投稿者「mars」のアーカイブ

streamlitで動画を再生

# -*- coding: utf-8 -*-

import streamlit as st
import time
import datetime
import os
import glob
import cv2
from PIL import Image
from datetime import datetime, date, time

PATH='/home/mars/pWork/DATA/'

def disp(device):
    cap = cv2.VideoCapture(device)
    image_loc = st.empty()
    while cap.isOpened:
        ret, img = cap.read()
        if ret:
            img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
            image_loc.image(img)
        else:
            break

    cap.release()
    st.button('Replay')

def main():
    st.header("流星観測データの表示")
    date=st.date_input('Select date')
    path=PATH+date.strftime("%Y%m%d")
    #st.write(path)
    if os.path.exists(path):
        files=glob.glob(path+'/*avi')
        option = st.selectbox('Select file:',files)
        disp(option)
    else:
        st.write('No data exists!')

if __name__ == '__main__':
    main()

ファイルの選択対象を、様々な条件で絞りこむコードを追加したい。

cudaとThread対応のテスト

複数台のネットワークカメラに対応し、cuda(GPU)とThread機能を活用できるバージョン。

import cv2
import numpy as np
import time
import datetime
import os
import sys
GPU=True
THREAD=True
if len(sys.argv)>1:
    if sys.argv[1]=='A':
        cPATH='rtsp://5173:6703@192.168.68.74/live'
    else:
        cPATH='rtsp://admin:@192.168.68.128:554/1/h264major'
else:
        cPATH='rtsp://5173:6703@192.168.68.74/live'
PATH="/home/mars/pWork/DATA"
#

class ThreadingVideoCapture:
    def __init__(self, src, max_queue_size=256):
        self.video = cv2.VideoCapture(src)
        self.q = queue.Queue(maxsize=max_queue_size)
        self.stopped = False

    def start(self):
        thread = threading.Thread(target=self.update, daemon=True)
        thread.start()
        return self

    def update(self):
        while True:
            if self.stopped:
                return
            if not self.q.full():
                ok, frame = self.video.read()
                self.q.put((ok, frame))
                if not ok:
                    self.stop()
                    return

    def read(self):
        return self.q.get()

    def stop(self):
        self.stopped = True

    def release(self):
        self.stopped = True
        self.video.release()

    def isOpened(self):
        return self.video.isOpened()

    def get(self, i):
        return self.video.get(i)

def key(k):
    global th, tc,track,reverse
    if k == ord('2'):
        th = th - 1
    elif k == ord('3'):
        th = th + 1
    elif k == ord('4'):
        tc = tc -5
    elif k == ord('5'):
        tc = tc +5
    elif k == ord('t'):
        track = not track
    elif k == ord('r'):
        reverse = not reverse

fontFace =cv2.FONT_HERSHEY_SIMPLEX
track, reverse = False,False
avg=None
writer = None
th = 30
tc = 30
x,y=0,0
detect_counts = 0
red,blue,green = (0,0,255),(255,0,0),(0,255,0)
time_start = time.time()
frame=0
log=PATH+'/metro.log'
if cPATH=='rtsp://5173:6703@192.168.68.74/live':
    TITLE="ATOM"
    HEAD ='ATOM'
else:
    TITLE="ONVIF"
    HEAD='ONVIF'

if THREAD:
    import threading
    import queue
    TITLE=TITLE+"-T"
    capture = ThreadingVideoCapture(cPATH)
    capture.start()
    if not capture.isOpened():
        raise RuntimeError
else:
    capture=cv2.VideoCapture(cPATH)

if GPU:
    TITLE=TITLE+"-G"
    img_gpu_src = cv2.cuda_GpuMat() # Allocate device memory only once, as memory allocation seems to take time...
    img_gpu_dst = cv2.cuda_GpuMat()
    img_gpu_gray= cv2.cuda_GpuMat()

