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| yolox_pose/configs/yolox-pose_l_4xb64-300e_coco.py: init_cfg=dict(checkpoint='https://download.openmmlab.com/mmyolo/v0/yolox/' | |
| yolox_pose/configs/yolox-pose_l_4xb64-300e_coco.py- 'yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_' | |
| -- | |
| uniformer/configs/td-hm_uniformer-s-8xb64-210e_coco-448x320.py: checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' | |
| uniformer/configs/td-hm_uniformer-s-8xb64-210e_coco-448x320.py- 'uniformer/uniformer_small_in1k.pth'))) | |
| -- | |
| yolox_pose/configs/py_yolox_pose_s_8xb32_300e_coco.py: checkpoint='https://download.openmmlab.com/mmyolo/v0/yolox/' | |
| yolox_pose/configs/py_yolox_pose_s_8xb32_300e_coco.py- 'yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_' | |
| -- | |
| uniformer/configs/td-hm_uniformer-b-8xb128-210e_coco-256x192.py: checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' |
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| wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_hrnet-w32_8xb64-210e_coco-wholebody-384x288.py: checkpoint='https://download.openmmlab.com/mmpose/' | |
| wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_hrnet-w32_8xb64-210e_coco-wholebody-384x288.py- 'pretrain_models/hrnet_w32-36af842e.pth'), | |
| -- | |
| wholebody_2d_keypoint/rtmpose/ubody/rtmpose-m_8xb64-270e_coco-ubody-wholebody-256x192.py: checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' | |
| wholebody_2d_keypoint/rtmpose/ubody/rtmpose-m_8xb64-270e_coco-ubody-wholebody-256x192.py- 'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-f2f7d6f6_20230130.pth' # noqa: E501 | |
| -- | |
| wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_hrnet-w32_dark-8xb64-210e_coco-wholebody-256x192.py: checkpoint='https://download.openmmlab.com/mmpose/' | |
| wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/td-hm_hrnet-w32_dark-8xb64-210e_coco-wholebody-256x192.py- 'pretrain_models/hrnet_w32-36af842e.pth') |
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| from pyflann import * | |
| import numpy as np | |
| dataset = np.array( | |
| [[1., 1, 1, 2, 3], | |
| [10, 10, 10, 3, 2], | |
| [100, 100, 2, 30, 1] | |
| ]) | |
| testset = np.array( | |
| [[1., 1, 1, 1, 1], |
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| import numpy as np | |
| import scipy.optimize | |
| a = np.array([[0, 0], [1, 1], [2, 2]]) | |
| b = np.array([[0, 1], [2, 2.2], [1, 1.2], [0, 0]]) | |
| indices = np.indices((len(a), len(b))) | |
| # array([[[0, 0, 0, 0], | |
| # [1, 1, 1, 1], | |
| # [2, 2, 2, 2]], |
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| import pandas as pd | |
| import errno | |
| import numpy as np | |
| import sys | |
| import warnings | |
| def save_mot(filename, df): | |
| df.to_csv(filename, header=False, index=False) |
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| import matplotlib.pylab as plt | |
| import numpy as np | |
| fig = plt.figure() | |
| closed = False | |
| def handle_close(evt): | |
| global closed | |
| closed = True |
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| Množiny. Teorie matic. Soustavy lineárních rovnic. | |
| Gaussova eliminace | |
| Diskriminant | |
| Invertibilni matice, singularni matice | |
| Linearni zobrazeni, eigenvalues | |
| Základy matematické analýzy. Metody aproximace funkcí. Metoda nejmenších čtverců. Aplikace pro hledání modelů z naměřených reálných dat. | |
| Lagrangeho multiplikatory | |
| Newtonova metoda | |
| Linearni regrese |
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| #!/usr/bin/env python | |
| from PySide.QtCore import * | |
| from PySide.QtGui import * | |
| import cv2 | |
| import sys | |
| class MainApp(QWidget): | |
| def __init__(self): |
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