{"id":1496,"date":"2024-05-17T05:35:25","date_gmt":"2024-05-17T05:35:25","guid":{"rendered":"https:\/\/www.nicekj.com\/?p=1496"},"modified":"2024-05-17T05:35:33","modified_gmt":"2024-05-17T05:35:33","slug":"wodeaixuexizhilunianduzongjie","status":"publish","type":"post","link":"https:\/\/www.nicekj.com\/wodeaixuexizhilunianduzongjie.html","title":{"rendered":"\u6211\u7684AI\u5b66\u4e60\u4e4b\u65c5\u5e74\u5ea6\u603b\u7ed3"},"content":{"rendered":"

1.\u524d\u8a00<\/h1>\n

\u4ece1956\u5e74AI\u6982\u5ff5\u7684\u63d0\u51fa\u81f3\u4eca\uff0c\u4eba\u5de5\u667a\u80fd\u6280\u672f\u5df2\u53d1\u5c55\u4e8660\u591a\u5e74\uff0c22\u5e7412\u6708\u5e95ChatGPT\u7684\u6a2a\u7a7a\u51fa\u4e16\u4f7f\u5f97\u751f\u6210\u5f0fAI\u7684\u5168\u7403\u7206\u706b\uff0cChatGPT\u7684\u706b\u7206\u51fa\u5708\uff0c\u4e5f\u76f8\u7ee7\u51fa\u73b0\u4e86\u6587\u5fc3\u4e00\u8a00\u3001Midjourney\u7b49\u521b\u65b0\u6027\u7684 AI \u4ea7\u54c1\uff0c\u4e92\u8054\u7f51\u6380\u8d77\u7684 AI \u98ce\u66b4\u53ef\u4ee5\u8bf4\u5df2\u7ecf\u5e2d\u5377\u4e86\u5168\u7403\u3002<\/p>\n

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2.\u9879\u76ee\u603b\u7ed3\u4e0e\u5fc3\u5f97<\/h1>\n

\u5e74\u521d\u51c6\u5907\u8fdb\u884c\u5b66\u4e60AI\u7684\u65f6\u5019\uff0c\u6070\u5de7\u78b0\u5230\u9047\u5230\u5b66\u6821\u4e5f\u5f00\u8bbe\u4e86\u8fd9\u95e8\u9009\u4fee\u8bfe\u7a0b\uff0c\u81ea\u7136\u662f\u975e\u5e38\u9ad8\u5174\u7684\u53c2\u52a0\u4e86\u8bfe\u7a0b\u7684\u5b66\u4e60\u3002\u901a\u8fc7\u81ea\u5df1\u548c\u5b66\u4e60\u5c0f\u7ec4\u7684\u5171\u540c\u51b3\u5b9a\uff0c\u6211\u4eec\u9009\u62e9\u4e86\u6df1\u5ea6\u5b66\u4e60\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u65b9\u5411\u8fdb\u884c\u4e86\u5b66\u4e60\uff0c\u5e76\u4e14\u4e5f\u8fdb\u884c\u4e86\u5b9e\u8df5\uff0c\u5bf9\u4e8e\u4e00\u4e2aAI\u5c0f\u767d\u6765\u8bf4\u6548\u679c\u8fd8\u662f\u4e0d\u9519\u7684\u3002<\/p>\n

\u6df1\u5ea6\u5b66\u4e60\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u662fAI\u9886\u57df\u4e24\u4e2a\u6bd4\u8f83\u6838\u5fc3\u7684\u6a21\u5757\uff0c\u662f\u76f8\u4e92\u5173\u8054\u7684\uff0c\u4e5f\u7ecf\u5e38\u4e00\u8d77\u88ab\u7528\u4e8e\u5b9e\u9645\u95ee\u9898\u4e2d\uff0c\u6211\u4eec\u5c0f\u7ec4\u8bfe\u9898\u662f\u7814\u53d1\u4e00\u6b3e\u5f71\u54cd\u8bc6\u522b\u529f\u80fd\u7684\u65b9\u6848\u3002\u53ef\u80fd\u662f\u6bd4\u8f83\u611f\u5174\u8da3\u7684\u539f\u56e0\uff0c\u6211\u4eec\u8bfe\u4e0b\u4e5f\u7ecf\u5e38\u4e00\u8d77\u5b66\u4e60\u63a2\u8ba8\uff0c\u5171\u540c\u8fdb\u6b65\u3002\u4e0b\u9762\u5927\u81f4\u603b\u7ed3\u9879\u76ee\u4e2d\u7684\u4e00\u4e9b\u77e5\u8bc6\u3002<\/p>\n

