diff --git a/samples/python2/browse.py b/samples/python2/browse.py
index d75b85bc23..64f23b8d5e 100644
--- a/samples/python2/browse.py
+++ b/samples/python2/browse.py
@@ -34,5 +34,5 @@ def onmouse(event, x, y, flags, param):
     cv2.imshow('zoom', zoom)
 
 cv2.imshow('preview', small)
-cv2.setMouseCallback('preview', onmouse, None)
+cv2.setMouseCallback('preview', onmouse)
 cv2.waitKey()
diff --git a/samples/python2/camshift.py b/samples/python2/camshift.py
new file mode 100644
index 0000000000..3a576e111c
--- /dev/null
+++ b/samples/python2/camshift.py
@@ -0,0 +1,104 @@
+import numpy as np
+import cv2
+import video
+
+help_message = '''USAGE: camshift.py [<video source>]
+
+Select a bright colored object to track.
+
+Keys:
+  ESC   - exit
+  b     - toggle back-projected probability visualization
+'''
+
+
+class App(object):
+    def __init__(self, video_src):
+        self.cam = video.create_capture(video_src)
+        ret, self.frame = self.cam.read()
+        cv2.namedWindow('camshift')
+        cv2.setMouseCallback('camshift', self.onmouse)
+
+        self.selection = None
+        self.drag_start = None
+        self.tracking_state = 0
+        self.show_backproj = False
+
+    def onmouse(self, event, x, y, flags, param):
+        x, y = np.int16([x, y]) # BUG
+        if event == cv2.EVENT_LBUTTONDOWN:
+            self.drag_start = (x, y)
+            self.tracking_state = 0
+        if self.drag_start: 
+            if flags & cv2.EVENT_FLAG_LBUTTON:
+                h, w = self.frame.shape[:2]
+                xo, yo = self.drag_start
+                x0, y0 = np.maximum(0, np.minimum([xo, yo], [x, y]))
+                x1, y1 = np.minimum([w, h], np.maximum([xo, yo], [x, y]))
+                self.selection = None
+                if x1-x0 > 0 and y1-y0 > 0:
+                    self.selection = (x0, y0, x1, y1)
+            else:
+                self.drag_start = None
+                if self.selection is not None:
+                    self.tracking_state = 1
+
+    def show_hist(self):
+        bin_count = self.hist.shape[0]
+        bin_w = 24
+        img = np.zeros((256, bin_count*bin_w, 3), np.uint8)
+        for i in xrange(bin_count):
+            h = int(self.hist[i])
+            cv2.rectangle(img, (i*bin_w+2, 255), ((i+1)*bin_w-2, 255-h), (int(180.0*i/bin_count), 255, 255), -1)
+        img = cv2.cvtColor(img, cv2.COLOR_HSV2BGR)
+        cv2.imshow('hist', img)
+
+    def run(self):
+        while True:
+            ret, self.frame = self.cam.read()
+            vis = self.frame.copy()
+            hsv = cv2.cvtColor(self.frame, cv2.COLOR_BGR2HSV)
+            mask = cv2.inRange(hsv, np.array((0, 60, 32)), np.array((180, 255, 255)))
+
+            if self.selection:
+                x0, y0, x1, y1 = self.selection
+                self.track_window = (x0, y0, x1-x0, y1-y0)
+                hsv_roi = hsv[y0:y1, x0:x1]
+                mask_roi = mask[y0:y1, x0:x1]
+                hist = cv2.calcHist( [hsv_roi], [0], mask_roi, [16], [0, 180] )
+                cv2.normalize(hist, hist, 0, 255, cv2.NORM_MINMAX);
+                self.hist = hist.reshape(-1)
+                self.show_hist()
+                
+                vis_roi = vis[y0:y1, x0:x1]
+                cv2.bitwise_not(vis_roi, vis_roi)
+                vis[mask == 0] = 0
+
+            if self.tracking_state == 1:
