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import glob
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import json
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import os.path as osp
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import numpy as np
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from tqdm import tqdm
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import matplotlib.pyplot as plt
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import seaborn as sns
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from pandas import DataFrame
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import pandas as pd
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def L2(p1, p2):
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return np.linalg.norm(p1 - p2)
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def NME(landmarks_gt, landmarks_pv):
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pts_num = landmarks_gt.shape[0]
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if pts_num == 29:
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left_index = 16
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right_index = 17
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elif pts_num == 68:
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left_index = 36
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right_index = 45
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elif pts_num == 98:
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left_index = 60
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right_index = 72
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nme = 0
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eye_span = L2(landmarks_gt[left_index], landmarks_gt[right_index])
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nmeList = []
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for i in range(pts_num):
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error = L2(landmarks_pv[i], landmarks_gt[i])
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_nme = error / eye_span
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nmeList.append(_nme)
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nme += _nme
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nme /= pts_num
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return nme, nmeList
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def NME_analysis(listA):
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for jsonA in listA:
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pred = np.array(jsonA['pred'])
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gt = np.array(jsonA['gt'])
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nme, nmeList = NME(gt, pred)
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jsonA['nme'] = nme
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jsonA['nmeList'] = nmeList
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return listA
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def nme_analysis(listA):
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bdy_nmeList = []
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scene_nmeList = []
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for jsonA in tqdm(listA):
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nme = jsonA['nmeList']
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nme = np.array(nme)
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bdy_nme = np.mean(nme[:33])
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scene_nme = np.mean(nme[33:])
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bdy_nmeList.append(bdy_nme)
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scene_nmeList.append(scene_nme)
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print('bdy nme: {:.4f}'.format(np.mean(bdy_nmeList)))
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print('scene_nmeList: {:.4f}'.format(np.mean(scene_nmeList)))
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def Energy_analysis(listA, easyThresh=0.02, easyNum=10, hardThresh=0.07, hardNum=10):
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easyDict = {'energy': [], 'nme': []}
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hardDict = {'energy': [], 'nme': []}
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_easyNum, _hardNum = 0, 0
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def cal_energy(evalues):
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evalues = np.array(evalues)
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eccentricity = evalues.max(1) / evalues.min(1)
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_energy = np.mean(eccentricity)
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return _energy
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for jsonA in tqdm(listA):
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nme = jsonA['nme']
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evalues = jsonA['evalues']
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if _easyNum == easyNum and _hardNum == hardNum:
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break
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if nme < easyThresh and _easyNum < easyNum:
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energy = cal_energy(evalues)
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easyDict['energy'].append(energy)
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easyDict['nme'].append(nme)
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_easyNum += 1
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elif nme > hardThresh and _hardNum < hardNum:
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energy = cal_energy(evalues)
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hardDict['energy'].append(energy)
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hardDict['nme'].append(nme)
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_hardNum += 1
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print('easyThresh: < {}; hardThresh > {}'.format(easyThresh, hardThresh))
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print(' |nme |energy |num |')
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print('easy samples: |{:.4f} |{:.4f} |{} |'.format(np.mean(easyDict['nme']),
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np.mean(easyDict['energy']),
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len(easyDict['energy'])))
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print('hard samples: |{:.4f} |{:.4f} |{} |'.format(np.mean(hardDict['nme']),
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np.mean(hardDict['energy']),
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len(hardDict['energy'])))
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return easyDict, hardDict
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def Eccentricity_analysis(listA):
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eyecornerList = []
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boundaryList = []
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for jsonA in listA:
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evalues = np.array(jsonA['evalues'])
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eccentricity = evalues.max(1) / evalues.min(1)
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eyecorner = np.mean(eccentricity[[60, 64, 68, 72]])
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boundary = np.mean(eccentricity[0:33])
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eyecornerList.append(eyecorner)
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boundaryList.append(boundary)
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print('eyecorner: {:.4f}'.format(np.mean(eyecornerList)))
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print('boundary: {:.4f}'.format(np.mean(boundaryList)))
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return eyecornerList, boundaryList
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def plot_bar(dataList):
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x = list(range(98))
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assert len(x) == len(dataList)
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_x = 'Landmark Index'
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_y = 'PCA Analyze (λ1/λ2)'
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data = {
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_x: x,
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_y: dataList
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}
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df = DataFrame(data)
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plt.figure(figsize=(10, 4))
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sns.barplot(x=_x, y=_y, data=df)
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plt.show()
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def Eccentricity_analysis2(listA, is_vis=False):
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landmarksList = [[] for i in range(98)]
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for jsonA in listA:
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evalues = np.array(jsonA['evalues'])
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eccentricity = evalues.max(1) / evalues.min(1)
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for i, e in enumerate(eccentricity):
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landmarksList[i].append(e)
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print('Mean value: {:.4f}'.format(np.mean(np.array(landmarksList))))
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landmarksList = [np.mean(l) for l in landmarksList]
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if is_vis:
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plot_bar(landmarksList)
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return landmarksList
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def std_analysis2():
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save_dir = '/apdcephfs/share_1134483/charlinzhou/experiment/cvpr-23/wflw_results'
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l2_npy = glob.glob(osp.join(save_dir, '*MHNLoss_v2_l2*.npy'))
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def npy2std(npyList):
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datas = [np.load(npy)[np.newaxis, :] for npy in npyList]
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datas = np.concatenate(datas, axis=0)
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datas = (datas + 1) * 256 / 2
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mean = datas.mean(axis=0)[np.newaxis, :]
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dist = np.linalg.norm(datas - mean, axis=-1)
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std = np.std(dist, 0)
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print('min: {}, max:{}, mean:{}'.format(std.min(), std.max(), std.mean()))
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return std
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std1 = npy2std(l2_npy)
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std1 = std1.mean(0)
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bdy_std = np.mean(std1[:33])
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cofw_std = np.mean(std1[[33, 35, 40, 38,
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60, 62, 96, 66, 64,
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50, 44, 48, 46,
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68, 70, 97, 74, 72,
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54, 55, 57, 59,
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76, 82, 79, 90, 94, 85, 16]])
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print('bdy_std: {:.4f}, cofw_std: {:.4f}'.format(bdy_std, cofw_std))
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print('the ratio of Boundary std and ALL std: {:.4f} / {:.4f}'.format(np.sum(std1[:33]), np.sum(std1)))
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if __name__ == '__main__':
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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'
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listA = json.load(open(json_path, 'r'))
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print('Load Done!')
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listA = NME_analysis(listA)
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print('NME analysis Done!')
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easyDict, hardDict = Energy_analysis(listA, easyNum=2500, hardNum=2500, easyThresh=0.03, hardThresh=0.08)
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