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import streamlit as st
from ultralytics import YOLO
import cv2
import tempfile
import os
from gtts import gTTS
# Load YOLOv8 model
@st.cache_resource
def load_model():
return YOLO('yolov8n.pt') # Automatically downloads YOLOv8 pre-trained model
model = load_model()
# Streamlit app title
st.title("Object Detection in Video")
st.write("Upload a video, and the application will detect and label objects frame by frame, and generate a summary.")
# File uploader
uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])
if uploaded_video:
# Save the uploaded video to a temporary file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
temp_file.write(uploaded_video.read())
video_path = temp_file.name
# Open the video file
video = cv2.VideoCapture(video_path)
frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(video.get(cv2.CAP_PROP_FPS))
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
# Create an output video file
output_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
out = cv2.VideoWriter(output_file.name, fourcc, fps, (frame_width, frame_height))
# Initialize a set to collect unique detected objects
detected_objects = set()
# Process video frame by frame
st.write("Processing video...")
progress_bar = st.progress(0)
for i in range(total_frames):
ret, frame = video.read()
if not ret:
break
# Object detection on the current frame
results = model(frame)
# Collect unique object names
detected_objects.update([model.names[int(box.cls)] for box in results[0].boxes])
# Annotate frame with bounding boxes
annotated_frame = results[0].plot()
# Write annotated frame to the output video
out.write(annotated_frame)
# Update progress bar
progress_bar.progress((i + 1) / total_frames)
# Release resources
video.release()
out.release()
# Generate text summary
if detected_objects:
detected_objects_list = ", ".join(detected_objects)
summary_text = f"In this video, the following objects were detected: {detected_objects_list}."
else:
summary_text = "No objects were detected in the video."
st.write("Summary:")
st.write(summary_text)
# Generate audio summary using gTTS
tts = gTTS(text=summary_text, lang='en')
audio_file = os.path.join(tempfile.gettempdir(), "summary.mp3")
tts.save(audio_file)
# Display the output video
st.write("Video processing complete! Download or watch the labeled video below:")
st.video(output_file.name)
st.download_button(
label="Download Labeled Video",
data=open(output_file.name, "rb").read(),
file_name="labeled_video.mp4",
mime="video/mp4"
)
# Provide audio playback
st.audio(audio_file, format="audio/mp3")
st.download_button(
label="Download Audio Summary",
data=open(audio_file, "rb").read(),
file_name="summary.mp3",
mime="audio/mp3"
)
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