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