_IO_ENABLE_OPENEXR"] = "1"
import cv2
try:
from pillow_heif import register_heif_opener
register_heif_opener()
heif_support_enabled = True
except ImportError:
heif_support_enabled = False
ImgNorm = tvf.Compose([tvf.ToTensor(), tvf.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
def imread_cv2(path, options=cv2.IMREAD_COLOR):
"""Open an image or a depthmap with opencv-python."""
if path.endswith((".exr", "EXR")):
options = cv2.IMREAD_ANYDEPTH
img = cv2.imread(path, options)
if img is None:
raise IOError(f"Could not load image={path} with {options=}")
if img.ndim == 3:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
return img
def rgb(ftensor, true_shape=None):
if isinstance(ftensor, list):
return [rgb(x, true_shape=true_shape) for x in ftensor]
if isinstance(ftensor, torch.Tensor):
ftensor = ftensor.detach().cpu().numpy() # H,W,3
if ftensor.ndim == 3 and ftensor.shape[0] == 3:
ftensor = ftensor.transpose(1, 2, 0)
elif ftensor.ndim == 4 and ftensor.shape[1] == 3:
ftensor = ftensor.transpose(0, 2, 3, 1)
if true_shape is not None:
H, W = true_shape
ftensor = ftensor[:H, :W]
if ftensor.dtype == np.uint8:
img = np.float32(ftensor) / 255
else:
img = (ftensor * 0.5) + 0.5
return img.clip(min=0, max=1)
def _resize_pil_image(img, long_edge_size):
S = max(img.size)
if S > long_edge_size:
interp = PIL.Image.LANCZOS
elif S <= long_edge_size:
interp = PIL.Image.BICUBIC
new_size = tuple(int(round(x * long_edge_size / S)) for x in img.size)
return img.resize(new_size, interp)
def load_images(folder_or_list, size, square_ok=False, verbose=True, rotate_clockwise_90=False, crop_to_landscape=False):
"""open and convert all images in a list or folder to proper input format for DUSt3R"""
if isinstance(folder_or_list, str):
if verbose:
print(f">> Loading images from {folder_or_list}")
root, folder_content = folder_or_list, sorted(os.listdir(folder_or_list))
elif isinstance(folder_or_list, list):
if verbose:
print(f">> Loading a list of {len(folder_or_list)} images")
root, folder_content = "", folder_or_list
else:
raise ValueError(f"bad {folder_or_list=} ({type(folder_or_list)})")
supported_images_extensions = [".jpg", ".jpeg", ".png"]
if heif_support_enabled:
supported_images_extensions += [".heic", ".heif"]
supported_images_extensions = tuple(supported_images_extensions)
imgs = []
for path in folder_content:
if not path.lower().endswith(supported_images_extensions):
continue
img = exif_transpose(PIL.Image.open(os.path.join(root, path))).convert("RGB")
if rotate_clockwise_90:
img = img.rotate(-90, expand=True)
if crop_to_landscape:
# Crop to a landscape aspect ratio (e.g., 16:9)
desired_aspect_ratio = 4 / 3
width, height = img.size
current_aspect_ratio = width / height
if current_aspect_ratio > desired_aspect_ratio:
# Wider than landscape: crop width
new_width = int(height * desired_aspect_ratio)
left = (width - new_width) // 2
right = left + new_width
top = 0
bottom = height
else:
# Taller than landscape: crop height
new_height = int(width / desired_aspect_ratio)
top = (height - new_height) // 2
bottom = top + new_height
left = 0
right = width
img = img.crop((left, top, right, bottom))
W1, H1 = img.size
if size == 224:
# resize short side to 224 (then crop)
img = _resize_pil_image(img, round(size * max(W1 / H1, H1 / W1)))
else:
# resize long side to 512
img = _resize_pil_image(img, size)
W, H = img.size
cx, cy = W // 2, H // 2
if size == 224:
half = min(cx, cy)
img = img.crop((cx - half, cy - half, cx + half, cy + half))
else:
halfw, halfh = ((2 * cx) // 16) * 8, ((2 * cy) // 16) * 8
if not (square_ok) and W == H:
halfh = 3 * halfw / 4
img = img.crop((cx - halfw, cy - halfh, cx + halfw, cy + halfh))
W2, H2 = img.size
if verbose:
print(f" - adding {path} with resolution {W1}x{H1} --> {W2}x{H2}")
imgs.append(
dict(
img=ImgNorm(img)[None],
true_shape=np.int32([img.size[::-1]]),
idx=len(imgs),
instance=str(len(imgs)),
)
)
assert imgs, "no images foud at " + root
if verbose:
print(f" (Found {len(imgs)} images)")
return imgs
def process_image(img_path):
img = Image.open(img_path)
if img.mode == 'RGBA':
# Convert RGBA to RGB by removing alpha channel
img = img.convert('RGB')
# Resize to maintain aspect ratio and then center crop to 448x448
width, height = img.size
if width > height:
new_height = 448
new_width = int(width * (new_height / height))
else:
new_width = 448
new_height = int(height * (new_width / width))
img = img.resize((new_width, new_height))
# Center crop
left = (new_width - 448) // 2
top = (new_height - 448) // 2
right = left + 448
bottom = top + 448
img = img.crop((left, top, right, bottom))
img_tensor = torchvision.transforms.ToTensor()(img) * 2.0 - 1.0 # [-1, 1]
return img_tensor |