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	<div class="highlight"><pre><span></span><span class="ch">#!/usr/bin/python</span>
	<span class="c1"># The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt</span>
	<span class="c1">#</span>
	<span class="c1"># This example shows how to run a CNN based face detector using dlib. The</span>
	<span class="c1"># example loads a pretrained model and uses it to find faces in images. The</span>
	<span class="c1"># CNN model is much more accurate than the HOG based model shown in the</span>
	<span class="c1"># <a href="face_detector.py.html">face_detector.py</a> example, but takes much more computational power to</span>
	<span class="c1"># run, and is meant to be executed on a GPU to attain reasonable speed.</span>
	<span class="c1">#</span>
	<span class="c1"># You can download the pre-trained model from:</span>
	<span class="c1"># http://dlib.net/files/mmod_human_face_detector.dat.bz2</span>
	<span class="c1">#</span>
	<span class="c1"># The examples/faces folder contains some jpg images of people. You can run</span>
	<span class="c1"># this program on them and see the detections by executing the</span>
	<span class="c1"># following command:</span>
	<span class="c1"># ./<a href="cnn_face_detector.py.html">cnn_face_detector.py</a> mmod_human_face_detector.dat ../examples/faces/*.jpg</span>
	<span class="c1">#</span>
	<span class="c1">#</span>
	<span class="c1"># COMPILING/INSTALLING THE DLIB PYTHON INTERFACE</span>
	<span class="c1"># You can install dlib using the command:</span>
	<span class="c1"># pip install dlib</span>
	<span class="c1">#</span>
	<span class="c1"># Alternatively, if you want to compile dlib yourself then go into the dlib</span>
	<span class="c1"># root folder and run:</span>
	<span class="c1"># python setup.py install</span>
	<span class="c1">#</span>
	<span class="c1"># Compiling dlib should work on any operating system so long as you have</span>
	<span class="c1"># CMake installed. On Ubuntu, this can be done easily by running the</span>
	<span class="c1"># command:</span>
	<span class="c1"># sudo apt-get install cmake</span>
	<span class="c1">#</span>
	<span class="c1"># Also note that this example requires Numpy which can be installed</span>
	<span class="c1"># via the command:</span>
	<span class="c1"># pip install numpy</span>

	<span class="kn">import</span> <span class="nn">sys</span>
	<span class="kn">import</span> <span class="nn">dlib</span>

	<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span> <span class="o"><</span> <span class="mi">3</span><span class="p">:</span>
	<span class="k">print</span><span class="p">(</span>
	<span class="s2">"Call this program like this:</span><span class="se">\n</span><span class="s2">"</span>
	<span class="s2">" ./<a href="cnn_face_detector.py.html">cnn_face_detector.py</a> mmod_human_face_detector.dat ../examples/faces/*.jpg</span><span class="se">\n</span><span class="s2">"</span>
	<span class="s2">"You can get the mmod_human_face_detector.dat file from:</span><span class="se">\n</span><span class="s2">"</span>
	<span class="s2">" http://dlib.net/files/mmod_human_face_detector.dat.bz2"</span><span class="p">)</span>
	<span class="nb">exit</span><span class="p">()</span>

	<span class="n">cnn_face_detector</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">cnn_face_detection_model_v1</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
	<span class="n">win</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">image_window</span><span class="p">()</span>

	<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">[</span><span class="mi">2</span><span class="p">:]:</span>
	<span class="k">print</span><span class="p">(</span><span class="s2">"Processing file: {}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">f</span><span class="p">))</span>
	<span class="n">img</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">load_rgb_image</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
	<span class="c1"># The 1 in the second argument indicates that we should upsample the image</span>
	<span class="c1"># 1 time. This will make everything bigger and allow us to detect more</span>
	<span class="c1"># faces.</span>
	<span class="n">dets</span> <span class="o">=</span> <span class="n">cnn_face_detector</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
	<span class="sd">'''</span>
	<span class="sd"> This detector returns a mmod_rectangles object. This object contains a list of mmod_rectangle objects.</span>
	<span class="sd"> These objects can be accessed by simply iterating over the mmod_rectangles object</span>
	<span class="sd"> The mmod_rectangle object has two member variables, a dlib.rectangle object, and a confidence score.</span>
	<span class="sd"> </span>
	<span class="sd"> It is also possible to pass a list of images to the detector.</span>
	<span class="sd"> - like this: dets = cnn_face_detector([image list], upsample_num, batch_size = 128)</span>

	<span class="sd"> In this case it will return a mmod_rectangless object.</span>
	<span class="sd"> This object behaves just like a list of lists and can be iterated over.</span>
	<span class="sd"> '''</span>
	<span class="k">print</span><span class="p">(</span><span class="s2">"Number of faces detected: {}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dets</span><span class="p">)))</span>
	<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">dets</span><span class="p">):</span>
	<span class="k">print</span><span class="p">(</span><span class="s2">"Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
	<span class="n">i</span><span class="p">,</span> <span class="n">d</span><span class="o">.</span><span class="n">rect</span><span class="o">.</span><span class="n">left</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">rect</span><span class="o">.</span><span class="n">top</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">rect</span><span class="o">.</span><span class="n">right</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">rect</span><span class="o">.</span><span class="n">bottom</span><span class="p">(),</span> <span class="n">d</span><span class="o">.</span><span class="n">confidence</span><span class="p">))</span>

	<span class="n">rects</span> <span class="o">=</span> <span class="n">dlib</span><span class="o">.</span><span class="n">rectangles</span><span class="p">()</span>
	<span class="n">rects</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">rect</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dets</span><span class="p">])</span>

	<span class="n">win</span><span class="o">.</span><span class="n">clear_overlay</span><span class="p">()</span>
	<span class="n">win</span><span class="o">.</span><span class="n">set_image</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
	<span class="n">win</span><span class="o">.</span><span class="n">add_overlay</span><span class="p">(</span><span class="n">rects</span><span class="p">)</span>
	<span class="n">dlib</span><span class="o">.</span><span class="n">hit_enter_to_continue</span><span class="p">()</span>
	</pre></div>
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