Update app.py

app.py

@@ -1,133 +1,137 @@
-import os
-import cv2
-import streamlit as st
-from datetime import datetime
-from tempfile import NamedTemporaryFile
-from ultralytics import YOLO
-
-# Initialize YOLOv8 model
-model = YOLO("best.pt")  # Replace with your YOLOv8 model path
-
-# Output folder for storing detected clips
-output_folder = "detected_clips"
-os.makedirs(output_folder, exist_ok=True)
-
-# Initialize session state variables
-if "log" not in st.session_state:
-    st.session_state.log = ""
-if "stop_camera" not in st.session_state:
-    st.session_state.stop_camera = False
-
-# Streamlit UI
-st.title("Weapon Detection System")
-st.write("This is a weapon detection system using YOLOv8 and OpenCV.")
-
-# Placeholder for video feed (Camera Stream)
-frame_placeholder = st.empty()
-
-# Placeholder for logs (⚠ Now defined before use)
-log_placeholder = st.empty()
-
-# Function to write log messages in real-time
-def write_log(message):
-    st.session_state.log += message + "\n"
-    # Display logs in a scrolling text area
-    log_placeholder.text_area("Detection Log", value=st.session_state.log, height=300, max_chars=5000, disabled=True)
-
-# Function to process video feed and detect weapons
-def process_video_feed(video_file=None):
-    st.session_state.stop_camera = False
-
-    # Open webcam or video file
-    if video_file:
-        # Save the uploaded video to a temporary file
-        with NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
-            temp_file.write(video_file.read())
-            temp_file_path = temp_file.name
-
-        cap = cv2.VideoCapture(temp_file_path)
-    else:
-        cap = cv2.VideoCapture(0)
-
-    if not cap.isOpened():
-        st.error("Error opening camera")
-        return
-
-    recording = False
-    out = None
-
-    stop_button = st.button("🛑 Stop Camera Feed")  # Stop button
-
-    while not st.session_state.stop_camera:
-        ret, frame = cap.read()
-        if not ret:
-            break
-
-        # Run YOLOv8 inference
-        results = model(frame, verbose=False)
-        annotated_frame = results[0].plot()
-
-        detected = False
-        weapon_name = ""
-        now = datetime.now()
-
-        # Check for weapon detection
-        for result in results[0].boxes:
-            class_id = int(result.cls[0])
-            confidence = float(result.conf[0])
-            if class_id == 0 and confidence > 0.5:
-                detected = True
-                weapon_name = "Weapon"
-                break
-
-        # If weapon detected, start saving the video
-        if detected:
-            if not recording:
-                formatted_date = now.strftime("%d-%m-%y")
-                formatted_time = now.strftime("%H-%M-%S")  
-                output_video_path = os.path.join(output_folder, f"{weapon_name}_Date_{formatted_date}_Time_{formatted_time}.mp4")
-                
-                fourcc = cv2.VideoWriter_fourcc(*"mp4v")
-                out = cv2.VideoWriter(output_video_path, fourcc, 30, (frame.shape[1], frame.shape[0]))
-
-                if not out.isOpened():
-                    st.error(f"Error opening video writer for {output_video_path}")
-                    break
-                
-                recording = True
-                write_log(f"🎥 Recording started for {output_video_path}")
-            
-            out.write(frame)
-
-            # Add detection log
-            timestamp = now.strftime("%d/%m/%y %I:%M:%S %p")
-            write_log(f"⚠️ {weapon_name} detected at {timestamp}")
-
-        else:
-            if recording:
-                recording = False
-                out.release()
-                write_log(f"📁 Recording stopped for {output_video_path}")
-
-        # Convert frame to RGB and display in Streamlit
-        annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
-        frame_placeholder.image(annotated_frame, channels="RGB", use_container_width=True)
-
-        if stop_button:
-            st.session_state.stop_camera = True
-            break
-
-    cap.release()
-    if recording and out:
-        out.release()
-
-# File upload component
-video_file = st.file_uploader("📂 Upload Video", type=["mp4", "avi", "mov"])
-
-# Start Camera Feed Button
-if not video_file and st.button("🎥 Start Camera Feed"):
-    process_video_feed()
-
-# Process uploaded video
-if video_file:
-    process_video_feed(video_file)
+import os
+import cv2
+import streamlit as st
+from datetime import datetime
+from tempfile import NamedTemporaryFile
+from ultralytics import YOLO
+import os
+os.system("pip install opencv-python")
+import cv2
+
+
+# Initialize YOLOv8 model
+model = YOLO("best.pt")  # Replace with your YOLOv8 model path
+
+# Output folder for storing detected clips
+output_folder = "detected_clips"
+os.makedirs(output_folder, exist_ok=True)
+
+# Initialize session state variables
+if "log" not in st.session_state:
+    st.session_state.log = ""
+if "stop_camera" not in st.session_state:
+    st.session_state.stop_camera = False
+
+# Streamlit UI
+st.title("Weapon Detection System")
+st.write("This is a weapon detection system using YOLOv8 and OpenCV.")