+                self.selection = None
+                prob = cv2.calcBackProject([hsv], [0], self.hist, [0, 180], 1)
+                prob &= mask
+                term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
+                track_box, self.track_window = cv2.CamShift(prob, self.track_window, term_crit)
+                
+                if self.show_backproj:
+                    vis[:] = prob[...,np.newaxis]
+                try: cv2.ellipse(vis, track_box, (0, 0, 255), 2)
+                except: print track_box
+                
+            cv2.imshow('camshift', vis)
+
+            ch = cv2.waitKey(5)
+            if ch == 27:
+                break
+            if ch == ord('b'):
+                self.show_backproj = not self.show_backproj
+
+
+if __name__ == '__main__':
+    import sys
+    try: video_src = sys.argv[1]
+    except: video_src = video.presets['chess']
+    print help_message
+    App(video_src).run()
+
diff --git a/samples/python2/letter_recog.py b/samples/python2/letter_recog.py
index c25809b57d..0d4d18f54b 100644
--- a/samples/python2/letter_recog.py
+++ b/samples/python2/letter_recog.py
@@ -7,11 +7,27 @@ def load_base(fn):
     return samples, responses
 
 class LetterStatModel(object):
+    class_n = 26
     train_ratio = 0.5
+
     def load(self, fn):
         self.model.load(fn)
     def save(self, fn):
         self.model.save(fn)
+    
+    def unroll_samples(self, samples):
+        sample_n, var_n = samples.shape
+        new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
+        new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
+        new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
+        return new_samples
+    
+    def unroll_responses(self, responses):
+        sample_n = len(responses)
+        new_responses = np.zeros(sample_n*self.class_n, np.int32)
+        resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
+        new_responses[resp_idx] = 1
+        return new_responses
 
 class RTrees(LetterStatModel):
     def __init__(self):
@@ -43,7 +59,6 @@ class KNearest(LetterStatModel):
 class Boost(LetterStatModel):
     def __init__(self):
         self.model = cv2.Boost()
-        self.class_n = 26
     
     def train(self, samples, responses):
         sample_n, var_n = samples.shape
@@ -60,20 +75,6 @@ class Boost(LetterStatModel):
         pred = pred.reshape(-1, self.class_n).argmax(1)
         return pred
 
-    def unroll_samples(self, samples):
-        sample_n, var_n = samples.shape
-        new_samples = np.zeros((sample_n * self.class_n, var_n+1), np.float32)
-        new_samples[:,:-1] = np.repeat(samples, self.class_n, axis=0)
-        new_samples[:,-1] = np.tile(np.arange(self.class_n), sample_n)
-        return new_samples
-    
-    def unroll_responses(self, responses):
-        sample_n = len(responses)
-        new_responses = np.zeros(sample_n*self.class_n, np.int32)
-        resp_idx = np.int32( responses + np.arange(sample_n)*self.class_n )
-        new_responses[resp_idx] = 1
-        return new_responses
-
 
 class SVM(LetterStatModel):
     train_ratio = 0.1
@@ -90,11 +91,34 @@ class SVM(LetterStatModel):
         return np.float32( [self.model.predict(s) for s in samples] )
 
 
+class MLP(LetterStatModel):
+    def __init__(self):
+        self.model = cv2.ANN_MLP()
+
+    def train(self, samples, responses):