W = capture.get(cv2.CAP_PROP_FRAME_WIDTH)
H = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
W2=int(W/2)
H2=int(H/2)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
print('Camera:',cPATH)
print('Size:',W,H)
while(True):
    ret, img = capture.read()
    if ret:
        org = img.copy()
        if GPU:
            img_gpu_src.upload(img)
            img_gpu_dst = cv2.cuda.resize(img_gpu_src, dsize=(W2, H2))
            img_gpu_dst = cv2.cuda_GpuMat(img_gpu_dst,[0,int(H2*0.85)],[0,W2])
            img_gpu_gray=cv2.cuda.cvtColor(img_gpu_dst,cv2.COLOR_BGR2GRAY)
            org_img=img_gpu_dst.download()
            gray = img_gpu_gray.download()
        else:
            org_img = cv2.resize(img, dsize=(W2, H2))
            org_img=img[0:int(H2*0.85),0:W2]
            gray = cv2.cvtColor(org_img, cv2.COLOR_BGR2GRAY)
        if reverse:
            gray=cv2.bitwise_not(gray)
            avg=cv2.bitwise_not(avg)
        if avg is None:
            avg = gray.copy().astype("float")
            continue

        #wtiter,fname = moving(img,avg)
        cv2.accumulateWeighted(gray, avg, 0.5)
        frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
        thresh = cv2.threshold(frameDelta, th, 255, cv2.THRESH_BINARY)[1]

        contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        detect=False
        for i in range(0,len(contours)):
            if len(contours[i]) > 0:
                 if cv2.contourArea(contours[i]) > tc:
                    detect=True
                    time_start = time.time()
                    if writer is None and track:
                        detect_counts = 0
                        now=datetime.datetime.today()
                        date=now.strftime("%Y%m%d")
                        cDIR=PATH+'/'+date
                        if not(os.path.exists(cDIR)):
                            os.mkdir(cDIR)
                        fname=cDIR+'/'+ HEAD + now .strftime("%Y%m%d_%H%M%S")+".avi"
                        writer = cv2.VideoWriter(fname, fourcc, 15, (int(W), int(H)))
                    rect = contours[i]
                    x, y, w, h = cv2.boundingRect(rect)
                    cv2.rectangle(org_img, (x-w, y-h), (x + w*2, y + h*2), red, 3)
        if detect:
            detect_counts=detect_counts + 1
        if time.time() -  time_start  > 5:
            if writer is not None:
                writer.release()
                new_name=fname.replace(HEAD,HEAD+'_' + f'{detect_counts:04}'+'_')
                os.rename(fname,new_name)
                frame=0
                writer = None
        now=datetime.datetime.today()
        text=now.strftime("%Y%m%d %H%M%S")+' No:'+str(frame)+ ' '+" TH:"+str(th)+" SZ:"+str(tc)
        org_img = cv2.putText(org_img, text, (30,50), fontFace,1,color=green)
        org = cv2.putText(org, text, (30,50), fontFace,1,color=green)
        text1="REC:"+str(track) + "  reverse:" + str(reverse)
        if writer is not None:
            frame=frame+1
            text1=fname+' '+text1
        org_img = cv2.putText(org_img, text1, (30,80), fontFace,1,color=green)
        #cv2.imshow('thresh-level',thresh)

        cv2.imshow(TITLE,org_img)

        if writer is not None:
            writer.write(org)
    else:
        now=datetime.datetime.today()
        date=now.strftime("%Y%m%d_%H%M%S")
        print('reconnect:',date)
        capture.release()
        avg=None
        if THREAD:
            capture = ThreadingVideoCapture(cPATH)
            capture.start()
        else:
            capture = cv2.VideoCapture(cPATH)
    k=cv2.waitKey(1) & 0xFF
    key(k)
    if k== ord('q'):
        break

capture.release()
if writer is not None:
    writer.release()
    new_name=fname.replace(HEAD,HEAD+'_' + f'{detect_counts:04}'+'_')
    os.rename(fname,new_name)
cv2.destroyAllWindows()

jetson nanoでcv2(cuda有効化)

ソースからインストール

python3でcv2をimportすると crashしてコアーダンプ。

問題の解決:export OPENBLAS_CORETYPE=ARMV8

こちらを参照して解決。Jetson NanoのPython3環境でIllegal instruction (cpre dumped)