\u3000\u3000\u6211\u4eec\u7684\u8bbe\u8ba1\u601d\u8def\u662f\uff0c\u7b2c\u4e00\u6b65\u5148\u8fdb\u884c\u6570\u636e\u6536\u96c6\u548c\u4e0e\u5904\u7406\u5de5\u4f5c\u3002<\/p>\n

\u3000\u3000\u5f71\u50cf\u8bc6\u522b\u4e00\u822c\u5c31\u5305\u62ec\u4e00\u4e9b\u533b\u5b66\u5f71\u50cf\uff0c\u6bd4\u5982X\u5c04\u7ebf\uff0cMRI\u7b49\u7b49\u5f71\u50cf\u6570\u636e\uff0c\u8fd9\u4e9b\u6570\u636e\u5f88\u597d\u83b7\u5f97\uff0c\u6211\u4eec\u5c0f\u7ec4\u662f\u53bb\u5b66\u6821\u9644\u8fd1\u7684\u533b\u9662\u8fdb\u884c\u6c9f\u901a\uff0c\u83b7\u5f97\u4e86\u4e00\u4e9b\u5e9f\u5f03\u7684\u5f71\u50cf\u6570\u636e\u7b49\uff0c\u6216\u8005\u4ece\u7f51\u7edc\u62c9\u53bb\u4e5f\u53ef\uff0c\u65b9\u5f0f\u591a\u6837\u3002\u6536\u96c6\u6570\u636e\u540e\u8fdb\u884c\u6570\u636e\u7684\u6807\u6ce8\uff0c\u6bd4\u5982\u75be\u75c5\u90e8\u4f4d\u6216\u662f\u5f02\u5e38\u60c5\u51b5\uff0c\u4f5c\u4e3a\u6df1\u5ea6\u5b66\u4e60\u7b97\u6cd5\u7684\u8bad\u7ec3\u6807\u7b7e\u3002\u4e4b\u540e\u5c31\u662f\u9884\u5904\u7406\u5de5\u4f5c\uff0c\u8fd9\u91cc\u8bbe\u8ba1\u7684\u6bd4\u8f83\u590d\u6742\uff0c\u5305\u62ec\u53bb\u566a\uff0c\u5f52\u4e00\u5316\uff0c\u88c1\u526a\u7b49\u7b49\u64cd\u4f5c\uff0c\u4e5f\u662f\u6bd4\u8f83\u8017\u65f6\u7684\u90e8\u5206\uff0c\u7ed3\u675f\u540e\u6750\u6599\u7528\u4e8e\u6df1\u5ea6\u6a21\u578b\u7684\u8bad\u7ec3\u3002\u8fd9\u91cc\u4e5f\u5e94\u7528\u5230\u4e86\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u4e00\u4e9b\u77e5\u8bc6\uff0c\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u7684\u5c31\u662f\u7cfb\u7edf\u53ef\u4ee5\u81ea\u52a8\u63d0\u53d6\u5f71\u50cf\u7279\u5f81\u3002<\/p>\n

\u8fd9\u91cc\u6211\u7b80\u5355\u6f14\u793a\u5982\u4f55\u8fdb\u884cMRI\u5f71\u50cf\u6570\u636e\u7684\u6807\u6ce8\u548c\u9884\u5904\u7406\u3002<\/p>\n

