Debug: parsing detections

yolov8.py

@@ -12,20 +12,18 @@ import gradio as gr
 COLORS = np.random.uniform(0, 255, size=(80, 3))
 
 def parse_detections(results):
-    detections = results.pandas().xyxy[0].to_dict()
     boxes, colors, names = [], [], []
-    for i in range(len(detections["xmin"])):
-        confidence = detections["confidence"][i]
-        if confidence < 0.2:
-            continue
-        xmin, ymin = int(detections["xmin"][i]), int(detections["ymin"][i])
-        xmax, ymax = int(detections["xmax"][i]), int(detections["ymax"][i])
-        name, category = detections["name"][i], int(detections["class"][i])
-        boxes.append((xmin, ymin, xmax, ymax))
-        colors.append(COLORS[category])
-        names.append(name)
-    return boxes, colors, names
+    for result in results:
+        # Accessing boxes directly from the result
+        for box in result.boxes:
+            xmin, ymin, xmax, ymax = box.xyxy[0].int().tolist()  # Convert to list of integers
+            category = int(box.cls[0].item())  # Class index
+            name = result.names[category]  # Get class name from names
+            boxes.append((xmin, ymin, xmax, ymax))
+            colors.append(COLORS[category])  # Ensure COLORS is defined elsewhere in your code
+            names.append(name)
 
+    return boxes, colors, names
 
 def draw_detections(boxes, colors, names, img):
     for box, color, name in zip(boxes, colors, names):
@@ -58,8 +56,7 @@ def xai_yolov8n(image):
 
     target_layers = [model.model.model[-2]]  # Grad-CAM target layer
     results = model([image])
-    print(results)
-    boxes, colors, names = parse_detections(results[0])
+    boxes, colors, names = parse_detections(results)
     detections_img = draw_detections(boxes, colors, names, image.copy())
     img_float = np.float32(image) / 255
     transform = transforms.ToTensor()