Yudai Kato yudai09

## open3d_capture_birdeyeview.py
import numpy as np
import laspy as lp
import open3d as o3d
import time


def main():
    pcd = o3d.geometry.PointCloud()
    points, colors = load_las_file()

## errors_come_from_camera_orientaton.ipynb

      
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                yudai09
                / errors_come_from_camera_orientaton.ipynb
            
            
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              March 6, 2023 14:41
            
              
                errors_come_from_camera_orientaton.ipynb
              
          
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## lensdistortion-webgl.html
<!DOCTYPE html>
<html lang="en">

<head>
	<title>Lens distortion WebGL sample using three.js. Giliam de Carpentier, 2015. BSD licensed. See
		www.decarpentier.nl/lens-distortion for more details</title>
	<meta charset="utf-8">
	<style>
		body {
			margin: 0px;

## yolov5_soft_NMS.py
import torch
import torchvision
import numpy as np
import time


def main():
    # wget 'https://ultralytics.com/images/zidane.jpg' in advance.
    model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
    model = model.to('cuda')

## opticalflow_TVL-1.py
import numpy as np
import cv2 as cv


# Most of the code come from https://docs.opencv.org/master/d4/dee/tutorial_optical_flow.html
# But, The algorithm is changed to TVL-1
cap = cv.VideoCapture(cv.samples.findFile("vtest.avi"))
ret, frame1 = cap.read()
prvs = cv.cvtColor(frame1, cv.COLOR_BGR2GRAY)
hsv = np.zeros_like(frame1)

## snake_active_contour.py
from skimage import io, filters, draw, color
import numpy as np


# A Fast algorithm for active contours and curvature estimation
# https://www.cs.princeton.edu/courses/archive/spr02/cs496/williams_shah.pdf
def main():
    # ループの終了条件
    max_iter = 10000
    thresh_finish = 10.0

## focal_loss_pytroch.py
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.autograd import Function
import numpy as np


def one_hot(index, classes):
    size = index.size() + (classes,)

## memory_leak_pytorch.py
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import time
from sklearn import datasets
import numpy
from PIL import Image

class ImageDataSet(Dataset):
    """Image dataset."""

## keras-vis-stanford40action.py
import os
import glob
import numpy as np
import keras
from keras.callbacks import ModelCheckpoint
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Input

from vis.visualization import visualize_cam, overlay

## monitor_smtp_delay_by_pop.py
# -*- coding: utf-8 -*-
import email
import poplib
import time
import smtplib
import sys
import traceback
# ------目的---------------------
# このスクリプトはメール配送にかかっている時間を測定する。
# Zabbix等と組み合わせてメールの遅延状況を監視するために使うことを想定している。
	import numpy as np
	import laspy as lp
	import open3d as o3d
	import time


	def main():
	pcd = o3d.geometry.PointCloud()
	points, colors = load_las_file()
	<!DOCTYPE html>
	<html lang="en">

	<head>
	<title>Lens distortion WebGL sample using three.js. Giliam de Carpentier, 2015. BSD licensed. See
	www.decarpentier.nl/lens-distortion for more details</title>
	<meta charset="utf-8">
	<style>
	body {
	margin: 0px;
	import torch
	import torchvision
	import numpy as np
	import time


	def main():
	# wget 'https://ultralytics.com/images/zidane.jpg' in advance.
	model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
	model = model.to('cuda')
	import numpy as np
	import cv2 as cv


	# Most of the code come from https://docs.opencv.org/master/d4/dee/tutorial_optical_flow.html
	# But, The algorithm is changed to TVL-1
	cap = cv.VideoCapture(cv.samples.findFile("vtest.avi"))
	ret, frame1 = cap.read()
	prvs = cv.cvtColor(frame1, cv.COLOR_BGR2GRAY)
	hsv = np.zeros_like(frame1)
	from skimage import io, filters, draw, color
	import numpy as np


	# A Fast algorithm for active contours and curvature estimation
	# https://www.cs.princeton.edu/courses/archive/spr02/cs496/williams_shah.pdf
	def main():
	# ループの終了条件
	max_iter = 10000
	thresh_finish = 10.0
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.nn.parameter import Parameter
	from torch.autograd import Function
	import numpy as np


	def one_hot(index, classes):
	size = index.size() + (classes,)
	import torch
	from torch.utils.data import Dataset, DataLoader
	from torchvision import transforms
	import time
	from sklearn import datasets
	import numpy
	from PIL import Image

	class ImageDataSet(Dataset):
	"""Image dataset."""
	import os
	import glob
	import numpy as np
	import keras
	from keras.callbacks import ModelCheckpoint
	from keras.preprocessing import image
	from keras.preprocessing.image import ImageDataGenerator
	from keras.layers import Input

	from vis.visualization import visualize_cam, overlay
	# -- coding: utf-8 --
	import email
	import poplib
	import time
	import smtplib
	import sys
	import traceback
	# ------目的---------------------
	# このスクリプトはメール配送にかかっている時間を測定する。
	# Zabbix等と組み合わせてメールの遅延状況を監視するために使うことを想定している。