Shaun ZhouandClaude Opus 5
154677032f
去掉强制非重叠：detect.py 不再给 DetectionValidator.postprocess 打补丁， 配置里移除 train.max_overlap 与 agnostic_nms，可视化脚本恢复为直接使用模型原始输出， engine/detection 里的 suppress_overlaps / filter_overlapping_outputs 一并移除。 约束会把 mAP50 从 0.555 压到 0.483（precision 0.536->0.612），代价是按置信度抑制时 高分错框会压掉本可命中的低分框。需要时用 git revert 这次提交即可取回。 按类别分目录的可视化产出结构（36fff98）保留。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
132 lines
4.4 KiB
Python
132 lines
4.4 KiB
Python
"""检测结果的匹配与绘制。"""
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import pytest
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pytest.importorskip("PIL")
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from engine.detection import ( # noqa: E402
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COLOR_GT,
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COLOR_MATCHED,
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COLOR_UNMATCHED,
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Box,
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draw_boxes,
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from_yolo,
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iou,
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match_boxes,
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parse_label,
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)
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def test_from_yolo_converts_center_to_corners():
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assert from_yolo(2, 0.5, 0.5, 0.2, 0.4) == Box(2, 0.4, 0.3, 0.6, 0.7)
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def test_parse_label_reads_boxes_and_empty_files():
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boxes = parse_label("0 0.5 0.5 0.2 0.2\n3 0.1 0.1 0.1 0.1\n")
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assert [b.class_id for b in boxes] == [0, 3]
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assert parse_label("") == [] # 负样本
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assert parse_label("\n\n") == []
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@pytest.mark.parametrize(
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"a,b,expected",
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[
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((0, 0, 1, 1), (0, 0, 1, 1), 1.0), # 完全重合
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((0, 0, 1, 1), (2, 2, 3, 3), 0.0), # 不相交
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((0, 0, 1, 1), (1, 1, 2, 2), 0.0), # 只碰到角
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((0, 0, 2, 2), (1, 1, 3, 3), 1 / 7), # 交 1，并 7
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],
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)
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def test_iou(a, b, expected):
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assert iou(Box(0, *a), Box(0, *b)) == pytest.approx(expected)
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def test_matches_by_confidence_first():
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"""两个预测抢同一个真值时，置信度高的赢——与 mAP 的规则一致。"""
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truth = [from_yolo(0, 0.5, 0.5, 0.2, 0.2)]
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weak = Box(0, 0.4, 0.4, 0.6, 0.6, 0.3)
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strong = Box(0, 0.41, 0.41, 0.59, 0.59, 0.9)
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match = match_boxes([weak, strong], truth, iou_threshold=0.3)
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assert match.pairs == [(1, 0)] # 下标 1 = strong
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assert match.unmatched_predictions == [0]
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assert match.unmatched_ground_truth == []
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def test_low_iou_counts_as_both_miss_and_false_positive():
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truth = [from_yolo(0, 0.2, 0.2, 0.1, 0.1)]
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prediction = Box(0, 0.7, 0.7, 0.9, 0.9, 0.9)
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match = match_boxes([prediction], truth)
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assert match.pairs == []
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assert match.unmatched_predictions == [0] # 误检
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assert match.unmatched_ground_truth == [0] # 同时也是漏检
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def test_class_mismatch_is_not_a_hit_unless_allowed():
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"""定位准但分类错：默认不算命中，same_class=False 时可单看位置。"""
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truth = [from_yolo(1, 0.5, 0.5, 0.2, 0.2)]
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prediction = Box(0, 0.4, 0.4, 0.6, 0.6, 0.9)
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assert match_boxes([prediction], truth).pairs == []
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assert match_boxes([prediction], truth, same_class=False).pairs == [(0, 0)]
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def test_each_ground_truth_is_claimed_once():
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"""一个真值只能被一个预测认领，多出来的预测算误检。"""
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truth = [from_yolo(0, 0.5, 0.5, 0.4, 0.4)]
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predictions = [
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Box(0, 0.31, 0.31, 0.69, 0.69, 0.9),
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Box(0, 0.32, 0.32, 0.68, 0.68, 0.8),
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]
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match = match_boxes(predictions, truth)
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assert len(match.pairs) == 1
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assert len(match.unmatched_predictions) == 1
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def test_no_prediction_on_background_is_clean():
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match = match_boxes([], [])
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assert match == ([], [], [])
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def test_multiple_boxes_are_matched_pairwise():
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truth = [from_yolo(0, 0.2, 0.2, 0.1, 0.1), from_yolo(1, 0.8, 0.8, 0.1, 0.1)]
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predictions = [
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Box(1, 0.75, 0.75, 0.85, 0.85, 0.7),
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Box(0, 0.15, 0.15, 0.25, 0.25, 0.9),
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]
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match = match_boxes(predictions, truth)
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assert sorted(match.pairs) == [(0, 1), (1, 0)]
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assert not match.unmatched_predictions and not match.unmatched_ground_truth
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def _colors(image):
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return {pixel for pixel in image.getdata()}
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def test_draw_uses_a_distinct_colour_per_role():
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"""三种角色三种颜色：绿=真值，蓝=命中的预测，红=没对上的预测。"""
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from PIL import Image
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truth = [from_yolo(0, 0.25, 0.25, 0.2, 0.2), from_yolo(0, 0.75, 0.25, 0.2, 0.2)]
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predictions = [
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Box(0, 0.16, 0.16, 0.34, 0.34, 0.9), # 命中第一个
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Box(0, 0.5, 0.8, 0.6, 0.9, 0.4), # 没有对应真值
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]
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match = match_boxes(predictions, truth, iou_threshold=0.5)
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assert match.pairs == [(0, 0)]
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image = Image.new("RGB", (200, 200), "black")
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draw_boxes(image, truth, predictions, ["blackdot"], match)
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present = _colors(image)
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assert COLOR_GT in present and COLOR_MATCHED in present and COLOR_UNMATCHED in present
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def test_draw_handles_boxes_at_the_border():
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"""框贴着上边缘时，标签文字要画到框内，不能画到图外去。"""
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from PIL import Image
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image = Image.new("RGB", (64, 64), "black")
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prediction = Box(0, 0.0, 0.0, 0.5, 0.05, 0.9)
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draw_boxes(image, [], [prediction], ["blackdot"], match_boxes([prediction], []))
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assert COLOR_UNMATCHED in _colors(image)
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