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.
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| #pip install opencv-python dlib facenet-pytorch | |
| #xamta infotech, guidelins to install dependancies. hi@xamta.in | |
| 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) |