処理速度の比較測定:約3倍

$ sudo nvpmodel -m 0
$ sudo jetson_clocks
$ python3 opencv_cuda.py
CPU = 2.7655137538909913[msec]
GPU = 1.0501614570617677[msec]
1
$ python3 opencv_cuda.py
CPU = 2.7816075325012206[msec]
GPU = 0.9869620561599731[msec]
1

opencv_cuda.py

import sys
import time
import cv2

### VALUES
NUM_REPEAT = 10000

### Read source image
img_src = cv2.imread("resource/lena.jpg")
cv2.imshow('img_src', img_src)


### Run with CPU
time_start = time.time()
for i in range (NUM_REPEAT):
    img_dst = cv2.resize(img_src, (300, 300))
time_end = time.time()
print ("CPU = {0}".format((time_end - time_start) * 1000 / NUM_REPEAT) + "[msec]")
cv2.imshow('CPU', img_dst)


### Run with GPU
img_gpu_src = cv2.cuda_GpuMat() # Allocate device memory only once, as memory allocation seems to take time...
img_gpu_dst = cv2.cuda_GpuMat()
time_start = time.time()
for i in range (NUM_REPEAT):
    img_gpu_src.upload(img_src)
    img_gpu_dst = cv2.cuda.resize(img_gpu_src, (300, 300))
    img_dst = img_gpu_dst.download()
time_end = time.time()
print ("GPU = {0}".format((time_end - time_start) * 1000 / NUM_REPEAT) + "[msec]")
cv2.imshow('GPU', img_dst)


key = cv2.waitKey(0)
cv2.destroyAllWindows()

print(cv2.cuda.getCudaEnabledDeviceCount())

