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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,166 +1,166 @@ | ||
| import cv2 | ||
| import mediapipe as mp | ||
| import numpy as np | ||
| import pandas as pd | ||
| # place holders and global variables | ||
| x = 0 # X axis head pose | ||
| y = 0 # Y axis head pose | ||
|
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||
| X_AXIS_CHEAT = 0 | ||
| Y_AXIS_CHEAT = 0 | ||
|
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| statements = [] | ||
|
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|
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| def pose(): | ||
| global VOLUME_NORM, x, y, X_AXIS_CHEAT, Y_AXIS_CHEAT | ||
| ############################# | ||
| mp_face_mesh = mp.solutions.face_mesh | ||
| face_mesh = mp_face_mesh.FaceMesh( | ||
| min_detection_confidence=0.5, min_tracking_confidence=0.5) | ||
| cap = cv2.VideoCapture(0) | ||
| mp_drawing = mp.solutions.drawing_utils | ||
| # mp_drawing_styles = mp.solutions | ||
|
|
||
| while cap.isOpened(): | ||
| success, image = cap.read() | ||
| # Flip the image horizontally for a later selfie-view display | ||
| # Also convert the color space from BGR to RGB | ||
| image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB) | ||
|
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||
| # To improve performance | ||
| image.flags.writeable = False | ||
|
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||
| # Get the result | ||
| results = face_mesh.process(image) | ||
|
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||
| # To improve performance | ||
| image.flags.writeable = True | ||
|
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||
| # Convert the color space from RGB to BGR | ||
| image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | ||
|
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| img_h, img_w, img_c = image.shape | ||
| face_3d = [] | ||
| face_2d = [] | ||
|
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| face_ids = [33, 263, 1, 61, 291, 199] | ||
|
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||
| if results.multi_face_landmarks: | ||
| for face_landmarks in results.multi_face_landmarks: | ||
| mp_drawing.draw_landmarks( | ||
| image=image, | ||
| landmark_list=face_landmarks, | ||
| connections=mp_face_mesh.FACEMESH_CONTOURS, | ||
| landmark_drawing_spec=None) | ||
| for idx, lm in enumerate(face_landmarks.landmark): | ||
| # print(lm) | ||
| if idx in face_ids: | ||
| if idx == 1: | ||
| nose_2d = (lm.x * img_w, lm.y * img_h) | ||
| nose_3d = (lm.x * img_w, lm.y * img_h, lm.z * 8000) | ||
|
|
||
| x, y = int(lm.x * img_w), int(lm.y * img_h) | ||
|
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||
| # Get the 2D Coordinates | ||
| face_2d.append([x, y]) | ||
|
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||
| # Get the 3D Coordinates | ||
| face_3d.append([x, y, lm.z]) | ||
|
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||
| # Convert it to the NumPy array | ||
| face_2d = np.array(face_2d, dtype=np.float64) | ||
|
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||
| # Convert it to the NumPy array | ||
| face_3d = np.array(face_3d, dtype=np.float64) | ||
|
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||
| # The camera matrix | ||
| focal_length = 1 * img_w | ||
|
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| cam_matrix = np.array([[focal_length, 0, img_h / 2], | ||
| [0, focal_length, img_w / 2], | ||
| [0, 0, 1]]) | ||
|
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||
| # The Distance Matrix | ||
| dist_matrix = np.zeros((4, 1), dtype=np.float64) | ||
|
|
||
| # Solve PnP | ||
| success, rot_vec, trans_vec = cv2.solvePnP( | ||
| face_3d, face_2d, cam_matrix, dist_matrix) | ||
|
|
||
| # Get rotational matrix | ||
| rmat, jac = cv2.Rodrigues(rot_vec) | ||
|
|
||
| # Get angles | ||
| angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat) | ||
|
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||
| # Get the y rotation degree | ||
| x = angles[0] * 360 | ||
| y = angles[1] * 360 | ||
|
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||
| # print(y) | ||
|
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||
| # See where the user's head tilting | ||
| if y < -10: | ||
| text = "Looking Left" | ||
| statements.append(text) | ||
| # print("looking left") | ||
| elif y > 10: | ||
| text = "Looking Right" | ||
| statements.append(text) | ||
| # print("looking right") | ||
| elif x < -10: | ||
| text = "Looking Down" | ||
| statements.append(text) | ||
| # print("looking down") | ||
| else: | ||
| text = "Looking Straight" | ||
| statements.append(text) | ||
| # print("looking straight") | ||
| text = str(int(x)) + "::" + str(int(y)) + text | ||
| # print(str(int(x)) + "::" + str(int(y))) | ||
| # print("x: {x} | y: {y} | sound amplitude: {amp}".format(x=int(x), y=int(y), amp=audio.SOUND_AMPLITUDE)) | ||
|
|
||
| # Y is left / right | ||
| # X is up / down | ||
| if y < -10 or y > 10: | ||
| X_AXIS_CHEAT = 1 | ||
| else: | ||
| X_AXIS_CHEAT = 0 | ||
|
|
||
| if x < -5: | ||
| Y_AXIS_CHEAT = 1 | ||
| else: | ||
| Y_AXIS_CHEAT = 0 | ||
|
|
||
| # print(X_AXIS_CHEAT, Y_AXIS_CHEAT) | ||
| # Display the nose direction | ||
| nose_3d_projection, jacobian = cv2.projectPoints( | ||
| nose_3d, rot_vec, trans_vec, cam_matrix, dist_matrix) | ||
|
|
||
| p1 = (int(nose_2d[0]), int(nose_2d[1])) | ||
| p2 = (int(nose_3d_projection[0][0][0]), int( | ||
| nose_3d_projection[0][0][1])) | ||
|
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||
| cv2.line(image, p1, p2, (255, 0, 0), 2) | ||
|
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||
| # Add the text on the image | ||
| cv2.putText(image, text, (20, 20), | ||
| cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) | ||
|
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||
| cv2.imshow('Head Pose Estimation', image) | ||
|
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||
| if cv2.waitKey(1) & 0xFF == ord('q'): | ||
| break | ||
|
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||
| df = pd.DataFrame(data={"statement": statements}) | ||
| statement_counts = df['statement'].value_counts() | ||
| statement_percentages = (statement_counts / len(statements)) * 100 | ||
| print(statement_percentages) | ||
|
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||
| cap.release() | ||
|
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||
|
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||
| ############################# | ||
| if __name__ == "__main__": | ||
| pose() | ||
| import cv2 | ||
| import mediapipe as mp | ||
| import numpy as np | ||
| import pandas as pd | ||
| # place holders and global variables | ||
| x = 0 # X axis head pose | ||
| y = 0 # Y axis head pose | ||
|
|
||
| X_AXIS_CHEAT = 0 | ||
| Y_AXIS_CHEAT = 0 | ||
|
|
||
| statements = [] | ||
|
|
||
|
|
||
| def pose(): | ||
| global VOLUME_NORM, x, y, X_AXIS_CHEAT, Y_AXIS_CHEAT | ||
| ############################# | ||
| mp_face_mesh = mp.solutions.face_mesh | ||
| face_mesh = mp_face_mesh.FaceMesh( | ||
| min_detection_confidence=0.5, min_tracking_confidence=0.5) | ||
| cap = cv2.VideoCapture(0) | ||
| mp_drawing = mp.solutions.drawing_utils | ||
| # mp_drawing_styles = mp.solutions | ||
|
|
||
| while cap.isOpened(): | ||
| success, image = cap.read() | ||
| # Flip the image horizontally for a later selfie-view display | ||
| # Also convert the color space from BGR to RGB | ||
| image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB) | ||
|
|
||
| # To improve performance | ||
| image.flags.writeable = False | ||
|
|
||
| # Get the result | ||
| results = face_mesh.process(image) | ||
|
|
||
| # To improve performance | ||
| image.flags.writeable = True | ||
|
|
||
