Update README.md

README.md

@@ -19,27 +19,25 @@ tagger_model_meta = tagger_model.get_modelmeta().custom_metadata_map
 tagger_tags = eval(tagger_model_meta['tags'])
 
 def tagger_predict(image, score_threshold):
-        h, w = image.shape[:2]
-        r = min(512 / w, 512 / h)
-        h, w = int(h * r), int(w * r)
-        image = cv2.resize(image, (w, h))
-        pdx = 512 - w
-        pdy = 512 - h
-        img_new = np.full([512, 512, 3], 1, dtype=np.float32)
-        img_new[pdy // 2:pdy // 2 + h, pdx // 2:pdx // 2 + w] = image
-        image = img_new[np.newaxis, :]
-        probs = tagger_model.run(None, {"input_1": image})[0][0]
-        probs = probs.astype(np.float32)
-        res = []
-        for prob, label in zip(probs.tolist(), tagger_tags):
-            if prob < score_threshold:
-                continue
-            res.append(label)
-        return res
+    s = 512
+    h, w = image.shape[:-1]
+    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
+    ph, pw = s - h, s - w
+    image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
+    image = cv2.copyMakeBorder(image, ph // 2, ph - ph // 2, pw // 2, pw - pw // 2, cv2.BORDER_REPLICATE)
+    image = image.astype(np.float32) / 255
+    image = img_new[np.newaxis, :]
+    probs = tagger_model.run(None, {"input_1": image})[0][0]
+    probs = probs.astype(np.float32)
+    res = []
+    for prob, label in zip(probs.tolist(), tagger_tags):
+        if prob < score_threshold:
+            continue
+        res.append(label)
+    return res
 
 img = cv2.imread("test.jpg")
 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-img = img.astype(np.float32) / 255
 tags = tagger_predict(img, 0.5)
 print(tags)
 ```