Create similarity.py

similarity.py

@@ -0,0 +1,40 @@
+import base64
+import io
+from typing import List
+from skimage.metrics import structural_similarity as ssim
+import cv2
+import numpy as np
+import requests
+from PIL import Image
+
+from models import RequestModel, ResponseModel
+
+
+def load_image_url(source):
+    if source.startswith('http'):
+        img = Image.open(requests.get(source, stream=True).raw)
+    else:
+        img_data = base64.b64decode(source)
+        img = Image.open(io.BytesIO(img_data))
+
+    img = np.array(img.convert('L'))
+    return img
+
+
+def check_similarity(images: List[RequestModel]):
+    print(f'checking similarity...')
+
+    original_image = load_image_url(images[0].source)
+    original_image_shape = original_image.shape
+
+    results = []
+
+    for i in range(1, len(images)):
+        image = load_image_url(images[i].source)
+        image = cv2.resize(image, original_image_shape[::-1])
+        s, _ = ssim(original_image, image, full=True)
+        similarity_score = (s + 1) * 50
+        response = ResponseModel(originId=images[i].originId, sequence=images[i].sequence,
+                                 assetCode=images[i].assetCode, similarity=similarity_score)
+        results.append(response)
+    return results