+        sample_n, var_n = samples.shape
+        new_responses = self.unroll_responses(responses).reshape(-1, self.class_n)
+
+        layer_sizes = np.int32([var_n, 100, 100, self.class_n])
+        self.model.create(layer_sizes)
+        
+        # CvANN_MLP_TrainParams::BACKPROP,0.001
+        params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
+                       train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP, 
+                       bp_dw_scale = 0.001,
+                       bp_moment_scale = 0.0 )
+        self.model.train(samples, np.float32(new_responses), None, params = params)
+
+    def predict(self, samples):
+        ret, resp = self.model.predict(samples)
+        return resp.argmax(-1)
+
+
 if __name__ == '__main__':
     import getopt
     import sys
 
-    models = [RTrees, KNearest, Boost, SVM] # MLP, NBayes
+    models = [RTrees, KNearest, Boost, SVM, MLP] # NBayes
     models = dict( [(cls.__name__.lower(), cls) for cls in models] )
 
     print 'USAGE: letter_recog.py [--model <model>] [--data <data fn>] [--load <model fn>] [--save <model fn>]'
diff --git a/samples/python2/people_hog.txt b/samples/python2/people_hog.txt
deleted file mode 100644
index 3c0ce13dd3..0000000000
--- a/samples/python2/people_hog.txt
+++ /dev/null
@@ -1,806 +0,0 @@
-# from HOGDescriptor::getDefaultPeopleDetector()
-0.05359386, -0.14721455, -0.05532170, 0.05077307,
-0.11547081, -0.04268804, 0.04635834, -0.05468199, 0.08232084,
-0.10424068, -0.02294518, 0.01108519, 0.01378693, 0.11193510,
-0.01268418, 0.08528346, -0.06309239, 0.13054633, 0.08100729,
--0.05209739, -0.04315529, 0.09341384, 0.11035026, -0.07596218,
--0.05517511, -0.04465296, 0.02947334, 0.04555536,
--3.55954492e-003, 0.07818956, 0.07730991, 0.07890715, 0.06222893,
-0.09001380, -0.03574381, 0.03414327, 0.05677258, -0.04773581,
-0.03746637, -0.03521175, 0.06955440, -0.03849038, 0.01052293,
-0.01736112, 0.10867710, 0.08748853, 3.29739624e-003, 0.10907028,
-0.07913758, 0.10393070, 0.02091867, 0.11594022, 0.13182420,
-0.09879354, 0.05362710, -0.06745391, -7.01260753e-003,
-5.24702156e-003, 0.03236255, 0.01407916, 0.02207983, 0.02537322,
-0.04547948, 0.07200756, 0.03129894, -0.06274468, 0.02107014,
-0.06035208, 0.08636236, 4.53164103e-003, 0.02193363, 0.02309801,
-0.05568166, -0.02645093, 0.04448695, 0.02837519, 0.08975694,
-0.04461516, 0.08975355, 0.07514391, 0.02306982, 0.10410084,
-0.06368385, 0.05943464, 4.58420580e-003, 0.05220337, 0.06675851,
-0.08358569, 0.06712101, 0.06559004, -0.03930482, -9.15936660e-003,
--0.05897915, 0.02816453, 0.05032348, 0.06780671, 0.03377650,
--6.09417039e-004, -0.01795146, -0.03083684, -0.01302475,
--0.02972313, 7.88706727e-003, -0.03525961, -2.50397739e-003,
-0.05245084, 0.11791293, -0.02167498, 0.05299332, 0.06640524,
-0.05190265, -8.27316567e-003, 0.03033127, 0.05842173,
--4.01050318e-003, -6.25105947e-003, 0.05862958, -0.02465461,
-0.05546781, -0.08228195, -0.07234028, 0.04640540, -0.01308254,
--0.02506191, 0.03100746, -0.04665651, -0.04591486, 0.02949927,
-0.06035462, 0.02244646, -0.01698639, 0.01040041, 0.01131170,
-0.05419579, -0.02130277, -0.04321722, -0.03665198, 0.01126490,
--0.02606488, -0.02228328, -0.02255680, -0.03427236,
--7.75165204e-003, -0.06195229, 8.21638294e-003, 0.09535975,
--0.03709979, -0.06942501, 0.14579427, -0.05448192, -0.02055904,
-0.05747357, 0.02781788, -0.07077577, -0.05178314, -0.10429011,
