Update app.py

app.py

@@ -1,57 +1,65 @@
 import gradio as gr
 import numpy as np
 import cv2
+import os
 
-# Load model data
+# Load Haar cascade for face detection
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+# Load precomputed model data
 mean = np.load("mean.npy")
 eigenfaces = np.load("eigenfaces.npy")
 projected_faces = np.load("projected_faces.npy")
 labels = np.load("labels.npy")
 
-# Resize and flatten the image
+# Preprocess the image: detect face, crop, grayscale, resize, flatten
 def preprocess(img):
     try:
         gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+        gray = cv2.equalizeHist(gray)  # Improve lighting
     except Exception as e:
         raise ValueError(f"Failed to convert to grayscale: {e}")
 
-    resized = cv2.resize(gray, (100, 100))
-    return resized.flatten()
+    # Detect face
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5)
+    if len(faces) == 0:
+        raise ValueError("❌ No face detected in the image.")
 
-# Predict function with debug
-def predict(img):
-    print("📸 Raw input image:", type(img))
+    # Crop the first face found
+    x, y, w, h = faces[0]
+    face_roi = gray[y:y+h, x:x+w]
+    resized_face = cv2.resize(face_roi, (100, 100))
+
+    return resized_face.flatten()
 
+# Predict function
+def predict(img):
     if img is None:
-        return "❌ No image provided"
+        return "❌ No image provided."
 
     try:
-        # Ensure the input is a NumPy array
-        if not isinstance(img, np.ndarray):
-            return f"❌ Unexpected input type: {type(img)}"
-
-        # Preprocess image
         img_vector = preprocess(img)
         img_centered = img_vector - mean
         img_projected = np.dot(eigenfaces.T, img_centered)
 
-        # Compare with dataset
+        # Calculate Euclidean distances
         distances = np.linalg.norm(projected_faces - img_projected, axis=1)
         min_index = np.argmin(distances)
         predicted_label = labels[min_index]
+        confidence = distances[min_index]
 
-        return f"✅ Match found: Person {predicted_label} (distance={distances[min_index]:.2f})"
+        return f"✅ Match: Person {predicted_label} (distance = {confidence:.2f})"
 
     except Exception as e:
-        return f"⚠️ Error during prediction: {str(e)}"
+        return f"⚠️ Error: {str(e)}"
 
 # Gradio interface
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="numpy", label="Upload Face Image"),
+    inputs=gr.Image(type="numpy", label="Upload or Capture a Face"),
     outputs=gr.Textbox(label="Prediction Result"),
-    title="Eigenfaces Face Recognition",
-    description="Upload a face image (JPG/PNG) to identify the most similar person from the dataset."
+    title="Enhanced Eigenfaces Face Recognition",
+    description="Improved with face detection and lighting normalization. Upload a clear frontal face image."
 )
 
 iface.launch()