print(' ---- Detection stage ---- ')
print('Face detection')
face_bboxes, face_bboxes_confidences = face_det_tracker.predict(image)
res_face_detection = face_det_tracker.get_result()
#face_detector.visualize(image, face_bboxes, face_bboxes_confidences, color=(0, 0, 255))
print('Initialize tracking...')
multi_faces_tracker.initialize(image, face_bboxes)
print('Facial landmarks estimation')
facial_landmarks, facial_landmarks_confidences = facial_landmarks_estimator.predict(image, face_bboxes, face_bboxes_confidences)
res_facial_landmarks_estimation = facial_landmarks_estimator.get_result()
#facial_landmarks_estimator.visualize(image, facial_landmarks, facial_landmarks_confidences, color=(0, 0, 255))
pipeline.match(res_face_detection.bounding_boxes, res_facial_landmarks_estimation.facial_landmarks)
pipeline.visualize(image)
cv2.putText(image, 'Detection stage', (5, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255))
elif pipeline.regime == 'tracking':
print(' ---- Tracking stage ---- ')
print('Face tracking')
face_bboxes, face_bboxes_confidences = multi_faces_tracker.update(image)
#face_detector.visualize(image, face_bboxes, face_bboxes_confidences, color=(0, 255, 0))
print('Facial landmarks estimation')
facial_landmarks, facial_landmarks_confidences = facial_landmarks_estimator.predict(image, face_bboxes, face_bboxes_confidences)
#facial_landmarks_estimator.visualize(image, facial_landmarks, facial_landmarks_confidences, color=(0, 255, 0))
image, face_bboxes, face_bboxes_confidences)
res_facial_emotion_classification = facial_emotion_classifier.get_result(
)
#facial_emotion_classifier.visualize(image, facial_emotions, facial_emotions_confidences)
if action_recognition:
print('Action classification')
action_classifier.predict(image)
#action_classifier.predict(res_humans)
#res_action_recognition = action_classifier.get_result()
#actions, actions_confidences = res_action_recognition.convert_to_list()
#action_classifier.visualize(image)
pipeline.match(res_human_detection.bounding_boxes, \
res_poses_estimation.poses, \
res_face_detection.bounding_boxes, \
res_facial_landmarks_estimation.facial_landmarks, \
res_facial_emotion_classification.facial_emotions)
pipeline.visualize(image)
cv2.putText(image, 'Detection stage', (5, 25),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0))
pipeline.update_regime()
elif pipeline.regime == 'tracking':
print(' ---- Tracking stage ---- ')
#if len(multi_human_tracker.trackers) == 0:
# pass