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<a href="_detect_object_algorithm_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="preprocessor">#include "<a class="code" href="_detect_object_algorithm_8h.html">DetectObjectAlgorithm.h</a>"</span></div><div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="keyword">using namespace </span><a class="code" href="namespacecv.html">cv</a>;</div><div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="keyword">using namespace </span><a class="code" href="namespacestd.html">std</a>;</div><div class="line"><a name="l00004"></a><span class="lineno"> 4</span> </div><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> </div><div class="line"><a name="l00006"></a><span class="lineno"><a class="line" href="class_detect_object_algorithm.html#aacc81ac010eef96af9889e791708c637"> 6</a></span> <a class="code" href="class_detect_object_algorithm.html#aacc81ac010eef96af9889e791708c637">DetectObjectAlgorithm::DetectObjectAlgorithm</a>()</div><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> {</div><div class="line"><a name="l00008"></a><span class="lineno"> 8</span> </div><div class="line"><a name="l00009"></a><span class="lineno"> 9</span> }</div><div class="line"><a name="l00010"></a><span class="lineno"> 10</span> </div><div class="line"><a name="l00011"></a><span class="lineno"><a class="line" href="class_detect_object_algorithm.html#ad499d4ed7e0c89676816c9dcf0a763c2"> 11</a></span> <span class="keywordtype">void</span> <a class="code" href="class_detect_object_algorithm.html#ad499d4ed7e0c89676816c9dcf0a763c2">DetectObjectAlgorithm::detectObjectsCustom</a>(<span class="keyword">const</span> Mat &img, CascadeClassifier &cascade, std::vector<Rect> &objects, <span class="keywordtype">int</span> scaledWidth, <span class="keywordtype">int</span> flags, Size minFeatureSize, <span class="keywordtype">float</span> searchScaleFactor, <span class="keywordtype">int</span> minNeighbors)</div><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> {</div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  MyDebug;</div><div class="line"><a name="l00014"></a><span class="lineno"> 14</span>  <span class="comment">// If the input image is not grayscale, then convert the BGR or BGRA color image to grayscale.</span></div><div class="line"><a name="l00015"></a><span class="lineno"> 15</span>  Mat gray;</div><div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  <span class="keywordflow">if</span> (img.channels() == 3)</div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span>  {</div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span>  cvtColor(img, gray, COLOR_BGR2GRAY);</div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span>  }</div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (img.channels() == 4)</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span>  {</div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  cvtColor(img, gray, COLOR_BGRA2GRAY);</div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span>  }</div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  {</div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span>  <span class="comment">// Access the input image directly, since it is already grayscale.</span></div><div class="line"><a name="l00027"></a><span class="lineno"> 27</span>  gray = img;</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  }</div><div class="line"><a name="l00029"></a><span class="lineno"> 29</span> </div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="comment">// Possibly shrink the image, to run much faster.</span></div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  Mat smallImg;</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// 小林的代码中scaledWidth使用的DETECTION_WIDTH，可以在输入参数中替换</span></div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="keywordtype">float</span> scale = img.cols / (float)scaledWidth;</div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="keywordflow">if</span> (scaledWidth < 0)</div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  {</div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  smallImg = gray;</div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  }</div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordflow">else</span> <span class="keywordflow">if</span> (img.cols > scaledWidth)</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  {</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  <span class="comment">// Shrink the image while keeping the same aspect ratio.</span></div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  <span class="keywordtype">int</span> scaledHeight = cvRound(img.rows / scale);</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  resize(gray, smallImg, Size(scaledWidth, scaledHeight));</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  }</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  <span class="keywordflow">else</span></div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  {</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="comment">// Access the input image directly, since it is already small.</span></div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  smallImg = gray;</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  }</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="comment">// Standardize the brightness and contrast to improve dark images.</span></div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  Mat equlizedImg;</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  equalizeHist(smallImg, equlizedImg);</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span> </div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="comment">//---->应用分类器的检测算法</span></div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="comment">// Detect objects in the small grayscale image.</span></div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="comment">// 小林代码中objects使用的faces，可以在参数中替换，searchScaleFactor=>searchfload，minNeighbors=>minneighbors</span></div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  cascade.detectMultiScale(equlizedImg, objects, searchScaleFactor, minNeighbors, flags, minFeatureSize);</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span> </div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  <span class="comment">// Enlarge the results if the image was temporarily shrunk before detection.