Spaces:
Runtime error
Runtime error
File size: 5,711 Bytes
ce190ee |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
import sys
from pathlib import Path
from skimage.io import imread, imsave
from skimage.transform import resize
from skimage.color import rgba2rgb
from argparse import ArgumentParser
import numpy as np
IMG_EXTENSIONS = set(
[".jpg", ".JPG", ".jpeg", ".JPEG", ".png", ".PNG", ".ppm", ".PPM", ".bmp", ".BMP"]
)
def is_image_file(filename):
"""Check that a file's name points to a known image format
"""
if isinstance(filename, Path):
return filename.suffix in IMG_EXTENSIONS
return Path(filename).suffix in IMG_EXTENSIONS
def find_images(path, recursive=False):
"""
Get a list of all images contained in a directory:
- path.glob("*") if not recursive
- path.glob("**/*") if recursive
"""
p = Path(path)
assert p.exists()
assert p.is_dir()
pattern = "*"
if recursive:
pattern += "*/*"
return [i for i in p.glob(pattern) if i.is_file() and is_image_file(i)]
def uint8(array):
return array.astype(np.uint8)
def crop_and_resize(image_path, label_path):
"""
Resizes an image so that it keeps the aspect ratio and the smallest dimensions
is 640, then crops this resized image in its center so that the output is 640x640
without aspect ratio distortion
Args:
image_path (Path or str): Path to an image
label_path (Path or str): Path to the image's associated label
Returns:
tuple((np.ndarray, np.ndarray)): (new image, new label)
"""
dolab = label_path is not None
img = imread(image_path)
if dolab:
lab = imread(label_path)
if img.shape[-1] == 4:
img = uint8(rgba2rgb(img) * 255)
if dolab and img.shape != lab.shape:
print("\nWARNING: shape mismatch. Entering breakpoint to investigate:")
breakpoint()
# resize keeping aspect ratio: smallest dim is 640
h, w = img.shape[:2]
if h < w:
size = (640, int(640 * w / h))
else:
size = (int(640 * h / w), 640)
r_img = resize(img, size, preserve_range=True, anti_aliasing=True)
r_img = uint8(r_img)
if dolab:
# nearest neighbor for labels
r_lab = resize(lab, size, preserve_range=True, anti_aliasing=False, order=0)
r_lab = uint8(r_lab)
# crop in the center
H, W = r_img.shape[:2]
top = (H - 640) // 2
left = (W - 640) // 2
rc_img = r_img[top : top + 640, left : left + 640, :]
if dolab:
rc_lab = r_lab[top : top + 640, left : left + 640, :]
else:
rc_lab = None
return rc_img, rc_lab
def label(img, label, alpha=0.4):
return uint8(alpha * label + (1 - alpha) * img)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument(
"-i", "--input_dir", type=str, help="Directory to recursively read images from"
)
parser.add_argument(
"-o",
"--output_dir",
type=str,
help="Where to writ the result of the script,"
+ " keeping the input dir's structure",
)
parser.add_argument(
"--no_labels",
action="store_true",
help="Only process images, don't look for labels",
)
parser.add_argument(
"--store_labeled",
action="store_true",
help="Store a superposition of the label and the image in out/labeled/",
)
args = parser.parse_args()
dolab = not args.no_labels
dolabeled = args.store_labeled
input_base = Path(args.input_dir).expanduser().resolve()
output_base = Path(args.output_dir).expanduser().resolve()
input_images = input_base / "imgs"
output_images = output_base / "imgs"
if dolab:
input_labels = input_base / "labels"
output_labels = output_base / "labels"
if dolabeled:
output_labeled = output_base / "labeled"
print("Input images:", str(input_images))
print("Output images:", str(output_images))
if dolab:
print("Input labels:", str(input_labels))
print("Output labels:", str(output_labels))
if dolabeled:
print("Output labeled:", str(output_labeled))
else:
print("NO LABEL PROCESSING (args.no_labels is specified)")
print()
assert input_images.exists()
if dolab:
assert input_labels.exists()
if output_base.exists():
if (
"n"
in input(
"WARNING: output dir already exists."
+ " Overwrite its content? (y/n, default: y)"
).lower()
):
sys.exit()
output_images.mkdir(parents=True, exist_ok=True)
if dolab:
output_labels.mkdir(parents=True, exist_ok=True)
if dolabeled:
output_labeled.mkdir(parents=True, exist_ok=True)
images_paths = list(
map(Path, sorted((map(str, find_images(input_images, recursive=True)))))
)
if dolab:
labels_paths = list(
map(Path, sorted((map(str, find_images(input_labels, recursive=True)))))
)
else:
labels_paths = [None] * len(images_paths)
for i, (image_path, label_path) in enumerate(zip(images_paths, labels_paths)):
print(
f"Processing {i + 1 :3} / {len(images_paths)} : {image_path.name}",
end="\r",
flush=True,
)
processed_image, processed_label = crop_and_resize(image_path, label_path)
imsave(output_images / f"{image_path.stem}.png", processed_image)
if dolab:
imsave(output_labels / f"{label_path.stem}.png", processed_label)
if dolabeled:
labeled = label(processed_image, processed_label)
imsave(output_labeled / f"{image_path.stem}.png", labeled)
print("\nDone.")
|