finished docker manylinux compliation for pypi
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@@ -72,11 +72,11 @@ def main():
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grid_spacing=20,
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arrow_scale=1.0
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)
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cv2.imshow("形变矢量场", arrows)
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cv2.imshow("arrow", arrows)
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# 步骤 3.2:显示接触区域和质心
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image_with_foe = orisys.util.draw_contact(img, contour, centroid)
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cv2.imshow("接触区域与质心", image_with_foe)
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cv2.imshow("contact", image_with_foe)
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# 步骤 3.3:显示深度图
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# 将深度值归一化到 0-255 范围
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@@ -87,7 +87,7 @@ def main():
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div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
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# 应用伪彩色映射
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cv2.imshow("深度图", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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cv2.imshow("depth", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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# 步骤 3.4:打印关键结果
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print(
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@@ -72,11 +72,11 @@ def main():
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grid_spacing=20,
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arrow_scale=1.0
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)
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cv2.imshow("形变矢量场", arrows)
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cv2.imshow("arrows", arrows)
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# 步骤 3.2:显示接触区域和质心
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image_with_foe = orisys.util.draw_contact(img, contour, centroid)
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cv2.imshow("接触区域与质心", image_with_foe)
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cv2.imshow("contact", image_with_foe)
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# 步骤 3.3:显示深度图
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# 将深度值归一化到 0-255 范围
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@@ -87,7 +87,7 @@ def main():
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div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
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# 应用伪彩色映射
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cv2.imshow("深度图", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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cv2.imshow("depth", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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# 步骤 3.4:打印关键结果
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print(
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