From 8d6d592c0b9f797137405bfeacb9e59e4eb2c0e6 Mon Sep 17 00:00:00 2001
From: Vadim Pisarevsky <no@email>
Date: Tue, 16 Aug 2011 18:31:43 +0000
Subject: [PATCH] updated Python samples from trunk

---
 samples/python2/browse.py       |   2 +-
 samples/python2/camshift.py     | 104 +++++
 samples/python2/letter_recog.py |  56 ++-
 samples/python2/people_hog.txt  | 806 --------------------------------
 samples/python2/peopledetect.py |   5 +-
 5 files changed, 146 insertions(+), 827 deletions(-)
 create mode 100644 samples/python2/camshift.py
 delete mode 100644 samples/python2/people_hog.txt

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,
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--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, ' - ',