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import os
os.system("pip install ultralytics")
from ultralytics import YOLO
import cv2
import gradio as gr
import numpy as np
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO("yolov8n.pt") # Using pre-trained YOLOv8 nano model
# Object classes in YOLOv8
CLASS_NAMES = model.names
HUMAN_CLASS_ID = 0 # Class ID for "person" in YOLO
def vid_inf(vid_path, contour_thresh):
cap = cv2.VideoCapture(vid_path)
# Get the video frames' width and height
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_size = (frame_width, frame_height)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
output_video = "output_recorded.mp4"
out = cv2.VideoWriter(output_video, fourcc, fps, frame_size)
backSub = cv2.createBackgroundSubtractorMOG2(history=200, varThreshold=25, detectShadows=True)
if not cap.isOpened():
print("Error opening video file")
return
count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# YOLOv8 Object Detection
results = model(frame)
detected_boxes = []
for result in results:
for box in result.boxes:
class_id = int(box.cls[0].item())
conf = box.conf[0].item()
if class_id != HUMAN_CLASS_ID and conf > 0.5: # Ignore humans, detect other objects
x1, y1, x2, y2 = map(int, box.xyxy[0]) # Bounding box coordinates
detected_boxes.append((x1, y1, x2, y2))
fg_mask = backSub.apply(frame)
retval, mask_thresh = cv2.threshold(fg_mask, 200, 255, cv2.THRESH_BINARY)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
mask_eroded = cv2.morphologyEx(mask_thresh, cv2.MORPH_OPEN, kernel)
contours, _ = cv2.findContours(mask_eroded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
min_contour_area = contour_thresh
large_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > min_contour_area]
frame_out = frame.copy()
# Draw bounding boxes only on non-human moving objects
for cnt in large_contours:
x, y, w, h = cv2.boundingRect(cnt)
for (x1, y1, x2, y2) in detected_boxes:
if x > x1 and y > y1 and (x + w) < x2 and (y + h) < y2: # Ensure it's inside an object
frame_out = cv2.rectangle(frame_out, (x, y), (x + w, y + h), (0, 0, 200), 3)
frame_out_final = cv2.cvtColor(frame_out, cv2.COLOR_BGR2RGB)
out.write(frame_out)
if not count % 12:
yield frame_out_final, None
count += 1
cap.release()
out.release()
cv2.destroyAllWindows()
yield None, output_video
# Gradio interface
input_video = gr.Video(label="Input Video")
contour_thresh = gr.Slider(0, 10000, value=4, label="Contour Threshold", info="Adjust the threshold based on package size.")
output_frames = gr.Image(label="Output Frames")
output_video_file = gr.Video(label="Output Video")
app = gr.Interface(
fn=vid_inf,
inputs=[input_video, contour_thresh],
outputs=[output_frames, output_video_file],
title="Package Tracking using YOLOv8 & Motion Detection",
description="A smart video analysis tool that uses YOLOv8 to track packages while ignoring human movement.",
allow_flagging="never",
examples=[["./sample/car.mp4", "1000"], ["./sample/motion_test.mp4", "5000"], ["./sample/home.mp4", "4500"]],
cache_examples=False,
)
app.queue().launch()
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