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import streamlit as st
import cv2
import numpy as np
import tempfile
def main():
st.title("SIFT Object Tracking in Video")
st.sidebar.title("Upload Files")
uploaded_image = st.sidebar.file_uploader("Upload an image (PNG or JPG)", type=["png", "jpg"])
uploaded_video = st.sidebar.file_uploader("Upload a video (MP4)", type=["mp4"])
if uploaded_image and uploaded_video:
st.sidebar.success("Files successfully uploaded!")
# Save the uploaded image to a temporary file
with tempfile.NamedTemporaryFile(suffix='.jpg') as tmp:
tmp.write(uploaded_image.read())
image = cv2.imread(tmp.name)
# Save the uploaded video to a temporary file
with tempfile.NamedTemporaryFile(suffix='.mp4') as tmp:
tmp.write(uploaded_video.read())
video = cv2.VideoCapture(tmp.name)
st.header("Uploaded Image")
st.image(image, caption="Uploaded Image", use_column_width=True)
st.header("Uploaded Video")
st.video(uploaded_video)
# Perform object tracking with SIFT
sift = cv2.SIFT_create()
keypoints_input, descriptors_input = sift.detectAndCompute(image, None)
bf = cv2.BFMatcher()
occurrences = 0
occurrence_start = 0
occurrence_duration = 0
prev_matches = []
while True:
ret, frame = video.read()
if not ret:
st.write("End of video reached.")
break
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
keypoints_frame, descriptors_frame = sift.detectAndCompute(frame_gray, None)
matches = bf.knnMatch(descriptors_input, descriptors_frame, k=2)
good_matches = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good_matches.append(m)
if len(good_matches) >= 6:
if not prev_matches:
occurrence_start = video.get(cv2.CAP_PROP_POS_MSEC) / 1000
occurrences += 1
prev_matches = good_matches
occurrence_duration = (video.get(cv2.CAP_PROP_POS_MSEC) / 1000) - occurrence_start
else:
if prev_matches:
st.write(f"Occurrence {occurrences}: Start time: {occurrence_start:.2f}s, Duration: {occurrence_duration:.2f}s")
prev_matches = []
video.release()
else:
st.sidebar.warning("Please upload both an image and a video.")
if __name__ == "__main__":
main() |