Update app.py

app.py

@@ -23,20 +23,23 @@ def snap(image, model, conf, iou):
     elif model == "S-Raw":
         results = s_raw_model(image, conf=conf, iou=iou)
 
+    
+
     # Convert the results list into an output image
-    for result in results:
-        classes = result.boxes.cls.cpu().numpy()
-        probs = result.boxes.conf.cpu().numpy()
-        boxes = result.boxes.xyxy.cpu().numpy()
+    result = results[0]
+    classes = result.boxes.cls.cpu().numpy()
+    probs = result.boxes.conf.cpu().numpy()
+    boxes = result.boxes.xyxy.cpu().numpy()
 
-        print("-------------------")
-        print(classes)
-        print("-------------------")
-        print(probs)
-        print("-------------------")
-        print(boxes)
+    print("-------------------")
+    print(classes)
+    print("-------------------")
+    print(probs)
+    print("-------------------")
+    print(boxes)
 
-        x1, y1, x2, y2 = boxes
+    for i in range(len(boxes)):
+        x1, y1, x2, y2 = boxes[i]
         x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
         cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
         cv2.putText(image, f"{classes} {probs:.2f}", (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
@@ -45,7 +48,7 @@ def snap(image, model, conf, iou):
     resulting_image = Image.fromarray(image)
 
     # Get the labels
-    labels = results.pandas().xyxy[0]["name"].values
+    # labels = results.pandas().xyxy[0]["name"].values
 
     # Sort the labels by their x-value first and then by their y-value
     # print(labels)