Update app.py

app.py

@@ -1,41 +1,51 @@
 import gradio as gr
 import cv2
-import numpy as np
-# Assuming YOLO and SORT are available as Python packages or modules
-from yolov3 import YOLO   # This is hypothetical; actual import will depend on your YOLO version
+from PIL import Image
+from yolov3 import YOLO  # Update this import based on your YOLO implementation
 from sort import Sort
 
 # Initialize YOLO and SORT
-yolo = YOLO()  # Hypothetical initialization; actual initialization will depend on YOLO implementation
-tracker = Sort() 
-
-def process_frame(frame):
-    # Your code to process the frame, detect people, and return bounding boxes
-    pass
+yolo = YOLO()
+tracker = Sort()
+
+def detect_people(frame):
+    try:
+        # Add YOLO detection logic here
+        # Return the detected bounding boxes
+        return []
+    except Exception as e:
+        print(f"Detection Error: {e}")
+        return []
+
+def track_people(detections):
+    try:
+        # Update SORT tracker with the detections
+        return tracker.update(detections)
+    except Exception as e:
+        print(f"Tracking Error: {e}")
+        return []
 
 def count_people(video_file):
     cap = cv2.VideoCapture(video_file)
-    unique_people = {}  # Dictionary to store unique people images
+    unique_people = {}
 
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
 
-        detections = process_frame(frame)  # Get detections from YOLO
-        tracked_objects = tracker.update(detections)  # Update SORT tracker
+        detections = detect_people(frame)
+        tracked_objects = track_people(detections)
 
         for obj in tracked_objects:
             obj_id = int(obj[4])
             if obj_id not in unique_people:
-                # Crop and store the image of the person
-                x1, y1, x2, y2 = int(obj[0]), int(obj[1]), int(obj[2]), int(obj[3])
+                x1, y1, x2, y2 = map(int, obj[:4])
                 person_img = frame[y1:y2, x1:x2]
                 unique_people[obj_id] = person_img
 
     cap.release()
 
-    # Convert images to a format suitable for Gradio output
     output_images = [Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) for img in unique_people.values()]
     return len(unique_people), output_images
 
@@ -48,4 +58,4 @@ iface = gr.Interface(
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()