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| from keras.models import load_model | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| import requests | |
| from keras.models import model_from_json | |
| import face_recognition | |
| import os | |
| from datetime import datetime | |
| #the following are to do with this interactive notebook code | |
| from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks | |
| import pylab # this allows you to control figure size | |
| pylab.rcParams['figure.figsize'] = (10.0, 8.0) # this controls figure size in the notebook | |
| import io | |
| import streamlit as st | |
| bytes_data=None | |
| Images = [] | |
| classnames = [] | |
| myList = os.listdir() | |
| #st.write(myList) | |
| for cls in myList: | |
| if os.path.splitext(cls)[1] == ".jpg" : | |
| curImg = cv2.imread(f'{cls}') | |
| Images.append(curImg) | |
| classnames.append(os.path.splitext(cls)[0]) | |
| st.write(classnames) | |
| json_file = open("facialemotionmodel.json", "r") | |
| model_json = json_file.read() | |
| json_file.close() | |
| model = model_from_json(model_json) | |
| model.load_weights("facialemotionmodel.h5") | |
| haar_file=cv2.data.haarcascades + 'haarcascade_frontalface_default.xml' | |
| face_cascade=cv2.CascadeClassifier(haar_file) | |
| def findEncodings(Images): | |
| encodeList = [] | |
| for img in Images: | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| encode = face_recognition.face_encodings(img)[0] | |
| encodeList.append(encode) | |
| return encodeList | |
| def extract_features(image): | |
| feature = np.array(image) | |
| feature = feature.reshape(1,48,48,1) | |
| return feature/255.0 | |
| labels = {0 : 'angry', 1 : 'disgust', 2 : 'fear', 3 : 'happy', 4 : 'neutral', 5 : 'sad', 6 : 'surprise'} | |
| encodeListknown = findEncodings(Images) | |
| st.write('Encoding Complete') | |
| img_file_buffer=st.camera_input("Take a picture") | |
| if img_file_buffer is not None: | |
| test_image = Image.open(img_file_buffer) | |
| st.image(test_image, use_column_width=True) | |
| image = np.asarray(test_image) | |
| ######################### | |
| imgS = cv2.resize(image,(0,0),None,0.25,0.25) | |
| imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB) | |
| facesCurFrame = face_recognition.face_locations(imgS) | |
| encodesCurFrame = face_recognition.face_encodings(imgS,facesCurFrame) | |
| for encodeFace,faceLoc in zip(encodesCurFrame,facesCurFrame): | |
| matches = face_recognition.compare_faces(encodeListknown,encodeFace) | |
| faceDis = face_recognition.face_distance(encodeListknown,encodeFace) | |
| #print(faceDis) | |
| matchIndex = np.argmin(faceDis) | |
| if matches[matchIndex]: | |
| name = classnames[matchIndex] | |
| st.write(name) | |
| y1, x2, y2, x1 = faceLoc | |
| y1, x2, y2, x1 = y1*4,x2*4,y2*4,x1*4 | |
| cv2.rectangle(image,(x1,y1),(x2,y2),(0,255,0),2) | |
| cv2.rectangle(image,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED) | |
| cv2.putText(image,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255, 255, 255),2) | |
| faces=face_cascade.detectMultiScale(image,1.3,5) | |
| st.write("close") | |
| try: | |
| for (p,q,r,s) in faces: | |
| cv2.rectangle(im,(p,q),(p+r,q+s),(255,0,0),2) | |
| image = cv2.resize(image,(48,48)) | |
| img = extract_features(image) | |
| pred = model.predict(img) | |
| prediction_label = labels[pred.argmax()] | |
| # print("Predicted Output:", prediction_label) | |
| # cv2.putText(im,prediction_label) | |
| cv2.putText(image, '% s' %(prediction_label), (p-10, q-10),cv2.FONT_HERSHEY_COMPLEX_SMALL,2, (0,0,255)) | |
| st.write("success") | |
| except: | |
| pass | |
| # ############## | |
| # url = "https://kiwi-whispering-plier.glitch.me/update" | |
| # data = { | |
| # 'name': name, | |
| # } | |
| # else: | |
| # st.write("Please smile") |