Delete app.py

app.py

@@ -1,102 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-
-import streamlit as st
-import cv2
-import numpy as np
-from ultralytics import YOLO
-#from sort.sort import Sort
-import tempfile
-import shutil
-from moviepy.editor import VideoFileClip, concatenate_videoclips, ImageSequenceClip
-import os
-
-# Load the pre-trained model and initialize the SORT tracker
-model_path = 'best.pt'  # Ensure this path points to the model file
-model = YOLO(model_path, device='cpu')
-tracker = Sort()
-
-def process_video(uploaded_file):
-    # Save the uploaded file to a temporary location
-    temp_file_path = "temp_video.mp4"
-    with open(temp_file_path, "wb") as temp_file:
-        temp_file.write(uploaded_file.getbuffer())
-
-    # Use moviepy to read the video file
-    video_clip = VideoFileClip(temp_file_path)
-    total_frames = int(video_clip.fps * video_clip.duration)
-    width, height = video_clip.size
-
-    # Temporary directory to save processed video frames
-    temp_dir = tempfile.mkdtemp()
-
-    unique_cars = set()
-    progress_bar = st.progress(0)
-
-    for frame_idx, frame in enumerate(video_clip.iter_frames()):
-        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-
-        progress_percentage = int((frame_idx + 1) / total_frames * 100)
-        progress_bar.progress(progress_percentage)
-
-        # Detection and tracking logic
-        results = model.predict(frame)
-        preds = results.pandas().xyxy[0]
-        detections = []
-
-        for index, row in preds.iterrows():
-            if row['name'] == 'car':
-                xmin, ymin, xmax, ymax, conf = row['xmin'], row['ymin'], row['xmax'], row['ymax'], row['confidence']
-                detections.append([xmin, ymin, xmax, ymax, conf])
-
-        if detections:
-            detections_np = np.array(detections)
-            trackers = tracker.update(detections_np)
-
-            for d in trackers:
-                unique_cars.add(int(d[4]))
-                xmin, ymin, xmax, ymax = map(int, d[:4])
-                cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
-                cv2.putText(frame, f'ID: {int(d[4])}', (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
-
-        cv2.putText(frame, f'Unique Cars: {len(unique_cars)}', (10, 35), cv2.FONT_HERSHEY_SIMPLEX, 1.25, (0, 255, 0), 2)
-        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)  # Convert back to RGB for moviepy
-        cv2.imwrite(f"{temp_dir}/{frame_idx:04d}.jpg", frame)
-
-    frames_files = [os.path.join(temp_dir, f"{i:04d}.jpg") for i in range(total_frames)]
-    clip = ImageSequenceClip(frames_files, fps=video_clip.fps)
-    output_video_path = 'processed_video.mp4'
-    clip.write_videofile(output_video_path, codec='libx264')  # Use libx264 codec for compatibility
-
-    # Remove temporary directory and temporary files
-    shutil.rmtree(temp_dir)
-
-    return output_video_path
-
-
-def main():
-    # Initialize session state variables if they don't exist
-    if 'output_video_path' not in st.session_state:
-        st.session_state.output_video_path = None
-
-    st.sidebar.image("logo.jpg", use_column_width=True)
-    uploaded_file = st.sidebar.file_uploader("Upload a video", type=['mp4'])
-
-    st.title("Car Detection and Tracking")
-
-    if uploaded_file is not None:
-        # Process the video only if it hasn't been processed yet or a new file is uploaded
-        if st.session_state.output_video_path is None or st.session_state.uploaded_file_name != uploaded_file.name:
-            st.session_state.uploaded_file_name = uploaded_file.name
-            st.session_state.output_video_path = process_video(uploaded_file)
-
-        # Display the processed video
-        st.video(st.session_state.output_video_path)
-
-        # Provide a download link for the processed video
-        with open(st.session_state.output_video_path, "rb") as file:
-            st.download_button("Download Processed Video", file, file_name="processed_video.mp4")
-
-
-if __name__ == "__main__":
-    main()