| # Convert the color space from RGB to BGR | ||
| image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) | ||
|
|
||
| img_h, img_w, img_c = image.shape | ||
| face_3d = [] | ||
| face_2d = [] | ||
|
|
||
| face_ids = [33, 263, 1, 61, 291, 199] | ||
|
|
||
| if results.multi_face_landmarks: | ||
| for face_landmarks in results.multi_face_landmarks: | ||
| mp_drawing.draw_landmarks( | ||
| image=image, | ||
| landmark_list=face_landmarks, | ||
| connections=mp_face_mesh.FACEMESH_CONTOURS, | ||
| landmark_drawing_spec=None) | ||
| for idx, lm in enumerate(face_landmarks.landmark): | ||
| # print(lm) | ||
| if idx in face_ids: | ||
| if idx == 1: | ||
| nose_2d = (lm.x * img_w, lm.y * img_h) | ||
| nose_3d = (lm.x * img_w, lm.y * img_h, lm.z * 8000) | ||
|
|
||
| x, y = int(lm.x * img_w), int(lm.y * img_h) | ||
|
|
||
| # Get the 2D Coordinates | ||
| face_2d.append([x, y]) | ||
|
|
||
| # Get the 3D Coordinates | ||
| face_3d.append([x, y, lm.z]) | ||
|
|
||
| # Convert it to the NumPy array | ||
| face_2d = np.array(face_2d, dtype=np.float64) | ||
|
|
||
| # Convert it to the NumPy array | ||
| face_3d = np.array(face_3d, dtype=np.float64) | ||
|
|
||
| # The camera matrix | ||
| focal_length = 1 * img_w | ||
|
|
||
| cam_matrix = np.array([[focal_length, 0, img_h / 2], | ||
| [0, focal_length, img_w / 2], | ||
| [0, 0, 1]]) | ||
|
|
||
| # The Distance Matrix | ||
| dist_matrix = np.zeros((4, 1), dtype=np.float64) | ||
|
|
||
| # Solve PnP | ||
| success, rot_vec, trans_vec = cv2.solvePnP( | ||
| face_3d, face_2d, cam_matrix, dist_matrix) | ||
|
|
||
| # Get rotational matrix | ||
| rmat, jac = cv2.Rodrigues(rot_vec) | ||
|
|
||
| # Get angles | ||
| angles, mtxR, mtxQ, Qx, Qy, Qz = cv2.RQDecomp3x3(rmat) | ||
|
|
||
| # Get the y rotation degree | ||
| x = angles[0] * 360 | ||
| y = angles[1] * 360 | ||
|
|
||
| # print(y) | ||
|
|
||
| # See where the user's head tilting | ||
| if y < -10: | ||
| text = "Looking Left" | ||
| statements.append(text) | ||
| # print("looking left") | ||
| elif y > 10: | ||
| text = "Looking Right" | ||
| statements.append(text) | ||
| # print("looking right") | ||
| elif x < -10: | ||
| text = "Looking Down" | ||
| statements.append(text) | ||
| # print("looking down") | ||
| else: | ||
| text = "Looking Straight" | ||
| statements.append(text) | ||
| # print("looking straight") | ||
| text = str(int(x)) + "::" + str(int(y)) + text | ||
| # print(str(int(x)) + "::" + str(int(y))) | ||
| # print("x: {x} | y: {y} | sound amplitude: {amp}".format(x=int(x), y=int(y), amp=audio.SOUND_AMPLITUDE)) | ||
|
|
||
| # Y is left / right | ||
| # X is up / down | ||
| if y < -10 or y > 10: | ||
| X_AXIS_CHEAT = 1 | ||
| else: | ||
| X_AXIS_CHEAT = 0 | ||
|
|
||
| if x < -5: | ||
| Y_AXIS_CHEAT = 1 | ||
| else: | ||
| Y_AXIS_CHEAT = 0 | ||
|
|
||
| # print(X_AXIS_CHEAT, Y_AXIS_CHEAT) | ||
| # Display the nose direction | ||
| nose_3d_projection, jacobian = cv2.projectPoints( | ||
| nose_3d, rot_vec, trans_vec, cam_matrix, dist_matrix) | ||
|
|
||
| p1 = (int(nose_2d[0]), int(nose_2d[1])) | ||
| p2 = (int(nose_3d_projection[0][0][0]), int( | ||
| nose_3d_projection[0][0][1])) | ||
|
|
||
| cv2.line(image, p1, p2, (255, 0, 0), 2) | ||
|
|
||
| # Add the text on the image | ||
| cv2.putText(image, text, (20, 20), | ||
| cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) | ||
|
|
||
| cv2.imshow('Head Pose Estimation', image) | ||
|
|
||
| if cv2.waitKey(1) & 0xFF == ord('q'): | ||
| break | ||
|
|
||
| df = pd.DataFrame(data={"statement": statements}) | ||
| statement_counts = df['statement'].value_counts() | ||
| statement_percentages = (statement_counts / len(statements)) * 100 | ||
| print(statement_percentages) | ||
|
|
||
| cap.release() | ||
|
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||
|
|
||
| ############################# | ||
| if __name__ == "__main__": | ||
| pose() |
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