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import streamlit as st
import cv2
from collections import defaultdict
import supervision as sv
from ultralytics import YOLO
import os
def ensure_dir(file_path):
if not os.path.exists(file_path):
os.makedirs(file_path)
def process_video(input_video_path, output_video_path):
model = YOLO('yolov8x-seg.pt')
cap = cv2.VideoCapture(input_video_path)
START = sv.Point(1250, -2)
END = sv.Point(1250, 1070)
track_history = defaultdict(list)
crossed_objects = {}
ensure_dir(os.path.dirname(output_video_path))
if cap.isOpened():
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Use H.264 codec
fourcc = cv2.VideoWriter_fourcc(*'avc1') # Alternatively, use 'X264' if 'avc1' doesn't work
out = cv2.VideoWriter(output_video_path, fourcc, 20.0, (width, height), True)
while cap.isOpened():
success, frame = cap.read()
if not success:
break
results = model.track(frame, conf=0.3, classes=[19], persist=True, save=True, tracker="bytetrack.yaml")
boxes = results[0].boxes.xywh.cpu()
track_ids = results[0].boxes.id.int().cpu().tolist()
annotated_frame = results[0].plot() if hasattr(results[0], 'plot') else frame
for box, track_id in zip(boxes, track_ids):
x, y, w, h = box
track = track_history[track_id]
track.append(x)
if len(track) > 1: # Checking if we have at least two points to compare
if track[-2] <= START.x < track[-1] or track[-2] >= START.x > track[-1]:
if track_id not in crossed_objects:
crossed_objects[track_id] = True
cv2.rectangle(annotated_frame, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (0, 255, 0), 2)
cv2.line(annotated_frame, (START.x, START.y), (END.x, END.y), (0, 255, 0), 2)
count_text = f"Objects crossed: {len(crossed_objects)}"
cv2.putText(annotated_frame, count_text, (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 255, 0), 4)
out.write(annotated_frame)
cap.release()
out.release()
def main():
st.title("Video Processing for Object Tracking")
video_file = st.file_uploader("Upload a video", type=['mp4', 'avi'])
if video_file is not None:
output_folder = "output_videos"
ensure_dir(output_folder)
file_path = os.path.join(output_folder, "uploaded_video.mp4")
file_name = os.path.join(output_folder, "processed_video.mp4")
# Save the uploaded file first
with open(file_path, "wb") as f:
f.write(video_file.getbuffer())
if st.button("Process Video"):
process_video(file_path, file_name)
st.video(file_name)
with open(file_name, "rb") as file:
st.download_button(
label="Download processed video",
data=file,
file_name="processed_video.mp4",
mime="video/mp4"
)
if __name__ == "__main__":
main()
=======
import streamlit as st
import cv2
from collections import defaultdict
import supervision as sv
from ultralytics import YOLO
import os
def ensure_dir(file_path):
if not os.path.exists(file_path):
os.makedirs(file_path)
def process_video(input_video_path, output_video_path):
model = YOLO('yolov8x-seg.pt')
cap = cv2.VideoCapture(input_video_path)
START = sv.Point(1250, -2)
END = sv.Point(1250, 1070)
track_history = defaultdict(list)
crossed_objects = {}
ensure_dir(os.path.dirname(output_video_path))
if cap.isOpened():
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Use H.264 codec
fourcc = cv2.VideoWriter_fourcc(*'avc1') # Alternatively, use 'X264' if 'avc1' doesn't work
out = cv2.VideoWriter(output_video_path, fourcc, 20.0, (width, height), True)
while cap.isOpened():
success, frame = cap.read()
if not success:
break
results = model.track(frame, conf=0.3, classes=[19], persist=True, save=True, tracker="bytetrack.yaml")
boxes = results[0].boxes.xywh.cpu()
track_ids = results[0].boxes.id.int().cpu().tolist()
annotated_frame = results[0].plot() if hasattr(results[0], 'plot') else frame
for box, track_id in zip(boxes, track_ids):
x, y, w, h = box
track = track_history[track_id]
track.append(x)
if len(track) > 1: # Checking if we have at least two points to compare
if track[-2] <= START.x < track[-1] or track[-2] >= START.x > track[-1]:
if track_id not in crossed_objects:
crossed_objects[track_id] = True
cv2.rectangle(annotated_frame, (int(x - w / 2), int(y - h / 2)), (int(x + w / 2), int(y + h / 2)), (0, 255, 0), 2)
cv2.line(annotated_frame, (START.x, START.y), (END.x, END.y), (0, 255, 0), 2)
count_text = f"Objects crossed: {len(crossed_objects)}"
cv2.putText(annotated_frame, count_text, (100, 80), cv2.FONT_HERSHEY_SIMPLEX, 3, (0, 255, 0), 4)
out.write(annotated_frame)
cap.release()
out.release()
def main():
st.title("Video Processing for Object Tracking")
video_file = st.file_uploader("Upload a video", type=['mp4', 'avi'])
if video_file is not None:
output_folder = "output_videos"
ensure_dir(output_folder)
file_path = os.path.join(output_folder, "uploaded_video.mp4")
file_name = os.path.join(output_folder, "processed_video.mp4")
# Save the uploaded file first
with open(file_path, "wb") as f:
f.write(video_file.getbuffer())
if st.button("Process Video"):
process_video(file_path, file_name)
st.video(file_name)
with open(file_name, "rb") as file:
st.download_button(
label="Download processed video",
data=file,
file_name="processed_video.mp4",
mime="video/mp4"
)
if __name__ == "__main__":
main()
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