<\/div>
ini<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import numpy as npimport cv2<\/span>\n#\u6807\u6ce8<\/span><\/span>\ndef annotate_image(image, annotations):<\/span>\n    annotated_image<\/span> = image.copy()<\/span>\n    for annotation in annotations:<\/span>\n        #\u5728\u5f71\u50cf\u4e0a\u7ed8\u5236\u77e9\u5f62\u8fb9\u754c\u6846<\/span><\/span>\n        x, y, w, h<\/span> = annotation<\/span>\n        cv2.rectangle(annotated_image, (x, y), (x+w, y+h), (0, 255, 0), 2)<\/span>\n    return annotated_image<\/span>\n#\u9884\u5904\u7406<\/span><\/span>\ndef preprocess_image(image):<\/span>\n    #\u4f7f\u7528\u9ad8\u65af\u6ee4\u6ce2\u53bb\u566a<\/span><\/span>\n    denoised_image<\/span> = cv2.GaussianBlur(image, (3<\/span>, 3<\/span>), 0<\/span>)<\/span>\n    #\u5c06\u50cf\u7d20\u503c\u6620\u5c04\u5230[0, 1]\u8303\u56f4<\/span><\/span>\n    normalized_image<\/span>=cv2.normalize(denoised_image,None,0<\/span>,1<\/span>,cv2.NORM_MINMAX, dtype=cv2.CV_32F)<\/span>\n    #\u6839\u636e\u9700\u8981\u8fdb\u884c\u88c1\u526a\u64cd\u4f5c<\/span><\/span>\n    cropped_image<\/span> = normalized_image[100<\/span>:300<\/span>, 100<\/span>:300<\/span>]<\/span>\n    return cropped_image<\/span>\n<\/span>\n#\u52a0\u8f7d\u539f\u59cb\u5f71\u50cf\u6570\u636e<\/span><\/span>\nimage<\/span> = cv2.imread('mri_image.jpg'<\/span>, 0<\/span>)<\/span>\n#\u6807\u6ce8\u6570\u636e<\/span><\/span>\nannotations<\/span> = [(50<\/span>, 50<\/span>, 100<\/span>, 100<\/span>), (200<\/span>, 200<\/span>, 150<\/span>, 150<\/span>)]<\/span>\nannotated_image<\/span> = annotate_image(image, annotations)<\/span>\n#\u9884\u5904\u7406\u6570\u636e<\/span><\/span>\npreprocessed_image<\/span> = preprocess_image(image)<\/span>\n#\u663e\u793a\u7ed3\u679c<\/span><\/span>\ncv2.imshow('Original Image', image)<\/span>\ncv2.imshow('Annotated Image', annotated_image)<\/span>\ncv2.imshow('Preprocessed Image', preprocessed_image)<\/span>\ncv2.waitKey(0)<\/span>\ncv2.destroyAllWindows()<\/span>\n<\/code><\/pre>\n
\u3000\u3000\u5b9e\u8df5\u8fdc\u8fdc\u6bd4\u6211\u4eec\u8bbe\u8ba1\u65f6\u56f0\u96be\uff0c\u8fd9\u4e5f\u662f\u5f88\u6b63\u5e38\u7684\u3002\u6bd5\u7adf\u4ece\u5b9e\u8df5\u8fc7\u7a0b\u4e2d\u6211\u4e5f\u901a\u8fc7\u6392\u9664\u56f0\u96be\u5b66\u4e60\u5230\u4e86\u65b0\u7684\u4e1c\u897f\u3002\u7136\u540e\u662f\u662f\u6211\u4eec\u5b66\u4e60\u7684\u4e3b\u9898\uff0c\u6df1\u5ea6\u5b66\u4e60\u4e86\u3002\u6211\u4eec\u9009\u62e9\u4e86\u6700\u5e38\u7528\u7684\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08CNN\uff09\uff0c\u5b83\u662f\u4e00\u4e2a\u975e\u5e38\u7ecf\u5178\