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