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import gradio as gr |
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import cv2 |
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import joblib |
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from skimage.feature import hog |
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import numpy as np |
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MODEL_PATH = 'models/hog_lreg_model_4.pkl' |
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clf = joblib.load(MODEL_PATH) |
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def ocr(pil_image): |
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im = np.array(pil_image) |
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im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) |
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im_gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) |
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ret, im_th = cv2.threshold(im_gray, 120, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) |
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ctrs, hier = cv2.findContours(im_th, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) |
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bboxes = [cv2.boundingRect(c) for c in ctrs] |
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sorted_bboxes = sorted(bboxes, key=lambda b: b[0]) |
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plate_char = [] |
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image_height, image_width = im.shape[:2] |
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height_threshold = image_height * 0.3 |
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width_threshold = image_width * 0.3 |
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for num, i_bboxes in enumerate(sorted_bboxes): |
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[x, y, w, h] = i_bboxes |
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if h > height_threshold and w < width_threshold: |
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roi = im_gray[y:y + h, x:x + w] |
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roi = cv2.resize(roi, (64, 128), interpolation=cv2.INTER_AREA) |
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roi_hog_fd = hog(roi, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(1, 1)) |
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nbr = clf.predict(np.array([roi_hog_fd])) |
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plate_char.append(str(nbr[0])) |
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return ''.join(plate_char) |
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interface = gr.Interface( |
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fn=ocr, |
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inputs=gr.Image(type="pil", label="Upload License Plate Image"), |
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outputs=gr.Textbox(label="Predicted License Plate"), |
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title="Automatic License Plate Recognition", |
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description="Upload an image of a license plate, and the system will predict the text on it.", |
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) |
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interface.launch() |
