{"id":78752,"date":"2020-06-26T15:54:04","date_gmt":"2020-06-26T10:24:04","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=78752"},"modified":"2021-03-14T16:52:01","modified_gmt":"2021-03-14T11:22:01","slug":"create-emoji-with-deep-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/create-emoji-with-deep-learning\/","title":{"rendered":"Emojify &#8211; Create your own emoji with Deep Learning"},"content":{"rendered":"<div class='__iawmlf-post-loop-links' style='display:none;' data-iawmlf-post-links='[{&quot;id&quot;:1064,&quot;href&quot;:&quot;https:\\\/\\\/www.kaggle.com\\\/msambare\\\/fer2013?&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20230921211909\\\/https:\\\/\\\/www.kaggle.com\\\/msambare\\\/fer2013&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 00:29:36&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2025-12-12 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01:43:03&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-28 03:29:29&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-05-31 06:52:58&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-03 08:13:41&quot;,&quot;http_code&quot;:200},{&quot;date&quot;:&quot;2026-06-06 09:50:32&quot;,&quot;http_code&quot;:200}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-06-06 09:50:32&quot;,&quot;http_code&quot;:200},&quot;process&quot;:&quot;done&quot;}]'><\/div>\n<p><strong>Deep Learning project for beginners &#8211; Taking you closer to your Data Science dream<\/strong><\/p>\n<p>Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also lead to increasing data science research dedicated to emoji-driven storytelling.<\/p>\n<p>With advancements in computer vision and deep learning, it is now possible to detect human emotions from images.\u00a0In this deep learning project, we will classify human facial expressions to filter and map corresponding emojis or avatars.<\/p>\n<h3>About the Dataset<\/h3>\n<p>The FER2013 dataset ( facial expression recognition) consists of 48*48 pixel grayscale face images. The images are centered and occupy an equal amount of space. This dataset consist of facial emotions of following categories:<\/p>\n<ul>\n<li>0:angry<\/li>\n<li>1:disgust<\/li>\n<li>2:feat<\/li>\n<li>3:happy<\/li>\n<li>4:sad<\/li>\n<li>5:surprise<\/li>\n<li>6:natural<\/li>\n<\/ul>\n<p><strong>Download Dataset:<\/strong> <a href=\"https:\/\/www.kaggle.com\/msambare\/fer2013?\">Facial Expression Recognition Dataset<\/a><\/p>\n<h3>Download Project Code<\/h3>\n<p>Before proceeding ahead, please download the source code: <strong><a href=\"https:\/\/data-flair.training\/blogs\/download-emoji-creation-project-source-code\/\">Emoji Creator Project Source Code<\/a><\/strong><\/p>\n<h2>Create your emoji with Deep Learning<\/h2>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/06\/create-emoji-with-deep-learning.gif\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-78770\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/06\/create-emoji-with-deep-learning.gif\" alt=\"create emoji with deep learning\" width=\"1909\" height=\"971\" \/><\/a><\/p>\n<p>We will build a deep learning model to classify facial expressions from the images. Then we will map the classified emotion to an emoji or an avatar.<\/p>\n<h3>Facial Emotion Recognition using CNN<\/h3>\n<p>In the below steps will build a convolution neural network architecture and train the model on FER2013 dataset for Emotion recognition from images.<\/p>\n<p>Download the dataset from the above link. Extract it in the data folder with separate train and test directories.<\/p>\n<p><strong>Make a file train.py and follow the steps:<\/strong><\/p>\n<p>1. Imports:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">import numpy as np\r\nimport cv2\r\n\r\nfrom keras.emotion_models import Sequential\r\nfrom keras.layers import Dense, Dropout, Flatten\r\nfrom keras.layers import Conv2D\r\nfrom keras.optimizers import Adam\r\nfrom keras.layers import MaxPooling2D\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\n<\/pre>\n<p>2. Initialize the training and validation generators:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">train_dir = 'data\/train'\r\nval_dir = 'data\/test'\r\ntrain_datagen = ImageDataGenerator(rescale=1.\/255)\r\nval_datagen = ImageDataGenerator(rescale=1.