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| import math | |
| import os | |
| import numpy as np | |
| import tensorflow as tf | |
| from utils.my_utils import normalize_wrt_maximum_distance_point, retrieve_interest_points | |
| def head_pose_estimation(kpt, detector, gaze_model, id_list=None): | |
| fps, shape = 20, (1280, 720) | |
| yaw_list, pitch_list, roll_list, yaw_u_list, pitch_u_list, roll_u_list = [], [], [], [], [], [] | |
| center_xy = [] | |
| for j, kpt_person in enumerate(kpt): | |
| # TODO here change order if openpose | |
| face_kpt = retrieve_interest_points(kpt_person, detector=detector) | |
| tdx = np.mean([face_kpt[k] for k in range(0, 15, 3) if face_kpt[k] != 0.0]) | |
| tdy = np.mean([face_kpt[k + 1] for k in range(0, 15, 3) if face_kpt[k + 1] != 0.0]) | |
| if math.isnan(tdx) or math.isnan(tdy): | |
| tdx = -1 | |
| tdy = -1 | |
| center_xy.append([tdx, tdy]) | |
| face_kpt_normalized = np.array(normalize_wrt_maximum_distance_point(face_kpt)) | |
| # print(type(face_kpt_normalized), face_kpt_normalized) | |
| aux = tf.cast(np.expand_dims(face_kpt_normalized, 0), tf.float32) | |
| yaw, pitch, roll = gaze_model(aux, training=False) | |
| # print(yaw[0].numpy()[0], pitch, roll) | |
| yaw_list.append(yaw[0].numpy()[0]) | |
| pitch_list.append(pitch[0].numpy()[0]) | |
| roll_list.append(roll[0].numpy()[0]) | |
| yaw_u_list.append(yaw[0].numpy()[1]) | |
| pitch_u_list.append(pitch[0].numpy()[1]) | |
| roll_u_list.append(roll[0].numpy()[1]) | |
| # print(id_lists[j]) | |
| # print('yaw: ', yaw[0].numpy()[0], 'yaw unc: ', yaw[0].numpy()[1], 'pitch: ', pitch[0].numpy()[0], | |
| # 'pitch unc: ', pitch[0].numpy()[1], 'roll: ', roll[0].numpy()[0], 'roll unc: ', roll[0].numpy()[1]) | |
| # draw_axis(yaw.numpy(), pitch.numpy(), roll.numpy(), im_pose, tdx, tdy) | |
| return center_xy, yaw_list, pitch_list, roll_list | |
| def hpe(gaze_model, kpt_person, detector): | |
| # TODO here change order if openpose | |
| face_kpt = retrieve_interest_points(kpt_person, detector=detector) | |
| tdx = np.mean([face_kpt[k] for k in range(0, 15, 3) if face_kpt[k] != 0.0]) | |
| tdy = np.mean([face_kpt[k + 1] for k in range(0, 15, 3) if face_kpt[k + 1] != 0.0]) | |
| if math.isnan(tdx) or math.isnan(tdy): | |
| tdx = -1 | |
| tdy = -1 | |
| # center_xy.append([tdx, tdy]) | |
| face_kpt_normalized = np.array(normalize_wrt_maximum_distance_point(face_kpt)) | |
| # print(type(face_kpt_normalized), face_kpt_normalized) | |
| aux = tf.cast(np.expand_dims(face_kpt_normalized, 0), tf.float32) | |
| yaw, pitch, roll = gaze_model(aux, training=False) | |
| return yaw, pitch, roll, tdx, tdy | |
| def project_ypr_in2d(yaw, pitch, roll): | |
| """ Project yaw pitch roll on image plane. Result is NOT normalised. | |
| :param yaw: | |
| :param pitch: | |
| :param roll: | |
| :return: | |
| """ | |
| pitch = pitch * np.pi / 180 | |
| yaw = -(yaw * np.pi / 180) | |
| roll = roll * np.pi / 180 | |
| x3 = (math.sin(yaw)) | |
| y3 = (-math.cos(yaw) * math.sin(pitch)) | |
| # normalize the components | |
| length = np.sqrt(x3**2 + y3**2) | |
| return [x3, y3] | |