Update app.py

app.py

@@ -2,8 +2,6 @@ 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)
@@ -19,41 +17,13 @@ model.max_det = 1000
 def detect(img):
 
 
-    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():
-      print( (row['xmin'], row['ymin']))
-      image = cv2.rectangle(image, (row['xmin'], row['ymin']), (row['xmax'], row['ymax']), (255, 0, 0), 2)
-    return image    
+    results = model(img, size=640)
+
+    predictions = results.pred[0]
+    boxes = predictions[:, :4] # x1, y1, x2, y2
+    scores = predictions[:, 4]
+    categories = predictions[:, 5]
+    return img.save()
 
 
 img = gr.inputs.Image(shape=(192, 192))