--0.11235505, 0.07529039, -0.07559302, -0.08786739, 0.02983843,
-0.02667585, 0.01382199, -0.01797496, -0.03141199, -0.02098101,
-0.09029204, 0.04955018, 0.13718739, 0.11379953, 1.80019124e-003,
--0.04577610, -1.11108483e-003, -0.09470536, -0.11596080,
-0.04489342, 0.01784211, 3.06850672e-003, 0.10781866,
-3.36498418e-003, -0.10842580, -0.07436839, -0.10535070,
--0.01866805, 0.16057891, -5.07316366e-003, -0.04295658,
--5.90488780e-003, 8.82003549e-003, -0.01492646, -0.05029279,
--0.12875880, 8.78831954e-004, -0.01297184, -0.07592774,
--0.02668831, -6.93787413e-004, 0.02406698, -0.01773298,
--0.03855745, -0.05877856, 0.03259695, 0.12826584, 0.06292590,
--4.10733931e-003, 0.10996531, 0.01332991, 0.02088735, 0.04037504,
--0.05210760, 0.07760046, 0.06399347, -0.05751930, -0.10053057,
-0.07505023, -0.02139782, 0.01796176, 2.34400877e-003, -0.04208319,
-0.07355055, 0.05093350, -0.02996780, -0.02219072, 0.03355330,
-0.04418742, -0.05580705, -0.05037573, -0.04548179, 0.01379514,
-0.02150671, -0.02194211, -0.13682702, 0.05464972, 0.01608082,
-0.05309116, 0.04701022, 1.33690401e-003, 0.07575664, 0.09625306,
-8.92647635e-003, -0.02819123, 0.10866830, -0.03439325,
--0.07092371, -0.06004780, -0.02712298, -7.07467366e-003,
--0.01637020, 0.01336790, -0.10313606, 0.04906582, -0.05732445,
--0.02731079, 0.01042235, -0.08340668, 0.03686501, 0.06108340,
-0.01322748, -0.07809529, 0.03774724, -0.03413248, -0.06096525,
--0.04212124, -0.07982176, -1.25973229e-003, -0.03045501,
--0.01236493, -0.06312395, 0.04789570, -0.04602066, 0.08576570,
-0.02521080, 0.02988098, 0.10314583, 0.07060035, 0.04520544,
--0.04426654, 0.13146530, 0.08386490, 0.02164590, -2.12280243e-003,
--0.03686353, -0.02074944, -0.03829959, -0.01530596, 0.02689708,
-0.11867401, -0.06043470, -0.02785023, -0.04775074, 0.04878745,
-0.06350956, 0.03494788, 0.01467400, 1.17890188e-003, 0.04379614,
-2.03681854e-003, -0.03958609, -0.01072688, 6.43705716e-003,
-0.02996500, -0.03418507, -0.01960307, -0.01219154,
--4.37000440e-003, -0.02549453, 0.02646318, -0.01632513,
-6.46516960e-003, -0.01929734, 4.78711911e-003, 0.04962371,
-0.03809111, 0.07265724, 0.05758125, -0.03741554, 0.01648608,
--8.45285598e-003, 0.03996826, -0.08185477, 0.02638875,
--0.04026615, -0.02744674, -0.04071517, 1.05096330e-003,
--0.04741232, -0.06733172, 8.70434940e-003, -0.02192543,
-1.35350740e-003, -0.03056974, -0.02975521, -0.02887780,
--0.01210713, -0.04828526, -0.09066251, -0.09969629, -0.03665164,
--8.88111943e-004, -0.06826669, -0.01866150, -0.03627640,
--0.01408288, 0.01874239, -0.02075835, 0.09145175, -0.03547291,
-0.05396780, 0.04198981, 0.01301925, -0.03384354, -0.12201976,
-0.06830920, -0.03715654, 9.55848210e-003, 5.05685573e-003,
-0.05659294, 3.90764466e-003, 0.02808490, -0.05518097, -0.03711621,
--0.02835565, -0.04420464, -0.01031947, 0.01883466,
--8.49525444e-003, -0.09419250, -0.01269387, -0.02133371,
--0.10190815, -0.07844430, 2.43644323e-003, -4.09610150e-003,
-0.01202551, -0.06452291, -0.10593818, -0.02464746, -0.02199699,
--0.07401930, 0.07285886, 8.87513801e-004, 9.97662079e-003,
-8.46779719e-003, 0.03730333, -0.02905126, 0.03573337, -0.04393689,
--0.12014472, 0.03176554, -2.76015815e-003, 0.10824566, 0.05090732,
--3.30179278e-003, -0.05123822, 5.04784798e-003, -0.05664124,
--5.99415926e-003, -0.05341901, -0.01221393, 0.01291318,
-9.91760660e-003, -7.56987557e-003, -0.06193124, -2.24549137e-003,
