diff --git a/dlib/dnn/loss.h b/dlib/dnn/loss.h
index e4776aa43..5b5c53ce8 100644
--- a/dlib/dnn/loss.h
+++ b/dlib/dnn/loss.h
@@ -1135,7 +1135,7 @@ namespace dlib
                 // Prevent calls to tensor_to_dets() from running for a really long time
                 // due to the production of an obscene number of detections.
                 const unsigned long max_num_initial_dets = max_num_dets*100;
-                if (dets.size() >= max_num_initial_dets)
+                if (dets.size() > max_num_initial_dets)
                 {
                     det_thresh_speed_adjust = std::max(det_thresh_speed_adjust,dets[max_num_initial_dets].detection_confidence + options.loss_per_false_alarm);
                 }
diff --git a/tools/python/src/face_recognition.cpp b/tools/python/src/face_recognition.cpp
index 03c19b0c9..d0a0b0094 100644
--- a/tools/python/src/face_recognition.cpp
+++ b/tools/python/src/face_recognition.cpp
@@ -206,6 +206,36 @@ py::list chinese_whispers_clustering(py::list descriptors, float threshold)
     return clusters;
 }
 
+py::list chinese_whispers_raw(py::list edges)
+{
+    py::list clusters;
+    size_t num_edges = py::len(edges);
+
+    std::vector<sample_pair> edges_pairs;
+    std::vector<unsigned long> labels;
+    for (size_t idx = 0; idx < num_edges; ++idx)
+    {
+        py::tuple t = edges[idx].cast<py::tuple>();
+        if ((len(t) != 2) && (len(t) != 3))
+        {
+            PyErr_SetString( PyExc_IndexError, "Input must be a list of tuples with 2 or 3 elements.");
+            throw py::error_already_set();
+        }
+        size_t i = t[0].cast<size_t>();
+        size_t j = t[1].cast<size_t>();
+        double distance = (len(t) == 3) ? t[2].cast<double>(): 1;
+
+        edges_pairs.push_back(sample_pair(i, j, distance));
+    }
+
+    chinese_whispers(edges_pairs, labels);
+    for (size_t i = 0; i < labels.size(); ++i)
+    {
+        clusters.append(labels[i]);
+    }
+    return clusters;
+}
+
 void save_face_chips (
     numpy_image<rgb_pixel> img,
     const std::vector<full_object_detection>& faces,
@@ -296,5 +326,10 @@ void bind_face_recognition(py::module &m)
     m.def("chinese_whispers_clustering", &chinese_whispers_clustering, py::arg("descriptors"), py::arg("threshold"),
         "Takes a list of descriptors and returns a list that contains a label for each descriptor. Clustering is done using dlib::chinese_whispers."
         );
+    m.def("chinese_whispers", &chinese_whispers_raw, py::arg("edges"),
+        "Given a graph with vertices represented as numbers indexed from 0, this algorithm takes a list of edges and returns back a list that contains a labels (found clusters) for each vertex. "
+        "Edges are tuples with either 2 elements (integers presenting indexes of connected vertices) or 3 elements, where additional one element is float which presents distance weight of the edge). "
+        "Offers direct access to dlib::chinese_whispers."
+        );
 }
 
diff --git a/tools/python/test/test_chinese_whispers.py b/tools/python/test/test_chinese_whispers.py
new file mode 100644
index 000000000..0e6b07854
--- /dev/null
+++ b/tools/python/test/test_chinese_whispers.py
@@ -0,0 +1,56 @@
+from random import Random
+
+from dlib import chinese_whispers
+from pytest import raises
+
+
+def test_chinese_whispers():
+    assert len(chinese_whispers([])) == 0
+    assert len(chinese_whispers([(0, 0), (1, 1)])) == 2
+
+    # Test that values from edges are actually used and that correct values are returned
+    labels = chinese_whispers([(0, 0), (0, 1), (1, 1)])
+    assert len(labels) == 2
+    assert labels[0] == labels[1]
+    labels = chinese_whispers([(0, 0), (1, 1)])
+    assert len(labels) == 2
+    assert labels[0] != labels[1]
+
+
+def test_chinese_whispers_with_distance():
+    assert len(chinese_whispers([(0, 0, 1)])) == 1
+    assert len(chinese_whispers([(0, 0, 1), (0, 1, 0.5), (1, 1, 1)])) == 2
+
+    # Test that values from edges and distances are actually used and that correct values are returned
+    labels = chinese_whispers([(0, 0, 1), (0, 1, 1), (1, 1, 1)])
+    assert len(labels) == 2
+    assert labels[0] == labels[1]
+    labels = chinese_whispers([(0, 0, 1), (0, 1, 0.0), (1, 1, 1)])
+    assert len(labels) == 2
+    assert labels[0] != labels[1]
+
+    # Non-trivial test
+    edges = []
+    r = Random(0)
+    for i in range(100):
+        edges.append((i, i, 1))
+        edges.append((i, r.randint(0, 99), r.random()))
+    assert len(chinese_whispers(edges)) == 100
+
+
+def test_chinese_whispers_type_checks():
+    """
+    Tests contract (expected errors) in case client provides wrong types
+    """
+    with raises(TypeError):
+        chinese_whispers()
+    with raises(TypeError):
+        chinese_whispers('foo')
+    with raises(RuntimeError):
+        chinese_whispers(['foo'])
+    with raises(IndexError):
+        chinese_whispers([(0,)])
+    with raises(IndexError):
+        chinese_whispers([(0, 1, 2, 3)])
+    with raises(RuntimeError):
+        chinese_whispers([('foo', 'bar')])