Update app.py

app.py

@@ -1,89 +1,47 @@
-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 cv2
 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)
-
-
-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)
+def detect_faces():
+    st.title("Real-time Face Detection App")
+    st.sidebar.title("Parameters")
     
-     #########################
-     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)
-
-             ##############
-             url = "https://kiwi-whispering-plier.glitch.me/update" 
-             
-             data = {
-                     'name': name,
-             }
+    # 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()
+    
+    detect_in_video()
 
-             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 ")
-                 
-     ##############################
-     st.image(image)
-if bytes_data is None:
-     st.stop()  
+if __name__ == "__main__":
+    detect_faces()