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"# 主題：健康上網AI守門員！ 色情圖片辨識\n",
"### 組員：陳怡升、張睿傑、李志恆<br/>\n",
"[發表簡報請點我](https://docs.google.com/presentation/d/19mwSTLK1vLj8kBd4NtedvkfHCA17YikbTqA5CD2D2Tg/edit?usp=sharing)\n",
"<br>\n",
"### 概述：\n",
"我們打造出一個新型態的<span style=\"color:red\">健康上網守門員</span><br/>透過色情圖片辨識，當偵測到使用者在瀏覽<span style=\"color:red\">色情內容</span>時，將會自動播放歌曲[Big Enough](https://www.youtube.com/watch?v=rvrZJ5C_Nwg)中[牛仔的尖叫片段](https://www.youtube.com/watch?v=Qcp2W1-SFt4)，<br/>然後讓滑鼠亂飄移，並<span style=\"color:red\">按下Alt+F4關閉視窗</span>讓使用者無法正常瀏覽色情內容。\n",
"\n",
"<br/>程式實際使用示範影片：[有碼版本示範影片](https://www.youtube.com/watch?v=AsDYYk-qPA8&feature=youtu.be)\n",
"<br/>同場加映：[上傳到PornHub的無碼版本示範影片](https://www.pornhub.com/view_video.php?viewkey=ph5cfbb88976949) (內含18+畫面，慎入！！)\n",
"\n",
"\n",
"### 目的：\n",
"本來我們想做色情圖片辨識，不過想想辨識出來也要有個用途，靈光一閃就想到這個\"健康上網AI守門員\"的idea<br/>\n",
"我們有空的話，會在暑假將其改良成開機後自動在背景執行，為我國青少年身心健康發展盡一份心力。"
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"### 具體做法：\n",
"* **資料取得：**<br/>\n",
"我們將透過github上的[NSFW數據庫](https://github.com/GantMan/nsfw_model)來取得資料進行訓練。<br/>\n",
"將資料集中corrupted image 移除後剩下的**212038**張圖片被分為5種類別，分別為：<br/>\n",
"\n",
"<br/><br/>\n",
"* **模型訓練：** <br/>\n",
"使用Keras中的<span style=\"color:red\">InceptionV3</span>模型中ImageNet 的權重來進行transfer learning，再加上兩層<span style=\"color:red\">DenseNet</span>提高辨識度。<br/><br/>\n",
"* **訓練結果：** <br/>\n",
"在經過長時間訓練後，模型實際使用時正確率很高，在將圖片分類上能夠達到<span style=\"color:red\">90%</span> 以上的正確率。<br/><br/>\n",
"訓練模型建置的github 連結: https://github.com/juichiehchang/NCTS5017-PythonFinalProject<br/><br/>\n",
"* **程式建置：** <br/>\n",
"我們把訓練完成的**模型**與**權重**讀入，打造出一個能夠辨識輸入圖片類別的辨識器。<br/>\n",
"接著使用PIL套件截圖，並將圖片分割為<span style=\"color:red\">18</span>個小圖片進行辨識，如下圖<br/>\n",
"\n",
"(我們將螢幕分割為3x5共15張圖片，並在影片播放位置的**重點區域**多剪兩張以及整個畫面一張，總共18張)<br/><br/>\n",
"該程式將<span style=\"color:red\">定期做螢幕截圖</span>並辨識圖片中是否有色情內容。<br/>\n",
"如果色情內容的比例超過一定水準，將會使用winsound套件自動播放歌曲Big Enough中牛仔的尖叫片段，<br/>\n",
"並使用PyAutoGUI讓<span style=\"color:red\">滑鼠隨機飄移無法操控</span>並在十五秒後<span style=\"color:red\">按下ALT+F4關閉視窗</span><br/>\n",
"而如果判斷色情內容比例超過一定水準，但尚未達到該關閉的程度，<br/>\n",
"因為可能會有誤判也會達到該水平，故程式會播放「光頭哥哥的嘿嘿」音效來提醒使用者，並將記錄累加，如持續超過一定水準也會執行關閉程式。"
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"## 此程式的缺點：\n",
"使用本模型，有一個缺點，就是**非常耗CPU資源**，因為<span style=\"color:red\">判讀模型很大</span><br/>\n",
"當執行判斷時，我們的CPU使用率會衝到100%約3秒，如下方圖片所示。<br/>\n",
"(高峰為程式執行判斷的時候)<br/>\n",
"\n",
"未來如果要改進，需要尋找有無抑制CPU使用率的寫法，<br/>\n",
"或將截圖<span style=\"color:red\">上傳至雲端</span>，由雲端的伺服器做判斷後，再將結果回傳給用戶端執行。"
]
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"source": [
"# 程式碼內容\n",
"(歡迎複製過去自行使用，其他檔案請至[雲端硬碟下載](https://drive.google.com/file/d/1nRXHMx9T4MLmZBP2rfjc8G8krewEHhaD/view?usp=sharing)整合好的zip檔案)<br/>後面還有趣事(判斷失敗的例子)喔！"
]
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"C:\\Users\\Public\\ANACONDA3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n",
"Using TensorFlow backend.\n"
]
}
