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import gradio as gr
import cv2
import numpy as np
import torch
from ultralytics import YOLO
# Load YOLOv8 model (pre-trained on COCO dataset)
model = YOLO("yolov8x.pt") # Use "yolov8x.pt" for highest accuracy
# Class label for trucks (COCO dataset)
TRUCK_CLASS_ID = 7 # "truck" in COCO dataset
def count_trucks(video_path):
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
return "Error: Unable to open video file."
frame_count = 0
truck_count_per_frame = []
frame_skip = 5 # Process every 5th frame for efficiency
while True:
ret, frame = cap.read()
if not ret:
break # End of video
frame_count += 1
if frame_count % frame_skip != 0:
continue # Skip frames to improve efficiency
# Run YOLOv8 inference
results = model(frame, verbose=False)
truck_count = 0
for result in results:
for box in result.boxes:
class_id = int(box.cls.item()) # Get class ID
confidence = float(box.conf.item()) # Get confidence score
if class_id == TRUCK_CLASS_ID and confidence > 0.6:
truck_count += 1 # Count trucks with confidence > 0.6
truck_count_per_frame.append(truck_count)
cap.release()
return {
"Total Trucks in Video": int(np.max(truck_count_per_frame)) if truck_count_per_frame else 0 #,
#"Avg Trucks Per Frame": round(np.mean(truck_count_per_frame), 2) if truck_count_per_frame else 0
}
# Gradio UI function
def analyze_video(video_file):
result = count_trucks(video_file)
return "\n".join([f"{key}: {value}" for key, value in result.items()])
# Gradio Interface
interface = gr.Interface(
fn=analyze_video,
inputs=gr.Video(label="Upload Video"),
outputs=gr.Textbox(label="Truck Counting Results"),
title="YOLOv8-based Truck Counter",
description="Upload a video to detect and count trucks using YOLOv8."
)
# Launch app
interface.launch() |