</span></div><div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keywordflow">if</span> (img.cols > scaledWidth)</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  {</div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keywordflow">for</span> (<span class="keywordtype">long</span> <span class="keywordtype">long</span> <a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a> = 0; <a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a> < (<span class="keywordtype">long</span> long)objects.size(); <a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>++)</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  {</div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].x = cvRound(objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].<a class="code" href="_p_d_8m.html#a9336ebf25087d91c818ee6e9ec29f8c1">x</a> * scale);</div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].y = cvRound(objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].y * scale);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].width = cvRound(objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].width * scale);</div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].height = cvRound(objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].height * scale);</div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  }</div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  }</div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span> </div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="comment">// Make sure the object is completely within the image, in case it was on a border.</span></div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> <a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a> = 0; <a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a> < (int)objects.size(); <a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>++)</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  {</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  <span class="keywordflow">if</span> (objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].<a class="code" href="_p_d_8m.html#a9336ebf25087d91c818ee6e9ec29f8c1">x</a> < 0)</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].x = 0;</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="keywordflow">if</span> (objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].y < 0)</div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].y = 0;</div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordflow">if</span> (objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].<a class="code" href="_p_d_8m.html#a9336ebf25087d91c818ee6e9ec29f8c1">x</a> + objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].width > img.cols)</div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].x = img.cols - objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].width;</div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordflow">if</span> (objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].y + objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].height > img.rows)</div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].y = img.rows - objects[<a class="code" href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a>].height;</div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  }</div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span> }</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span> </div><div class="line"><a name="l00085"></a><span class="lineno"><a class="line" href="class_detect_object_algorithm.html#ab4ded486bad99a14285692d1d5f2afb1"> 85</a></span> <span class="keywordtype">void</span> <a class="code" href="class_detect_object_algorithm.html#ab4ded486bad99a14285692d1d5f2afb1">DetectObjectAlgorithm::detectLargestObject</a>(<span class="keyword">const</span> Mat &img, CascadeClassifier &cascade, Rect &largestObject, <span class="keywordtype">int</span> scaledWidth)</div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span> {</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  MyDebug;</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">// Only search for just 1 object (the biggest in the image).</span></div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordtype">int</span> flags = CASCADE_FIND_BIGGEST_OBJECT;<span class="comment">// | CASCADE_DO_ROUGH_SEARCH;</span></div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  <span class="comment">// Smallest object size.</span></div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  Size minFeatureSize = Size(20, 20);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  <span class="comment">// How detailed should the search be. Must be larger than 1.0.</span></div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordtype">float</span> searchScaleFactor = 1.1f;</div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// How much the detections should be filtered out. This should depend on how bad false detections are to your system.</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keywordtype">int</span> minNeighbors = 4;</div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span> </div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// Perform Object or Face Detection, looking for just 1 object (the biggest in the image).</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  vector<Rect> objects;</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <a class="code" href="class_detect_object_algorithm.html#ad499d4ed7e0c89676816c9dcf0a763c2">DetectObjectAlgorithm::detectObjectsCustom</a>(img, cascade, objects, scaledWidth, flags, minFeatureSize, searchScaleFactor, minNeighbors);</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="keywordflow">if</span> (objects.size() > 0) {</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  <span class="comment">// Return the only detected object.