Create app.py

app.py

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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()
+