cudaで利用できる機能を表示してみる

import cv2
cv2.__version__
dir(cv2.cuda)
['ALPHA_ATOP',
 'ALPHA_ATOP_PREMUL',
 'ALPHA_IN',
 'ALPHA_IN_PREMUL',
 'ALPHA_OUT',
 'ALPHA_OUT_PREMUL',
 'ALPHA_OVER',
 'ALPHA_OVER_PREMUL',
 'ALPHA_PLUS',
 'ALPHA_PLUS_PREMUL',
 'ALPHA_PREMUL',
 'ALPHA_XOR',
 'ALPHA_XOR_PREMUL',
 'BroxOpticalFlow_create',
 'COLOR_BAYER_BG2BGR_MHT',
 'COLOR_BAYER_BG2GRAY_MHT',
 'COLOR_BAYER_BG2RGB_MHT',
 'COLOR_BAYER_GB2BGR_MHT',
 'COLOR_BAYER_GB2GRAY_MHT',
 'COLOR_BAYER_GB2RGB_MHT',
 'COLOR_BAYER_GR2BGR_MHT',
 'COLOR_BAYER_GR2GRAY_MHT',
 'COLOR_BAYER_GR2RGB_MHT',
 'COLOR_BAYER_RG2BGR_MHT',
 'COLOR_BAYER_RG2GRAY_MHT',
 'COLOR_BAYER_RG2RGB_MHT',
 'COLOR_BayerBG2BGR_MHT',
 'COLOR_BayerBG2GRAY_MHT',
 'COLOR_BayerBG2RGB_MHT',
 'COLOR_BayerGB2BGR_MHT',
 'COLOR_BayerGB2GRAY_MHT',
 'COLOR_BayerGB2RGB_MHT',
 'COLOR_BayerGR2BGR_MHT',
 'COLOR_BayerGR2GRAY_MHT',
 'COLOR_BayerGR2RGB_MHT',
 'COLOR_BayerRG2BGR_MHT',
 'COLOR_BayerRG2GRAY_MHT',
 'COLOR_BayerRG2RGB_MHT',
 'CascadeClassifier_create',
 'DEVICE_INFO_COMPUTE_MODE_DEFAULT',
 'DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE',
 'DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE_PROCESS',
 'DEVICE_INFO_COMPUTE_MODE_PROHIBITED',
 'DYNAMIC_PARALLELISM',
 'DensePyrLKOpticalFlow_create',
 'DescriptorMatcher_createBFMatcher',
 'DeviceInfo_ComputeModeDefault',
 'DeviceInfo_ComputeModeExclusive',
 'DeviceInfo_ComputeModeExclusiveProcess',
 'DeviceInfo_ComputeModeProhibited',
 'EVENT_BLOCKING_SYNC',
 'EVENT_DEFAULT',
 'EVENT_DISABLE_TIMING',
 'EVENT_INTERPROCESS',
 'Event_BLOCKING_SYNC',
 'Event_DEFAULT',
 'Event_DISABLE_TIMING',
 'Event_INTERPROCESS',
 'Event_elapsedTime',
 'FEATURE_SET_COMPUTE_10',
 'FEATURE_SET_COMPUTE_11',
 'FEATURE_SET_COMPUTE_12',
 'FEATURE_SET_COMPUTE_13',
 'FEATURE_SET_COMPUTE_20',
 'FEATURE_SET_COMPUTE_21',
 'FEATURE_SET_COMPUTE_30',
 'FEATURE_SET_COMPUTE_32',
 'FEATURE_SET_COMPUTE_35',
 'FEATURE_SET_COMPUTE_50',
 'FarnebackOpticalFlow_create',
 'FastFeatureDetector_create',
 'GLOBAL_ATOMICS',
 'GpuMat_defaultAllocator',
 'GpuMat_setDefaultAllocator',
 'HOG_create',
 'HOST_MEM_PAGE_LOCKED',
 'HOST_MEM_SHARED',
 'HOST_MEM_WRITE_COMBINED',
 'HostMem_PAGE_LOCKED',
 'HostMem_SHARED',
 'HostMem_WRITE_COMBINED',
 'NATIVE_DOUBLE',
 'NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_FAST',
 'NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_MAX',
 'NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_MEDIUM',
 'NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_SLOW',
 'NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_UNDEFINED',
 'NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_FAST',
 'NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_MAX',
 'NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_MEDIUM',
 'NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_SLOW',
 'NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_UNDEFINED',
 'NvidiaOpticalFlow_1_0_create',
 'ORB_create',
 'OpticalFlowDual_TVL1_create',
 'SHARED_ATOMICS',
 'SURF_CUDA_ANGLE_ROW',
 'SURF_CUDA_HESSIAN_ROW',
 'SURF_CUDA_LAPLACIAN_ROW',
 'SURF_CUDA_OCTAVE_ROW',
 'SURF_CUDA_ROWS_COUNT',
 'SURF_CUDA_SIZE_ROW',
 'SURF_CUDA_X_ROW',
 'SURF_CUDA_Y_ROW',
 'SparsePyrLKOpticalFlow_create',
 'StereoBeliefPropagation_estimateRecommendedParams',
 'StereoConstantSpaceBP_estimateRecommendedParams',
 'Stream_Null',
 'TargetArchs_has',
 'TargetArchs_hasBin',
 'TargetArchs_hasEqualOrGreater',
 'TargetArchs_hasEqualOrGreaterBin',
 'TargetArchs_hasEqualOrGreaterPtx',
 'TargetArchs_hasEqualOrLessPtx',
 'TargetArchs_hasPtx',
 'WARP_SHUFFLE_FUNCTIONS',
 '__doc__',
 '__loader__',
 '__name__',
 '__package__',
 '__spec__',
 'abs',
 'absSum',
 'absdiff',
 'add',
 'addWeighted',
 'alphaComp',
 'bilateralFilter',
 'bitwise_and',
 'bitwise_not',
 'bitwise_or',
 'bitwise_xor',
 'blendLinear',
 'buildWarpAffineMaps',
 'buildWarpPerspectiveMaps',
 'calcAbsSum',
 'calcHist',
 'calcNorm',
 'calcNormDiff',
 'calcSqrSum',
 'calcSum',
 'cartToPolar',
 'compare',
 'copyMakeBorder',
 'countNonZero',
 'createBackgroundSubtractorMOG',
 'createBackgroundSubtractorMOG2',
 'createBoxFilter',
 'createBoxMaxFilter',
 'createBoxMinFilter',
 'createCLAHE',
 'createCannyEdgeDetector',
 'createColumnSumFilter',
 'createContinuous',
 'createConvolution',
 'createDFT',
 'createDerivFilter',
 'createDisparityBilateralFilter',
 'createGaussianFilter',
 'createGeneralizedHoughBallard',
 'createGeneralizedHoughGuil',
 'createGoodFeaturesToTrackDetector',
 'createHarrisCorner',
 'createHoughCirclesDetector',
 'createHoughLinesDetector',
 'createHoughSegmentDetector',
 'createLaplacianFilter',
 'createLinearFilter',
 'createLookUpTable',
 'createMedianFilter',
 'createMinEigenValCorner',
 'createMorphologyFilter',
 'createRowSumFilter',
 'createScharrFilter',
 'createSeparableLinearFilter',
 'createSobelFilter',
 'createStereoBM',
 'createStereoBeliefPropagation',
 'createStereoConstantSpaceBP',
 'createTemplateMatching',
 'cvtColor',
 'demosaicing',
 'dft',
 'divide',
 'drawColorDisp',
 'ensureSizeIsEnough',
 'equalizeHist',
 'evenLevels',
 'exp',
 'findMinMax',
 'findMinMaxLoc',
 'flip',
 'gammaCorrection',
 'gemm',
 'getCudaEnabledDeviceCount',
 'getDevice',
 'histEven',
 'histRange',
 'integral',
 'log',
 'magnitude',
 'magnitudeSqr',
 'max',
 'meanShiftFiltering',
 'meanShiftProc',
 'meanShiftSegmentation',
 'meanStdDev',
 'merge',
 'min',
 'minMax',
 'minMaxLoc',
 'mulAndScaleSpectrums',
 'mulSpectrums',
 'multiply',
 'norm',
 'normalize',
 'phase',
 'polarToCart',
 'pow',
 'printCudaDeviceInfo',
 'printShortCudaDeviceInfo',
 'pyrDown',
 'pyrUp',
 'rectStdDev',
 'reduce',
 'registerPageLocked',
 'remap',
 'reprojectImageTo3D',
 'resetDevice',
 'resize',
 'rotate',
 'setBufferPoolConfig',
 'setBufferPoolUsage',
 'setDevice',
 'split',
 'sqr',
 'sqrIntegral',
 'sqrSum',
 'sqrt',
 'subtract',
 'sum',
 'threshold',
 'transpose',
 'unregisterPageLocked',
 'warpAffine',
 'warpPerspective']

