Image Similarity Detection

backend/main.py

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+import test,train
+
+train_folder_dir="C:/Users/Sanjay/OneDrive/Documents/pixel-peep/test_cases/train_images"
+test_folder_dir="C:/Users/Sanjay/OneDrive/Documents/pixel-peep/test_cases/test_images"
+
+des_list=train.train_images(folder_dir=train_folder_dir)
+is_Match=test.test_images(folder_dir=test_folder_dir,des_from_train=des_list)
+
+for matches in is_Match:
+    print(matches,sep="\n")

backend/test.py

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+import train
+import cv2
+import glob
+import os
+
+
+
+def test_images(folder_dir,des_from_train):
+    match_list=[]
+    sift=cv2.SIFT_create()
+    bf=cv2.BFMatcher()
+    for test_img_path in glob.iglob(f"{folder_dir}/*"):
+        image=cv2.imread(test_img_path)
+        gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+        kp=sift.detect(gray,None)
+        kp,des_test=sift.compute(gray,kp)
+
+        for train_img_path,des_train in des_from_train.items():
+            matches=bf.knnMatch(des_train,des_test,k=2)
+            good=[]
+            for m,n in matches:
+                if m.distance<0.75*n.distance:
+                    good.append(m) 
+
+            # print(good)
+            if(len(good)>=30):
+                match_list.append(f"{os.path.basename(train_img_path)} matches with {os.path.basename(test_img_path)}")
+            else:
+                match_list.append(f"{os.path.basename(train_img_path)} doesn't match with {os.path.basename(test_img_path)}")
+
+
+
+    return match_list  

backend/train.py

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+import cv2
+import glob
+
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+def train_images(folder_dir):
+    des_list={}
+    sift=cv2.SIFT_create()
+    for train_img_path in glob.iglob(f'{folder_dir}/*'):
+        image=cv2.imread(train_img_path)
+        gray=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+
+        kp=sift.detect(gray,None)
+        kp,des_train=sift.compute(gray,kp)
+        des_list[train_img_path]=des_train
+
+    return des_list
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+#This is to show the image in a separate window.
+# cv2.imshow("gray image",gray)
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()