4 files changed, 39 insertions, 48 deletions
diff --git a/mask.jpg b/mask.jpg
new file mode 100644
index 0000000..aafa555
--- /dev/null
+++ b/mask.jpg
diff --git a/mask.png b/mask.png
deleted file mode 100644
index 2d97ca4..0000000
--- a/mask.png
+++ /dev/null
diff --git a/matches.jpg b/matches.jpg
new file mode 100644
index 0000000..d8ef11a
--- /dev/null
+++ b/matches.jpg
diff --git a/scan.py b/scan.py
index 75e14bb..792e852 100644
--- a/scan.py
+++ b/scan.py
@@ -1,65 +1,56 @@
+from __future__ import print_function
+
 import cv2
-import imutils
 import numpy as np
 
-
-def order_points(pts):
-    rect = np.zeros((4, 2), dtype="float32")
-    s = pts.sum(axis=1)
-    rect[0] = pts[np.argmin(s)]
-    rect[2] = pts[np.argmax(s)]
-    diff = np.diff(pts, axis=1)
-    rect[1] = pts[np.argmin(diff)]
-    rect[3] = pts[np.argmax(diff)]
-    return rect
+MAX_MATCHES = 500
+GOOD_MATCH_PERCENT = 0.15
 
 
-def four_point_transform(image, pts):
-    rect = order_points(pts)
-    (tl, tr, br, bl) = rect
-    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
-    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
-    maxWidth = max(int(widthA), int(widthB))
+def align_images(im1, im2):
+    im1_gra = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
+    im2_gray = cv2.cvtColor(im2, cv2.COLOR_BGR2GRAY)
 
-    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
-    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
-    maxHeight = max(int(heightA), int(heightB))
+    orb = cv2.ORB_create(MAX_MATCHES)
+    keypoints1, descriptors1 = orb.detectAndCompute(im1_gra, None)
+    keypoints2, descriptors2 = orb.detectAndCompute(im2_gray, None)
 
-    dst = np.array([
-        [0, 0],
-        [maxWidth - 1, 0],
-        [maxWidth - 1, maxHeight - 1],
-        [0, maxHeight - 1]], dtype="float32")
+    matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_BRUTEFORCE_HAMMING)
+    matches = matcher.match(descriptors1, descriptors2, None)
 
-    M = cv2.getPerspectiveTransform(rect, dst)
-    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
+    matches.sort(key=lambda x: x.distance, reverse=False)
 
-    return warped
+    num_good_matches = int(len(matches) * GOOD_MATCH_PERCENT)
+    matches = matches[:num_good_matches]
 
+    im_matches = cv2.drawMatches(im1, keypoints1, im2, keypoints2, matches, None)
+    cv2.imwrite("matches.jpg", im_matches)
 
-image = cv2.imread("example.jpg")
-ratio = image.shape[0] / 500.0
-orig = image.copy()
+    points1 = np.zeros((len(matches), 2), dtype=np.float32)
+    points2 = np.zeros((len(matches), 2), dtype=np.float32)
 
-gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-gray = cv2.GaussianBlur(gray, (5, 5), 0)
-edged = cv2.Canny(gray, 75, 200)
+    for i, match in enumerate(matches):
+        points1[i, :] = keypoints1[match.queryIdx].pt
+        points2[i, :] = keypoints2[match.trainIdx].pt
 
-cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
-cnts = imutils.grab_contours(cnts)
-cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]
+    h, mask = cv2.findHomography(points1, points2, cv2.RANSAC)
 
-for c in cnts:
-    peri = cv2.arcLength(c, True)
-    approx = cv2.approxPolyDP(c, 0.02 * peri, True)
-    if len(approx) == 4:
-        screenCnt = approx
-        break
+    height, width, channels = im2.shape
+    im1_reg = cv2.warpPerspective(im1, h, (width, height))
 
-cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
+    return im1_reg, h
 
-warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
 
-cv2.imshow("Original", imutils.resize(orig, height=650))
-cv2.imshow("Scanned", imutils.resize(warped, height=650))
-cv2.waitKey(0)
+if __name__ == '__main__':
+    ref_filename = "mask.jpg"
+    print("Reading reference image : ", ref_filename)
+    im_reference = cv2.imread(ref_filename, cv2.IMREAD_COLOR)
+    im_filename = "example.jpg"
+    print("Reading image to align : ", im_filename)
+    im = cv2.imread(im_filename, cv2.IMREAD_COLOR)
+    print("Aligning images ...")
+    im_req, h = align_images(im, im_reference)
+    out_filename = "aligned.jpg"
+    print("Saving aligned image : ", out_filename)
+    cv2.imwrite(out_filename, im_req)
+    print("Estimated homography : \n", h)