diff --git a/A2/GUI.py b/A2/GUI.py
index cfb9b2fa53a4308942fe74bf262b4417398cf204..fa29348d546c1d287a0106fb4fa449faa550b957 100644
--- a/A2/GUI.py
+++ b/A2/GUI.py
@@ -154,8 +154,8 @@ class GUI(object):
if __name__ == '__main__':
# Todo: Add smooth filter
"""
- This program is for editing images with ORB keypoints.
- Those keypoints are then been grouped with k-means.
+ This program implements the CCV.
+ Since the DFS is very costly please take images of: /images
Preparation:
Make sure you have already installed all packages by running:
diff --git a/A2/Graph.py b/A2/Graph.py
index 49105bb2d9e7c5411f99dd09098eb3c5a8541328..0747d5b20be61bed0f7de753b278d7e5cf1adf56 100644
--- a/A2/Graph.py
+++ b/A2/Graph.py
@@ -9,6 +9,15 @@ from KEYS import KEYS
class Graph(object):
def __init__(self, row, col, g, bins, threshold) -> None:
+ """
+ This constructs the graph for the CCV.
+
+ :param row: Amount of rows
+ :param col: Amount if columns
+ :param g: Graph for recursion
+ :param bins: Amount of bins
+ :param threshold: What is the threshold for plotting
+ """
self.ROW = row
self.COL = col
self.graph = g
@@ -16,10 +25,28 @@ class Graph(object):
self.bins = bins
self.threshold = threshold
- def isSafe(self, i, j, visited, g):
+ def isSafe(self, i, j, visited, g) -> bool:
+ """
+ Check if the neighbor is in the matrix.
+
+ :param i: row coordinate
+ :param j: column coordinate
+ :param visited: matrix which holds the visited fileds
+ :param g: graph
+ :return: Is the neighbor in the matrix ?
+ """
return 0 <= i < self.ROW and 0 <= j < self.COL and not visited[i][j] and g[i][j]
- def DFS(self, i, j, visited, g):
+ def DFS(self, i, j, visited, g) -> None:
+ """
+ Depth first search to get the connected components in the matrix.
+
+ :param i: row coordinate
+ :param j: column coordinate
+ :param visited: matrix which holds the visited fileds
+ :param g: graph
+ :return: None
+ """
rowNbr = [-1, -1, -1, 0, 0, 1, 1, 1]
colNbr = [-1, 0, 1, -1, 1, -1, 0, 1]
@@ -29,7 +56,15 @@ class Graph(object):
if self.isSafe(i + rowNbr[k], j + colNbr[k], visited, g):
self.DFS(i + rowNbr[k], j + colNbr[k], visited, g)
- def label_connected_components(self, visited, label, count):
+ def label_connected_components(self, visited, label, count) -> None:
+ """
+ Label the connected components.
+
+ :param visited: matrix which holds the visited fileds
+ :param label: label of the color
+ :param count: label of the component
+ :return:
+ """
for x in range(self.ROW):
for y in range(self.COL):
if self.connected_components[x][y] is None and visited[x][y] is True:
@@ -38,7 +73,12 @@ class Graph(object):
KEYS.COLOR.name: label
}
- def detect_connected_components(self):
+ def detect_connected_components(self) -> None:
+ """
+ This method detect all connected components.
+
+ :return: None
+ """
visited = [[False for _ in range(self.COL)] for _ in range(self.ROW)]
count = 0
for numb in range(1, self.bins + 1):
@@ -49,10 +89,18 @@ class Graph(object):
self.DFS(i, j, visited, g)
self.label_connected_components(visited, numb, count)
count += 1
- print(np.array(visited))
return count
- def binary_matrix(self, number, g, r, c):
+ def binary_matrix(self, number, g, r, c) -> np.array:
+ """
+ Turn the matrix into a given binary matrix to detect connected components.
+
+ :param number: how many colors labels are in the matrix
+ :param g: the graph
+ :param r: row
+ :param c: column
+ :return: matrix
+ """
adjacency_matrix = copy.deepcopy(g)
for i in range(r):
for j in range(c):
diff --git a/A2/README.md b/A2/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4834ac7e0f4a8fa8cdcccde88aa71b0ef6846842
--- /dev/null
+++ b/A2/README.md
@@ -0,0 +1,17 @@
+# Ex 2
+
+Es wäre möglich die die coherenten und incoherenten Pixel über den Farbraum RGB zu berechnen.
+
+Daraus könnten dann Histogramme erzeugt werden.
+Jeder Bin repräsentiert dann ein Farbe und die Information "coherent" bzw. "incoherent".
+
+Die modifizierten CCV sehen dann bspw. so aus:
+
+ RotCoherent: 10,
+ RotIncoherent: 1,
+ GrünCoherent: 1,
+ GrünIncoherent: 10,
+ BlueCoherent: 5,
+ BlueIncoherent: 5
+
+Anschließend können solche Histogramme mit der "Earth Mover Distance" verglichen werden.
\ No newline at end of file
diff --git a/A2/images/astronauts/Eileen-Collins-2.jpg b/A2/images/astronauts/Eileen-Collins-2.jpg
deleted file mode 100644
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diff --git a/A2/images/original.jpg b/A2/images/original.jpg
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diff --git a/A2/images/result.jpg b/A2/images/result.jpg
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