+
+# Placeholder for video feed (Camera Stream)
+frame_placeholder = st.empty()
+
+# Placeholder for logs (⚠ Now defined before use)
+log_placeholder = st.empty()
+
+# Function to write log messages in real-time
+def write_log(message):
+    st.session_state.log += message + "\n"
+    # Display logs in a scrolling text area
+    log_placeholder.text_area("Detection Log", value=st.session_state.log, height=300, max_chars=5000, disabled=True)
+
+# Function to process video feed and detect weapons
+def process_video_feed(video_file=None):
+    st.session_state.stop_camera = False
+
+    # Open webcam or video file
+    if video_file:
+        # Save the uploaded video to a temporary file
+        with NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
+            temp_file.write(video_file.read())
+            temp_file_path = temp_file.name
+
+        cap = cv2.VideoCapture(temp_file_path)
+    else:
+        cap = cv2.VideoCapture(0)
+
+    if not cap.isOpened():
+        st.error("Error opening camera")
+        return
+
+    recording = False
+    out = None
+
+    stop_button = st.button("🛑 Stop Camera Feed")  # Stop button
+
+    while not st.session_state.stop_camera:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Run YOLOv8 inference
+        results = model(frame, verbose=False)
+        annotated_frame = results[0].plot()
+
+        detected = False
+        weapon_name = ""
+        now = datetime.now()
+
+        # Check for weapon detection
+        for result in results[0].boxes:
+            class_id = int(result.cls[0])
+            confidence = float(result.conf[0])
+            if class_id == 0 and confidence > 0.5:
+                detected = True
+                weapon_name = "Weapon"
+                break
+
+        # If weapon detected, start saving the video
+        if detected:
+            if not recording:
+                formatted_date = now.strftime("%d-%m-%y")
+                formatted_time = now.strftime("%H-%M-%S")  
+                output_video_path = os.path.join(output_folder, f"{weapon_name}_Date_{formatted_date}_Time_{formatted_time}.mp4")
+                
+                fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+                out = cv2.VideoWriter(output_video_path, fourcc, 30, (frame.shape[1], frame.shape[0]))
+
+                if not out.isOpened():
+                    st.error(f"Error opening video writer for {output_video_path}")
+                    break
+                
+                recording = True
+                write_log(f"🎥 Recording started for {output_video_path}")
+            
+            out.write(frame)
+
+            # Add detection log
+            timestamp = now.strftime("%d/%m/%y %I:%M:%S %p")
+            write_log(f"⚠️ {weapon_name} detected at {timestamp}")
+
+        else:
+            if recording:
+                recording = False
+                out.release()
+                write_log(f"📁 Recording stopped for {output_video_path}")
+
+        # Convert frame to RGB and display in Streamlit
+        annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
+        frame_placeholder.image(annotated_frame, channels="RGB", use_container_width=True)
+
+        if stop_button:
+            st.session_state.stop_camera = True
+            break
+
+    cap.release()
+    if recording and out:
+        out.release()
+
+# File upload component
+video_file = st.file_uploader("📂 Upload Video", type=["mp4", "avi", "mov"])
+
+# Start Camera Feed Button
+if not video_file and st.button("🎥 Start Camera Feed"):
+    process_video_feed()
+
+# Process uploaded video
+if video_file:
+    process_video_feed(video_file)