- refactored into utils.py

- added ocv_template_matching.py for evaluationof template matcher
- fixed template matching from tracker bounding box

template_matcher.py

@@ -0,0 +1,33 @@
+import cv2
+import sys
+import numpy as np
+import imutils as im
+
+
+def template_match_rotation(source, template, angle, center, rot_min=-1.0, rot_max=+1.0, n_steps=10, scale=1):
+	best_angle = 0
+	best_r = 0
+	best_max_loc = 0
+	r = 0
+	im_source = im.resize(source, scale * source.shape[0], scale * source.shape[1])
+	for dangle in np.linspace(rot_min, rot_max, n_steps):
+		# rotated and scale up to IMG_SCALE_UP-size
+		im_template = im.rotate(template, angle + dangle, center=center, scale=scale)
+
+		# Perform template match operations
+		res = cv2.matchTemplate(im_source, im_template, cv2.TM_CCOEFF_NORMED)
+		(minVal, maxVal, minLoc, maxLoc) = cv2.minMaxLoc(res)
+
+		r = maxVal
+		if r > best_r:
+			best_r = r
+			best_angle = dangle
+			best_max_loc = (maxLoc[0]/scale, maxLoc[1]/scale)
+		else:
+			break
+
+	angle += best_angle
+	loc_x = best_max_loc[0]
+	loc_y = best_max_loc[1]
+
+	return angle, (loc_x, loc_y)