Ajustando limite de resultados

image_similarity.py

@@ -18,7 +18,7 @@ class ImageSimilarity(object):
         # all other images and returns a list with the pairs that have the highest 
         # cosine similarity score
         processed_images = util.paraphrase_mining_embeddings(encoded_image)
-        NUM_SIMILAR_IMAGES = 10 
+        # NUM_SIMILAR_IMAGES = 10 
 
         # =================
         # DUPLICATES
@@ -43,10 +43,10 @@ class ImageSimilarity(object):
         # you will get larger clusters which have less similar images in it. Threshold 0 - 1.00
         # A threshold of 1.00 means the two images are exactly the same. Since we are finding near 
         # duplicate images, we can set it at 0.99 or any number 0 < X < 1.00.
-        threshold = 0.90
+        threshold = 0.99
         near_duplicates = [image for image in processed_images if image[0] < threshold]
 
-        for score, image_id1, image_id2 in near_duplicates[0:NUM_SIMILAR_IMAGES]:
+        for score, image_id1, image_id2 in near_duplicates:
             results.append({
                 'score': score,
                 'image1': pil_images[image_id1]["key"],