removed tracking option in pose estimation + fixed tests so that synchonization is not done in multiperson Demo + fixed multitab plots crashing
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davidpagnon
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6fd237ecc9
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07afe0b0fb
@ -152,7 +152,7 @@ make_c3d = true # save triangulated data in c3d format in addition to trc
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[filtering]
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type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
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display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
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display_figures = true # true or false (lowercase)
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make_c3d = true # also save triangulated data in c3d format
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[filtering.butterworth]
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@ -152,7 +152,7 @@
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# [filtering]
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# type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
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# display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
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# display_figures = true # true or false (lowercase)
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# make_c3d = true # also save triangulated data in c3d format
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# [filtering.butterworth]
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@ -152,7 +152,7 @@ keypoints_to_consider = 'all' # 'all' if all points should be considered, for ex
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# [filtering]
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# type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
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# display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
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# display_figures = true # true or false (lowercase)
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# make_c3d = false # also save triangulated data in c3d format
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# [filtering.butterworth]
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@ -152,7 +152,7 @@ make_c3d = false # save triangulated data in c3d format in addition to trc
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[filtering]
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type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
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display_figures = false # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
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display_figures = true # true or false (lowercase)
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make_c3d = false # also save triangulated data in c3d format
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[filtering.butterworth]
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@ -152,7 +152,7 @@ make_c3d = true # save triangulated data in c3d format in addition to trc
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[filtering]
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type = 'butterworth' # butterworth, kalman, gaussian, LOESS, median, butterworth_on_speed
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display_figures = true # true or false (lowercase) # fails when run multiple times https://github.com/superjax/plotWindow/issues/7
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display_figures = true # true or false (lowercase)
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make_c3d = true # also save triangulated data in c3d format
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[filtering.butterworth]
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@ -129,17 +129,19 @@ class TestWorkflow(unittest.TestCase):
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config_dict.get("synchronization").update({"display_sync_plots":False})
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config_dict['filtering']['display_figures'] = False
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# Step by step
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Pose2Sim.calibration(config_dict)
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Pose2Sim.poseEstimation(config_dict)
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Pose2Sim.synchronization(config_dict)
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# Pose2Sim.synchronization(config_dict) # No synchronization for multi-person for now
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Pose2Sim.personAssociation(config_dict)
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Pose2Sim.triangulation(config_dict)
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Pose2Sim.filtering(config_dict)
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Pose2Sim.markerAugmentation(config_dict)
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# Pose2Sim.kinematics(config_dict)
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# Run all
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config_dict.get("pose").update({"overwrite_pose":False})
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Pose2Sim.runAll(config_dict)
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Pose2Sim.runAll(config_dict, do_synchronization=False)
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####################
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@ -149,7 +151,7 @@ class TestWorkflow(unittest.TestCase):
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project_dir = '../Demo_Batch'
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os.chdir(project_dir)
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Pose2Sim.runAll()
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Pose2Sim.runAll(do_synchronization=False)
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if __name__ == '__main__':
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@ -8,6 +8,10 @@
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##################################################
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Determine gait on and off from a TRC file of point coordinates.
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N.B.: Could implement the methods listed there in the future.
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Please feel free to make a pull-request or keep me informed if you do so!
