Source code for pyVHR.realtime.params

from pyVHR.extraction.utils import MagicLandmarks
from pyVHR.BVP.methods import *
from pyVHR.BVP.filters import *
from pyVHR.extraction.utils import MagicLandmarks

[docs]class Params: # DEFAULT PARAMETERS videoFileName = '' fps_fixed = None tot_sec = 0 winSize = 6 stride = 1 cuda = False skin_extractor = 'convexhull' # or faceparsing approach = 'patches' # or holistic patches = 'squares' # or rects type = 'mean' landmarks_list = MagicLandmarks.equispaced_facial_points squares_dim = 30.0 rects_dims = [] # extractor params skin_color_low_threshold = 75 skin_color_high_threshold = 230 sig_color_low_threshold = 75 sig_color_high_threshold = 230 # rgb_filter_th params color_low_threshold = 75 color_high_threshold = 230 # visualize skin and patches visualize_skin = True visualize_patches = True visualize_landmarks = True visualize_landmarks_number = True font_size = 0.3 font_color = (255, 0, 0, 255) # Pre filtering # dictionary of {filter_func, params} pre_filter = [{'filter_func': BPfilter, 'params': { 'minHz': 0.7, 'maxHz': 3.0, 'fps': 'adaptive', 'order': 6}}] # BVP method # dictionary of {method_func, device_type, params} method = {'method_func': cpu_CHROM, 'device_type': 'cpu', 'params': {}} # Post filtering # dictionary of {filter_func, params} post_filter = [{'filter_func': BPfilter, 'params': { 'minHz': 0.7, 'maxHz': 3.0, 'fps': 'adaptive', 'order': 6}}] # BPM params minHz = 0.7 maxHz = 4.0 # WELCH: CPU, GPU # PSD_CLUSTERING: CPU, GPU # USE psd_clustering only with patches! BPM_extraction_type = "welch" # or 'psd_clustering' # Utils fake_delay = False resize = True out_path = None