find_obj.py sample added

diff --git a/samples/python2/find_obj.py b/samples/python2/find_obj.py
new file mode 100644 (file)
index 0000000..9881d00
--- /dev/null
+++ b/samples/python2/find_obj.py
@@ -0,0 +1,72 @@
+import numpy as np\r
+import cv2, cv\r
+from common import anorm\r
+\r
+help_message = '''SURF image match \r
+\r
+USAGE: findobj.py [ <image1> <image2> ]\r
+'''\r
+\r
+\r
+def match(desc1, desc2, r_threshold = 0.75):\r
+    res = []\r
+    for i in xrange(len(desc1)):\r
+        dist = anorm( desc2 - desc1[i] )\r
+        n1, n2 = dist.argsort()[:2]\r
+        r = dist[n1] / dist[n2]\r
+        if r < r_threshold:\r
+            res.append((i, n1))\r
+    return np.array(res)\r
+\r
+def draw_match(img1, img2, p1, p2, status = None, H = None):\r
+    h1, w1 = img1.shape[:2]\r
+    h2, w2 = img2.shape[:2]\r
+    vis = np.zeros((max(h1, h2), w1+w2), np.uint8)\r
+    vis[:h1, :w1] = img1\r
+    vis[:h2, w1:w1+w2] = img2\r
+    vis = cv2.cvtColor(vis, cv.CV_GRAY2BGR)\r
+\r
+    if H is not None:\r
+        corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])\r
+        corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )\r
+        cv2.polylines(vis, [corners], True, (255, 255, 255))\r
+    \r
+    if status is None:\r
+        status = np.ones(len(p1), np.bool_)\r
+    green = (0, 255, 0)\r
+    red = (0, 0, 255)\r
+    for (x1, y1), (x2, y2), inlier in zip(np.int32(p1), np.int32(p2), status):\r
+        col = [red, green][inlier]\r
+        if not inlier:\r
+            cv2.line(vis, (x1, y1), (x2+w1, y2), col)\r
+        cv2.circle(vis, (x1, y1), 2, col, -1)\r
+        cv2.circle(vis, (x2+w1, y2), 2, col, -1)\r
+    return vis\r
+\r
+if __name__ == '__main__':\r
+    import sys\r
+    try: fn1, fn2 = sys.argv[1:3]\r
+    except:\r
+        fn1 = '../c/box.png'\r
+        fn2 = '../c/box_in_scene.png'\r
+    print help_message\r
+\r
+    img1 = cv2.imread(fn1, 0)\r
+    img2 = cv2.imread(fn2, 0)\r
+\r
+    surf = cv2.SURF(1000)\r
+    kp1, desc1 = surf.detect(img1, None, False)\r
+    kp2, desc2 = surf.detect(img2, None, False)\r
+    desc1.shape = (-1, surf.descriptorSize())\r
+    desc2.shape = (-1, surf.descriptorSize())\r
+    print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))\r
+\r
+    m = match(desc1, desc2)\r
+    matched_p1 = np.array([kp1[i].pt for i, j in m])\r
+    matched_p2 = np.array([kp2[j].pt for i, j in m])\r
+    H, status = cv2.findHomography(matched_p1, matched_p2, cv2.RANSAC, 10.0)\r
+    print '%d / %d  inliers/matched' % (np.sum(status), len(status))\r
+\r
+    vis = draw_match(img1, img2, matched_p1, matched_p2, status, H)\r
+    cv2.imshow('find_obj SURF', vis)\r
+    cv2.waitKey()
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