ven-kyoshiro

import numpy as np
import matplotlib.pyplot as plt
def ccw(A,B,C):
    return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])

def intersect(A,B,C,D):
    # check AB vs CD
    return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D)

class LineSegment:

ven-kyoshiro

import numpy as np
def ccw(A,B,C):
    return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])

def intersect(A,B,C,D):
    # check AB vs CD
    return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D)

class LineSegment:
    def __init__(self,xy1,xy2):

ven-kyoshiro

import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
"""
marge two scatter animation


"""

fig = plt.figure()

ven-kyoshiro

def bb_intersection_over_union(boxA, boxB):
    """ calculate IOU score between boxA and boxB
    Args:
        boxA : [hori_lo,virt_lo,hori_hi,virt_hi]
        boxB : [hori_lo,virt_lo,hori_hi,virt_hi]
    Returns:
        iou_score
    """
    # determine the (x, y)-coordinates of the intersection rectangle
    xA = max(boxA[0], boxB[0])

ven-kyoshiro

"""
# 変更点
- video2activityで画像を出力するフォルダを指定できるように
  - video2activity('200922_4_10min.mp4','test.csv',visualize=True,visualize_dir='hoge')
  - ↑の場合 hoge以下に画像が出力される
- 動画内でずっと黒色になっている部分を検出して，白く塗りつぶす処理を追加
  - video2activity('200922_4_10min.mp4','test.csv',remove_dontmove=True)
"""
import tqdm
import time

ven-kyoshiro

import tqdm
import time
import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt

from sklearn.cluster import DBSCAN

def measure_position(arr,mouse_area_max,mouse_area_min,

ven-kyoshiro

import matplotlib.pyplot as plt
import numpy as np

def val2color(x):
    assert x<=1, 'x should be in the [0,1]'
    base =0.5
    if x<1/3:
        g=x*3.
        r= 1-g
        b=0

ven-kyoshiro

import pprint
import numpy as np
class PolynomialPredictor(object):
    def __init__(self,u,v,n=1,debug=True): 
        self.n = n
        u = np.array(u)
        A = np.array([u**i for i in range(self.n,-1,-1)]).T
        b = np.array([v]).T
        self.alpha = np.matmul(np.matmul( np.linalg.inv( np.matmul(A.T,A) ), A.T ), b )
        self.sum_of_se = np.sum((np.matmul(A,np.fliplr(self.alpha))-b)**2)

ven-kyoshiro

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as pat
rot = lambda th:np.array([[np.cos(th),-np.sin(th)],[np.sin(th),np.cos(th)]])

mu = np.array([0.,0.]) 
S = np.array([[1,0],[0,6]]) @ rot(np.pi/3)
x = np.random.multivariate_normal(mu, S,10000)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)

ven-kyoshiro

import matplotlib.pyplot as plt
import math
def xy2color(x,y):
    norm = min(1.0,(x*x + y*y)**(1/2))
    rad = math.atan2(x,y)+math.pi
    if rad < math.pi*2/3:
        ratio = rad/(math.pi*2/3)
        r = 1.-ratio
        g = ratio
        b = 0