Cleaning up the strings

app.py

@@ -20,6 +20,8 @@ COLORS = [
     [0.301, 0.745, 0.933]
 ]
 
+YOLOV8_LABELS = ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
+
 def make_prediction(img, feature_extractor, model):
     inputs = feature_extractor(img, return_tensors="pt")
     outputs = model(**inputs)
@@ -64,8 +66,8 @@ def detect_objects(model_name,url_input,image_input,threshold):
 
         model = YOLO(model_name)
         # set model parameters
-        model.overrides['conf'] = 0.25  # NMS confidence threshold
-        model.overrides['iou'] = 0.45  # NMS IoU threshold
+        model.overrides['conf'] = 0.15  # NMS confidence threshold
+        model.overrides['iou'] = 0.05  # NMS IoU threshold             https://www.google.com/search?client=firefox-b-1-d&q=intersection+over+union+meaning
         model.overrides['agnostic_nms'] = False  # NMS class-agnostic
         model.overrides['max_det'] = 1000  # maximum number of detections per image
 
@@ -81,11 +83,14 @@ def detect_objects(model_name,url_input,image_input,threshold):
             boxes = result.boxes.cpu().numpy()
             for i, box in enumerate(boxes):
                 # r = box.xyxy[0].astype(int)
-                final_str_abv += str(box.xyxy[0].astype(int)) + "__" + str(box.cls) + "__" + str(box.conf) + "__" + str(box) + "\n"
-            # if r.boxes.conf >= threshold:
-            #     final_str_abv += str(r.boxes) + "\n"
-            # else:
-            #     final_str_else += str(r.boxes) + "\n"
+                coordinates = box.xyxy[0].astype(int)
+                label = YOLOV8_LABELS[box.cls]
+                confi = box.conf
+                # final_str_abv += str() + "__" + str(box.cls) + "__" + str(box.conf) + "__" + str(box) + "\n"
+                if confi >= threshold:
+                    final_str_abv += f"Detected `{label}` with confidence `{round(confi, 3)}` at location `{coordinates}`\n"
+                else:
+                    final_str_else += f"Detected `{label}` with confidence `{round(confi, 3)}` at location `{coordinates}`\n"
 
         final_str = "{:*^50}\n".format("ABOVE THRESHOLD OR EQUAL") + final_str_abv + "\n{:*^50}\n".format("BELOW THRESHOLD")+final_str_else