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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 | |
| from keras.models import model_from_json | |
| from keras.preprocessing.image import img_to_array | |
| from PIL import Image | |
| 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) | |
| # load model | |
| emotion_dict = {0:'angry', 1 :'happy', 2: 'neutral', 3:'sad', 4: 'surprise'} | |
| # load json and create model | |
| json_file = open('emotion_model1.json', 'r') | |
| loaded_model_json = json_file.read() | |
| json_file.close() | |
| classifier = model_from_json(loaded_model_json) | |
| # load weights into new model | |
| classifier.load_weights("emotion_model1.h5") | |
| #load face | |
| try: | |
| face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
| except Exception: | |
| st.write("Error loading cascade classifiers") | |
| 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 | |
| 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) | |
| image1 = Image.open(img_file_buffer) | |
| st.image(test_image, use_column_width=True) | |
| image = np.asarray(test_image) | |
| img = np.asarray(image1) | |
| img = cv2.resize(img,(0,0),None,0.25,0.25) | |
| #image gray | |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| faces = face_cascade.detectMultiScale( | |
| image=img_gray, scaleFactor=1.3, minNeighbors=5) | |
| try: | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle(img=img, pt1=(x, y), pt2=( | |
| x + w, y + h), color=(255, 0, 0), thickness=2) | |
| roi_gray = img_gray[y:y + h, x:x + w] | |
| roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA) | |
| if np.sum([roi_gray]) != 0: | |
| roi = roi_gray.astype('float') / 255.0 | |
| roi = img_to_array(roi) | |
| roi = np.expand_dims(roi, axis=0) | |
| prediction = classifier.predict(roi)[0] | |
| maxindex = int(np.argmax(prediction)) | |
| finalout = emotion_dict[maxindex] | |
| output = str(finalout) | |
| st.write(output) | |
| label_position = (x, y) | |
| img = cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) | |
| st.image(img, use_column_width=True) | |
| except: | |
| st.write("face is not clear") | |
| ######################### | |
| 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) | |
| 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) | |
| ############## | |
| if name: | |
| if output=='happy': | |
| url = "https://kiwi-whispering-plier.glitch.me/update" | |
| data = { | |
| 'name': name, | |
| } | |
| response = requests.get(url, params=data) | |
| if response.status_code == 200 : | |
| st.write(" data updated on : https://kiwi-whispering-plier.glitch.me" ) | |
| st.image(image) | |
| else : | |
| st.write("data not updated ") | |
| ############################## | |
| else: | |
| st.write("Please smile") | |
| else: | |
| st.write("Failed") | |