Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,60 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
from ultralytics import YOLO
|
3 |
+
import cv2
|
4 |
+
from deep_sort_realtime.deepsort_tracker import DeepSort
|
5 |
+
import tempfile
|
6 |
+
|
7 |
+
# Initialize YOLO model
|
8 |
+
model = YOLO("yolov8l.pt") # Load YOLOv8 model
|
9 |
+
tracker = DeepSort(max_age=30, n_init=3, nn_budget=100)
|
10 |
+
|
11 |
+
def count_people_in_video(video_file):
|
12 |
+
cap = cv2.VideoCapture(video_file) # Load video
|
13 |
+
total_ids = set() # Track unique IDs
|
14 |
+
|
15 |
+
while cap.isOpened():
|
16 |
+
ret, frame = cap.read()
|
17 |
+
if not ret:
|
18 |
+
break
|
19 |
+
|
20 |
+
# Run YOLO inference on the frame
|
21 |
+
results = model(frame)
|
22 |
+
detections = []
|
23 |
+
|
24 |
+
# Parse YOLO detections
|
25 |
+
for result in results:
|
26 |
+
for box, cls, conf in zip(result.boxes.xyxy, result.boxes.cls, result.boxes.conf):
|
27 |
+
if result.names[int(cls)] == "person" and conf > 0.5: # Detect "person" class
|
28 |
+
x1, y1, x2, y2 = map(int, box)
|
29 |
+
bbox = [x1, y1, x2 - x1, y2 - y1] # Convert to [x, y, width, height]
|
30 |
+
detections.append((bbox, conf, "person"))
|
31 |
+
|
32 |
+
# Update DeepSORT tracker with detections
|
33 |
+
tracks = tracker.update_tracks(detections, frame=frame)
|
34 |
+
|
35 |
+
# Add unique IDs from confirmed tracks
|
36 |
+
for track in tracks:
|
37 |
+
if track.is_confirmed():
|
38 |
+
total_ids.add(track.track_id)
|
39 |
+
|
40 |
+
cap.release()
|
41 |
+
return len(total_ids)
|
42 |
+
|
43 |
+
# Gradio Interface
|
44 |
+
def process_video(video_file):
|
45 |
+
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
|
46 |
+
temp_file.write(video_file.read())
|
47 |
+
temp_file.flush()
|
48 |
+
total_people = count_people_in_video(temp_file.name)
|
49 |
+
return f"Total unique people in the video: {total_people}"
|
50 |
+
|
51 |
+
interface = gr.Interface(
|
52 |
+
fn=process_video,
|
53 |
+
inputs=gr.Video(label="Upload a Video"),
|
54 |
+
outputs="text",
|
55 |
+
title="Person Counting with YOLOv8 and DeepSORT",
|
56 |
+
description="Upload a video to count the number of unique people using YOLOv8 and DeepSORT."
|
57 |
+
)
|
58 |
+
|
59 |
+
if __name__ == "__main__":
|
60 |
+
interface.launch()
|