Shaun ZhouandClaude Opus 5
474b7b50a0
训练数据已经是固定边长的片，推理还把整图交给 ultralytics 就会被 letterbox 缩放， 与模型见过的形态不符。三个调用方（可视化、批量推送预测、ML backend）统一改成 切片推理再拼回原图。 - engine/tiling.merge_tiles / tiled_predict：映射回原图、裁掉镜像填充区的框、跨片去重。 推理框架留在调用方，几何部分可单测。 - engine/detection.nms：标准 NMS，另加按包含比例抑制。缺陷被切割线切开时，相邻片 只看到一角，给出的小残框与完整框 IoU 很小（面积悬殊），光靠 IoU 压不掉。 - engine/tiled_inference：ultralytics 的接线，内部分批，避免片数多时 OOM。 - 三处调用点加 --tile-size / --tile-overlap（后端为 YOLO_TILE_SIZE / YOLO_TILE_OVERLAP）， 默认 160 / 32；设 0 退回整图推理，仅用于和旧模型对比。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
171 lines
5.7 KiB
Python
171 lines
5.7 KiB
Python
"""检测结果的匹配与绘制。"""
|
|
|
|
import pytest
|
|
|
|
pytest.importorskip("PIL")
|
|
|
|
from engine.detection import ( # noqa: E402
|
|
COLOR_GT,
|
|
COLOR_MATCHED,
|
|
COLOR_UNMATCHED,
|
|
Box,
|
|
draw_boxes,
|
|
from_yolo,
|
|
iou,
|
|
match_boxes,
|
|
nms,
|
|
parse_label,
|
|
)
|
|
|
|
|
|
def test_from_yolo_converts_center_to_corners():
|
|
assert from_yolo(2, 0.5, 0.5, 0.2, 0.4) == Box(2, 0.4, 0.3, 0.6, 0.7)
|
|
|
|
|
|
def test_parse_label_reads_boxes_and_empty_files():
|
|
boxes = parse_label("0 0.5 0.5 0.2 0.2\n3 0.1 0.1 0.1 0.1\n")
|
|
assert [b.class_id for b in boxes] == [0, 3]
|
|
assert parse_label("") == [] # 负样本
|
|
assert parse_label("\n\n") == []
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"a,b,expected",
|
|
[
|
|
((0, 0, 1, 1), (0, 0, 1, 1), 1.0), # 完全重合
|
|
((0, 0, 1, 1), (2, 2, 3, 3), 0.0), # 不相交
|
|
((0, 0, 1, 1), (1, 1, 2, 2), 0.0), # 只碰到角
|
|
((0, 0, 2, 2), (1, 1, 3, 3), 1 / 7), # 交 1，并 7
|
|
],
|
|
)
|
|
def test_iou(a, b, expected):
|
|
assert iou(Box(0, *a), Box(0, *b)) == pytest.approx(expected)
|
|
|
|
|
|
def test_matches_by_confidence_first():
|
|
"""两个预测抢同一个真值时，置信度高的赢——与 mAP 的规则一致。"""
|
|
truth = [from_yolo(0, 0.5, 0.5, 0.2, 0.2)]
|
|
weak = Box(0, 0.4, 0.4, 0.6, 0.6, 0.3)
|
|
strong = Box(0, 0.41, 0.41, 0.59, 0.59, 0.9)
|
|
match = match_boxes([weak, strong], truth, iou_threshold=0.3)
|
|
|
|
assert match.pairs == [(1, 0)] # 下标 1 = strong
|
|
assert match.unmatched_predictions == [0]
|
|
assert match.unmatched_ground_truth == []
|
|
|
|
|
|
def test_low_iou_counts_as_both_miss_and_false_positive():
|
|
truth = [from_yolo(0, 0.2, 0.2, 0.1, 0.1)]
|
|
prediction = Box(0, 0.7, 0.7, 0.9, 0.9, 0.9)
|
|
match = match_boxes([prediction], truth)
|
|
assert match.pairs == []
|
|
assert match.unmatched_predictions == [0] # 误检
|
|
assert match.unmatched_ground_truth == [0] # 同时也是漏检
|
|
|
|
|
|
def test_class_mismatch_is_not_a_hit_unless_allowed():
|
|
"""定位准但分类错：默认不算命中，same_class=False 时可单看位置。"""
|
|
truth = [from_yolo(1, 0.5, 0.5, 0.2, 0.2)]
|
|
prediction = Box(0, 0.4, 0.4, 0.6, 0.6, 0.9)
|
|
|
|
assert match_boxes([prediction], truth).pairs == []
|
|
assert match_boxes([prediction], truth, same_class=False).pairs == [(0, 0)]
|
|
|
|
|
|
def test_each_ground_truth_is_claimed_once():
|
|
"""一个真值只能被一个预测认领，多出来的预测算误检。"""
|
|
truth = [from_yolo(0, 0.5, 0.5, 0.4, 0.4)]
|
|
predictions = [
|
|
Box(0, 0.31, 0.31, 0.69, 0.69, 0.9),
|
|