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"#import predict\n",
"import cv2\n",
"import numpy as np\n",
"from PIL import Image\n",
"from PIL import ImageGrab\n",
"import time\n",
"import pyautogui\n",
"import winsound\n",
"#import sys\n",
"#sys.setrecursionlimit(50)\n",
"import numpy as np\n",
"import os\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
"import keras\n",
"\n",
"def load_image(img_path, size):\n",
" image = keras.preprocessing.image.load_img(img_path, target_size = size)\n",
" image = keras.preprocessing.image.img_to_array(image)\n",
" image /= 255\n",
" return np.expand_dims(image, axis=0)\n",
"\n",
"class predictor():\n",
"\n",
" model = None\n",
" categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']\n",
" size = None\n",
" \n",
" def __init__(self, model_path):\n",
" if('inceptionV3' in model_path):\n",
" self.size = (299, 299)\n",
" self.model = keras.models.load_model(model_path)\n",
" \n",
" def predict_from_path(self, img_path):\n",
" \n",
" image = load_image(img_path, self.size)\n",
" prediction = self.model.predict(image)\n",
" #print(prediction)\n",
" return self.categories[np.argmax(prediction)]\n",
"\n",
" def predict_from_array(self, img):\n",
"\n",
" image = img/255\n",
" prediction = self.model.predict(np.expand_dims(image, axis=0))\n",
" return self.categories[np.argmax(prediction)]\n"
]
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"C:\\Users\\Public\\ANACONDA3\\lib\\site-packages\\keras\\engine\\saving.py:327: UserWarning: Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
" warnings.warn('Error in loading the saved optimizer '\n"
]
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"text": [
"Judge start\n",
"讚喔\n",
"1\n",
"drawings: 10\n",
"hentai: 0\n",
"neutral: 6\n",
"porn: 0\n",
"sexy: 2\n",
"Judge end\n",
"\n",
"Judge start\n",
"母湯喔\n",
"0\n",
"drawings: 1\n",
"hentai: 0\n",
"neutral: 7\n",
"porn: 10\n",
"sexy: 0\n",
"Judge end\n",
"\n",
"Judge start\n",
"讚喔\n",
"1\n",
"drawings: 1\n",
"hentai: 0\n",
"neutral: 17\n",
"porn: 0\n",
"sexy: 0\n",
"Judge end\n",
"\n",
"Judge start\n",
"讚喔\n",
"1\n",
"drawings: 9\n",
"hentai: 0\n",
"neutral: 9\n",
"porn: 0\n",
"sexy: 0\n",
"Judge end\n",
"\n",
"Judge start\n",
"讚喔\n",
"1\n",
"drawings: 12\n",
"hentai: 0\n",
"neutral: 6\n",
"porn: 0\n",
"sexy: 0\n",
"Judge end\n",
"\n",
"Judge start\n",
"讚喔\n",
"1\n",
"drawings: 7\n",
"hentai: 0\n",
"neutral: 10\n",
"porn: 1\n",
"sexy: 0\n",
"Judge end\n",
"\n",
"Judge start\n",
"讚喔\n",
"1\n",
"drawings: 2\n",
"hentai: 0\n",
"neutral: 15\n",
"porn: 1\n",
"sexy: 0\n",
"Judge end\n",
"\n",
"Judge start\n",
"讚喔\n",
"1\n",
"drawings: 5\n",
"hentai: 0\n",
"neutral: 13\n",
"porn: 0\n",
"sexy: 0\n",
"Judge end\n",
"\n"
]
}
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"source": [
"weights_path = \"C:/Users/user/Downloads/pythonF/inceptionV3.299x299.h5\" #請自行修改路徑\n",
"\n",
"p = predictor(weights_path)\n",
"categories = ['drawings', 'hentai', 'neutral', 'porn', 'sexy']\n",
