init

data.csv

@@ -0,0 +1,12 @@
+#,X,Y,Z,Planeet
+2,-0.98181527372170,0.00000000000000,-0.18983879552606,
+3,-0.73564182776852,0.64449044863310,-0.20847820715387,
+4,-0.73564182776852,-0.64449044863310,-0.20847820715387,
+11,-0.33732482508860,-0.39831700267993,0.85296865578697,
+1,-0.20847820715387,0.73564182776852,-0.64449044863310,
+10,0.00000000000000,-0.18983879552606,-0.98181527372170,
+7,0.33732482508860,-0.39831700267993,-0.85296865578697,
+6,0.39831700267993,-0.85296865578697,-0.33732482508860,
+9,0.39831700267993,-0.85296865578697,0.33732482508860,Zauber
+8,0.64449044863310,-0.20847820715387,0.73564182776852,
+5,0.98181527372170,0.00000000000000,-0.18983879552606,

finder.py

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+import cv2
+from matplotlib import pyplot as plt
+from numpy import floor
+
+method = cv2.TM_SQDIFF_NORMED
+
+# Read the images from the file
+energy_image = cv2.imread('glyphs-a-energy.png', cv2.IMREAD_GRAYSCALE)
+large_image = cv2.imread('radofdispear.png', cv2.IMREAD_GRAYSCALE)
+
+orb = cv2.ORB_create()
+
+# screenshot = cv2.resize(large_image, down_points, interpolation= cv2.INTER_LINEAR)
+
+
+for i in range(64):
+    mod8 = i % 8
+
+    x1 = (int(floor(i / 8))) * 256
+    y1 = (int(mod8)) * 192
+
+    x2 = x1 + 256
+    y2 = y1 + 192
+
+    # print(x1, y1, x2, y2)
+    small_image = energy_image[y1:y2, x1:x2]
+    # print(small_image)
+    plt.imshow(small_image)
+    plt.show()
+
+    kp1, des1 = orb.detectAndCompute(small_image, None)
+    kp2, des2 = orb.detectAndCompute(large_image, None)
+
+    img1 = small_image
+    img2 = large_image
+
+    # result = cv2.matchTemplate(small_image, large_image, method)
+
+    # print(result)
+    bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
+    # Match descriptors.
+    matches = bf.match(des1, des2)
+    # Sort them in the order of their distance.
+    matches = sorted(matches, key=lambda x: x.distance)
+    # Draw first 10 matches.
+    img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
+
+    img3 = cv2.resize(img3, dsize=(int(3840 / 2), int(2180 / 2)))
+
+    cv2.imshow('output', img3)
+    cv2.waitKey(0)
+    break
+    # break
+    # # We want the minimum squared difference
+    # mn, _, mnLoc, _ = cv2.minMaxLoc(result)
+    # #
+    # # Draw the rectangle:
+    # # Extract the coordinates of our best match
+    # MPx, MPy = mnLoc
+    #
+    # print(MPx, MPy)
+    # #
+    # # # Step 2: Get the size of the template. This is the same size as the match.
+    # # trows, tcols = small_image.shape[:2]
+    # #
+    # # # Step 3: Draw the rectangle on large_image
+    # # cv2.rectangle(large_image, (MPx, MPy), (MPx + tcols, MPy + trows), (0, 0, 255), 2)
+    # cv2.imshow('output', small_image)
+    # cv2.waitKey(0)
+    # break
+# # Display the original image with the rectangle around the match.
+# cv2.imshow('output', large_image)
+#
+# # The image is only displayed if we call this
+#