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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| st.title("Object Tracking in Video using SIFT") | |
| # Upload files | |
| uploaded_image = st.file_uploader("Upload an image", type=['png', 'jpg']) | |
| uploaded_video = st.file_uploader("Upload a video", type=['mp4']) | |
| if uploaded_image and uploaded_video: | |
| # Convert uploaded files to OpenCV-compatible formats | |
| image_bytes = np.asarray(bytearray(uploaded_image.read()), dtype=np.uint8) | |
| input_image = cv2.imdecode(image_bytes, cv2.IMREAD_GRAYSCALE) | |
| video_bytes = np.asarray(bytearray(uploaded_video.read()), dtype=np.uint8) | |
| cap = cv2.VideoCapture() | |
| cap.open(uploaded_video.name) | |
| # Initialize SIFT | |
| sift = cv2.SIFT_create() | |
| bf = cv2.BFMatcher() | |
| # Detect keypoints and descriptors for the input image | |
| keypoints_input, descriptors_input = sift.detectAndCompute(input_image, None) | |
| occurrences = 0 | |
| occurrence_start = 0 | |
| occurrence_duration = 0 | |
| prev_matches = [] | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| st.write("End of video reached.") | |
| break | |
| frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| # Detect keypoints and descriptors for the video frame | |
| keypoints_frame, descriptors_frame = sift.detectAndCompute(frame_gray, None) | |
| # Match descriptors | |
| 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 = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000 | |
| occurrences += 1 | |
| prev_matches = good_matches | |
| occurrence_duration = (cap.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 = [] | |
| cap.release() | |