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import gradio as gr
import torch
import cv2
import pytesseract
import numpy as np
from PIL import Image
import sys
import os
import torch
import torch.serialization
from ultralytics import YOLO
from ultralytics.nn.tasks import YOLOv10DetectionModel
# Register the custom class for safe loading
torch.serialization.add_safe_globals({'YOLOv10DetectionModel': YOLOv10DetectionModel})
# Load the model inside a safe context
with torch.serialization.safe_globals({'YOLOv10DetectionModel': YOLOv10DetectionModel}):
model = YOLO('/home/user/app/best.pt') # Load your YOLOv10 model
# Frame processing function
def process_frame(frame):
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
img_tensor = torch.from_numpy(img).permute(2, 0, 1).float() / 255.0
img_tensor = img_tensor.unsqueeze(0)
results = model(img_tensor, augment=False)
results = model.nms(results)[0]
extracted_texts = []
confidences = []
for det in results:
x1, y1, x2, y2, conf, cls = det.tolist()
if conf > 0.5:
x1, y1, x2, y2 = map(int, (x1, y1, x2, y2))
cls = int(cls)
label_map = {0: "Analog", 1: "Digital", 2: "Non-LP"}
label = label_map.get(cls, "Unknown")
percent = f"{conf * 100:.2f}%"
# Draw box & label
cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 0, 0), 2)
cv2.putText(frame, f"{label}: {percent}", (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
# OCR
lp_crop = frame[y1:y2, x1:x2]
gray = cv2.cvtColor(lp_crop, cv2.COLOR_BGR2GRAY)
text = pytesseract.image_to_string(gray, config="--psm 6 -l ben")
extracted_texts.append(text.strip())
confidences.append(percent)
return frame, "\n".join(extracted_texts), ", ".join(confidences)
# Input handler
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", ""
processed_frame, text, confidence = process_frame(frame)
processed_pil = Image.fromarray(cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB))
return processed_pil, text, confidence
# Gradio Interface
interface = gr.Interface(
fn=process_input,
inputs=gr.File(type="file", 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 plates and extracts Bangla text using OCR (CPU)."
)
interface.launch()
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