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59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
import cv2
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import mediapipe as mp
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mp_drawing = mp.solutions.drawing_utils
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mp_face_mesh = mp.solutions.face_mesh
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drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=1)
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cap = cv2.VideoCapture(0)
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with mp_face_mesh.FaceMesh(
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max_num_faces=1,
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refine_landmarks=True,
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min_detection_confidence=0.5,
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min_tracking_confidence=0.5) as face_mesh:
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while cap.isOpened():
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success, image = cap.read()
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if not success:
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print("Ignoring empty camera frame.")
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continue
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# Initialize the face mesh model
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face_mesh = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.5)
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# Load the input image
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# lecture de la vidéo
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ret, frame = cap.read()
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# conversion de l'image en RGB
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image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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# Process the image and extract the landmarks
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results = face_mesh.process(image)
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if results.multi_face_landmarks:
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landmarks = results.multi_face_landmarks[0]
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# Define the landmark indices for the corners of the eyes and the tip of the nose
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left_eye = [33, 133, 246, 161, 160, 159, 158, 157, 173, 133]
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right_eye = [362, 263, 373, 380, 381, 382, 384, 385, 386, 362]
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nose_tip = 4
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# Calculate the distance between the eyes and the nose tip
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left_eye_x = landmarks.landmark[left_eye[0]].x * image.shape[1]
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right_eye_x = landmarks.landmark[right_eye[0]].x * image.shape[1]
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nose_x = landmarks.landmark[nose_tip].x * image.shape[1]
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eye_distance = abs(left_eye_x - right_eye_x)
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nose_distance = abs(nose_x - (left_eye_x + right_eye_x) / 2)
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# Determine the gender based on the eye and nose distances
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if eye_distance > 1.5 * nose_distance:
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gender = "Female"
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else:
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gender = "Male"
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# Draw the landmarks on the image
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cv2.putText(image, gender, (10, 50),cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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# affichage de la vidéo
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cv2.imshow('Video', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
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if cv2.waitKey(10) & 0xFF == ord('q'):
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break
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# libération de la caméra et des ressources
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cap.release()
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cv2.destroyAllWindows() |