pyanomaly/datatools/evaluate/utils.py
def amc_score(frame, frame_hat, flow, flow_hat, wf, wi, kernel_size=16, stride=4, lambada_s=0.2):
    '''
    wf, wi is different from videos
    '''
    loss_appe, loss_flow = simple_diff(frame, frame_hat, flow, flow_hat)
    max_patch_appe, max_patch_flow, app_cord, flow_crod = find_max_patch(loss_appe, loss_flow, kernel_size=kernel_size, stride=stride)
    final_score = amc_normal_score(wf, max_patch_appe, wi, max_patch_flow, lambada_s=lambada_s)

    return final_score, app_cord, flow_crod

    wf, wi is different from videos
    
wf, wi is different from videos
wf wi is different from videos
def amc_score(frame, frame_hat, flow, flow_hat, wf, wi, kernel_size=16, stride=4, lambada_s=0.2):
    loss_appe, loss_flow = simple_diff(frame, frame_hat, flow, flow_hat)
    max_patch_appe, max_patch_flow, app_cord, flow_crod = find_max_patch(loss_appe, loss_flow, kernel_size=kernel_size, stride=stride)
    final_score = amc_normal_score(wf, max_patch_appe, wi, max_patch_flow, lambada_s=lambada_s)
    return final_score, app_cord, flow_crod
def amc_score ( frame , frame_hat , flow , flow_hat , wf , wi , kernel_size = 16 , stride = 4 , lambada_s = 0.2 ) : loss_appe , loss_flow = simple_diff ( frame , frame_hat , flow , flow_hat ) max_patch_appe , max_patch_flow , app_cord , flow_crod = find_max_patch ( loss_appe , loss_flow , kernel_size = kernel_size , stride = stride ) final_score = amc_normal_score ( wf , max_patch_appe , wi , max_patch_flow , lambada_s = lambada_s ) return final_score , app_cord , flow_crod
wf, wi is different from videos
wf wi is different from videos
'''
    wf, wi is different from videos
    '''
frame
frame_hat
flow
flow_hat
wf
wi
kernel_size
stride
lambada_s