Update anti_spoofing.py

anti_spoofing.py

@@ -1,50 +1,15 @@
+import gradio as gr
 import cv2
 import dlib
 import numpy as np
-import os
-import time
-import mediapipe as mp
 from skimage import feature
 
+# Initialize your AntiSpoofingSystem class as previously defined
 class AntiSpoofingSystem:
     def __init__(self):
         self.detector = dlib.get_frontal_face_detector()
-        self.anti_spoofing_completed = False
-        self.blink_count = 0
-        self.image_captured = False
-        self.captured_image = None
         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.cap = cv2.VideoCapture(0)
-        self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
-        self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
-
-        self.save_directory = "Person"
-        if not os.path.exists(self.save_directory):
-            os.makedirs(self.save_directory)
-
-        self.net_smartphone = cv2.dnn.readNet('yolov4.weights', 'Pretrained_yolov4 (1).cfg')
-        with open('PreTrained_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
-
-        # New attributes for student data
-        self.student_id = None
-        self.student_name = None
 
     def calculate_ear(self, eye):
         A = np.linalg.norm(eye[1] - eye[5])
@@ -60,165 +25,42 @@ class AntiSpoofingSystem:
         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':
-                        center_x = int(obj[0] * frame.shape[1])
-                        center_y = int(obj[1] * frame.shape[0])
-                        width = int(obj[2] * frame.shape[1])
-                        height = int(obj[3] * frame.shape[0])
-                        left = int(center_x - width / 2)
-                        top = int(center_y - height / 2)
-
-                        cv2.rectangle(frame, (left, top), (left + width, top + height), (0, 0, 255), 2)
-                        cv2.putText(frame, 'Smartphone Detected', (left, top - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
-                        
-                        self.smartphone_detected = True
-                        self.left_blink_counter = 0
-                        self.right_blink_counter = 0
-                        return
-
-        self.frame_count += 1
-        self.smartphone_detected = False
-
-    def access_verified_image(self):
-        ret, frame = self.cap.read()
-        if not ret:
-            return None
-
-        # Perform anti-spoofing checks
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    def process_image(self, image):
+        # Convert the image to grayscale and detect faces
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
         faces = self.detector(gray)
-        
-        # Check if a face is detected
+
+        # If no face is detected, return a message
         if len(faces) == 0:
-            return None
-        
-        # Assume the first detected face is the subject
+            return "No face detected. Please try again."
+
+        # Process the first detected face
         face = faces[0]
         landmarks = self.predictor(gray, face)
-        
-        # Check for blink detection (assuming you have this method correctly implemented)
         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 not self.detect_blink(ear_left, ear_right):
-            return None
-
-        # Check for hand gesture (assuming you have this method correctly implemented)
-        if not self.detect_hand_gesture(frame):
-            return None
-
-        # Check if a smartphone is detected
-        self.detect_smartphone(frame)
-        if self.smartphone_detected:
-            return None
-        
-        # Check texture for liveness (assuming you have this method correctly implemented)
-        (x, y, w, h) = (face.left(), face.top(), face.width(), face.height())
-        expanded_region = 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 not self.analyze_texture(expanded_region):
-            return None
-
-        return frame
-
-    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
-
-        if self.left_blink_counter > 0 and self.right_blink_counter > 0:
-            self.left_blink_counter = 0
-            self.right_blink_counter = 0
-            return True
-        else:
-            return False
-
-    def run(self):
-        ret, frame = self.cap.read()
-        if not ret:
-            return None
-
-        # Detect smartphone in the frame
-        self.detect_smartphone(frame)
-
-        if self.smartphone_detected:
-            cv2.putText(frame, "Mobile phone detected, can't record attendance", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
-        else:
-            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):
-                    self.blink_count += 1
-                    cv2.putText(frame, f"Blink Count: {self.blink_count}", (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
 
-                # Check if conditions for image capture are met
-                if self.blink_count >= 5 and not self.image_captured:
-                    # Capture the image and reset blink count
-                    self.save_image(frame)
-                    self.blink_count = 0
-                    self.image_captured = True
+        # Determine if a blink is detected
+        blink_detected = (ear_left < self.EAR_THRESHOLD and ear_right < self.EAR_THRESHOLD)
+        return "Blink detected!" if blink_detected else "No blink detected."
 
-        return frame
+# Define the Gradio interface
+anti_spoofing_system = AntiSpoofingSystem()
 
-    def save_image(self, frame):
-        # Implement logic to save the frame as an image
-        timestamp = int(time.time())
-        image_name = f"captured_{timestamp}.png"
-        cv2.imwrite(os.path.join(self.save_directory, image_name), frame)
-        self.captured_image = frame
-        print(f"Image captured and saved as {image_name}")
+def detect_blink(image):
+    result = anti_spoofing_system.process_image(image)
+    return result
 
-    def get_captured_image(self):
-        # Return the captured image with preprocessing applied (if necessary)
-        captured_frame = self.captured_image
-        if captured_frame is not None:
-            return captured_frame
-        return None
+iface = gr.Interface(
+    fn=detect_blink,
+    inputs=gr.Image(shape=(720, 1280)),
+    outputs="text",
+    title="Anti-Spoofing Detection System",
+    description="Upload an image with a face to detect if a blink is detected."
+)
 
-if __name__ == "__main__":
-    anti_spoofing_system = AntiSpoofingSystem()
-    anti_spoofing_system.run()
+# Launch the Gradio interface
+iface.launch()