import glob import json import os.path as osp import numpy as np from tqdm import tqdm import matplotlib.pyplot as plt import seaborn as sns from pandas import DataFrame import pandas as pd def L2(p1, p2): return np.linalg.norm(p1 - p2) def NME(landmarks_gt, landmarks_pv): pts_num = landmarks_gt.shape[0] if pts_num == 29: left_index = 16 right_index = 17 elif pts_num == 68: left_index = 36 right_index = 45 elif pts_num == 98: left_index = 60 right_index = 72 nme = 0 eye_span = L2(landmarks_gt[left_index], landmarks_gt[right_index]) nmeList = [] for i in range(pts_num): error = L2(landmarks_pv[i], landmarks_gt[i]) _nme = error / eye_span nmeList.append(_nme) nme += _nme nme /= pts_num return nme, nmeList def NME_analysis(listA): for jsonA in listA: pred = np.array(jsonA['pred']) gt = np.array(jsonA['gt']) nme, nmeList = NME(gt, pred) jsonA['nme'] = nme jsonA['nmeList'] = nmeList return listA def nme_analysis(listA): bdy_nmeList = [] scene_nmeList = [] for jsonA in tqdm(listA): nme = jsonA['nmeList'] nme = np.array(nme) bdy_nme = np.mean(nme[:33]) scene_nme = np.mean(nme[33:]) # scene_nme = np.mean(nme[[33, 35, 40, 38, # 60, 62, 96, 66, 64, # 50, 44, 48, 46, # 68, 70, 97, 74, 72, # 54, 55, 57, 59, # 76, 82, 79, 90, 94, 85, 16]]) bdy_nmeList.append(bdy_nme) scene_nmeList.append(scene_nme) print('bdy nme: {:.4f}'.format(np.mean(bdy_nmeList))) print('scene_nmeList: {:.4f}'.format(np.mean(scene_nmeList))) def Energy_analysis(listA, easyThresh=0.02, easyNum=10, hardThresh=0.07, hardNum=10): easyDict = {'energy': [], 'nme': []} hardDict = {'energy': [], 'nme': []} _easyNum, _hardNum = 0, 0 def cal_energy(evalues): evalues = np.array(evalues) # _energy = _energy.max(1) eccentricity = evalues.max(1) / evalues.min(1) # _energy = _energy.sum() / 2 _energy = np.mean(eccentricity) return _energy for jsonA in tqdm(listA): nme = jsonA['nme'] evalues = jsonA['evalues'] if _easyNum == easyNum and _hardNum == hardNum: break if nme < easyThresh and _easyNum < easyNum: energy = cal_energy(evalues) easyDict['energy'].append(energy) easyDict['nme'].append(nme) _easyNum += 1 elif nme > hardThresh and _hardNum < hardNum: energy = cal_energy(evalues) hardDict['energy'].append(energy) hardDict['nme'].append(nme) _hardNum += 1 print('easyThresh: < {}; hardThresh > {}'.format(easyThresh, hardThresh)) print(' |nme |energy |num |') print('easy samples: |{:.4f} |{:.4f} |{} |'.format(np.mean(easyDict['nme']), np.mean(easyDict['energy']), len(easyDict['energy']))) print('hard samples: |{:.4f} |{:.4f} |{} |'.format(np.mean(hardDict['nme']), np.mean(hardDict['energy']), len(hardDict['energy']))) return easyDict, hardDict def Eccentricity_analysis(listA): eyecornerList = [] boundaryList = [] for jsonA in listA: evalues = np.array(jsonA['evalues']) eccentricity = evalues.max(1) / evalues.min(1) eyecorner = np.mean(eccentricity[[60, 64, 68, 72]]) boundary = np.mean(eccentricity[0:33]) eyecornerList.append(eyecorner) boundaryList.append(boundary) print('eyecorner: {:.4f}'.format(np.mean(eyecornerList))) print('boundary: {:.4f}'.format(np.mean(boundaryList))) return eyecornerList, boundaryList def plot_bar(dataList): x = list(range(98)) assert len(x) == len(dataList) _x = 'Landmark Index' # _y = 'elliptical eccentricity (λ1/λ2)' _y = 'PCA Analyze (λ1/λ2)' data = { _x: x, _y: dataList } df = DataFrame(data) plt.figure(figsize=(10, 4)) sns.barplot(x=_x, y=_y, data=df) plt.show() def Eccentricity_analysis2(listA, is_vis=False): landmarksList = [[] for i in range(98)] for jsonA in listA: evalues = np.array(jsonA['evalues']) eccentricity = evalues.max(1) / evalues.min(1) for i, e in enumerate(eccentricity): landmarksList[i].append(e) print('Mean value: {:.4f}'.format(np.mean(np.array(landmarksList)))) landmarksList = [np.mean(l) for l in landmarksList] if is_vis: plot_bar(landmarksList) return landmarksList def std_analysis2(): save_dir = '/apdcephfs/share_1134483/charlinzhou/experiment/cvpr-23/wflw_results' # l2_npy = glob.glob(osp.join(save_dir, '*DSNT*.npy')) l2_npy = glob.glob(osp.join(save_dir, '*MHNLoss_v2_l2*.npy')) def npy2std(npyList): datas = [np.load(npy)[np.newaxis, :] for npy in npyList] datas = np.concatenate(datas, axis=0) # denormalization datas = (datas + 1) * 256 / 2 mean = datas.mean(axis=0)[np.newaxis, :] dist = np.linalg.norm(datas - mean, axis=-1) std = np.std(dist, 0) print('min: {}, max:{}, mean:{}'.format(std.min(), std.max(), std.mean())) return std std1 = npy2std(l2_npy) std1 = std1.mean(0) # plot_bar(std1) bdy_std = np.mean(std1[:33]) cofw_std = np.mean(std1[[33, 35, 40, 38, 60, 62, 96, 66, 64, 50, 44, 48, 46, 68, 70, 97, 74, 72, 54, 55, 57, 59, 76, 82, 79, 90, 94, 85, 16]]) print('bdy_std: {:.4f}, cofw_std: {:.4f}'.format(bdy_std, cofw_std)) print('the ratio of Boundary std and ALL std: {:.4f} / {:.4f}'.format(np.sum(std1[:33]), np.sum(std1))) if __name__ == '__main__': # 4.29模型 json_path = '/apdcephfs/share_1134483/charlinzhou/ckpts/STAR/WFLW/WFLW_256x256_adam_ep500_lr0.001_bs128_STARLoss_smoothl1_1_b0183746-161a-4b76-9cb9-8a2059090233/results.json' # 无初始化 # json_path = '/apdcephfs/share_1134483/charlinzhou/ckpts/STAR/WFLW/WFLW_256x256_adam_ep500_lr0.001_bs128_STARLoss_smoothl1_1_9cff3656-8ca8-4c3d-a95d-da76f9f76ea5/results.json' # 4.02模型 # json_path = '/apdcephfs/share_1134483/charlinzhou/ckpts/STAR/WFLW/WFLW_256x256_adam_ep500_lr0.001_bs128_STARLoss_smoothl1_1_AAM_2d2bb70e-6fdb-459c-baf7-18c89e7a165f/results.json' listA = json.load(open(json_path, 'r')) print('Load Done!') listA = NME_analysis(listA) print('NME analysis Done!') # Exp1: 分析简单样本和困难样本的能量差异 easyDict, hardDict = Energy_analysis(listA, easyNum=2500, hardNum=2500, easyThresh=0.03, hardThresh=0.08) # Exp2.1: 分析眼角点和轮廓点的斜率差异 # eyecornerList, boundaryList = Eccentricity_analysis(listA) # Exp2.2: 可视化所有点的斜率分布 # landmarksList = Eccentricity_analysis2(listA, is_vis=True) # Exp2.3: 可视化所有点的方差分布 # std_analysis2() # Exp3: 五官和轮廓NME分析 # nme_analysis(listA) # print(easyDict) # print(hardDict) # nmeList = [jsonA['nme'] for jsonA in listA] # print(len(nmeList))