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import gradio as gr
import cv2
import easyocr
import numpy as np
import os
from PIL import Image
from ultralytics import YOLO
from datetime import datetime
# Load YOLO model
model = YOLO("/home/user/app/best.pt")
# Label map
label_map = {0: "Analog", 1: "Digital", 2: "Non-LP"}
# EasyOCR Bengali
reader = easyocr.Reader(['bn'])
def annotate_frame(frame):
input_img = cv2.resize(frame, (640, 640))
results = model(input_img)[0]
detections = results.boxes.data.cpu().numpy()
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
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]
if cropped.size > 0:
ocr_result = reader.readtext(cropped)
for i, item in enumerate(ocr_result):
text = item[1].strip()
conf = item[2]
cv2.putText(input_img, text, (x1, y2 + 20 + i*25),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
return cv2.cvtColor(input_img, cv2.COLOR_BGR2RGB)
def process_input(input_file):
file_path = input_file.name
ext = os.path.splitext(file_path)[-1].lower()
if ext in ['.mp4', '.avi', '.mov']:
cap = cv2.VideoCapture(file_path)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# Output path
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
output_path = f"annotated_{timestamp}.mp4"
out = cv2.VideoWriter(output_path, fourcc, fps, (640, 640))
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
annotated = annotate_frame(frame)
annotated_resized = cv2.resize(annotated, (640, 640))
out.write(cv2.cvtColor(annotated_resized, cv2.COLOR_RGB2BGR))
cap.release()
out.release()
return output_path, "", ""
else:
# Image case
frame = cv2.imread(file_path)
if frame is None:
return None, "Invalid image", ""
annotated = annotate_frame(frame)
pil_img = Image.fromarray(annotated)
return pil_img, "", ""
interface = gr.Interface(
fn=process_input,
inputs=gr.File(type="filepath", label="Upload Image or Video"),
outputs=[
gr.Video(label="Output Video or Image") | gr.Image(type="pil", label="Output"),
gr.Textbox(label="Detected Text (Bangla)"),
gr.Textbox(label="Confidence (%)")
],
title="YOLOv5 License Plate Detector (Bangla OCR)",
description="Upload an image or video. Detects license plates and extracts Bangla text using EasyOCR."
)
interface.launch()
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