tejastank · December 28, 2023 07:30 · Dec 28, 2023
diff --git a/tongue-detection-and-recognition.py b/tongue-detection-and-recognition.py
@@ -0,0 +1,59 @@
+#pip install opencv-python dlib facenet-pytorch
+#xamta infotech, guidelins to install dependancies. [email protected] 
+
+import cv2
+import dlib
+from facenet_pytorch import MTCNN
+import numpy as np
+
+def detect_tongue(image_path):
+    # Load the image
+    image = cv2.imread(image_path)
+
+    # Initialize the MTCNN face detection model
+    mtcnn = MTCNN(keep_all=True)
+
+    # Detect faces in the image
+    boxes, probs = mtcnn.detect(image)
+
+    if boxes is None:
+        print("No face detected in the image.")
+        return
+
+    # Assuming the first detected face is the correct one
+    face_box = boxes[0].astype(int)
+
+    # Extract the region of interest (ROI) around the detected face
+    face_roi = image[face_box[1]:face_box[3], face_box[0]:face_box[2]]
+
+    # Display the detected face
+    cv2.imshow("Detected Face", face_roi)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+    # Load the pre-trained facial landmark predictor from dlib
+    predictor_path = "shape_predictor_68_face_landmarks.dat"
+    predictor = dlib.shape_predictor(predictor_path)
+
+    # Convert the face ROI to grayscale for facial landmark detection
+    gray_face = cv2.cvtColor(face_roi, cv2.COLOR_BGR2GRAY)
+
+    # Detect facial landmarks
+    landmarks = predictor(gray_face, dlib.rectangle(0, 0, face_roi.shape[1], face_roi.shape[0]))
+
+    # Extract the tongue region (assuming landmarks 54-59 represent the tongue)
+    tongue_landmarks = np.array([(landmarks.part(i).x, landmarks.part(i).y) for i in range(54, 60)])
+
+    # Create a mask for the tongue region
+    mask = np.zeros_like(gray_face)
+    cv2.fillPoly(mask, [tongue_landmarks], 255)
+
+    # Display the detected tongue
+    tongue = cv2.bitwise_and(gray_face, gray_face, mask=mask)
+    cv2.imshow("Detected Tongue", tongue)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    image_path = "path/to/your/image.jpg"
+    detect_tongue(image_path)