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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+preset_videos/soccer.mp4 filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,118 @@
+import streamlit as st
+import os
+import cv2
+import time
+import tempfile
+
+from ultralytics import YOLO
+from huggingface_hub import hf_hub_url, cached_download
+
+@st.cache_resource
+def load_model():
+
+    repo_id = 'navoditamathur/Soccer_yolo'
+    model_filename = 'soccer_ball.pt'
+    # Create a URL for the model file on the Hugging Face Hub
+    model_url = hf_hub_url(repo_id, model_filename)
+
+    # Download the model file from the Hub and cache it locally
+    cached_model_path = cached_download(model_url)
+
+    # Rename the file to have a .pt extension
+    new_cached_model_path = f"{cached_model_path}.pt"
+    os.rename(cached_model_path, new_cached_model_path)
+
+    print(f"Downloaded model to {new_cached_model_path}")
+
+    # Load the model using YOLO from the cached model file
+    return YOLO(new_cached_model_path)
+
+def process_video(video_path, output_path):
+  cap = cv2.VideoCapture(video_path)
+  width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+  height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+  fps = int(cap.get(cv2.CAP_PROP_FPS))
+  total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+  fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+  out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+  progress_text = "Please wait..."
+  progress_bar = st.progress(0)
+  progress_bar.text(progress_text)
+
+  status_text = st.empty()
+  time_text = st.empty()  
+
+  start_time = time.time()
+
+  for i in range(total_frames):
+    ret, frame = cap.read()
+    if not ret:
+      break
+
+    boxes = model(frame)
+    annotated_frame = boxes[0].plot()
+    out.write(annotated_frame)
+
+    progress = (i + 1) / total_frames
+    progress_bar.progress(progress)
+
+    elasped_time = time.time() - start_time
+    time_per_frame = elasped_time / (i + 1)
+    remaining_time = (total_frames - (i + 1)) * time_per_frame
+
+    status_text.text(f"Processing frame {i + 1} of {total_frames}")
+    time_text.text(f"Time remaining: {remaining_time:.2f} seconds")
+  cap.release()
+  out.release()
+  status_text.text("Video processing completed.")
+  progress_bar.empty()
+  time_text.empty()
+
+
+model = load_model()
+st.title("Soccer Ball Detection App")
+
+# Sidebar for options
+st.sidebar.header("Options")
+video_option = st.sidebar.radio("Choose video source:", ("Use preset video", "Upload video"))
+
+if video_option == "Upload video":
+    uploaded_file = st.sidebar.file_uploader("Choose a video file", type=["mp4", "avi", "mov"])
+    if uploaded_file is not None:
+        tfile = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+        tfile.write(uploaded_file.read())
+        video_path = tfile.name
+else:
+        preset_videos = {
+            "Soccer Video": "preset_videos/soccer.mp4"
+        }
+        selected_video = st.sidebar.selectbox("Select a preset video", list(preset_videos.keys()))
+        video_path = preset_videos[selected_video]
+
+if 'video_path' in locals():
+    st.header("Original Video")
+    st.video(video_path)
+
+if st.button("Detect"):
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    output_path = temp_file.name
+    process_video(video_path, output_path)
+    with open(output_path, 'rb') as video_file:
+        video_bytes = video_file.read()
+        st.header("Detected Video")
+        
+        # Debugging: Display video size
+        st.write(f"Processed video size: {len(video_bytes)} bytes")
+
+        if len(video_bytes) > 0:
+            st.video(video_bytes)
+            
+        # Generate a download button
+        btn = st.download_button(
+            label="Download Processed Video",
+            data=video_bytes,
+            file_name="processed_video.mp4",
+            mime="video/mp4"
+        )

preset_videos/soccer.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6a565de9d3440544fb3592e952ce13e4fa3eadf1cc3f3feb785a89f34b756d1
+size 6412733

requirements.txt

@@ -0,0 +1,5 @@
+streamlit==1.25.0
+torch==2.0.1
+ultralytics==8.0.109
+opencv-python==4.8.0.76
+Pillow==9.4.0