</span></div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  largestObject = (Rect)objects.at(0);</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  }</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  <span class="keywordflow">else</span> {</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  <span class="comment">// Return an invalid rect.</span></div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  largestObject = Rect(-1,-1,-1,-1);</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  }</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> }</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"><a class="line" href="class_detect_object_algorithm.html#a093f17ac1c223b21255383dbef4f5262"> 111</a></span> <span class="keywordtype">void</span> <a class="code" href="class_detect_object_algorithm.html#a093f17ac1c223b21255383dbef4f5262">DetectObjectAlgorithm::detectManyObjects</a>(<span class="keyword">const</span> Mat &img, CascadeClassifier &cascade, vector<Rect> &objects, <span class="keywordtype">int</span> scaledWidth)</div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> {</div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  MyDebug;</div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  <span class="comment">// Search for many objects in the one image.</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="keywordtype">int</span> flags = CASCADE_SCALE_IMAGE;</div><div class="line"><a name="l00116"></a><span class="lineno"> 116</span> </div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="comment">// Smallest object size.</span></div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  Size minFeatureSize = Size(20, 20);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  <span class="comment">// How detailed should the search be. Must be larger than 1.0.</span></div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="keywordtype">float</span> searchScaleFactor = 1.1f;</div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// How much the detections should be filtered out. This should depend on how bad false detections are to your system.</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// minNeighbors=2 means lots of good+bad detections, and minNeighbors=6 means only good detections are given but some are missed.</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keywordtype">int</span> minNeighbors = 2;</div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span> </div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="comment">// Perform Object or Face Detection, looking for many objects in the one image.</span></div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  <a class="code" href="class_detect_object_algorithm.html#ad499d4ed7e0c89676816c9dcf0a763c2">DetectObjectAlgorithm::detectObjectsCustom</a>(img, cascade, objects, scaledWidth, flags, minFeatureSize, searchScaleFactor, minNeighbors);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> }</div><div class="ttc" id="_detect_object_algorithm_8h_html"><div class="ttname"><a href="_detect_object_algorithm_8h.html">DetectObjectAlgorithm.h</a></div></div>
<div class="ttc" id="process__1_8m_html_a8c4e28b0ee15a1005df90b45f3b82ea8"><div class="ttname"><a href="process__1_8m.html#a8c4e28b0ee15a1005df90b45f3b82ea8">i</a></div><div class="ttdeci">Speed_z target for i</div><div class="ttdef"><b>Definition:</b> <a href="process__1_8m_source.html#l00081">process_1.m:81</a></div></div>
<div class="ttc" id="class_detect_object_algorithm_html_ab4ded486bad99a14285692d1d5f2afb1"><div class="ttname"><a href="class_detect_object_algorithm.html#ab4ded486bad99a14285692d1d5f2afb1">DetectObjectAlgorithm::detectLargestObject</a></div><div class="ttdeci">static void detectLargestObject(const cv::Mat &img, cv::CascadeClassifier &cascade, cv::Rect &largestObject, int scaledWidth=320)</div><div class="ttdoc">detectLargestObject 识别图片中的一个物体，例如最大的脸，将结果储存到‘argestObject’中， 可以用Haar c...</div><div class="ttdef"><b>Definition:</b> <a href="_detect_object_algorithm_8cpp_source.html#l00085">DetectObjectAlgorithm.cpp:85</a></div></div>
<div class="ttc" id="namespacestd_html"><div class="ttname"><a href="namespacestd.html">std</a></div></div>
<div class="ttc" id="_p_d_8m_html_a9336ebf25087d91c818ee6e9ec29f8c1"><div class="ttname"><a href="_p_d_8m.html#a9336ebf25087d91c818ee6e9ec29f8c1">x</a></div><div class="ttdeci">x</div><div class="ttdef"><b>Definition:</b> <a href="_p_d_8m_source.html#l00010">PD.m:10</a></div></div>
<div class="ttc" id="namespacecv_html"><div class="ttname"><a href="namespacecv.html">cv</a></div></div>
<div class="ttc" id="class_detect_object_algorithm_html_aacc81ac010eef96af9889e791708c637"><div class="ttname"><a href="class_detect_object_algorithm.html#aacc81ac010eef96af9889e791708c637">DetectObjectAlgorithm::DetectObjectAlgorithm</a></div><div class="ttdeci">DetectObjectAlgorithm()</div><div class="ttdef"><b>Definition:</b> <a href="_detect_object_algorithm_8cpp_source.html#l00006">DetectObjectAlgorithm.cpp:6</a></div></div>
<div class="ttc" id="class_detect_object_algorithm_html_a093f17ac1c223b21255383dbef4f5262"><div class="ttname"><a href="class_detect_object_algorithm.html#a093f17ac1c223b21255383dbef4f5262">DetectObjectAlgorithm::detectManyObjects</a></div><div class="ttdeci">static void detectManyObjects(const cv::Mat &img, cv::CascadeClassifier &cascade, std::vector< cv::Rect > &objects, int scaledWidth=320)</div><div class="ttdef"><b>Definition:</b> <a href="_detect_object_algorithm_8cpp_source.html#l00111">DetectObjectAlgorithm.cpp:111</a></div></div>
<div class="ttc" id="class_detect_object_algorithm_html_ad499d4ed7e0c89676816c9dcf0a763c2"><div class="ttname"><a href="class_detect_object_algorithm.html#ad499d4ed7e0c89676816c9dcf0a763c2">DetectObjectAlgorithm::detectObjectsCustom</a></div><div class="ttdeci">static void detectObjectsCustom(const cv::Mat &img, cv::CascadeClassifier &cascade, std::vector< cv::Rect > &objects, int scaledWidth, int flags, cv::Size minFeatureSize, float searchScaleFactor, int minNeighbors)</div><div class="ttdef"><b>Definition:</b> <a href="_detect_object_algorithm_8cpp_source.html#l00011">DetectObjectAlgorithm.cpp:11</a></div></div>
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