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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()

requirements.txt

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+absl-py==2.1.0
+altair==5.2.0
+attrs==23.2.0
+blinker==1.7.0
+boto3==1.34.64
+botocore==1.34.64
+cachetools==5.3.3
+certifi==2023.7.22
+chardet==4.0.0
+charset-normalizer==3.3.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.1.1
+cycler==0.10.0
+decorator==4.4.2
+ffmpeg==1.4
+filelock==3.13.1
+filterpy==1.4.5
+fire==0.6.0
+fonttools==4.50.0
+fsspec==2024.3.0
+gitdb==4.0.11
+GitPython==3.1.42
+google-auth==2.28.2
+google-auth-oauthlib==1.0.0
+grpcio==1.62.1
+huggingface-hub==0.21.4
+idna==2.10
+imageio==2.34.0
+imageio-ffmpeg==0.4.9
+importlib_metadata==7.0.2
+importlib_resources==6.3.1
+Jinja2==3.1.3
+jmespath==1.0.1
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+lazy_loader==0.3
+Markdown==3.6
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.7.5
+mdurl==0.1.2
+moviepy==1.0.3
+mpmath==1.3.0
+networkx==3.1
+numpy==1.24.4
+oauthlib==3.2.2
+opencv-python==4.7.0.72
+opencv-python-headless==4.8.0.74
+packaging==23.2
+pandas==2.0.3
+pillow==10.2.0
+pkgutil_resolve_name==1.3.10
+proglog==0.1.10
+protobuf==4.25.3
+psutil==5.9.8
+py-cpuinfo==9.0.0
+pyarrow==15.0.1
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pybboxes==0.1.6
+pydeck==0.8.1b0
+Pygments==2.17.2
+pyparsing==3.1.2
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-magic==0.4.27
+pytz==2024.1
+PyWavelets==1.4.1
+PyYAML==6.0.1
+referencing==0.34.0
+requests==2.31.0
+requests-oauthlib==1.4.0
+requests-toolbelt==1.0.0
+rich==13.7.1
+roboflow==1.1.24
+rpds-py==0.18.0
+rsa==4.9
+s3transfer==0.10.1
+sahi==0.11.15
+scikit-image==0.21.0
+scipy==1.10.1
+seaborn==0.13.2
+shapely==2.0.3
+six==1.16.0
+smmap==5.0.1
+streamlit==1.32.2
+sympy==1.12
+tenacity==8.2.3
+tensorboard==2.14.0
+tensorboard-data-server==0.7.2
+termcolor==2.4.0
+terminaltables==3.1.10
+thop==0.1.1.post2209072238
+tifffile==2023.7.10
+toml==0.10.2
+toolz==0.12.1
+torch==2.2.1
+torchaudio==2.2.1
+torchvision==0.17.1
+tornado==6.4
+tqdm==4.66.2
+typing_extensions==4.10.0
+tzdata==2024.1
+ultralytics==8.1.29
+urllib3==1.26.18
+watchdog==4.0.0
+Werkzeug==3.0.1
+yolov5==7.0.13
+zipp==3.18.1