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import gradio as gr
import torch
import cv2
import pytesseract
import numpy as np
from PIL import Image
from ultralytics import YOLO
# Load model
model = YOLO("/home/user/app/best.pt")
# Label map
label_map = {0: "Analog", 1: "Digital", 2: "Non-LP"}
def process_frame(frame):
# Resize to YOLO input shape
input_img = cv2.resize(frame, (640, 640))
results = model(input_img)[0]
detections = results.boxes.data.cpu().numpy()
extracted_texts = []
confidences = []
for det in detections:
if len(det) < 6:
continue
x1, y1, x2, y2, conf, cls = det
x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])
label = label_map.get(int(cls), "Unknown")
percent = f"{conf * 100:.2f}%"
# Draw box and label on image
cv2.rectangle(input_img, (x1, y1), (x2, y2), (255, 0, 0), 2)
cv2.putText(input_img, f"{label}: {percent}", (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
# OCR
cropped = frame[y1:y2, x1:x2] # Use original frame for OCR
if cropped.size > 0:
gray = cv2.cvtColor(cropped, cv2.COLOR_BGR2GRAY)
text = pytesseract.image_to_string(gray, config="--psm 6 -l ben")
extracted_texts.append(text.strip())
confidences.append(percent)
# Convert to PIL
annotated = cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
pil_img = Image.fromarray(annotated)
return pil_img, "\n".join(extracted_texts), ", ".join(confidences)
def process_input(input_file):
file_path = input_file.name
if file_path.endswith(('.mp4', '.avi', '.mov')):
cap = cv2.VideoCapture(file_path)
ret, frame = cap.read()
cap.release()
if not ret:
return None, "Couldn't read video", ""
else:
frame = cv2.imread(file_path)
if frame is None:
return None, "Invalid image", ""
return process_frame(frame)
interface = gr.Interface(
fn=process_input,
inputs=gr.File(type="filepath", label="Upload Image or Video"),
outputs=[
gr.Image(type="pil", label="Detected Output"),
gr.Textbox(label="Detected Text (Bangla)"),
gr.Textbox(label="Confidence (%)")
],
title="YOLOv10n License Plate Detector (Bangla)",
description="Upload an image or video. Detects license plates and extracts Bangla text using OCR."
)
interface.launch()
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