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\uff0c\u5728\u5904\u7406\u56fe\u50cf\u6570\u636e\u65b9\u9762\u8868\u73b0\u4e5f\u5341\u5206\u4f18\u5f02\u3002\u901a\u8fc7\u4f7f\u7528\u6570\u636e\u5bf9\u6df1\u5ea6\u6a21\u578b\u8fdb\u884c\u8bad\u7ec3\u540e\uff0c\u901a\u8fc7\u8fed\u4ee3\u6a21\u578b\u53c2\u6570\uff0c\u5c31\u80fd\u66f4\u51c6\u786e\u7684\u5b66\u4e60\u56fe\u50cf\u7279\u5f81\u5e76\u8fdb\u884c\u4e00\u4e9b\u8bca\u65ad\u9884\u6d4b\u4e86\u3002\u8bf4\u8d77\u6765\u51e0\u7b14\u5e26\u8fc7\u7684\u8fc7\u7a0b\uff0c\u5176\u5b9e\u4e5f\u82b1\u4e86\u4e0d\u5c11\u7cbe\u529b\uff0c\u4f46\u662f\u5feb\u6709\u6210\u679c\u7684\u65f6\u5019\uff0c\u603b\u80fd\u6fc0\u52b1\u6211\u4eec\u66f4\u52a0\u52aa\u529b\u3002<\/p>\n
\n
\"\u6211\u7684AI\u5b66\u4e60\u4e4b\u65c5\u5e74\u5ea6\u603b\u7ed3\"<\/figure>\n<\/p>\n
\u5728\u6a21\u578b\u90e8\u7f72\u548c\u4f18\u5316\u65b9\u9762\uff0c\u6211\u4eec\u9009\u62e9\u4e86 Distribution of OpenVINO\u2122\u5de5\u5177\u5957\u4ef6\u6765\u8fdb\u884c\u6a21\u578b\u7684\u90e8\u7f72\uff0c\u5e76\u4e14\u5229\u7528Intel\u00ae VTune\u2122 Profiler\u5bf9\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u4e86\u6027\u80fd\u4f18\u5316\uff0c\u63d0\u9ad8\u4e86\u8ba1\u7b97\u6548\u7387\u3002<\/p>\n
\u5927\u81f4\u6b65\u9aa4\uff1a<\/p>\n
1.\u5b89\u88c5Intel\u00ae Distribution for Python\u548cIntel\u00ae oneAPI Base Toolkit\uff0c\u786e\u4fdd\u5b89\u88c5\u9002\u5408\u7684CPU\u7684\u4f18\u5316\u5e93\u548c\u9a71\u52a8\u7a0b\u5e8f\u3002<\/p>\n
2.\u4f7f\u7528Intel\u00ae VTune\u2122 Profiler\u5bf9\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u8fdb\u884c\u6027\u80fd\u5206\u6790\uff0c\u4ee5\u8bc6\u522b\u74f6\u9888\u5e76\u8c03\u6574\u53c2\u6570\u3002<\/p>\n
<\/div>
ini<\/span>
\u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import tensorflow as tffrom tensorflow.keras import layers, modelsfrom keras.applications.vgg16 import VGG16import numpy as npfrom PIL import Imageimport time<\/span>\n#\u52a0\u8f7d<\/span><\/span>\nvgg_model<\/span> = VGG16(weights='imagenet'<\/span>)<\/span>\n#\u8f93\u5165\u6570\u636e<\/span><\/span>\nimg_path<\/span> = 'test.jpg'<\/span><\/span>\nimg<\/span> = Image.open(img_path)<\/span>\nimg<\/span> = img.resize((224<\/span>, 224<\/span>))<\/span>\nx<\/span> = np.asarray(img)<\/span>\nx<\/span> = np.expand_dims(x, axis=0<\/span>)<\/span>\n#\u8fdb\u884c\u6027\u80fd\u5206\u6790with tf.device('\/CPU:0'):<\/span><\/span>\n    start_time<\/span> = time.time()<\/span>\n    preds<\/span> = vgg_model.predict(x)<\/span>\n    end_time<\/span> = time.time()<\/span>\n<\/code><\/pre>\n
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