Merge pull request larymak#353 from MBSA-INFINITY/emotion-detector

Realtime Emotion Detector using Python (Google's Teachable Machine Learning)

Author: larymak

MachineLearning Projects/Emotion Detector using Google's Teachable ML/README.md

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+<!--Please do not remove this part-->
+![Star Badge](https://img.shields.io/static/v1?label=%F0%9F%8C%9F&message=If%20Useful&style=style=flat&color=BC4E99)
+![Open Source Love](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)
+
+# Realtime Emotion Detector using Python (Google's Teachable Machine Learning)
+
+## 🛠️ Description
+This project is about developing a system that can detect the emotions of a person in realtime from the video on the basis of a pre-trained **keras** model. This model was trained using Google's [Teachable Machine Learning](https://teachablemachine.withgoogle.com/). 
+
+The project can detect the following emotions:- 
+**Angry**, **Happy**, **Sad**, **Smile**, **Surprise**
+
+
+## ⚙️ Languages or Frameworks Used
+ - Python, Mediapipe, Keras
+ - Teachable Machine Learning (For model training)
+
+
+## 🌟 How to run
+ - ### Install all the requirements
+    Run `pip install -r requirements.txt` to install all the requirements.
+
+-  ###  Run the project
+    To the run the project, go to the terminal and run `python main.py`. This will popup two windows, one for capturing the `video input` and the other for displyaing the `emotion output.`
+    
+ > Note: The Model (.h5 file) has been trained using the Teachable Machine Learning which is an esay to use ML Training Platform by **Google**. Do checkout that platform.
+
+
+## 📺 Demo
+Do checkout the Below Video for Demo of the Project.
+
+[Youtube Link](https://youtu.be/ER4avLksQfU)
+
+## 🤖 Author
+Github - [MBSA-INFINITY](https://github.com/MBSA-INFINITY)
+LinkedIn - [MBSAIADITYA](https://www.linkedin.com/in/mbsaiaditya/)
+Portfolio - [MBSA](https://mbsaiaditya.in/)
+Instagram - [MBSAIADITYA](https://instagram.com/mbsaiaditya)

MachineLearning Projects/Emotion Detector using Google's Teachable ML/Teachable ML Data/converted_keras.zip

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+0 happy
+1 angry
+2 sad
+3 smile
+4 surprise

MachineLearning Projects/Emotion Detector using Google's Teachable ML/Teachable ML Data/keras_model.h5

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+import cv2
+import numpy as np
+import mediapipe as mp
+from keras.models import  load_model
+from keras.preprocessing import image
+# from tensorflow.keras.utils import img_to_array
+from PIL import Image, ImageOps
+
+mpFaceDetection = mp.solutions.face_detection
+mpDraw = mp.solutions.drawing_utils
+faceDetection = mpFaceDetection.FaceDetection()
+
+model = load_model('./Teachable ML Data/keras_model.h5')
+
+cap = cv2.VideoCapture(1)
+
+results_detect = {0:"😁",1:"😠",2:"☹️",3:"😊",4:"😲"}
+results_detect_str = {0:"happy",1:"angry",2:"sad",3:"smile",4:"surprise"}
+
+# pTime = 0
+while cap.isOpened():
+    _,img = cap.read()
+    imgRGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+    results = faceDetection.process(imgRGB)
+    if results.detections:
+        ih,iw,ic = img.shape
+        for id,detection in enumerate(results.detections):
+            bBoxC = detection.location_data.relative_bounding_box
+            bBox = int(bBoxC.xmin * iw),int(bBoxC.ymin * ih),int(bBoxC.width * iw),int(bBoxC.height * ih)
+            # cv2.rectangle(img,bBox,(255,0,255),2)
+            roi_gray = img[bBox[1]:bBox[1] + bBox[2], bBox[0]:bBox[0] + bBox[3]]
+            roi_gray = cv2.resize(roi_gray, (224, 224))
+            cv2.imwrite("image.jpg",roi_gray)
+
+
+            data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+            image = Image.open('image.jpg')
+            size = (224, 224)
+            image = ImageOps.fit(image, size, Image.ANTIALIAS)
+            image_array = np.asarray(image)
+            normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
+            data[0] = normalized_image_array
+            prediction = model.predict(data)
+            res = np.argmax(prediction)
+
+            # predictions = np.argmax(model.predict(np.array([roi_gray])))
+            # cv2.putText(img, results_detect[res], (150,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+            temp_emotion = cv2.imread(f"./emotions/{results_detect_str[res]}.jfif")
+            cv2.imshow("emotion", temp_emotion)
+            print(results_detect[res])
+
+    cv2.imshow("Image",img)
+
+    key = cv2.waitKey(1)
+    if key == ord('q'):
+        cv2.destroyAllWindows()
+        break