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含有忍耐度的TP计算(Precision、Rcall、F1 score).html
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含有忍耐度的TP计算(Precision、Rcall、F1 score).html
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<span>含有忍耐度的TP计算(Precision、Rcall、F1 score)</span>
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发布 :
2020-03-30
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分类 :
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深度学习
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浏览 : <span class="article-timer" data-identity="含有忍耐度的TP计算(Precision、Rcall、F1 score).html"></span>
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<p>对于边缘分割类任务，边缘部分因为人为标记不准确等原因，在评估模型时，需要对这些区域设置一定忍耐度以保证模型评估更完整。这里提供代码参考。（允许两个像素差距）</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> cv2</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> time</span><br><span class="line"></span><br><span class="line">gt = cv2.imread(<span class="string">"/home/harvey/Datasets/DeepCrack/OurResult/11125-1_gt.png"</span>, cv2.IMREAD_UNCHANGED)</span><br><span class="line">p_msk = cv2.imread(<span class="string">"/home/harvey/Datasets/DeepCrack/OurResult/11125-1_pred.png"</span>, cv2.IMREAD_UNCHANGED)</span><br><span class="line">thres = <span class="number">10</span></span><br><span class="line">kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (<span class="number">5</span>, <span class="number">5</span>))</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">rolling_cal</span><span class="params">(ground, pred_msk, thres=<span class="number">10</span>)</span>:</span></span><br><span class="line"> H, W = ground.shape</span><br><span class="line"> total = H * W</span><br><span class="line"> filter_size = <span class="number">5</span></span><br><span class="line"> msk_t = (pred_msk > thres).astype(np.int)</span><br><span class="line"> ground = (ground > <span class="number">0</span>).astype(np.int)</span><br><span class="line"> H, W = ground.shape</span><br><span class="line"> gt = np.pad(ground, (<span class="number">2</span>, <span class="number">2</span>), <span class="string">'constant'</span>, constant_values=(<span class="number">0</span>, <span class="number">0</span>))</span><br><span class="line"> tp, fp, fn = <span class="number">0.0</span>, <span class="number">0.0</span>, <span class="number">0.0</span></span><br><span class="line"> <span class="keyword">for</span> r <span class="keyword">in</span> range(<span class="number">0</span>, H):</span><br><span class="line"> <span class="keyword">for</span> c <span class="keyword">in</span> range(<span class="number">0</span>, W):</span><br><span class="line"> <span class="comment"># 池化大小的输入区域</span></span><br><span class="line"> gti = gt[r:r + filter_size, c:c + filter_size]</span><br><span class="line"> cur_pexl = msk_t[r, c]</span><br><span class="line"> gt_patch = (gti * kernel).sum()</span><br><span class="line"> <span class="keyword">if</span> cur_pexl != <span class="number">0</span> <span class="keyword">and</span> gt_patch != <span class="number">0</span>:</span><br><span class="line"> tp += <span class="number">1</span></span><br><span class="line"> <span class="keyword">elif</span> cur_pexl != <span class="number">0</span> <span class="keyword">and</span> gti[<span class="number">2</span>][<span class="number">2</span>] == <span class="number">0</span>:</span><br><span class="line"> fp += <span class="number">1</span></span><br><span class="line"> <span class="keyword">elif</span> cur_pexl == <span class="number">0</span> <span class="keyword">and</span> gti[<span class="number">2</span>][<span class="number">2</span>] != <span class="number">0</span>:</span><br><span class="line"> fn += <span class="number">1</span></span><br><span class="line"> <span class="keyword">return</span> tp, fp, fn, total</span><br><span class="line"></span><br><span class="line">start = time.time()</span><br><span class="line">tp, fp, fn, total = rolling_cal(gt, p_msk, <span class="number">10</span>)</span><br><span class="line">print(<span class="string">"time:"</span>, time.time() - start)</span><br><span class="line">print(tp, fp, fn, total)</span><br><span class="line">recall = tp / (tp + fn)</span><br><span class="line">precision = tp / (tp + fp)</span><br><span class="line">f1 = <span class="number">2</span>*tp / (<span class="number">2</span>*tp + fp + fn)</span><br><span class="line">print(precision, recall, f1)</span><br></pre></td></tr></table></figure>
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本文作者 : HeoLis <br>
原文链接 : <a href="">https://ishero.net/%E5%90%AB%E6%9C%89%E5%BF%8D%E8%80%90%E5%BA%A6%E7%9A%84TP%E8%AE%A1%E7%AE%97(Precision%E3%80%81Rcall%E3%80%81F1%20score).html</a><br>
版权声明 : 本博客所有文章除特别声明外，均采用 <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" rel="external nofollow noopener noreferrer">CC BY-NC-SA 4.0</a> 许可协议。转载请注明出处！
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