From 1589076974e64d3e710834ebb11580a1261de4c0 Mon Sep 17 00:00:00 2001
From: mmattmcw <matt@sixteenmillimeter.com>
Date: Mon, 30 Jan 2023 09:26:43 -0500
Subject: [PATCH] Work on matching registration marks against document.
 Segmenting image without copying is causing errors.

---
 fourcell/calibrate.py | 41 ++++++++++++++++++++++++++++++++---------
 1 file changed, 32 insertions(+), 9 deletions(-)

diff --git a/fourcell/calibrate.py b/fourcell/calibrate.py
index ee32b9f..1ffc941 100644
--- a/fourcell/calibrate.py
+++ b/fourcell/calibrate.py
@@ -8,8 +8,7 @@ from json import load
 
 #clockwise from top left
 order = [ 0, 2, 3, 5, 4, 1 ]
-matchMethods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
-            'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
+matchMethods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR', 'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
 
 def read_text (textPath) :
 	holePunches = {}
@@ -52,6 +51,7 @@ original = cv2.imread(normalImage)
 img = original.copy()
 height, width = img.shape[:2]
 orientation = height > width
+marks = []
 
 if not orientation :
 	print(f'Scan is not in portrait mode, exiting...')
@@ -79,9 +79,11 @@ def group_points(points):
                 groups[key].append(points[i])
                 points[i] = None
         points = list(filter(lambda x: x is not None, points))
-    # perform average operation on each group
     return list([[int(np.mean(list([x[0] for x in groups[arr]]))), int(np.mean(list([x[1] for x in groups[arr]])))] for arr in groups])
 
+def find_closest (pt, pts) :
+	return pts[min(range(len(pts)), key = lambda i: get_distance(pts[i], pt))]
+
 def find_in_half (half) :
 	halfGray = cv2.cvtColor(half, cv2.COLOR_BGR2GRAY)
 	res = cv2.matchTemplate(halfGray, registrationMark, cv2.TM_CCOEFF_NORMED)
@@ -89,19 +91,40 @@ def find_in_half (half) :
 	loc = np.where( res >= threshold)
 	for pt in zip(*loc[::-1]):
 		cv2.rectangle(half, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)
-	#display(half)
 	return list(zip(*loc[::-1]))
 
-topHalf = img[holePunches['0']['y']-round(height*0.05):holePunches['1']['y']+round(height*0.1), holePunches['0']['x']:holePunches['2']['x']]
+
+
+ttly = holePunches['0']['y']-round(height*0.05)
+ttlx = holePunches['0']['x']
+topHalf = img[ttly:holePunches['1']['y']+round(height*0.1), ttlx:holePunches['2']['x']]
 topHalfPts = find_in_half(topHalf)
 thpts = group_points(topHalfPts)
 for pt in thpts :
-	print(f'{pt[0]},{pt[1]}')
+	#print(f'{ttlx + pt[0]},{ttly + pt[1]}')
+	marks.append((ttlx + pt[0], ttly + pt[1],))
 
-bottomHalf = img[holePunches['4']['y']-round(height*0.1):holePunches['5']['y']+round(height*0.05), holePunches['3']['x']:holePunches['4']['x']]
+print(f'Found {len(thpts)} points')
+#display(topHalf)
+
+btly = holePunches['4']['y']-round(height*0.1)
+btlx = holePunches['3']['x']
+bottomHalf = img[btly:holePunches['5']['y']+round(height*0.05), btlx:holePunches['4']['x']]
 bottomHalfPts = find_in_half(bottomHalf)
 bhpts = group_points(bottomHalfPts)
 for pt in bhpts :
-	print(f'{pt[0]},{pt[1]}')
-display(bottomHalf)
+	#print(f'{btlx + pt[0]},{btly + pt[1]}')
+	marks.append((btlx + pt[0], btly + pt[1], ))
+
+print(f'Found {len(bhpts)} points')
+
+if len(marks) != 16 :
+	print(f'{len(marks)} != 16 marks')
+	exit(1)
+
+print(find_closest((0,0,), marks))
+print(find_closest((width,0,), marks))
+
+print(find_closest((0,height,), marks))
+print(find_closest((width,height,), marks))