Shaun ZhouandClaude Opus 5
65bc6b2045
背景差分：大半径方框均值估背景 → 残差（可选极性）→ 平滑 → 有无判定 → 按峰值归一化， 再交给与 gradcam / shap 同一套 cam_to_boxes 出框。只依赖 numpy。 与归因方法的差别：不需要 --checkpoint，分类器还没训练出来就能先跑一版框，也不受分类 精度影响；类别只能来自目录（--target label）；找的是偏离局部背景的区域，而不是模型 据以判断类别的区域，所以对局部目标好、对铺满整图的纹理类给不出有意义的框。 有无缺陷是显式判定的：残差峰值要同时高于绝对对比度下限与噪声水平（MAD 估计）的 15 倍， 否则返回全零图。这条是必需的——实测纯噪声图的"峰值/MAD"稳定在 9 附近（最大 10.9， 几乎与噪声大小无关），有 0.25 对比度黑点时是 25~330；若只按峰值归一化，正常图的噪声 会被拉到 1，满图假框。阈值 15 取在两者之间。 顺带把积分图方框均值从 shap_explain.smooth 提到 engine/classical.box_mean 共用， 避免两处实现各自漂移。 实测：单张 0.011 秒（全量 11k 约 2 分钟）；真实薄膜图上框能套住黑点、虫状物与划痕。 已知局限：背景估计在图像边界处对明暗阶跃有响应，贴边的交界可能出假框。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
138 lines
5.2 KiB
Python
138 lines
5.2 KiB
Python
"""传统 CV 的缺陷显著图（背景差分）。"""
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import pytest
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np = pytest.importorskip("numpy")
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from engine.classical import box_mean, defect_saliency, to_gray # noqa: E402
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from engine.gradcam import cam_to_boxes # noqa: E402
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BOX_KWARGS = {"threshold": 0.4, "min_area_ratio": 0.002, "max_boxes": 1, "peak_ratio": 0.75}
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def _image(size=96, level=0.5, seed=0, noise=0.01):
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rng = np.random.default_rng(seed)
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return np.clip(level + rng.normal(0, noise, (size, size)), 0, 1).astype(np.float32)
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def _with_blob(image, center=(30, 70), radius=4, delta=-0.25):
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ys, xs = np.ogrid[: image.shape[0], : image.shape[1]]
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blob = (ys - center[0]) ** 2 + (xs - center[1]) ** 2 <= radius**2
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out = image.copy()
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out[blob] = np.clip(out[blob] + delta, 0, 1)
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return out
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def test_locates_a_dark_spot():
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"""黑点应该落在框里，而且框不能大到没有意义。"""
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saliency = defect_saliency(_with_blob(_image()))
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boxes = cam_to_boxes(saliency, **BOX_KWARGS)
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assert len(boxes) == 1
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x1, y1, x2, y2 = boxes[0]
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assert x1 <= 70 / 96 <= x2 and y1 <= 30 / 96 <= y2 # 覆盖缺陷中心
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assert (x2 - x1) * (y2 - y1) < 0.1 # 不到整图的 10%
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@pytest.mark.parametrize("noise", [0.005, 0.01, 0.03])
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def test_flat_image_gets_no_box(noise):
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"""只有噪声的正常图必须出 0 个框，噪声大小不同都要成立。
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回归：若只按峰值归一化，噪声峰值会被拉到 1，满图假框；改成先判定有无缺陷。
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"""
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saliency = defect_saliency(_image(noise=noise, seed=1))
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assert not saliency.any()
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assert cam_to_boxes(saliency, **BOX_KWARGS) == []
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def test_illumination_gradient_is_not_a_defect():
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"""大尺度亮度不均（照明、暗角）属于背景，不该被当成缺陷。"""
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ramp = np.linspace(0.3, 0.7, 96, dtype=np.float32)
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gradient = np.repeat(ramp[None, :], 96, axis=0)
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assert cam_to_boxes(defect_saliency(gradient), **BOX_KWARGS) == []
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# 同一张图上的黑点仍要找得到
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boxes = cam_to_boxes(defect_saliency(_with_blob(gradient)), **BOX_KWARGS)
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assert len(boxes) == 1
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@pytest.mark.parametrize(
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"polarity,delta,expected",
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[("dark", -0.25, 1), ("dark", 0.25, 0), ("bright", 0.25, 1), ("bright", -0.25, 0),
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("both", -0.25, 1), ("both", 0.25, 1)],
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)
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def test_polarity_selects_dark_or_bright(polarity, delta, expected):
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image = _with_blob(_image(), delta=delta)
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saliency = defect_saliency(image, polarity=polarity)