-0.01987562, -0.02018840, -0.06975540, -0.06601523, -0.03349112,
--0.08910118, -0.03371435, -0.07406893, -0.02248047, -0.06159951,
-2.77751544e-003, -0.05723337, -0.04792468, 0.07518548,
-2.77279224e-003, 0.04211938, 0.03100502, 0.05278448, 0.03954679,
--0.03006846, -0.03851741, -0.02792403, -0.02875333, 0.01531280,
-0.02186953, -0.01989829, 2.50679464e-003, -0.10258728,
--0.04785743, -0.02887216, 3.85063468e-003, 0.01112236,
-8.29218887e-003, -0.04822981, -0.04503597, -0.03713100,
--0.06988008, -0.11002295, -2.69209221e-003, 1.85383670e-003,
--0.05921049, -0.06105053, -0.08458050, -0.04527602,
-8.90329306e-004, -0.05875023, -2.68602883e-003, -0.01591195,
-0.03631859, 0.05493166, 0.07300330, 5.53333294e-003, 0.06400407,
-0.01847740, -5.76280477e-003, -0.03210877, 4.25160583e-003,
-0.01166520, -1.44864211e-003, 0.02253744, -0.03367080, 0.06983195,
--4.22323542e-003, -8.89401045e-003, -0.07943393, 0.05199728,
-0.06065201, 0.04133492, 1.44032843e-003, -0.09585235, -0.03964731,
-0.04232114, 0.01750465, -0.04487902, -7.59733608e-003, 0.02011171,
-0.04673622, 0.09011173, -0.07869188, -0.04682482, -0.05080139,
--3.99383716e-003, -0.05346331, 0.01085723, -0.03599333,
--0.07097908, 0.03551549, 0.02680387, 0.03471529, 0.01790393,
-0.05471273, 9.62048303e-003, -0.03180215, 0.05864431, 0.02330614,
-0.01633144, -0.05616681, -0.10245429, -0.08302189, 0.07291322,
--0.01972590, -0.02619633, -0.02485327, -0.04627592,
-1.48853404e-003, 0.05514185, -0.01270860, -0.01948900, 0.06373586,
-0.05002292, -0.03009798, 8.76216311e-003, -0.02474238,
--0.05504891, 1.74034527e-003, -0.03333667, 0.01524987, 0.11663762,
--1.32344989e-003, -0.06608453, 0.05687166, -6.89525274e-004,
--0.04402352, 0.09450210, -0.04222684, -0.05360983, 0.01779531,
-0.02561388, -0.11075410, -8.77790991e-003, -0.01099504,
--0.10380266, 0.03103457, -0.02105741, -0.07371717, 0.05146710,
-0.10581432, -0.08617968, -0.02892107, 0.01092199, 0.14551543,
--2.24320893e-003, -0.05818033, -0.07390742, 0.05701261,
-0.12937020, -0.04986651, 0.10182415, 0.05028650, 0.12515625,
-0.09175041, 0.06404983, 0.01523394, 0.09460562, 0.06106631,
--0.14266998, -0.02926703, 0.02762171, 0.02164151,
--9.58488265e-004, -0.04231362, -0.09866509, 0.04322244,
-0.05872034, -0.04838847, 0.06319253, 0.02443798, -0.03606876,
-9.38737206e-003, 0.04289991, -0.01027411, 0.08156885, 0.08751175,
--0.13191354, 8.16054735e-003, -0.01452161, 0.02952677, 0.03615945,
--2.09128903e-003, 0.02246693, 0.09623287, 0.09412123, -0.02924758,
--0.07815186, -0.02203079, -2.02566991e-003, 0.01094733,
--0.01442332, 0.02838561, 0.11882371, 7.28798332e-003, -0.10345965,
-0.07561217, -0.02049661, 4.44177445e-003, 0.01609347, -0.04893158,
--0.08758243, -7.67420698e-003, 0.08862378, 0.06098121, 0.06565887,
-7.32981879e-003, 0.03558407, -0.03874352, -0.02490055,
--0.06771075, 0.09939223, -0.01066077, 0.01382995, -0.07289080,
-7.47184316e-003, 0.10621431, -0.02878659, 0.02383525, -0.03274646,
-0.02137008, 0.03837290, 0.02450992, -0.04296818, -0.02895143,
-0.05327370, 0.01499020, 0.04998732, 0.12938657, 0.09391870,
-0.04292390, -0.03359194, -0.06809492, 0.01125796, 0.17290455,
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--0.02631102, -0.03458253, -0.04749985, -0.06070716,
-4.71884012e-003, 0.06445789, -0.02450038, -0.05483776,
--0.04657237, -0.02030717, -0.03480766, -0.09397731, -0.06399718,
