use refined corners (Zwischenstand)

ocv_template_matching.py

@@ -1,10 +1,26 @@
 import cv2
 import sys
 import imutils as im
+import numpy as np
 
 from util import bbox_extend, bbox_round, image_crop, to_rect, bbox_add_position, bbox_center
 from ocv_corner_tracker import CornerTracker
 
+
+def refine_corner(src_gray, corners):
+	# Set the needed parameters to find the refined corners
+	winSize = (5, 5)
+	zeroZone = (-1, -1)
+	criteria = (cv2.TERM_CRITERIA_EPS + cv2.TermCriteria_COUNT, 40, 0.001)
+
+	# Calculate the refined corner locations
+	corners = cv2.cornerSubPix(src_gray, corners, winSize, zeroZone, criteria)
+
+	# Write them down
+	for i in range(corners.shape[0]):
+		print(" -- Refined Corner [", i, "] (", corners[i, 0, 0], ",", corners[i, 0, 1], ")")
+
+
 COLOR_TRACKER = (0, 255, 0)
 COLOR_TRACKER_EXT = (0, 255, 255)
 COLOR_MATCHER = (0, 0, 255)
@@ -76,6 +92,7 @@ while True:
 
 	if ok:
 		if mode:
+			corners = []
 			for ct in ct_list:
 				matcher_bbox_local, matcher = ct.process(crop)
 				matcher_top_left_local, matcher_bottom_right_local = to_rect(bbox_round(matcher_bbox_local))
@@ -95,11 +112,14 @@ while True:
 
 				# Draw path
 				line_to = bbox_center(matcher_bbox)
+				corners.append([line_to])
 				ct.path_add(line_to)
 
 				for p in ct.path:
 					cv2.line(image_anno, p['from'], p['to'], (0, 0, 255), 2)
 
+			refine_corner(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), np.array(corners, dtype=np.float32))
+
 		else:
 			# Perform template match operations
 			crop_scaled = im.resize(crop.copy(), IMG_SCALE_UP * crop.shape[1], IMG_SCALE_UP * crop.shape[0])
@@ -159,4 +179,3 @@ while True:
 	k = cv2.waitKey(-1) & 0xff
 	if k == 27:
 		break
-