\/255)\r\n\r\ntrain_generator = train_datagen.flow_from_directory(\r\n        train_dir,\r\n        target_size=(48,48),\r\n        batch_size=64,\r\n        color_mode=\"gray_framescale\",\r\n        class_mode='categorical')\r\n\r\nvalidation_generator = val_datagen.flow_from_directory(\r\n        val_dir,\r\n        target_size=(48,48),\r\n        batch_size=64,\r\n        color_mode=\"gray_framescale\",\r\n        class_mode='categorical')\r\n<\/pre>\n<p>3. Build the convolution network architecture:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">emotion_model = Sequential()\r\n\r\nemotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))\r\nemotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\r\nemotion_model.add(MaxPooling2D(pool_size=(2, 2)))\r\nemotion_model.add(Dropout(0.25))\r\n\r\nemotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\r\nemotion_model.add(MaxPooling2D(pool_size=(2, 2)))\r\nemotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\r\nemotion_model.add(MaxPooling2D(pool_size=(2, 2)))\r\nemotion_model.add(Dropout(0.25))\r\n\r\nemotion_model.add(Flatten())\r\nemotion_model.add(Dense(1024, activation='relu'))\r\nemotion_model.add(Dropout(0.5))\r\nemotion_model.add(Dense(7, activation='softmax'))\r\n<\/pre>\n<p>4. Compile and train the model:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">emotion_model.compile(loss='categorical_crossentropy',optimizer=Adam(lr=0.0001, decay=1e-6),metrics=['accuracy'])\r\n\r\nemotion_model_info = emotion_model.fit_generator(\r\n        train_generator,\r\n        steps_per_epoch=28709 \/\/ 64,\r\n        epochs=50,\r\n        validation_data=validation_generator,\r\n        validation_steps=7178 \/\/ 64)\r\n<\/pre>\n<p>5. Save the model weights:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">emotion_model.save_weights('model.h5')\r\n<\/pre>\n<p>6. Using openCV haarcascade xml detect the bounding boxes of face in the webcam and predict the emotions:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">cv2.ocl.setUseOpenCL(False)\r\n\r\nemotion_dict = {0: \"Angry\", 1: \"Disgusted\", 2: \"Fearful\", 3: \"Happy\", 4: \"Neutral\", 5: \"Sad\", 6: \"Surprised\"}\r\n\r\ncap = cv2.VideoCapture(0)\r\nwhile True:\r\n    ret, frame = cap.read()\r\n    if not ret:\r\n        break\r\n    bounding_box = cv2.CascadeClassifier('\/home\/shivam\/.local\/lib\/python3.6\/site-packages\/cv2\/data\/haarcascade_frontalface_default.xml')\r\n    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2gray_frame)\r\n    num_faces = bounding_box.detectMultiScale(gray_frame,scaleFactor=1.3, minNeighbors=5)\r\n\r\n    for (x, y, w, h) in num_faces:\r\n        cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)\r\n        roi_gray_frame = gray_frame[y:y + h, x:x + w]\r\n        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)\r\n        emotion_prediction = emotion_model.predict(cropped_img)\r\n        maxindex = int(np.argmax(emotion_prediction))\r\n        cv2.putText(frame, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\r\n\r\n    cv2.imshow('Video', cv2.resize(frame,(1200,860),interpolation = cv2.INTER_CUBIC))\r\n    if cv2.waitKey(1) &amp; 0xFF == ord('q'):\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n    break\r\n<\/pre>\n<h3>Code for GUI and mapping with emojis<\/h3>\n<p>Create a folder named emojis and save the emojis corresponding to each of the seven emotions in the dataset.<\/p>\n<p>Paste the below code in gui.py and run the file.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">import tkinter as tk\r\nfrom tkinter import *\r\nimport cv2\r\nfrom PIL import Image, ImageTk\r\nimport os\r\nimport numpy as np\r\nimport cv2\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Flatten\r\nfrom keras.layers import Conv2D\r\nfrom keras.optimizers import Adam\r\nfrom keras.layers import MaxPooling2D\r\nfrom keras.preprocessing.image import ImageDataGenerator\r\n\r\nemotion_model = Sequential()\r\n\r\nemotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))\r\nemotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\r\nemotion_model.add(MaxPooling2D(pool_size=(2, 2)))\r\nemotion_model.add(Dropout(0.25))\r\n\r\nemotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\r\nemotion_model.add(MaxPooling2D(pool_size=(2, 2)))\r\nemotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\r\nemotion_model.add(MaxPooling2D(pool_size=(2, 2)))\r\nemotion_model.add(Dropout(0.25))\r\n\r\nemotion_model.add(Flatten())\r\nemotion_model.add(Dense(1024, activation='relu'))\r\nemotion_model.add(Dropout(0.5))\r\nemotion_model.add(Dense(7, activation='softmax'))\r\nemotion_model.load_weights('model.h5')\r\n\r\ncv2.ocl.setUseOpenCL(False)\r\n\r\nemotion_dict = {0: \"   Angry   \", 1: \"Disgusted\", 2: \"  Fearful  \", 3: \"   Happy   \", 4: \"  Neutral  \", 5: \"    Sad    \", 6: \"Surprised\"}\r\n\r\n\r\nemoji_dist={0:\".\/emojis\/angry.png\",2:\".\/emojis\/disgusted.png\",2:\".\/emojis\/fearful.png\",3:\".\/emojis\/happy.png\",4:\".\/emojis\/neutral.png\",5:\".\/emojis\/sad.png\",6:\".