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app.py

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+import matplotlib
+matplotlib.use('Agg')
+
+import streamlit as st
+import cv2
+import numpy as np
+from yolov5 import YOLOv5
+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 = 'yolov5s.pt'  # Ensure this path points to the model file
+model = YOLOv5(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()

yolov5s.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8b3b748c1e592ddd8868022e8732fde20025197328490623cc16c6f24d0782ee
+size 14808437