" \n",
"#截圖程式\n",
"#自動在螢幕產生15個截圖\n",
"def GrabAndCut():\n",
" images = []\n",
" width = 300 \n",
" height = 300\n",
" im=ImageGrab.grab() #截圖\n",
" wid_1 = -300\n",
" hei_1 = -300\n",
" for i in range(3):\n",
" wid_1 = -300\n",
" for i in range(5):\n",
" im_temp = im.crop(( #裁切圖片，裁切出3*5個\n",
" width +wid_1,\n",
" height + hei_1,\n",
" width +wid_1 + 300,\n",
" height + hei_1 + 300))\n",
" images.append(im_temp.resize((299,299)))\n",
" wid_1+=300\n",
" hei_1+=300 \n",
" images.append(im.crop(( #多剪兩張在平常的播放位置的內容\n",
" 100,\n",
" 200,\n",
" 800,\n",
" 800)).resize((299,299))) #將圖片大小設定為299x299\n",
" images.append(im.crop((\n",
" 500,\n",
" 200,\n",
" 1100,\n",
" 800)).resize((299,299)))\n",
" images.append(im.resize((299,299)))\n",
" return images\n",
"\n",
"def CheckImages(images): #分類函式 將圖片分類\n",
" result = []\n",
" for i in range(len(images)):\n",
" result.append(p.predict_from_array(np.array(images[i])))\n",
" return result\n",
" #判斷時要使用np.array判斷，所以我們將圖片轉換成array\n",
" #然後將判斷結果連接到一個叫result的陣列\n",
"\n",
"\n",
"def Judge(result, beforehand): #根據結果做出判斷的程式\n",
" testing = result.count(\"porn\")*2 + result.count(\"hentai\")\n",
" #使用自訂的判斷權重，因為在看動漫時較容易判斷出hentai故hentai需要比較高的權重\n",
" if testing >=9 or (testing + beforehand) >=11:\n",
" winsound.PlaySound(\"AHHH.wav\", winsound.SND_FILENAME|winsound.SND_ASYNC)\n",
" #使用winsound播放音樂， winsound.SND_FILENAME 是指播放該檔名的檔案\n",
" #winsound.SND_ASYNC 是指播放後就繼續執行剩下的程式碼\n",
" for i in range(40): #亂動滑鼠的部分\n",
" pyautogui.moveTo(np.random.randint(1,1910), np.random.randint(1,1070),0.25)\n",
" pyautogui.keyDown(\"ALT\") #使用PyAutoGUI的套件，模擬按下ALT+F4\n",
" pyautogui.keyDown(\"F4\")\n",
" pyautogui.keyUp(\"ALT\")\n",
" #x, y =pyautogui.size() #移動到右上角關閉視窗\n",
" #pyautogui.moveTo(x-10, 10, 10) #後來選擇ALT+F4，故此段註解\n",
" #pyautogui.click() \n",
" print(\"母湯喔\")\n",
" Judges = 0\n",
" elif testing >=5:\n",
" print(\"嘿嘿\") #播放光頭哥哥的「嘿嘿」音效，警告使用者\n",
" winsound.PlaySound(\"hehe.wav\", winsound.SND_FILENAME|winsound.SND_ASYNC)\n",
" Judges = 1\n",
" beforehand = testing #將結果回傳，如果重複出現危險內容將會執行關閉程式\n",
" else:\n",
" print(\"讚喔\")\n",
" Judges = 1\n",
" beforehand = 0\n",
" return Judges, beforehand\n",
"\n",
"beforehand = 0\n",
"#while(1): #正式程式，將內容串在一起\n",
"for i in range(8): #本來是使用while迴圈，不過因為要呈現不想出現KeyboardInterrupt\n",
" #所以使用for迴圈做八次\n",
" print(\"Judge start\")\n",
" images = GrabAndCut()\n",
" result = CheckImages(images)\n",
" Judges, beforehand = Judge(result, beforehand)\n",
" print(Judges)\n",
" for i in range(len(categories)): #打印出分類模型究竟分類出來的結果\n",
" print(\"%s: %d\"%(categories[i], result.count(categories[i])))\n",
" print(\"Judge end\\n\")\n",
" time.sleep(1)"
]
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"### 趣聞 (判斷失敗的例子) ：\n",
"此程式準確率還是有待加強，比方會將一些新聞的圖片判斷為porn\n",
"(如下方圖片)<br/>\n",
"推測可能是因為原先training data **並沒有文字**，同時切割過的圖片較不易判斷<br/>所以我們使用指定公式，當porn分類超過5個，才執行關閉程式。\n",
""
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