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2026-01-02 12:40:44 +02:00
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src/utils/image_splitter.py Normal file
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import numpy as np
from pathlib import Path
from tifffile import imread, imwrite
from shapely.geometry import LineString
from copy import deepcopy
# debug
from src.utils.image import Image
from show_yolo_seg import draw_annotations
import pylab as plt
import cv2
class Label:
def __init__(self, yolo_annotation: str):
class_id, bbox, polygon = self.parse_yolo_annotation(yolo_annotation)
self.class_id = class_id
self.bbox = bbox
self.polygon = polygon
def parse_yolo_annotation(self, yolo_annotation: str):
class_id, *coords = yolo_annotation.split()
class_id = int(class_id)
bbox = np.array(coords[:4], dtype=np.float32)
polygon = (
np.array(coords[4:], dtype=np.float32).reshape(-1, 2)
if len(coords) > 4
else None
)
return class_id, bbox, polygon
def offset_label(
self, distance: float = 3.0, cap_style: int = 2, join_style: int = 2
):
if self.polygon is None:
self.bbox = np.array(
[
self.bbox[0] - distance if self.bbox[0] - distance > 0 else 0,
self.bbox[1] - distance if self.bbox[1] - distance > 0 else 0,
self.bbox[2] + distance if self.bbox[2] + distance < 1 else 1,
self.bbox[3] + distance if self.bbox[3] + distance < 1 else 1,
],
dtype=np.float32,
)
return self.bbox
line = LineString(self.polygon)
# Buffer distance in pixels
buffered = line.buffer(
distance=distance, cap_style=cap_style, join_style=join_style
)
self.polygon = np.array(buffered.exterior.coords, dtype=np.int32)
self.bbox = np.array(
[np.min(self.polygon[:, 0]), np.min(self.polygon[:, 1])], dtype=np.int32
)
return self.bbox, self.polygon
def translate(self, x, y, scale_x, scale_y):
self.bbox[0] -= x
self.bbox[0] *= scale_x
self.bbox[1] -= y
self.bbox[1] *= scale_y
self.bbox[2] *= scale_x
self.bbox[3] *= scale_y
if self.polygon is not None:
self.polygon[:, 0] -= x
self.polygon[:, 0] *= scale_x
self.polygon[:, 1] -= y
self.polygon[:, 1] *= scale_y
def in_range(self, hrange, wrange):
xc, yc, h, w = self.bbox
x1 = xc - w / 2
y1 = yc - h / 2
x2 = xc + w / 2
y2 = yc + h / 2
truth_val = (
xc >= wrange[0]
and x1 <= wrange[1]
and x2 >= wrange[0]
and x2 <= wrange[1]
and y1 >= hrange[0]
and y1 <= hrange[1]
and y2 >= hrange[0]
and y2 <= hrange[1]
)
print(x1, x2, wrange, y1, y2, hrange, truth_val)
return truth_val
def to_string(self, bbox: list = None, polygon: list = None):
if bbox is None:
bbox = self.bbox
if polygon is None:
polygon = self.polygon
coords = " ".join([f"{x:.6f}" for x in self.bbox])
if self.polygon is not None:
coords += " " + " ".join([f"{x:.6f} {y:.6f}" for x, y in self.polygon])
return f"{self.class_id} {coords}"
def __str__(self):
return f"Class: {self.class_id}, BBox: {self.bbox}, Polygon: {self.polygon}"
class YoloLabelReader:
def __init__(self, label_path: Path):
self.label_path = label_path
self.labels = self._read_labels()
def _read_labels(self):
with open(self.label_path, "r") as f:
labels = [Label(line) for line in f.readlines()]
return labels
def get_labels(self, hrange, wrange):
"""hrange and wrange are tuples of (start, end) normalized to [0, 1]"""
labels = []
# print(hrange, wrange)
for lbl in self.labels:
# print(lbl)
if lbl.in_range(hrange, wrange):
labels.append(lbl)
return labels if len(labels) > 0 else None
class ImageSplitter:
def __init__(self, image_path: Path, label_path: Path):
self.image = imread(image_path)
self.image_path = image_path
self.label_path = label_path
self.labels = YoloLabelReader(label_path)
def split(self, patch_size: tuple = (2, 2)):
"""Split image into patches of size patch_size"""
hstep, wstep = (
self.image.shape[0] // patch_size[0],
self.image.shape[1] // patch_size[1],
)
h, w = self.image.shape[:2]
for i in range(patch_size[0]):
for j in range(patch_size[1]):
tile_reference = f"i{i}j{j}"
hrange = (i * hstep / h, (i + 1) * hstep / h)
wrange = (j * wstep / w, (j + 1) * wstep / w)
labels = deepcopy(self.labels.get_labels(hrange, wrange))
tile = self.image[
i * hstep : (i + 1) * hstep, j * wstep : (j + 1) * wstep
]
print(id(labels))
if labels is not None:
print(hrange[0], wrange[0])
for l in labels:
print(l.bbox)
[l.translate(wrange[0], hrange[0], 2, 2) for l in labels]
print("translated")
for l in labels:
print(l.bbox)
# print(labels)
yield tile_reference, tile, labels
def main(args):
for image_path in (args.input / "images").glob("*.tif"):
data = ImageSplitter(
image_path=image_path,
label_path=(args.input / "labels" / image_path.stem).with_suffix(".txt"),
)
for tile_reference, tile, labels in data.split(patch_size=args.patch_size):
print()
print(
tile_reference, tile.shape, labels
) # len(labels) if labels else None)
# { debug
plt.figure(figsize=(10, 10 * tile.shape[0] / tile.shape[1]))
if labels is None:
plt.imshow(tile, cmap="gray")
plt.axis("off")
plt.title(f"{image_path.name} ({tile_reference})")
plt.show()
continue
print(labels[0].bbox)
# Draw annotations
out = draw_annotations(
cv2.cvtColor(
(tile / tile.max() * 255).astype(np.uint8), cv2.COLOR_GRAY2BGR
),
[l.to_string() for l in labels],
alpha=0.1,
)
# Convert BGR -> RGB for matplotlib display
out_rgb = cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
plt.imshow(out_rgb)
plt.axis("off")
plt.title(f"{image_path.name} ({tile_reference})")
plt.show()
# } debug
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", type=Path)
parser.add_argument("-o", "--output", type=Path)
parser.add_argument(
"-p",
"--patch-size",
nargs=2,
type=int,
default=[2, 2],
help="Number of patches along height and width, rows and columns, respectively",
)
args = parser.parse_args()
main(args)

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src/utils/show_yolo_seg.py Symbolic link
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../../tests/show_yolo_seg.py