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Three available methods, each of them with their own pros and cons:
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- "forward_coordinates":
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@ -22,9 +22,9 @@ import sys
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import matplotlib as mpl
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mpl.use('qt5agg')
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mpl.rc('figure', max_open_warning=0)
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from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTabWidget, QVBoxLayout
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from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
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from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
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from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QTabWidget, QVBoxLayout
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import warnings
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warnings.filterwarnings("ignore", category=UserWarning, module="c3d")
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@ -482,7 +482,8 @@ class plotWindow():
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def __init__(self, parent=None):
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self.app = QApplication(sys.argv)
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self.MainWindow = QMainWindow()
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if not self.app:
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self.app = QApplication(sys.argv)
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self.MainWindow.__init__()
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self.MainWindow.setWindowTitle("Multitabs figure")
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self.canvases = []
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@ -319,7 +319,7 @@ def median_filter_1d(config_dict, frame_rate, col):
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return col_filtered
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def display_figures_fun(Q_unfilt, Q_filt, time_col, keypoints_names):
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def display_figures_fun(Q_unfilt, Q_filt, time_col, keypoints_names, person_id=0):
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'''
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Displays filtered and unfiltered data for comparison
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@ -334,6 +334,7 @@ def display_figures_fun(Q_unfilt, Q_filt, time_col, keypoints_names):
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'''
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pw = plotWindow()
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pw.MainWindow.setWindowTitle('Person '+ str(person_id) + ' coordinates')
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for id, keypoint in enumerate(keypoints_names):
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f = plt.figure()
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@ -472,7 +473,7 @@ def filter_all(config_dict):
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trc_f_out = [f'{os.path.basename(t).split(".")[0]}_filt_{filter_type}.trc' for t in trc_path_in]
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trc_path_out = [os.path.join(pose3d_dir, t) for t in trc_f_out]
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for t_in, t_out in zip(trc_path_in, trc_path_out):
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for person_id, t_in, t_out in enumerate(zip(trc_path_in, trc_path_out)):
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# Read trc header
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with open(t_in, 'r') as trc_file:
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header = [next(trc_file) for line in range(5)]
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@ -489,7 +490,7 @@ def filter_all(config_dict):
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if display_figures:
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# Retrieve keypoints
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keypoints_names = pd.read_csv(t_in, sep="\t", skiprows=3, nrows=0).columns[2::3].to_numpy()
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display_figures_fun(Q_coord, Q_filt, time_col, keypoints_names)
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display_figures_fun(Q_coord, Q_filt, time_col, keypoints_names, person_id)
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# Reconstruct trc file with filtered coordinates
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with open(t_out, 'w') as trc_o:
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@ -127,14 +127,13 @@ def sort_people_rtmlib(pose_tracker, keypoints, scores):
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return sorted_keypoints, sorted_scores
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def process_video(video_path, pose_tracker, tracking, output_format, save_video, save_images, display_detection, frame_range):
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def process_video(video_path, pose_tracker, output_format, save_video, save_images, display_detection, frame_range):
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'''
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Estimate pose from a video file
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INPUTS:
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- video_path: str. Path to the input video file
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- pose_tracker: PoseTracker. Initialized pose tracker object from RTMLib
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- tracking: bool. Whether to give consistent person ID across frames
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- output_format: str. Output format for the pose estimation results ('openpose', 'mmpose', 'deeplabcut')
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- save_video: bool. Whether to save the output video
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- save_images: bool. Whether to save the output images
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@ -186,10 +185,6 @@ def process_video(video_path, pose_tracker, tracking, output_format, save_video,
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# Perform pose estimation on the frame
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keypoints, scores = pose_tracker(frame)
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# Reorder keypoints, scores
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if tracking:
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keypoints, scores = sort_people_rtmlib(pose_tracker, keypoints, scores)
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# Save to json
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if 'openpose' in output_format:
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json_file_path = os.path.join(json_output_dir, f'{video_name_wo_ext}_{frame_idx:06d}.json')
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@ -225,7 +220,7 @@ def process_video(video_path, pose_tracker, tracking, output_format, save_video,
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cv2.destroyAllWindows()
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def process_images(image_folder_path, vid_img_extension, pose_tracker, tracking, output_format, fps, save_video, save_images, display_detection, frame_range):