Box(0, 0.32, 0.32, 0.68, 0.68, 0.8),
|
|
]
|
|
match = match_boxes(predictions, truth)
|
|
assert len(match.pairs) == 1
|
|
assert len(match.unmatched_predictions) == 1
|
|
|
|
|
|
def test_no_prediction_on_background_is_clean():
|
|
match = match_boxes([], [])
|
|
assert match == ([], [], [])
|
|
|
|
|
|
def test_multiple_boxes_are_matched_pairwise():
|
|
truth = [from_yolo(0, 0.2, 0.2, 0.1, 0.1), from_yolo(1, 0.8, 0.8, 0.1, 0.1)]
|
|
predictions = [
|
|
Box(1, 0.75, 0.75, 0.85, 0.85, 0.7),
|
|
Box(0, 0.15, 0.15, 0.25, 0.25, 0.9),
|
|
]
|
|
match = match_boxes(predictions, truth)
|
|
assert sorted(match.pairs) == [(0, 1), (1, 0)]
|
|
assert not match.unmatched_predictions and not match.unmatched_ground_truth
|
|
|
|
|
|
def _colors(image):
|
|
return {pixel for pixel in image.getdata()}
|
|
|
|
|
|
def test_draw_uses_a_distinct_colour_per_role():
|
|
"""三种角色三种颜色：绿=真值，蓝=命中的预测，红=没对上的预测。"""
|
|
from PIL import Image
|
|
|
|
truth = [from_yolo(0, 0.25, 0.25, 0.2, 0.2), from_yolo(0, 0.75, 0.25, 0.2, 0.2)]
|
|
predictions = [
|
|
Box(0, 0.16, 0.16, 0.34, 0.34, 0.9), # 命中第一个
|
|
Box(0, 0.5, 0.8, 0.6, 0.9, 0.4), # 没有对应真值
|
|
]
|
|
match = match_boxes(predictions, truth, iou_threshold=0.5)
|
|
assert match.pairs == [(0, 0)]
|
|
|
|
image = Image.new("RGB", (200, 200), "black")
|
|
draw_boxes(image, truth, predictions, ["blackdot"], match)
|
|
present = _colors(image)
|
|
assert COLOR_GT in present and COLOR_MATCHED in present and COLOR_UNMATCHED in present
|
|
|
|
|
|
def test_draw_handles_boxes_at_the_border():
|
|
"""框贴着上边缘时，标签文字要画到框内，不能画到图外去。"""
|
|
from PIL import Image
|
|
|
|
image = Image.new("RGB", (64, 64), "black")
|
|
prediction = Box(0, 0.0, 0.0, 0.5, 0.05, 0.9)
|
|
draw_boxes(image, [], [prediction], ["blackdot"], match_boxes([prediction], []))
|
|
assert COLOR_UNMATCHED in _colors(image)
|
|
|
|
|
|
# ---------------- NMS ----------------
|
|
|
|
|
|
def test_nms_removes_duplicates_of_the_same_defect():
|
|
high = Box(0, 0.30, 0.30, 0.50, 0.50, 0.9)
|
|
duplicate = Box(0, 0.31, 0.31, 0.51, 0.51, 0.7)
|
|
assert nms([duplicate, high]) == [1]
|
|
|
|
|
|
def test_nms_keeps_neighbours_below_the_threshold():
|
|
"""真正分开的两个缺陷，轻微相邻也要都留住。"""
|
|
a = Box(0, 0.0, 0.0, 0.5, 0.5, 0.9)
|
|
b = Box(0, 0.4, 0.4, 0.9, 0.9, 0.8) # IoU 约 0.02
|
|
assert nms([a, b]) == [0, 1]
|
|
|
|
|
|
def test_nms_is_per_class_by_default():
|
|
"""不同类别在同一位置的分歧是有意义的，不该互相压制。"""
|
|
a = Box(0, 0.3, 0.3, 0.6, 0.6, 0.9)
|
|
b = Box(4, 0.31, 0.31, 0.61, 0.61, 0.8)
|
|
assert nms([a, b]) == [0, 1]
|
|
assert nms([a, b], per_class=False) == [0]
|
|
|
|
|
|
def test_nms_handles_empty_input():
|
|
assert nms([]) == []
|
|
|
|
|
|
def test_nms_can_suppress_contained_fragments():
|
|
"""切片接缝上的残框：与完整框 IoU 很小，却几乎整个落在里面。"""
|
|
full = Box(0, 0.30, 0.50, 0.40, 0.60, 0.9)
|
|
fragment = Box(0, 0.39, 0.50, 0.40, 0.60, 0.8) # 只剩一条边
|
|
assert iou(full, fragment) < 0.15
|
|
|
|
assert nms([full, fragment]) == [0, 1] # 只看 IoU 压不掉
|
|
assert nms([full, fragment], contained_threshold=0.8) == [0] # 按包含比例能压掉
|