Update app.py

app.py

@@ -3,14 +3,14 @@ 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
+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
@@ -27,6 +27,23 @@ for cls in myList:
         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:
@@ -41,46 +58,78 @@ 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)
+    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)
     
-     #########################
-     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)
+    img = np.asarray(image1)
+    img = cv2.resize(img,(0,0),None,0.25,0.25)
 
-     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)
+    #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")
 
-         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)
-
-             ##############
-             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" )
-             else : st.write("data not updated ")
-                 
-            ##############################
+    #########################
+    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 ")
                  
-     st.image(image)
-if bytes_data is None:
-     st.stop()
+                    ##############################
+                   
+
+                else:
+                    st.write("Please smile")
+
+
+        else:
+            st.write("Failed")