Update app.py

app.py

@@ -1,29 +1,46 @@
 import gradio as gr
 import cv2
 import mediapipe as mp
-import numpy as np
 
-# Initialize MediaPipe People Detection model
-mp_people_detection = mp.solutions.pose
+# Initialize MediaPipe Pose model
+mp_pose = mp.solutions.pose
+pose = mp_pose.Pose()
 
-# Function to process the video and count unique people
 def count_people(video_file):
-    cap = cv2.VideoCapture(video_file)  # Use the file path directly
+    cap = cv2.VideoCapture(video_file)
 
-    with mp_people_detection.Pose(static_image_mode=False) as pose_detector:
-        unique_people_count = 0
-        # Your processing logic here
+    people_count = 0
+    frame_count = 0
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Convert the BGR image to RGB before processing
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Process the frame with MediaPipe Pose
+        results = pose.process(rgb_frame)
+
+        # Check if any pose is detected
+        if results.pose_landmarks:
+            people_count += 1
+
+        frame_count += 1
 
     cap.release()
-    return unique_people_count
-    
+
+    # For simplicity, return the count of frames where people were detected
+    return people_count
+
 # Create the Gradio interface
 iface = gr.Interface(
     fn=count_people,
     inputs=gr.Video(label="Upload Video"),
-    outputs=gr.Textbox(label="Number of Unique People"),
-    title="Unique People Counter",
-    description="Upload a video to count the number of unique people present."
+    outputs=gr.Textbox(label="Number of People Detected"),
+    title="People Counter",
+    description="Upload a video to count the number of people present."
 )
 
 if __name__ == "__main__":