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assert len(cam_to_boxes(saliency, **BOX_KWARGS)) == expected
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def test_rejects_unknown_polarity():
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with pytest.raises(ValueError, match="polarity"):
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defect_saliency(_image(), polarity="darker")
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def test_output_range_and_shape():
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saliency = defect_saliency(_with_blob(_image(size=64)))
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assert saliency.shape == (64, 64)
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assert saliency.min() >= 0.0 and saliency.max() <= 1.0
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def test_accepts_uint8_rgb_and_grayscale():
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"""uint8 / 浮点、单通道 / 三通道都要能进来，结果一致。"""
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gray = _with_blob(_image())
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as_uint8 = (gray * 255).astype(np.uint8)
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rgb = np.repeat(as_uint8[..., None], 3, axis=2)
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from_float = cam_to_boxes(defect_saliency(gray), **BOX_KWARGS) # noqa: F841
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from_uint8 = cam_to_boxes(defect_saliency(as_uint8), **BOX_KWARGS)
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from_rgb = cam_to_boxes(defect_saliency(rgb), **BOX_KWARGS)
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assert from_uint8 == from_rgb
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assert np.allclose(from_float, from_uint8, atol=0.05)
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def test_to_gray_rejects_odd_shapes():
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with pytest.raises(ValueError, match="HxW"):
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to_gray(np.zeros((5,), dtype=np.float32))
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def test_default_radius_scales_with_the_image():
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"""background_radius=0 时按 min(高,宽)//8 取值。"""
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image = _with_blob(_image(size=96), radius=3)
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assert np.array_equal(
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defect_saliency(image), defect_saliency(image, background_radius=12)
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)
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def test_faint_defect_is_rejected_by_min_contrast():
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"""对比度低于下限的起伏不算缺陷；调低下限就能找出来。"""
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faint = _with_blob(_image(noise=0.005), delta=-0.04)
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assert cam_to_boxes(defect_saliency(faint), **BOX_KWARGS) == []
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assert len(cam_to_boxes(defect_saliency(faint, min_contrast=0.01), **BOX_KWARGS)) == 1
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def test_noisy_image_needs_more_contrast():
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"""噪声大的图自动要求更高对比度：同一个弱缺陷在干净图上能找到，在噪声图上不算数。"""
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weak = -0.05 # 实测比值：σ=0.002 时 109，σ=0.04 时 9.8
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assert len(cam_to_boxes(
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defect_saliency(_with_blob(_image(noise=0.002), delta=weak), min_contrast=0.01),
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**BOX_KWARGS,
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)) == 1
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assert cam_to_boxes(
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defect_saliency(_with_blob(_image(noise=0.04), delta=weak), min_contrast=0.01),
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**BOX_KWARGS,
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) == []
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def test_box_mean_matches_a_naive_average():
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rng = np.random.default_rng(3)
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array = rng.random((12, 15))
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radius = 2
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padded = np.pad(array, radius, mode="edge")
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expected = np.empty_like(array)
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for y in range(array.shape[0]):
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for x in range(array.shape[1]):
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expected[y, x] = padded[y:y + 2 * radius + 1, x:x + 2 * radius + 1].mean()
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assert np.allclose(box_mean(array, radius), expected)
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assert np.array_equal(box_mean(array, 0), array) # radius=0 原样返回
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