Update app.py

app.py

@@ -1,64 +1,66 @@
 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()
 
+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!")
+        image = cv2.imdecode(np.fromstring(uploaded_image.read(), np.uint8), cv2.IMREAD_COLOR)
+        video = cv2.VideoCapture(uploaded_video)
+
+        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 video.isOpened():
+            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()