Final

Author: aman05382

ForgeryDetection.py

@@ -0,0 +1,44 @@
+from sklearn.cluster import DBSCAN
+import numpy as np
+import cv2
+
+
+class Detect(object):
+    def __init__(self, input):
+        self.image = cv2.imread(input)
+
+    def siftDetector(self):
+        sift = cv2.SIFT_create()
+        # sift = cv2.xfeatures2d.SIFT_create()
+        gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
+        self.key_points, self.descriptors = sift.detectAndCompute(gray, None)
+        return self.key_points, self.descriptors
+
+    def showSiftFeatures(self):
+        gray_image = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
+        sift_image = cv2.drawKeypoints(
+            self.image, self.key_points, self.image.copy())
+        return sift_image
+
+    def locateForgery(self, eps=40, min_sample=2):
+        clusters = DBSCAN(eps=eps, min_samples=min_sample).fit(
+            self.descriptors)
+        size = np.unique(clusters.labels_).shape[0]-1
+        forgery = self.image.copy()
+        if (size == 0) and (np.unique(clusters.labels_)[0] == -1):
+            print('No Forgery Found!!')
+            return None
+        if size == 0:
+            size = 1
+        cluster_list = [[] for i in range(size)]
+        for idx in range(len(self.key_points)):
+            if clusters.labels_[idx] != -1:
+                cluster_list[clusters.labels_[idx]].append(
+                    (int(self.key_points[idx].pt[0]), int(self.key_points[idx].pt[1])))
+        for points in cluster_list:
+            if len(points) > 1:
+                for idx1 in range(1, len(points)):
+                    # Green color in BGR
+                    cv2.line(forgery, points[0], points[idx1], (0, 255, 0), 5)
+                    # cv2.line(forgery, points[0], points[idx1], (255, 0, 0), 5)
+        return forgery