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-6.96210237e-003, -0.01860148, -0.04245830, -0.05850367,
--3.24417115e-003, 0.07700698, 0.11290991, 0.09923030, -0.02970599,
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-0.04612800, -0.03114098, -0.04650044, -0.03249795, -0.02425641,
--0.04311355, 0.04307659, -0.09401883, -0.04742785, -0.01254499,
--0.06598741, 3.41369561e-003, -0.05620445, -7.28127593e-003,
--0.05998361, -0.03274450, -0.07376868, 3.19015374e-003,
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--0.02607902, -0.02938001, 0.02720689, -0.06228397, -0.02970936,
--0.03426210, -0.10280876, -0.06739304, -0.05227850, 0.03360292,
--0.11278441, -0.06966180, -0.13937433, 9.10932291e-003,
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--0.01068920, -0.01323473, -0.11970232, -0.04616347, -0.12088681,
--0.06762606, -0.08676834, -0.06434575, 0.01772529, 0.03469615,
--0.10926618, 0.03013873, 0.14030397, 0.16130108, 0.17985588,
-0.11281928, 0.10530639, 0.08905948, 0.07733764, 0.06695238,
-0.02142088, 0.06438877, 0.09794453, 0.05745072, 0.02788557,
-0.02632830, 0.07985807, 4.24902979e-003, 8.47890321e-003,
--0.02679466, -5.28812688e-003, -0.02162580, -0.07490715,
--0.08251337, -0.02056576, -0.01026194, -1.15492963e-003,
--5.75720915e-004, -0.07210591, -0.07320981, -0.04883312,
--0.10897151, -0.07477258, -0.08867134, -0.09222437, -0.10924666,
--0.10430276, 0.07953499, 0.02767959, 0.11393359, 0.18779543,
-0.03313421, 0.02143700, 0.05852016, -2.12067598e-003,
--3.76984011e-003, 0.02774167, -0.03124610, 0.01465141, 0.01616004,
--0.01391913, -0.04404102, -0.05444227, -0.14684731, -0.15016587,
-0.04509468, 1.29563001e-003, 0.01398350, 0.05610404, -0.04868806,
--0.04776716, -8.16873740e-003, -2.30126386e-003, -0.02286313,
-0.11983398, -0.04703261, -0.08814441, -0.07585249, -0.10799607,
--0.03232087, 0.01509786, -0.04843464, -0.03967846, 0.09589416,
-0.01352560, -0.01458119, 0.01050829, -0.03038946, 0.01608388,
-1.11975556e-003, -0.01250656, 2.86211423e-003, 0.04333691,
--0.14603497, -0.01946543, -0.02327525, -0.01973944, 0.07944400,
--0.02224544, -0.06701808, 0.03476532, 0.11505594, -0.02712801,
--0.01665113, 0.06315716, -0.08205860, 0.07431999, 0.04915778,
--0.04468752, -0.01490402, 0.07400476, -0.11650901, 0.05102430,
-0.04559118, -0.05916039, 0.08840760, -0.01587902, -0.14890194,
-0.07857784, 0.04710254, -0.05381983, -0.07331945, -0.03604643,
-0.15611970, 0.07649943, -0.05959348, -0.02776607, 0.11098688,
-0.03758875, -0.04446875, 0.04933187, 0.01345535, 0.06921103,
-0.07364785, 0.05518956, 0.02899585, 0.09375840, 0.10518434,
--0.04420241, 0.01915282, -3.56386811e-003, 0.14586878, 0.10286101,
--0.04360626, -0.12723237, 0.09076386, 0.11119842, -0.06035013,
-0.09674817, 0.08938243, 0.07065924, 0.02603180, 5.84815582e-003,
--0.05922065, 0.12360309, 3.59695964e-003, 2.99844006e-003,
-0.03697936, 0.02043072, 0.04168725, 0.01025975, -0.01359980,
--0.01600920, 0.02581056, 0.02329250, 2.98100687e-003, 0.01629762,
-0.06652115, 0.05855627, 0.01237463, -0.01297135, 0.01761587,
-0.05090865, 0.06549342, -0.04425945, 2.43203156e-003,
-3.07327788e-003, 0.06678630, -0.04303836, 0.01082393, -0.06476044,
-0.04077786, 0.12441979, 0.08237778, 0.07424165, 0.04065890,
-0.06905543, 0.09556347, 0.12724875, -0.02132082, 0.08514154,
--0.04175328, -0.02666954, 0.01897836, 0.03317382, 9.45465732e-003,
--0.01238974, -0.04242500, -0.01419479, -0.03545213, -0.02440874,
