Update app.py

app.py

@@ -9,13 +9,64 @@ import os
 
 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)
+
+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)
+        classnames.append(os.path.splitext(cls)[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  ")
 
 if file_name is not None:
     col1, col2 = st.columns(2)
 
-    image = Image.open(file_name)
-    col1.image(image, use_column_width=True)
+    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)
+    encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
+
+    name = "Unknown"  # Default name for unknown faces
+    match_found = False  # Flag to track if a match is found
+
+    # Checking if faces are detected
+    if len(encodesCurFrame) > 0:
+        for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
+            # 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 = 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(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)
+            
+            st.image(image, use_column_width=True, output_format="PNG")
+    else:
+            st.warning("No faces detected in the image. Face recognition failed.")
+
+    # image = Image.open(file_name)
+    # col1.image(image, use_column_width=True)
 
 # pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")