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

some old cv api use cleaning

samples/python2/distrans.py

 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

 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/peopledetect.py

+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