Only predict one image at a time

.gitignore

@@ -0,0 +1,2 @@
+*.pyc
+yolov5x6.pt

Processor.py

@@ -10,18 +10,8 @@ class Processor():
         self.__model = torch.hub.load(
             'ultralytics/yolov5', 'yolov5x6', trust_repo=True)
 
-    def classify_images(self, images: List[NDArray]) -> List[str]:
-        result = []
-        class_names = self.__inference.dls.vocab
-
-        test_dl = self.__inference.dls.test_dl(images)
-        tensors = self.__inference.get_preds(dl=test_dl, with_decoded=True)[2]
-        preds = [int(t.item()) for t in tensors]
-
-        for i in preds:
-            result.append(class_names[i])
-
-        return result
+    def classify_image(self, images: NDArray) -> str:
+        return self.__inference.predict(images)
 
     def filter_image(self, image: Image) -> bool:
         results = self.__model(image)

app.py

@@ -14,13 +14,16 @@ def initialize_app():
 
 def process_images(images, processor: Processor):
     filtered_images = []
+    result = []
 
     for image in images:
         image = Image.open(image)
         if processor.filter_image(image):
             filtered_images.append(np.asarray(image))
+    
+    for img in filtered_images:
+        result.append(processor.classify_image(img)[0])
 
-    result = processor.classify_images(filtered_images)
     outfit = mode(result)
 
     with open(f'./texts/{outfit}.txt') as text: