Tongue Detection and Recognition System

{ Python Code } | { Xamta Infotech } | { Emai: }

Creating a comprehensive and professional tongue detection and recognition system involves advanced computer vision techniques and machine learning. Due to the complexity and scope of such a project, I'll provide a simplified example using a deep learning model for face detection and facial landmark detection.

For this example, I'll use the face detection and facial landmark detection models provided by the Dlib library. Additionally, we'll use the MTCNN (Multi-task Cascaded Convolutional Networks) model for face detection, which is available through the facenet-pytorch library.


#pip install opencv-python dlib facenet-pytorch
#xamta infotech, guidelins to install dependancies.
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.")
# 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)
# 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)
if __name__ == "__main__":
image_path = "path/to/your/image.jpg"

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