-0.08684119, 0.04212951, 0.02462858, -0.01104825, -5.01706870e-003,
-0.02968982, 0.02597476, -0.01568939, 0.04514892, 0.06974549,
-0.08670278, 0.06828108, 0.10238872, 0.05405957, 0.06548470,
--0.03763957, 0.01366090, 0.07069602, 0.05363748, 0.04798120,
-0.11706422, 0.05466456, -0.01869259, 0.06344382, 0.03106543,
-0.08432506, -0.02061096, 0.03821088, -6.92190882e-003,
-6.40467042e-003, -0.01271779, 6.89014705e-005, 0.04541415,
--0.01899539, -0.05020239, 0.03000903, 0.01090422, 4.52452758e-003,
-0.02573632, -0.02388454, -0.04200457, 1.72783900e-003,
--0.05978370, -0.02720562, 0.06573715, 0.01154317, 0.01265615,
-0.07375994, -9.19828378e-003, -0.04914120, 0.02124831, 0.06455322,
-0.04372910, -0.03310043, 0.03605788, -6.78055827e-003,
-9.36202332e-003, 0.01747596, -0.06406314, -0.06812935, 0.08080816,
--0.02778088, 0.02735260, 0.06393493, 0.06652229, 0.05676993,
-0.08640018, -7.59188086e-003, -0.02012847, -0.04741159,
--0.01657069, -0.01624399, 0.05547778, -2.33309763e-003,
-0.01120033, 0.06141156, -0.06285004, -0.08732341, -0.09313398,
--0.04267832, 5.57443965e-003, 0.04809862, 0.01773641,
-5.37361018e-003, 0.14842421, -0.06298012, -0.02935147, 0.11443478,
--0.05034208, 5.65494271e-003, 0.02076526, -0.04577984,
--0.04735741, 0.02961071, -0.09307127, -0.04417921, -0.04990027,
--0.03940028, 0.01306016, 0.06267900, 0.03758737, 0.08460117,
-0.13858789, 0.04862388, -0.06319809, -0.05655516, 0.01885816,
--0.03285607, 0.03371567, -0.07040928, -0.04514049, 0.01392166,
-0.08184422, -0.07230316, 0.02386871, 0.02184591, 0.02605764,
--0.01033954, 9.29878280e-003, 7.67351175e-003, 0.15189242,
-0.02069071, -0.09738296, -0.08894105, -0.07768748, 0.02332268,
--0.01778995, -0.03258888, -0.08180822, -0.08492987, 0.02290156,
--0.11368170, -0.03554465, -0.04533844, -0.02861580, 0.06782424,
-0.01113123, 0.02453644, 0.12721945, 0.08084814, -0.03607795,
-0.01109122, 0.04803548, -0.03489929, 0.03399536, -0.05682014,
-8.59533902e-003, -4.27904585e-003, 0.03230887, -0.01300198,
--0.01038137, -0.07930113, 8.33097473e-003, 0.02296994,
--0.01306500, -0.01881626, 0.04413369, 0.05729880, -0.03761553,
-0.01942326, 1.64540811e-003, -0.03811319, 0.04190650, -0.14978096,
--0.04514487, 0.01209545, -5.46460645e-003, -0.01647195,
-7.63064111e-003, -0.07494587, 0.08415288, 0.10020141, -0.01228561,
-0.06553826, 0.04554005, 0.07890417, 0.03041138, 0.01752007,
-0.09208256, -3.74419295e-004, 0.10549527, 0.04686913, 0.01894833,
--0.02651412, -4.34682379e-003, 5.44942822e-003, 0.01444484,
-0.05882156, -0.03336544, 0.04603891, -0.10432546, 0.01923928,
-0.01842845, -0.01712168, -0.02222766, 0.04693324, -0.06202956,
--0.01422159, 0.08732220, -0.07706107, 0.02661049, -0.04300238,
--0.03092422, -0.03552184, -0.01886088, -0.04979934, 0.03906401,
-0.04608644, 0.04966111, 0.04275464, -0.04621769, -0.02653212,
-8.57011229e-003, 0.03839684, 0.05818764, 0.03880796,
--2.76100676e-004, 0.03076511, -0.03266929, -0.05374557,
-0.04986527, -9.45429131e-003, 0.03582499, -2.64564669e-003,
--1.07461517e-003, 0.02962313, -0.01483363, 0.03060869, 0.02448327,
-0.01845641, 0.03282966, -0.03534438, -0.01084059, -0.01119136,
--1.85360224e-003, -5.94652840e-004, -0.04451817, 2.98327743e-003,
-0.06272484, -0.02152076, -3.05971340e-003, -0.05070828,
-0.01531762, 0.01282815, 0.05167150, 9.46266949e-003,
--3.34558333e-003, 0.11442288, -0.03906701, -2.67325155e-003,
-0.03069184, -0.01134165, 0.02949462, 0.02879886, 0.03855566,