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def process_images(image_folder_path, vid_img_extension, pose_tracker, output_format, fps, save_video, save_images, display_detection, frame_range):
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'''
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Estimate pose estimation from a folder of images
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@ -233,7 +228,6 @@ def process_images(image_folder_path, vid_img_extension, pose_tracker, tracking,
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- image_folder_path: str. Path to the input image folder
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- vid_img_extension: str. Extension of the image files
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- pose_tracker: PoseTracker. Initialized pose tracker object from RTMLib
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- tracking: bool. Whether to give consistent person ID across frames
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- output_format: str. Output format for the pose estimation results ('openpose', 'mmpose', 'deeplabcut')
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- save_video: bool. Whether to save the output video
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- save_images: bool. Whether to save the output images
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@ -275,17 +269,6 @@ def process_images(image_folder_path, vid_img_extension, pose_tracker, tracking,
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# Perform pose estimation on the image
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keypoints, scores = pose_tracker(frame)
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# Reorder keypoints, scores
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if tracking:
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max_id = max(pose_tracker.track_ids_last_frame)
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num_frames, num_points, num_coordinates = keypoints.shape
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keypoints_filled = np.zeros((max_id+1, num_points, num_coordinates))
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scores_filled = np.zeros((max_id+1, num_points))
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keypoints_filled[pose_tracker.track_ids_last_frame] = keypoints
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scores_filled[pose_tracker.track_ids_last_frame] = scores
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keypoints = keypoints_filled
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scores = scores_filled
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# Extract frame number from the filename
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if 'openpose' in output_format:
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@ -361,9 +344,7 @@ def rtm_estimator(config_dict):
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save_images = True if 'to_images' in config_dict['pose']['save_video'] else False
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display_detection = config_dict['pose']['display_detection']
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overwrite_pose = config_dict['pose']['overwrite_pose']
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det_frequency = config_dict['pose']['det_frequency']
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tracking = config_dict['pose']['tracking']
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# Determine frame rate
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video_files = glob.glob(os.path.join(video_dir, '*'+vid_img_extension))
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@ -407,9 +388,6 @@ def rtm_estimator(config_dict):
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logging.info(f'Inference run on every single frame.')
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else:
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raise ValueError(f"Invalid det_frequency: {det_frequency}. Must be an integer greater or equal to 1.")
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if tracking:
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logging.info(f'Pose estimation will attempt to give consistent person IDs across frames.\n')
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# Select the appropriate model based on the model_type
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if pose_model.upper() == 'HALPE_26':
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@ -433,7 +411,7 @@ def rtm_estimator(config_dict):
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mode=mode,
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backend=backend,
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device=device,
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tracking=tracking,
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tracking=False,
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to_openpose=False)
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@ -454,7 +432,7 @@ def rtm_estimator(config_dict):
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logging.info(f'Found video files with extension {vid_img_extension}.')
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for video_path in video_files:
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pose_tracker.reset()
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process_video(video_path, pose_tracker, tracking, output_format, save_video, save_images, display_detection, frame_range)
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process_video(video_path, pose_tracker, output_format, save_video, save_images, display_detection, frame_range)
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else:
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# Process image folders
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@ -463,4 +441,4 @@ def rtm_estimator(config_dict):
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for image_folder in image_folders:
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pose_tracker.reset()
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image_folder_path = os.path.join(video_dir, image_folder)
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process_images(image_folder_path, vid_img_extension, pose_tracker, tracking, output_format, frame_rate, save_video, save_images, display_detection, frame_range)
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process_images(image_folder_path, vid_img_extension, pose_tracker, output_format, frame_rate, save_video, save_images, display_detection, frame_range)
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@ -177,11 +177,6 @@ def time_lagged_cross_corr(camx, camy, lag_range, show=True, ref_cam_id=0, cam_i
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if isinstance(lag_range, int):
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lag_range = [-lag_range, lag_range]
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import hashlib
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print(repr(list(camx)), repr(list(camy)))
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hashlib.md5(pd.util.hash_pandas_object(camx).values).hexdigest()
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hashlib.md5(pd.util.hash_pandas_object(camy).values).hexdigest()
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pearson_r = [camx.corr(camy.shift(lag)) for lag in range(lag_range[0], lag_range[1])]
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offset = int(np.floor(len(pearson_r)/2)-np.argmax(pearson_r))
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if not np.isnan(pearson_r).all():
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