ATOMcam2の画像から流星を録画

ATOMcam2をベランダへ設置し、RTSP
で画像を流す。
小規模な火球をキャッチ(左上)。動画を拡大すると、画面上部中央に、おおぐま座(北斗七星)の一部が写っている。

処理は、Python(jupyter notebook)のスクリプト。cv2の中の移動検知のライブラリーを利用している。流星の他に、航空機、人工衛星、移動が激しい雲、鳥なども記録されるので、何らかの方法で、フィルタリングしたい。

とりあえず、一定のサイズ以下のファイルを削除するスクリプト(500kBの例)

find . -name "*.avi" -type 'f' -size -500k -delete
import cv2
import numpy as np
import time
import datetime
import os

capture = cv2.VideoCapture('rtsp://4190:2712@192.168.68.74/live')
#capture=cv2.VideoCapture(0)
PATH="/media/mars/ff2880cc-1a99-40bd-88c1-5cdc86fe9eed/home/mars/DATA"
W = capture.get(cv2.CAP_PROP_FRAME_WIDTH)
H = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
W2=int(W/2)
H2=int(H/2)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
print(W,H)

def key(k):
    global th, tc,track,reverse
    if k == ord('2'):
        th = th - 1
    elif k == ord('3'):
        th = th + 1
    elif k == ord('4'):
        tc = tc -5
    elif k == ord('5'):
        tc = tc +5
    elif k == ord('t'):
        track = not track
    elif k == ord('r'):
        reverse = not reverse

