Update app.py

app.py

@@ -1,47 +1,78 @@
-import cv2
+from keras.models import load_model
+from PIL import Image
+import numpy as np
+import cv2 
+import requests
+
+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
 
-def detect_faces():
-    st.title("Real-time Face Detection App")
-    st.sidebar.title("Parameters")
-    
-    # Function to detect faces in the video stream
-    def detect_in_video():
-        video_feed = st.sidebar.checkbox("Use Video Stream", True)
-        if video_feed:
-            video = cv2.VideoCapture(0)  # Access the default camera (change 0 to another number for different cameras)
-        else:
-            uploaded_file = st.sidebar.file_uploader("Upload a video file", type=["mp4", "avi"])
-            if uploaded_file is not None:
-                video = cv2.VideoCapture(uploaded_file)
-            else:
-                st.warning("Please upload a video file.")
-                return
-        
-        face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
-        
-        # Display the video feed and detect faces
-        while video.isOpened():
-            ret, frame = video.read()
-            if not ret:
-                st.error("Failed to capture the video.")
-                break
-            
-            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-            faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
-            
-            for (x, y, w, h) in faces:
-                cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 3)
-            
-            st.image(frame, channels="BGR", use_column_width=True)
-            
-            if cv2.waitKey(1) & 0xFF == ord('q'):
-                break
-        
-        video.release()
-        cv2.destroyAllWindows()
+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)
+
+
+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)
+     st.image(test_image, use_column_width=True)
+     
+     image = np.asarray(test_image)
     
-    detect_in_video()
+     #########################
+     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)
 
-if __name__ == "__main__":
-    detect_faces()
+             ##############
+             url = "https://kiwi-whispering-plier.glitch.me/update" 
+             
+             data = {
+                     'name': name,
+             }