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3ecb175 | 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 | """Read YOLO-seg .txt predictions and build a per-tuber morphometrics dataframe.
YOLO-seg .txt format (one detection per line):
class_id x1 y1 x2 y2 ... xN yN # coords normalized to [0, 1]
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
from PIL import Image
from .morphometrics import polygon_shape_metrics
IMG_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff"}
def read_yolo_seg_txt(txt_path: str | Path) -> list[dict]:
"""Parse a YOLO-seg .txt file into a list of {class_id, xy_norm}.
Lines with fewer than 3 vertices (6 coordinates) or an odd coordinate
count are skipped.
"""
polys: list[dict] = []
with open(txt_path) as f:
for line in f:
line = line.strip()
if not line:
continue
parts = line.split()
class_id = int(float(parts[0]))
coords = np.array(list(map(float, parts[1:])), dtype=float)
if coords.size < 6 or coords.size % 2 != 0:
continue
polys.append({"class_id": class_id, "xy_norm": coords.reshape(-1, 2)})
return polys
def _list_images(folder: Path) -> list[Path]:
return sorted(p for p in folder.rglob("*") if p.suffix.lower() in IMG_EXTS)
def build_morphometrics_dataframe(
trial_name: str,
images_dir: str | Path,
preds_dir: str | Path,
) -> pd.DataFrame:
"""Produce one row per detected tuber with all pixel-scale shape metrics.
Pairs each .txt file in `preds_dir` with the image of the same stem in
`images_dir` so we can read the image dimensions (needed both for
denormalizing polygon coordinates and for the downstream calibration).
"""
images_dir = Path(images_dir)
preds_dir = Path(preds_dir)
if not preds_dir.exists():
raise FileNotFoundError(f"[{trial_name}] preds_dir not found: {preds_dir}")
if not images_dir.exists():
raise FileNotFoundError(f"[{trial_name}] images_dir not found: {images_dir}")
image_map = {p.stem: p for p in _list_images(images_dir)}
txt_files = sorted(preds_dir.glob("*.txt"))
if not txt_files:
raise ValueError(f"[{trial_name}] No .txt files found in {preds_dir}")
rows: list[dict] = []
skipped_txt_no_image = 0
for txt in txt_files:
img_stem = txt.stem
img_path = image_map.get(img_stem)
if img_path is None:
skipped_txt_no_image += 1
continue
with Image.open(img_path) as im:
img_width, img_height = im.size
for line_idx, poly in enumerate(read_yolo_seg_txt(txt), start=1):
xy = poly["xy_norm"]
xs = xy[:, 0] * img_width
ys = xy[:, 1] * img_height
metrics = polygon_shape_metrics(xs, ys)
rows.append(
{
"trial": trial_name,
"image_id": img_stem,
"detection_id": f"{img_stem}_{line_idx}",
"class_id": poly["class_id"],
"img_width_px": img_width,
"img_height_px": img_height,
"n_points": len(xs),
**metrics,
}
)
df = pd.DataFrame(rows)
if skipped_txt_no_image:
print(
f"[{trial_name}] images={len(image_map)} txt={len(txt_files)} "
f"instances={len(df)} skipped_txt_no_image={skipped_txt_no_image}"
)
return df
def plot_id_from_image_id(image_id: pd.Series) -> pd.Series:
"""Derive a raw plot identifier from the last 8 characters of the image stem.
The result (e.g. "NCPT-129", "5_ADC101") is further normalized by
standardize_plot_id in the yield-prediction stage.
"""
return image_id.str[-8:]
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