fontFace =cv2.FONT_HERSHEY_SIMPLEX
track, reverse = False,False
avg=None
th = 10
tc = 25
x,y=0,0
writer = None
time_start = time.time()
frame=0
while(True):
    ret, img = capture.read()
    org = img.copy()
    #img = cv2.resize(im, dsize=(W2, H2))
    img=img[0:int(H*0.9),0:int(W)]    # 映像の下側10%を検知範囲から除外。
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    if reverse:
        gray=cv2.bitwise_not(gray)
        avg=cv2.bitwise_not(avg)
    if avg is None:
        avg = gray.copy().astype("float")
        continue

    cv2.accumulateWeighted(gray, avg, 0.5)
    frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
    thresh = cv2.threshold(frameDelta, th, 255, cv2.THRESH_BINARY)[1]
    contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    detect=False
    for i in range(0,len(contours)):
        if len(contours[i]) > 0:
             if cv2.contourArea(contours[i]) > tc:
                detect=True
                time_start = time.time()
                if writer is None and track:
                    now=datetime.datetime.today()
                    date=now.strftime("%Y%m%d")
                    cDIR=PATH+'/'+date
                    if not(os.path.exists(cDIR)):
                           os.mkdir(cDIR)
                    fname=cDIR+'/'+'E-'+now.strftime("%Y%m%d_%H:%M:%S")+".avi"
                    writer = cv2.VideoWriter(fname, fourcc, 15, (int(W), int(H)))
                rect = contours[i]
                x, y, w, h = cv2.boundingRect(rect)

                cv2.rectangle(img, (x-w, y-h), (x + w*2, y + h*2), (0, 0, 255), 2)
                
    if time.time() -  time_start  > 5:
        if writer is not None:
            writer.release()
            #f.close()
            frame=0
            writer = None
    now=datetime.datetime.today()    
    text=now.strftime("%Y/%m/%d %H:%M:%S")+' No:'+str(frame)+ ' '+" TH:"+str(th)+" SZ:"+str(tc)
    img = cv2.putText(img, text, (30,50), fontFace,1,color=(0, 255, 0))
    org = cv2.putText(org, text, (30,50), fontFace,1,color=(0, 255, 0))
    text1="REC:"+str(track) + "  reverse:" + str(reverse)
    if writer is not None:
        frame=frame+1
        text1=fname+' '+text1
    img = cv2.putText(img, text1, (30,80), fontFace,1,color=(0, 255, 0))
    #cv2.imshow('thresh-level',thresh)
    cv2.imshow("IMAGE",img)
    if writer is not None:
        writer.write(org)
        
    k=cv2.waitKey(1) & 0xFF
    key(k)
    if k== ord('q'):
        break

capture.release()
if writer is not None:
    writer.release()
cv2.destroyAllWindows()

Thread機能を利用して、処理の効率化を図る。

import cv2
import threading
import queue
import numpy as np
import time
import datetime
import os

cPATH='rtsp://4190:2712@192.168.68.74/live'
#cPATH= 'rtsp://admin:@192.168.68.128:554/1/h264major'
PATH="/home/pi/DATA"

fourcc = cv2.VideoWriter_fourcc(*"XVID")

class ThreadingVideoCapture:
    def __init__(self, src, max_queue_size=256):
        self.video = cv2.VideoCapture(src)
        self.q = queue.Queue(maxsize=max_queue_size)
        self.stopped = False

    def start(self):
        thread = threading.Thread(target=self.update, daemon=True)
        thread.start()
        return self

    def update(self):
        while True:
            if self.stopped:
                return
            if not self.q.full():
                ok, frame = self.video.read()
                self.q.put((ok, frame))
                if not ok:
                    self.stop()
                    return

    def read(self):
        return self.q.get()

    def stop(self):
        self.stopped = True

    def release(self):
        self.stopped = True
        self.video.release()

    def isOpened(self):
        return self.video.isOpened()

    def get(self, i):
        return self.video.get(i)
    
def key(k):
    global th, tc,track,reverse,disp
    if k == ord('2'):
        th = th - 1
    elif k == ord('3'):
        th = th + 1
    elif k == ord('4'):
        tc = tc -5
    elif k == ord('5'):
        tc = tc +5
    elif k == ord('t'):
        track = not track
    elif k == ord('d'):
        disp= not disp
    elif k == ord('r'):
        reverse = not reverse