\/emojis\/surpriced.png\"}\r\n\r\nglobal last_frame1                                    \r\nlast_frame1 = np.zeros((480, 640, 3), dtype=np.uint8)\r\nglobal cap1\r\nshow_text=[0]\r\ndef show_vid():      \r\n    cap1 = cv2.VideoCapture(0)                                 \r\n    if not cap1.isOpened():                             \r\n        print(\"cant open the camera1\")\r\n    flag1, frame1 = cap1.read()\r\n    frame1 = cv2.resize(frame1,(600,500))\r\n\r\n    bounding_box = cv2.CascadeClassifier('\/home\/shivam\/.local\/lib\/python3.6\/site-packages\/cv2\/data\/haarcascade_frontalface_default.xml')\r\n    gray_frame = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)\r\n    num_faces = bounding_box.detectMultiScale(gray_frame,scaleFactor=1.3, minNeighbors=5)\r\n\r\n    for (x, y, w, h) in num_faces:\r\n        cv2.rectangle(frame1, (x, y-50), (x+w, y+h+10), (255, 0, 0), 2)\r\n        roi_gray_frame = gray_frame[y:y + h, x:x + w]\r\n        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)\r\n        prediction = emotion_model.predict(cropped_img)\r\n        \r\n        maxindex = int(np.argmax(prediction))\r\n        cv2.putText(frame1, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)\r\n        show_text[0]=maxindex\r\n    if flag1 is None:\r\n        print (\"Major error!\")\r\n    elif flag1:\r\n        global last_frame1\r\n        last_frame1 = frame1.copy()\r\n        pic = cv2.cvtColor(last_frame1, cv2.COLOR_BGR2RGB)     \r\n        img = Image.fromarray(pic)\r\n        imgtk = ImageTk.PhotoImage(image=img)\r\n        lmain.imgtk = imgtk\r\n        lmain.configure(image=imgtk)\r\n        lmain.after(10, show_vid)\r\n    if cv2.waitKey(1) &amp; 0xFF == ord('q'):\r\n        exit()\r\n\r\n\r\ndef show_vid2():\r\n    frame2=cv2.imread(emoji_dist[show_text[0]])\r\n    pic2=cv2.cvtColor(frame2,cv2.COLOR_BGR2RGB)\r\n    img2=Image.fromarray(frame2)\r\n    imgtk2=ImageTk.PhotoImage(image=img2)\r\n    lmain2.imgtk2=imgtk2\r\n    lmain3.configure(text=emotion_dict[show_text[0]],font=('arial',45,'bold'))\r\n    \r\n    lmain2.configure(image=imgtk2)\r\n    lmain2.after(10, show_vid2)\r\n\r\nif __name__ == '__main__':\r\n    root=tk.Tk()   \r\n    img = ImageTk.PhotoImage(Image.open(\"logo.png\"))\r\n    heading = Label(root,image=img,bg='black')\r\n    \r\n    heading.pack() \r\n    heading2=Label(root,text=\"Photo to Emoji\",pady=20, font=('arial',45,'bold'),bg='black',fg='#CDCDCD')                                 \r\n    \r\n    heading2.pack()\r\n    lmain = tk.Label(master=root,padx=50,bd=10)\r\n    lmain2 = tk.Label(master=root,bd=10)\r\n\r\n    lmain3=tk.Label(master=root,bd=10,fg=\"#CDCDCD\",bg='black')\r\n    lmain.pack(side=LEFT)\r\n    lmain.place(x=50,y=250)\r\n    lmain3.pack()\r\n    lmain3.place(x=960,y=250)\r\n    lmain2.pack(side=RIGHT)\r\n    lmain2.place(x=900,y=350)\r\n    \r\n\r\n\r\n    root.title(\"Photo To Emoji\")            \r\n    root.geometry(\"1400x900+100+10\") \r\n    root['bg']='black'\r\n    exitbutton = Button(root, text='Quit',fg=\"red\",command=root.destroy,font=('arial',25,'bold')).pack(side = BOTTOM)\r\n    show_vid()\r\n    show_vid2()\r\n    root.mainloop()\r\n<\/pre>\n<h2>Summary<\/h2>\n<p>In this deep learning project for beginners, we have built a convolution neural network to recognize facial emotions. We have trained our model on the FER2013 dataset. Then we are mapping those emotions with the corresponding emojis or avatars.<\/p>\n<p>Using OpenCV&#8217;s haar cascade xml we are getting the bounding box of the faces in the webcam. Then we feed these boxes to the trained model for classification.<\/p>\n<p><strong>DataFlair<\/strong> is committed to provide all the resources to make you a data scientist, which includes detailed tutorials, practicals, use-cases as well as projects with source code.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep Learning project for beginners &#8211; Taking you closer to your Data Science dream Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product&#46;&#46;&#46;<\/p>\n","protected":false},"author":7,"featured_media":78777,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[22468,21686,22272,22469],"class_list":["post-78752","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-create-emoji-with-deep-learning","tag-deep-learning-project","tag-deep-learning-project-for-beginners","tag-deep-learning-project-with-source-code"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Emojify - Create your own emoji with Deep Learning - DataFlair<\/title>\n<meta name=\"description\" content=\"Create your own emoji with deep learning. 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