Update app.py

app.py

@@ -2,6 +2,8 @@ import torch
 import cv2
 import numpy as np
 import gradio as gr
+from sahi.prediction import ObjectPrediction
+from sahi.utils.cv import visualize_object_predictions, read_image
 
 
 model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
@@ -17,15 +19,35 @@ model.max_det = 1000
 def detect(img):
 
 
-    results = model(img, size=640)
-
-    predictions = results.pred[0]
-    boxes = predictions[:, :4] # x1, y1, x2, y2
-    scores = predictions[:, 4]
-    categories = predictions[:, 5]
-    dfResults = results.pandas().xyxy[0]
-    for i, box in enumerate(boxes):
-        cv2.rectangle(img, tuple(box[:2]), tuple(box[2:]), (0, 255, 0), 2)
+    results = model.predict(image, imgsz=image_size, return_outputs=True)
+
+    
+    object_prediction_list = []
+    for _, image_results in enumerate(results):
+        if len(image_results)!=0:
+            image_predictions_in_xyxy_format = image_results['det']
+            for pred in image_predictions_in_xyxy_format:
+                x1, y1, x2, y2 = (
+                    int(pred[0]),
+                    int(pred[1]),
+                    int(pred[2]),
+                    int(pred[3]),
+                )
+                bbox = [x1, y1, x2, y2]
+                score = pred[4]
+                category_name = model.model.names[int(pred[5])]
+                category_id = pred[5]
+                object_prediction = ObjectPrediction(
+                    bbox=bbox,
+                    category_id=int(category_id),
+                    score=score,
+                    category_name=category_name,
+                )
+                object_prediction_list.append(object_prediction)
+
+    image = read_image(image)
+    output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
+    return output_image['image']
 
 def drawRectangles(image, dfResults):
     for index, row in dfResults.iterrows():