def detect_mov(contours,detect):
    for i in range(0,len(contours)):
            if len(contours[i]) > 0:
                 if cv2.contourArea(contours[i]) > tc:
                    detect=detect+1
                    rect = contours[i]
                    x, y, w, h = cv2.boundingRect(rect)
                    cv2.rectangle(img, (x-w, y-h), (x + w*2, y + h*2), (0, 0, 255), 3)
    return img,detect
                    
fontFace =cv2.FONT_HERSHEY_SIMPLEX

video = ThreadingVideoCapture(cPATH)
video.start()
if not video.isOpened():
    raise RuntimeError

W = video.get(cv2.CAP_PROP_FRAME_WIDTH)
H = video.get(cv2.CAP_PROP_FRAME_HEIGHT)
W2,H2=int(W/2),int(H/2)
cv2.namedWindow('ATOM', cv2.WINDOW_AUTOSIZE)

track, reverse,disp = False,False,False
avg=None
th = 10
tc = 25
x,y=0,0
writer = None
time_start = time.time()
frame=0
fname=None
log=PATH+'/metro.log'
detect=0
while(True):
    ret, img = video.read()
    if ret:
        org = img.copy()
        img = cv2.resize(img, dsize=(W2,H2))
        img=img[0:int(H2*0.85),0:int(W2)]
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        if reverse:
            gray=cv2.bitwise_not(gray)
            avg=cv2.bitwise_not(avg)
        if avg is None:
            avg = gray.copy().astype("float")
            continue

        cv2.accumulateWeighted(gray, avg, 0.5)
        frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))
        thresh = cv2.threshold(frameDelta, th, 255, cv2.THRESH_BINARY)[1]

        contours,hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        img,detect=detect_mov(contours,detect)
        if writer is None and track and detect !=0:
            time_start = time.time()
            now=datetime.datetime.today()
            date=now.strftime("%Y%m%d")
            cDIR=PATH+'/'+date
            if not(os.path.exists(cDIR)):
                os.mkdir(cDIR)
            fname=cDIR+'/'+'E-'+now .strftime("%Y%m%d_%H%M%S")+".avi"
            writer = cv2.VideoWriter(fname, fourcc, 15, (int(W), int(H)))
            
        if time.time() -  time_start  > 5:
            if writer is not None:
                writer.release()
                #f.close()
                frame=0
                writer = None
        now=datetime.datetime.today()    
        text=now.strftime("%Y%m%d %H%M%S")+' No:'+str(frame)+ ' '+" TH:"+str(th)+" SZ:"+str(tc)
        img = cv2.putText(img, text, (30,50), fontFace,1,color=(0, 255, 0))
        org = cv2.putText(org, text, (30,50), fontFace,1,color=(0, 255, 0))
        text1="REC:"+str(track) + "  reverse:" + str(reverse)
        if writer is not None:
            frame=frame+1
            text1=fname+' '+text1
        img = cv2.putText(img, text1, (30,80), fontFace,1,color=(0, 255, 0))
        #cv2.imshow('thresh-level',thresh)
        if disp:
            cv2.imshow("ATOM",img)
        
        if writer is not None:
            writer.write(org)
    else:
        now=datetime.datetime.today()
        date=now.strftime("%Y%m%d_%H%M%S")
        print("disconected:",date)
        video.release()
        avg = None
        video = ThreadingVideoCapture(cPATH)
        video.start()
    k=cv2.waitKey(int(1000 / 30)) & 0xFF
    key(k)
    if k== ord('q'):
        break

video.release()
if writer is not None:
    writer.release()
cv2.destroyAllWindows()
print('Done.')

2重星団

ペルセウス座にある散開星団。カシオペヤ座との境界の近く、天の川のほぼ中央に位置している。2つの散開星団が近接しているためこの名で呼ばれる。NGCカタログ/Melカタログでのカタログ番号は、西側の星団が NGC 869/ Mel 13、東側の星団が NGC 884/ Mel 14。