Update cal_compatibility.py

cal_compatibility.py

@@ -1,43 +1,41 @@
-import vgg16
-import numpy as np
-
-
-def cosine_similarity(vector1, vector2):
-    # # 计算向量的点积
-    # dot_product = np.dot(vector1, vector2)
-    # # 计算向量的模长
-    # magnitude_vector1 = np.linalg.norm(vector1)
-    # magnitude_vector2 = np.linalg.norm(vector2)
-    # # 计算余弦相似度
-    # return dot_product / (magnitude_vector1 * magnitude_vector2)
-    flat_vector1 = vector1.reshape(-1)
-    flat_vector2 = vector2.reshape(-1)
-    dot_product = np.dot(flat_vector1, flat_vector2)
-    norm_vector1 = np.linalg.norm(flat_vector1)
-    norm_vector2 = np.linalg.norm(flat_vector2)
-    return dot_product / (norm_vector1 * norm_vector2)
-
-def cal_compatibility():
-    n = 4096
-    access_feature = []
-    cloth_feature = []
-    for item_id in range(1, 7):
-        access_feature.append(vgg16.extract_features('downloads/access_' + '%s.jpg' % item_id)[0])
-        cloth_feature.append(vgg16.extract_features('downloads/gen_cloth_' + '%s.jpeg' % item_id)[0])
-
-    best_score = float('-inf')
-    best_cloth = 0
-    best_access = 0
-    for i in range(1, 7):
-        for j in range(1, 6):
-            score = cosine_similarity(access_feature[i - 1], cloth_feature[j - 1])
-            if score > best_score:
-                best_score = score
-                best_cloth = j
-                best_access = i
-    print(best_cloth, best_access)
-    picture = [f"downloads/gen_cloth_{best_cloth}.jpeg", f"downloads/access_{best_access}.jpg"]
-    return picture
-
-
-# cal_compatibility()
+import vgg16
+import numpy as np
+
+
+def cosine_similarity(vector1, vector2):
+    # # 计算向量的点积
+    # dot_product = np.dot(vector1, vector2)
+    # # 计算向量的模长
+    # magnitude_vector1 = np.linalg.norm(vector1)
+    # magnitude_vector2 = np.linalg.norm(vector2)
+    # # 计算余弦相似度
+    # return dot_product / (magnitude_vector1 * magnitude_vector2)
+    flat_vector1 = vector1.reshape(-1)
+    flat_vector2 = vector2.reshape(-1)
+    dot_product = np.dot(flat_vector1, flat_vector2)
+    norm_vector1 = np.linalg.norm(flat_vector1)
+    norm_vector2 = np.linalg.norm(flat_vector2)
+    return dot_product / (norm_vector1 * norm_vector2)
+
+def cal_compatibility():
+    n = 4096
+    access_feature = []
+    cloth_feature = []
+    for item_id in range(1, 9):
+        access_feature.append(vgg16.extract_features('downloads/access_' + '%s.jpg' % item_id)[0])
+    for item_id in range(1, 7):
+        cloth_feature.append(vgg16.extract_features('downloads/gen_cloth_' + '%s.jpeg' % item_id)[0])
+
+    best_score = float('-inf')
+    best_cloth = 0
+    best_access = 0
+    for i in range(1, 9):
+        for j in range(1, 7):
+            score = cosine_similarity(access_feature[i - 1], cloth_feature[j - 1])
+            if score > best_score:
+                best_score = score
+                best_cloth = j
+                best_access = i
+    print(best_cloth, best_access)
+    picture = [f"downloads/gen_cloth_{best_cloth}.jpeg", f"downloads/access_{best_access}.jpg"]
+    return picture