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import gradio as gr
import torch
import cv2
import numpy as np
import pytesseract
from PIL import Image
# YOLOv5 modelini yükle (örnek: yolov5s)
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', force_reload=True)
def detect_and_ocr(image):
# YOLOv5 ile plaka tespiti
results = model(image)
labels, cords = results.xyxyn[0][:, -1], results.xyxyn[0][:, :-1]
img = np.array(image)
h, w, _ = img.shape
plates = []
for i, (label, cord) in enumerate(zip(labels, cords)):
if int(label) == 0: # 0: 'license-plate' olarak eğitilmişse
x1, y1, x2, y2, conf = cord
x1, y1, x2, y2 = int(x1*w), int(y1*h), int(x2*w), int(y2*h)
plate_img = img[y1:y2, x1:x2]
plates.append(plate_img)
if not plates:
return "Plaka bulunamadı."
# OCR
ocr_results = []
for plate in plates:
text = pytesseract.image_to_string(plate, config='--psm 7')
ocr_results.append(text.strip())
return "\n".join(ocr_results)
iface = gr.Interface(
fn=detect_and_ocr,
inputs=gr.Image(source="upload", tool="editor", type="pil", label="Fotoğraf yükle veya çek"),
outputs="text",
title="Araç Plaka Tanıma ve OCR",
description="Kamera ile fotoğraf çek veya yükle, plaka ve yazı otomatik tespit edilsin."
)
iface.launch() |