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85 lines
2.9 KiB
85 lines
2.9 KiB
import gradio as gr
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import cv2
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import time
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from ultralytics import YOLO
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from paddleocr import PaddleOCR
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import numpy as np
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import detect_tools as tools
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from imgTest import get_license_result
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import os
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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# 加载YOLOv8检测模型
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model_path = 'models/best.pt'
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yolo_model = YOLO(model_path, task='detect')
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# 加载车牌识别模型
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cls_model_dir = 'paddleModels/whl/cls/ch_ppocr_mobile_v2.0_cls_infer'
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rec_model_dir = 'paddleModels/whl/rec/ch/ch_PP-OCRv4_rec_infer'
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ocr = PaddleOCR(use_angle_cls=False, lang="ch", det=False, cls_model_dir=cls_model_dir, rec_model_dir=rec_model_dir)
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def Car_detection():
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def predict_image(query_image):
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start_time = time.time()
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# 图像预处理
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img = cv2.cvtColor(query_image, cv2.COLOR_BGR2RGB)
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print(f"Image preprocessing time: {time.time() - start_time:.2f}s")
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# 使用YOLOv8检测车辆和车牌位置
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yolo_start = time.time()
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results = yolo_model(img)[0]
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yolo_output = img.copy() # 复制原图像用于显示YOLO结果
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location_list = results.boxes.xyxy.tolist()
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print(f"YOLO detection time: {time.time() - yolo_start:.2f}s")
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# 处理每个检测到的车牌区域
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license_numbers = []
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for location in location_list:
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x1, y1, x2, y2 = list(map(int, location))
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crop_img = img[y1:y2, x1:x2]
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# 使用PaddleOCR识别车牌号
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license_num, confidence = get_license_result(ocr, crop_img)
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if license_num:
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license_numbers.append(license_num)
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else:
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license_numbers.append("无法识别")
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# 在YOLO结果图上绘制检测框
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cv2.rectangle(yolo_output, (x1, y1), (x2, y2), (0, 255, 0), 2)
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return yolo_output, "\n".join(license_numbers)
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title = "<h1 align='center'>基于Opencv图像处理的车牌检测与识别</h1>"
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description = "上传一张包含车辆的图像，系统将检测车辆并识别车牌号码"
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# examples = [['images/car.jpg'], ['images/car.png'], ['images/car_test.jpg']]
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with gr.Blocks() as demo:
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gr.Markdown(title)
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gr.Markdown(description)
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with gr.Row():
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with gr.Column(scale=1):
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img = gr.components.Image(label="上传图片")
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btn = gr.Button("点击检测与识别")
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with gr.Column(scale=1):
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out_1 = gr.components.Image(label="YOLO定位结果:", height="auto")
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out_2 = gr.components.Textbox(label="车牌识别结果:", type="text", lines=6)
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inputs = [img]
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outputs = [out_1, out_2]
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btn.click(fn=predict_image, inputs=inputs, outputs=outputs)
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# gr.Examples(examples, inputs=inputs)
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return demo
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if __name__ == "__main__":
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with gr.TabbedInterface(
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[Car_detection()],
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["Opencv车牌检测与识别"],
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) as demo:
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demo.launch(show_api=False, inbrowser=True)
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