peopledetect.py sample added (TODO: use builtin svm data instead of people_hog.txt)

some old cv api use cleaning

samples/python2/distrans.py

@@ -1,5 +1,6 @@
 import numpy as np
 import cv2
+import cv2.cv as cv
 from common import make_cmap
 
 help_message = '''USAGE: distrans.py [<image>]

samples/python2/facedetect.py

@@ -37,7 +37,7 @@ if __name__ == '__main__':
 
     while True:
         ret, img = cam.read()
-        gray = cv2.cvtColor(img, cv.CV_BGR2GRAY)
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
         gray = cv2.equalizeHist(gray)
         
         t = clock()

samples/python2/lk_track.py

@@ -1,6 +1,5 @@
 import numpy as np
 import cv2
-import cv2.cv as cv
 import video
 from common import anorm2, draw_str
 from time import clock

samples/python2/people_hog.txt

@@ -0,0 +1,806 @@
+# 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,
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+-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,
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+0.05309116, 0.04701022, 1.33690401e-003, 0.07575664, 0.09625306,
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+-0.07092371, -0.06004780, -0.02712298, -7.07467366e-003,
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samples/python2/peopledetect.py

@@ -0,0 +1,57 @@
+import numpy as np
+import cv2
+
+help_message = '''
+USAGE: peopledetect.py <image_names> ...
+
+Press any key to continue, ESC to stop. 
+'''
+
+def inside(r, q):
+    rx, ry, rw, rh = r
+    qx, qy, qw, qh = q
+    return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
+
+def draw_detections(img, rects, thickness = 1):
+    for x, y, w, h in rects:
+        # the HOG detector returns slightly larger rectangles than the real objects.
+        # so we slightly shrink the rectangles to get a nicer output.
+        pad_w, pad_h = int(0.15*w), int(0.05*h)
+        cv2.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
+
+
+if __name__ == '__main__':
+    import sys
+    from glob import glob
+    import itertools as it
+
+    print help_message
+
+    text = "".join(open('people_hog.txt').readlines()[1:])
+    data = np.fromstring(text, sep=',')
+    hog = cv2.HOGDescriptor()
+    hog.setSVMDetector(data)
+
+    for fn in it.chain(*map(glob, sys.argv[1:])):
+        print fn, ' - ',
+        try: 
+            img = cv2.imread(fn)
+        except:
+            print 'loading error'
+            continue
+
+        found = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
+        found_filtered = []
+        for ri, r in enumerate(found):
+            for qi, q in enumerate(found):
+                if ri != qi and inside(r, q):
+                    break
+            else:
+                found_filtered.append(r)
+        draw_detections(img, found)
+        draw_detections(img, found_filtered, 3)
+        print '%d (%d) found' % (len(found_filtered), len(found))
+        cv2.imshow('img', img)
+        ch = cv2.waitKey()
+        if ch == 27:
+            break