gray

app.py

@@ -1,13 +1,10 @@
 import gradio as gr
-#import torch
 from sahi.prediction import ObjectPrediction
 from sahi.utils.cv import visualize_object_predictions, read_image
 from ultralyticsplus import YOLO
+import cv2
+from PIL import Image
 
-# Images
-# torch.hub.download_url_to_file('https://raw.githubusercontent.com/kadirnar/dethub/main/data/images/highway.jpg', 'highway.jpg')
-# torch.hub.download_url_to_file('https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg', 'highway1.jpg')
-# torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')
 
 def yolov8_inference(
     image: gr.inputs.Image = None,
@@ -30,7 +27,14 @@ def yolov8_inference(
     model = YOLO(model_path)
     model.conf = conf_threshold
     model.iou = iou_threshold
-    results = model.predict(image, imgsz=image_size)
+    
+    image = read_image(image)
+    
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    thresh = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
+    thresh = Image.fromarray(thresh)
+    
+    results = model.predict(thresh, imgsz=image_size)
     object_prediction_list = []
     for image_results in results:
         if len(image_results)!=0:
@@ -53,7 +57,6 @@ def yolov8_inference(
                 )
                 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']