Update camera_view.py

camera_view.py

@@ -1,73 +1,81 @@
 import streamlit as st
 import cv2
+import tempfile
 import time
+import os
 
-# Initialize session state
-if "show_button" not in st.session_state:
-    st.session_state.show_button = True
+if 'video_processed' not in st.session_state:
+    st.session_state.video_processed = False
 
-if "camera_active" not in st.session_state:
-    st.session_state.camera_active = False
-
-class Camera_View:
+class Video_View:
     def __init__(self, app, model):
         self.app = app
         self.model = model
-
-    def toggle_camera(self):
-        st.session_state.camera_active = True
-        st.session_state.show_button = False  # Hide st.camera_input when camera is started
-
-    def stop_camera(self):
-        st.session_state.camera_active = False
-
+    def toggle_video_processing(self):
+        """Toggle video processing state."""
+        st.session_state.video_processed = False
     def show(self):
         # Top navigation
         col1_back, col2_back = st.columns([0.2, 0.8])
         with col1_back:
-            if st.button("Back", key='upload_back', icon=':material/arrow_back:', type='primary'):
+            if st.button("Back", key='video_back', icon=':material/arrow_back:', type='primary'):
                 self.app.change_page("Main")
 
-        st.markdown("<h1 style='text-align: center;'>🧠 Real-time Detection</h1>", unsafe_allow_html=True)
+        st.markdown("<h1 style='text-align: center;'>🧠 Video Detection</h1>", unsafe_allow_html=True)
         st.divider()
 
-        # Buttons to control camera
-        col1_button, col2_button = st.columns(2)
-        with col1_button:
-            st.button("Stop Camera",
-                      icon=':material/videocam_off:',
-                      type='secondary',
-                      on_click=self.stop_camera)  # Button to stop video
-        with col2_button:
-            st.button("Start Camera",
-                      icon=':material/videocam:',
-                      type='primary',
-                      on_click=self.toggle_camera)
+        uploaded_file = st.file_uploader("Upload a video", type=["mp4", "avi", "mov"],on_change=self.toggle_video_processing)
+        if not st.session_state.video_processed:
+            if uploaded_file is not None:
+                # Save to temp file
+                tfile = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+                tfile.write(uploaded_file.read())
+
+                cap = cv2.VideoCapture(tfile.name)
+                total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))  # Total number of frames
+                width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                fps = cap.get(cv2.CAP_PROP_FPS)
+
+                out_path = os.path.join(tempfile.gettempdir(), "predicted_video.mp4")
+                out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*'XVID'), fps, (width, height))
+
+                frame_count = 0  # To track the number of processed frames
+
+                with st.spinner("Processing video... ⏳"):
+                    progress_bar = st.progress(0)  # Create a progress bar
 
-        # Display st.camera_input if camera is not started yet
-        if st.session_state.show_button:
-            st.camera_input("")
+                    while cap.isOpened():
+                        ret, frame = cap.read()
+                        if not ret:
+                            break
 
-        frame_placeholder = st.empty()
+                        # Run YOLO model on the frame
+                        results = self.model(frame)[0]
 
-        # Run real-time detection if camera is active
-        if st.session_state.camera_active:
-            cap = cv2.VideoCapture(0)
-            while cap.isOpened():
-                ret, frame = cap.read()
-                if not ret or not st.session_state.camera_active:
-                    break
+                        for result in results.boxes.data.tolist():
+                            x1, y1, x2, y2, score, _ = result
+                            color = (0, 0, 255) if score > 0.5 else (0, 255, 0)
+                            label = f"{score:.2f}"
+                            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), color, 1)
+                            cv2.putText(frame, label, (int(x1), int(y1)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1)
 
-                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                results = self.model(frame)[0]
+                        out.write(frame)
 
-                for result in results.boxes.data.tolist():
-                    x1, y1, x2, y2, score, class_id = result
-                    color = (0, 0, 255) if score > 0.5 else (0, 255, 0)
-                    cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
+                        # Update the progress bar
+                        frame_count += 1
+                        progress_percentage = frame_count / total_frames  # Calculate the percentage
+                        progress_bar.progress(progress_percentage)  # Update the progress bar
 
-                frame_placeholder.image(frame, channels="RGB", use_container_width=True)
-                time.sleep(0.1)
+                cap.release()
+                out.release()
+                cv2.destroyAllWindows()
+                st.session_state.video_processed = True
+                st.success("✅ Detection complete!")
 
-            cap.release()
-            st.warning("Camera stopped.")
+                # Read and display the video
+                with open(out_path, 'rb') as video_file:
+                    video_bytes = video_file.read()
+                    st.video(uploaded_file, loop=True, autoplay=True, muted=False)
+                    st.download_button("📥 Download Predicted Video", video_bytes, file_name="predicted_video.mp4",
+                                       mime="video/mp4")