Update app.py

app.py

@@ -1,28 +1,169 @@
 import gradio as gr
-from anti_spoofing import AntiSpoofingSystem
+import tensorflow as tf
+import cv2
+import numpy as np
+import os
+import time
+import dlib
+import mediapipe as mp
+from skimage import feature
+# from your_cnn_model import YourCNNModel  # Import your CNN model
 
-# Instantiate your AntiSpoofingSystem
+class AntiSpoofingSystem:
+    def __init__(self):
+        self.detector = dlib.get_frontal_face_detector()
+        self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+        self.mp_hands = mp.solutions.hands
+        self.hands = self.mp_hands.Hands(static_image_mode=False, max_num_hands=1, min_detection_confidence=0.7)
+        self.net_smartphone = cv2.dnn.readNet('yolov4.weights', 'yolov4.cfg')
+        with open('coco.names', 'r') as f:
+            self.classes_smartphone = f.read().strip().split('\n')
+        self.EAR_THRESHOLD = 0.25
+        self.BLINK_CONSEC_FRAMES = 4
+        self.left_eye_state = False
+        self.right_eye_state = False
+        self.left_blink_counter = 0
+        self.right_blink_counter = 0
+        self.smartphone_detected = False
+        self.smartphone_detection_frame_interval = 30
+        self.frame_count = 0
+
+    def calculate_ear(self, eye):
+        A = np.linalg.norm(eye[1] - eye[5])
+        B = np.linalg.norm(eye[2] - eye[4])
+        C = np.linalg.norm(eye[0] - eye[3])
+        return (A + B) / (2.0 * C)
+
+    def analyze_texture(self, face_region):
+        gray_face = cv2.cvtColor(face_region, cv2.COLOR_BGR2GRAY)
+        lbp = feature.local_binary_pattern(gray_face, P=8, R=1, method="uniform")
+        lbp_hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, 58), range=(0, 58))
+        lbp_hist = lbp_hist.astype("float")
+        lbp_hist /= (lbp_hist.sum() + 1e-5)
+        return np.sum(lbp_hist[:10]) > 0.3
+
+    def detect_hand_gesture(self, frame):
+        results = self.hands.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+        return results.multi_hand_landmarks is not None
+
+    def detect_smartphone(self, frame):
+        if self.frame_count % self.smartphone_detection_frame_interval == 0:
+            blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
+            self.net_smartphone.setInput(blob)
+            output_layers_names = self.net_smartphone.getUnconnectedOutLayersNames()
+            detections = self.net_smartphone.forward(output_layers_names)
+            for detection in detections:
+                for obj in detection:
+                    scores = obj[5:]
+                    class_id = np.argmax(scores)
+                    confidence = scores[class_id]
+                    if confidence > 0.5 and self.classes_smartphone[class_id] == 'cell phone':
+                        self.smartphone_detected = True
+                        self.left_blink_counter = 0
+                        self.right_blink_counter = 0
+                        return
+        self.frame_count += 1
+        self.smartphone_detected = False
+
+    def detect_blink(self, left_ear, right_ear):
+        if self.smartphone_detected:
+            self.left_eye_state = False
+            self.right_eye_state = False
+            self.left_blink_counter = 0
+            self.right_blink_counter = 0
+            return False
+
+        if left_ear < self.EAR_THRESHOLD:
+            if not self.left_eye_state:
+                self.left_eye_state = True
+        else:
+            if self.left_eye_state:
+                self.left_eye_state = False
+                self.left_blink_counter += 1
+
+        if right_ear < self.EAR_THRESHOLD:
+            if not self.right_eye_state:
+                self.right_eye_state = True
+        else:
+            if self.right_eye_state:
+                self.right_eye_state = False
+                self.right_blink_counter += 1
+
+        return self.left_blink_counter > 0 and self.right_blink_counter > 0
+
+    def run(self, input_image):
+        frame = input_image
+        blink_count = 0
+        hand_gesture_detected = False
+        real_person_detected = False
+        cropped_face = None
+
+        self.detect_smartphone(frame)
+
+        if not self.smartphone_detected:
+            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            faces = self.detector(gray)
+
+            for face in faces:
+                landmarks = self.predictor(gray, face)
+                leftEye = np.array([(landmarks.part(n).x, landmarks.part(n).y) for n in range(36, 42)])
+                rightEye = np.array([(landmarks.part(n).x, landmarks.part(n).y) for n in range(42, 48)])
+
+                ear_left = self.calculate_ear(leftEye)
+                ear_right = self.calculate_ear(rightEye)
+
+                if self.detect_blink(ear_left, ear_right):
+                    blink_count += 1
+
+                hand_gesture_detected = self.detect_hand_gesture(frame)
+
+                (x, y, w, h) = (face.left(), face.top(), face.width(), face.height())
+                cropped_face = frame[max(y - h // 2, 0):min(y + 3 * h // 2, frame.shape[0]),
+                                     max(x - w // 2, 0):min(x + 3 * w // 2, frame.shape[1])]
+
+                if blink_count >= 5 and hand_gesture_detected and self.analyze_texture(cropped_face):
+                    real_person_detected = True
+                    break
+
+        return real_person_detected, cropped_face
+
+# Initialize the anti-spoofing system
 anti_spoofing_system = AntiSpoofingSystem()
 
+# Load your CNN model (this is a placeholder for your actual model loading code)
+supervised_embedding_model = tf.keras.models.load_model('v3_embedding_model (2).h5')
+#cnn_model.load_weights('v3_embedding_model (2).h5')
+
 def process_frame(image):
-    # Process the single image
-    # This is a placeholder for your actual processing logic
-    processed_image, blink_count, hand_gesture_detected, smartphone_detected = anti_spoofing_system.process_single_frame(image)
-    
-    # Return results
-    return processed_image, f"Blink Count: {blink_count}", f"Hand Gesture Detected: {hand_gesture_detected}", f"Smartphone Detected: {smartphone_detected}"
+    real_person_detected, cropped_face = anti_spoofing_system.run(image)
+
+    if not real_person_detected:
+        return image, "No real person detected or spoofing attempt."
+
+    # Placeholder for actual CNN model prediction
+    person_id, confidence = "PersonID", 0.99  # Replace with your CNN model logic
+
+    result_text = f"Person identified: {person_id} with confidence: {confidence}" if person_id else "Person not recognized. Registration required."
+    cv2.putText(image, result_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+    return image
+
+def video_stream():
+    cap = cv2.VideoCapture(0)
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        processed_frame = process_frame(frame)
+        yield processed_frame
 
 iface = gr.Interface(
-    fn=process_frame,
-    inputs=gr.Image(type="pil", label="Upload Image or Use Webcam"),  # Directly using gr.Image
-    outputs=[
-        gr.Image(type="pil", label="Processed Image"),  # Directly using gr.Image
-        gr.Textbox(label="Blink Count"),
-        gr.Textbox(label="Hand Gesture Detected"),
-        gr.Textbox(label="Smartphone Detected")
-    ],
-    title="Anti-Spoofing System",
-    description="Upload an image or capture from webcam to check for spoofing indicators."
+    fn=video_stream,
+    inputs=None,
+    outputs=gr.outputs.Video(label="Output Video"),
+    live=True,
+    title="Live Face Recognition and Verification System",
+    description="Live detection and verification of persons from a camera feed."
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()