--0.03450781, 0.09142872, -0.02156654, 0.06075062, -0.06220816,
-0.01944680, 6.68372354e-003, -0.06656796, 8.70784000e-003,
-0.03456013, 0.02434320, -0.13236357, -0.04177035, -0.02069627,
-0.01068112, 0.01505432, -0.07517391, -3.83571628e-003,
--0.06298508, -0.02881260, -0.13101046, -0.07221562,
--5.79945277e-003, -8.57300125e-003, 0.03782469, 0.02762164,
-0.04942456, -0.02936396, 0.09597211, 0.01921411, 0.06101191,
--0.04787507, -0.01379578, -7.40224449e-003, -0.02220136,
--0.01313756, 7.77558051e-003, 0.12296968, 0.02939998, 0.03594062,
--0.07788624, -0.01133144, 3.99316690e-004, -0.06090347,
--0.01122066, -4.68682544e-003, 0.07633100, -0.06748922,
--0.05640298, -0.05265681, -0.01139122, -0.01624347, -0.04715714,
--0.01099092, 0.01048561, 3.28499987e-003, -0.05810167,
--0.07699911, -0.03330683, 0.04185145, 0.03478536, 0.02275165,
-0.02304766, 6.66040834e-003, 0.10968148, -5.93013782e-003,
--0.04858336, -0.04203213, -0.09316786, -6.13074889e-003,
--0.02544625, 0.01366201, 9.18555818e-003, -0.01846578,
--0.05622401, -0.03989377, -0.07810296, 6.91275718e-003,
-0.05957597, -0.03901334, 0.01572002, -0.01193903,
--6.89400872e-003, -0.03093356, -0.04136098, -0.01562869,
--0.04604580, 0.02865234, -0.08678447, -0.03232484, -0.05364593,
--0.01445016, -0.07003860, -0.08669746, -0.04520775, 0.04274122,
-0.03117515, 0.08175703, 0.01081109, 0.06379741, 0.06199206,
-0.02865988, 0.02360346, 0.06725410, -0.03248780, -9.37702879e-003,
-0.08265898, -0.02245839, 0.05125763, -0.01862395, 0.01973453,
--0.01994494, -0.10770868, 0.03180375, 3.23935156e-003,
--0.02142080, -0.04256190, 0.04760900, 0.04282863, 0.05635953,
--0.01870849, 0.05540622, -0.03042666, 0.01455277, -0.06630179,
--0.05843807, -0.03739681, -0.09739155, -0.03220233, -0.05620182,
--0.10381401, 0.07400211, 4.20676917e-003, 0.03258535,
-2.14308966e-003, 0.05121966, -0.01274337, 0.02384761, 0.06335578,
--0.07905591, 0.08375625, -0.07898903, -0.06508528, -0.02498444,
-0.06535810, 0.03970535, 0.04895468, -0.01169566, -0.03980601,
-0.05682293, 0.05925463, -0.01165808, -0.07936699, -0.04208954,
-0.01333987, 0.09051196, 0.10098671, -0.03974256, 0.01238771,
--0.07501741, -0.03655440, -0.04301528, 0.09216860,
-4.63579083e-004, 0.02851115, 0.02142735, 1.28244064e-004,
-0.02879687, -0.08554889, -0.04838862, 0.08135369, -0.05756533,
-0.01413900, 0.03451880, -0.06619488, -0.03053130, 0.02961676,
--0.07384635, 0.01135692, 0.05283910, -0.07778034, -0.02107482,
--0.05511716, -0.13473752, 0.03030157, 0.06722020, -0.06218817,
--0.05826827, 0.06254654, 0.02895772, -0.01664000, -0.03620280,
--0.01612278, -1.46097376e-003, 0.14013411, -8.96181818e-003,
--0.03250246, 3.38630192e-003, 2.64779478e-003, 0.03359732,
--0.02411991, -0.04229729, 0.10666174, -6.66579151
diff --git a/samples/python2/peopledetect.py b/samples/python2/peopledetect.py
index 730587e718..27b24fbf63 100644
--- a/samples/python2/peopledetect.py
+++ b/samples/python2/peopledetect.py
@@ -27,11 +27,8 @@ if __name__ == '__main__':
 
     print help_message
 
-    # TODO should use built-in detector data
-    text = "".join(open('people_hog.txt').readlines()[1:])
-    data = np.fromstring(text, sep=',')
     hog = cv2.HOGDescriptor()
-    hog.setSVMDetector(data)
+    hog.setSVMDetector( cv2.HOGDescriptor_getDefaultPeopleDetector() )
 
     for fn in it.chain(*map(glob, sys.argv[1:])):
         print fn, ' - ',