work on obj_detect.py

added square_size cmd line param for calibrate.py
added _coverage.py script to compute cv2 api coverage by samples (just for interest, may be removed later)

samples/python2/_coverage.py

@@ -0,0 +1,21 @@
+from glob import glob
+import cv2
+import re
+
+cv2_callable = set(['cv2.'+name for name in dir(cv2) if callable( getattr(cv2, name) )])
+
+found = set()
+for fn in glob('*.py'):
+    print ' --- ', fn
+    code = open(fn).read()
+    found |= set(re.findall('cv2?\.\w+', code))
+
+cv2_used = found & cv2_callable
+
+r = 1.0 * len(cv2_used) / len(cv2_callable)
+print '\ncv2 api coverage: %d / %d  (%.1f%%)' % ( len(cv2_used), len(cv2_callable), r*100 )
+
+print '\nold (cv) symbols:'
+for s in found:
+    if s.startswith('cv.'):
+        print s

samples/python2/calibrate.py

@@ -4,7 +4,7 @@ import os
 from common import splitfn
 
 USAGE = '''
-USAGE: calib.py [--save <filename>] [--debug <output path>] [<image mask>] 
+USAGE: calib.py [--save <filename>] [--debug <output path>] [--square_size] [<image mask>] 
 '''
 
 
@@ -13,16 +13,18 @@ if __name__ == '__main__':
     import sys, getopt
     from glob import glob
 
-    args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug='])
+    args, img_mask = getopt.getopt(sys.argv[1:], '', ['save=', 'debug=', 'square_size='])
     args = dict(args)
     try: img_mask = img_mask[0]
     except: img_mask = '../cpp/left*.jpg'
     img_names = glob(img_mask)
     debug_dir = args.get('--debug')
+    square_size = float(args.get('--square_size', 1.0))
 
     pattern_size = (9, 6)
     pattern_points = np.zeros( (np.prod(pattern_size), 3), np.float32 )
     pattern_points[:,:2] = np.indices(pattern_size).T.reshape(-1, 2)
+    pattern_points *= square_size
 
     obj_points = []
     img_points = []
@@ -53,6 +55,6 @@ if __name__ == '__main__':
     img_n = len(img_points)
     rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), camera_matrix, dist_coefs)
     print "RMS:", rms
-    print "camera matrix: ", camera_matrix
-    print "distortion coefficients: ", dist_coefs
+    print "camera matrix:\n", camera_matrix
+    print "distortion coefficients: ", dist_coefs.ravel()
 

samples/python2/color_histogram.py

@@ -18,7 +18,7 @@ hist_scale = 10
 def set_scale(val):
     global hist_scale
     hist_scale = val
-cv.CreateTrackbar('scale', 'hist', hist_scale, 32, set_scale)
+cv2.createTrackbar('scale', 'hist', hist_scale, 32, set_scale)
 
 try: fn = sys.argv[1]
 except: fn = 'synth:bg=../cpp/baboon.jpg:class=chess:noise=0.05'

samples/python2/common.py

@@ -2,6 +2,8 @@ import numpy as np
 import cv2
 import os
 
+image_extensions = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.pbm', '.pgm', '.ppm']
+
 def splitfn(fn):
     path, fn = os.path.split(fn)
     name, ext = os.path.splitext(fn)

samples/python2/obj_detect.py

@@ -1,6 +1,5 @@
 import numpy as np
 import cv2, cv
-import common
 
 def detect(img, cascade):
     rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
@@ -44,7 +43,7 @@ def process_image(fn, cascade, extract_faces=True):
 
     faces = []
     if extract_faces:
-        path, name, ext = common.splitfn(fn)
+        path, name, ext = splitfn(fn)
         face_sz = 256
         for i, r in enumerate(rects):
             p1, p2, u = r.reshape(3, 2)
@@ -56,8 +55,6 @@ def process_image(fn, cascade, extract_faces=True):
             face = cv2.warpAffine(img, M, (face_sz, face_sz), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_AREA)
             faces.append(face)
 
-            cv2.imwrite('out/%s_%02d.bmp' % (name, i), face)
-        
     return small, rects, faces
     
     
@@ -66,23 +63,32 @@ if __name__ == '__main__':
     import sys
     import getopt
     from glob import glob
+    from common import splitfn, image_extensions
 
-    args, img_mask = getopt.getopt(sys.argv[1:], '', ['cascade='])
+    args, img_args = getopt.getopt(sys.argv[1:], '', ['cascade=', 'outdir='])
     args = dict(args)
-    # "../../data/haarcascades/haarcascade_frontalface_default.xml" #haarcascade_frontalface_default
     cascade_fn = args.get('--cascade', "../../data/haarcascades/haarcascade_frontalface_alt.xml")
+    outdir = args.get('--outdir')
+    
+    img_list = []
+    if len(img_args) == 0:
+        img_list = ['../cpp/lena.jpg']
+    else:
+        for mask in img_args:
+            img_list.extend(glob(mask))
+    img_list = [fn for fn in img_list if splitfn(fn)[-1].lower() in image_extensions]
 
     cascade = cv2.CascadeClassifier(cascade_fn)
 
-    mask = 'D:/Dropbox/Photos/2011-06-12 aero/img_08[2-9]*.jpg'
-    for fn in glob(mask):
-        print fn
+    for i, fn in enumerate(img_list):
+        print '%d / %d   %s' % (i+1, len(img_list), fn),
         vis, rects, faces = process_image(fn, cascade)
+        if len(faces) > 0 and outdir is not None:
+            path, name, ext = splitfn(fn)
+            cv2.imwrite('%s/%s_all.bmp' % (outdir, name), vis)
+            for face_i, face in enumerate(faces):
+                cv2.imwrite('%s/%s_obj%02d.bmp' % (outdir, name, face_i), face)
+        print ' - %d object(s) found' % len(faces)
         cv2.imshow('img', vis)
-        cv2.waitKey(100)
-
-    
-    #vis, rects = process_image('test.jpg', cascade)
-    #print rects
-    #cv2.imshow('img', vis)
+        cv2.waitKey(50)
     cv2.waitKey()