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import cv2
import numpy as np
import torch
import gradio as gr
from ultralytics import YOLO
# Load YOLOv12x model
MODEL_PATH = "yolov12x.pt" # Ensure the model is uploaded to the Hugging Face Space
model = YOLO(MODEL_PATH)
# COCO dataset class ID for trucks
TRUCK_CLASS_ID = 7 # "truck"
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_counts = []
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 YOLOv12x 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
# Count only trucks
if class_id == TRUCK_CLASS_ID and confidence > 0.5:
truck_count += 1
truck_counts.append(truck_count)
cap.release()
return {
"Trucks in a Frame": int(np.max(truck_counts)) if truck_counts 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()])
# Define Gradio interface
iface = gr.Interface(
fn=analyze_video,
inputs=gr.Video(label="Upload Video"),
outputs=gr.Textbox(label="Truck Count"),
title="YOLOv12x Truck Counter",
description="Upload a video to count trucks using YOLOv12x."
)
# Launch the Gradio app
if __name__ == "__main__":
iface.launch()
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