Update app.py

app.py

@@ -1,29 +1,44 @@
 import streamlit as st
+from PIL import Image
 import face_recognition
 import cv2
 import numpy as np
+import requests
 import os
 
-def load_images(directory):
-    images = []
-    classnames = []
-    file_list = os.listdir(directory)
+st.title("AIMLJan24 - Face Recognition")
+
+# create list of encoding of all images in photos folder
+# Load images for face recognition
+Images = []   # List to store Images
+classnames = []  # List to store classnames
+directory = "photos"
+myList = os.listdir(directory)
+
+st.write("Photographs found in folder : ")
+for cls in myList:
+    if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
+        img_path = os.path.join(directory, cls)
+        curImg = cv2.imread(img_path)
+        Images.append(curImg)
+        st.write(os.path.splitext(cls)[0])
+        classnames.append(os.path.splitext(cls)[0])
 
-    st.write("Photographs found in folder : ")
-    for file in file_list:
-        if os.path.splitext(file)[1] in [".jpg", ".jpeg"]:
-            img_path = os.path.join(directory, file)
-            cur_img = cv2.imread(img_path)
-            images.append(cur_img)
-            st.write(os.path.splitext(file)[0])
-            classnames.append(os.path.splitext(file)[0])
+# Load images for face recognition
+encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]
+
+# camera to take photo of user in question
+file_name = st.camera_input("Take a picture")  #st.file_uploader("Upload image  ")
 
-    return images, classnames
+if file_name is not None:
+    col1, col2 = st.columns(2)
 
-def recognize_faces(test_image, known_encodings, class_names):
-    imgS = cv2.resize(test_image, (0, 0), None, 0.25, 0.25)
+    test_image = Image.open(file_name)
+    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)
+    facesCurFrame   = face_recognition.face_locations(imgS)
     encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
 
     name = "Unknown"  # Default name for unknown faces
@@ -32,42 +47,21 @@ def recognize_faces(test_image, known_encodings, class_names):
     # Checking if faces are detected
     if len(encodesCurFrame) > 0:
         for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
-            matches = face_recognition.compare_faces(known_encodings, encodeFace)
-            faceDis = face_recognition.face_distance(known_encodings, encodeFace)
+            # Assuming that encodeListknown is defined and populated in your code
+            matches = face_recognition.compare_faces(encodeListknown, encodeFace)
+            faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
             matchIndex = np.argmin(faceDis)
 
             if matches[matchIndex]:
-                name = class_names[matchIndex].upper()
+                name = classnames[matchIndex].upper()
                 match_found = True  # Set the flag to True
                 
             y1, x2, y2, x1 = faceLoc
             y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
-            cv2.rectangle(test_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.rectangle(test_image, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
-            cv2.putText(test_image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
+            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)
             
-    return test_image
-
-st.title("AIMLJan24 - Face Recognition")
-
-# Load images for face recognition
-directory = "photos"
-Images, classnames = load_images(directory)
-
-# Load images for face recognition
-encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]
-
-# Camera input to take photo of user in question
-capture = cv2.VideoCapture(0)
-
-if capture.isOpened():
-    ret, frame = capture.read()
-    if ret:
-        # Recognize faces in the captured frame
-        image_with_recognition = recognize_faces(frame, encodeListknown, classnames)
-        st.image(image_with_recognition, channels="BGR", use_column_width=True)
-else:
-    st.error("Failed to open camera.")
-
-capture.release()
-cv2.destroyAllWindows()
+        st.image(image , use_column_width=True, output_format="PNG")
+    else:
+        st.warning("No faces detected in the image. Face recognition failed.")