Python video.create_capture函数代码示例

 def __init__(self, video_src):
     self.track_len = 10
     self.detect_interval = 3
     self.tracks = {}
     self.cam = video.create_capture(video_src)
     self.frame_idx = 0
     self.prev_gray = {}

def main():
	try:
		fn = sys.argv[1]
	except:
		fn = 0
	cv2.namedWindow('edge')

	cap = video.create_capture(fn)
	cv2.setMouseCallback('edge', onmouse)
	global ser
	ser = serial.Serial('COM4',9600)
	count =0
	while True:
		#print seed_pt[0]
		flag, img = cap.read()
		vis=img.copy()
		gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
		if count%2==0:
			main_check(vis,gray,seed_pt)
				
		cv2.imshow('edge', vis)
		count+=1
		ch = cv2.waitKey(5) & 0xFF
		if ch == 27:
			break

    def __init__(self, video_src, circles, csvFlag, rotation = 0):
        """
        Initializes main function for execution
        :param video_src: path to video to be analyzed
        :param circles: path to text file containing joints
        :param csvFlag: flag if video src is actually a csv that should be read in. ONLY USE THIS WITH DEBUG MODE!
        """
        self.roguePoints = []
        self.rotation = rotation
        self.csvFlag = csvFlag
        if not csvFlag:
            self.cam = video.create_capture(video_src)
        else:
            self.cam = csv.reader(open(video_src, 'r'), delimiter=',', quotechar='|')
        self.frame_idx = -1#This is because the frame index updates BEFORE anything is done.

        #Save frame to start at and the initial circles.
        self.allJoints = []
        f = open(circles, 'r')
        self.initalFrame = int(f.readline())
        for line in f:
            read = map(float, line.split())#x y r (with spaces)
            circle = j.Circle(read[0], read[1] , read[2])
            self.allJoints.append( j.Joint(circle, prm.TRACK_LEN ) )# circle in form [(x,y),r]
        f.close()

def main():
    import sys

    try:
        fn = sys.argv[1]
    except:
        fn = 0
    cap = video.create_capture(fn)

    leveln = 6
    cv.namedWindow('level control')
    for i in xrange(leveln):
        cv.createTrackbar('%d'%i, 'level control', 5, 50, nothing)

    while True:
        ret, frame = cap.read()

        pyr = build_lappyr(frame, leveln)
        for i in xrange(leveln):
            v = int(cv.getTrackbarPos('%d'%i, 'level control') / 5)
            pyr[i] *= v
        res = merge_lappyr(pyr)

        cv.imshow('laplacian pyramid filter', res)

        if cv.waitKey(1) == 27:
            break

    print('Done')

def dictionay_learning_MHOF_online(training_samples_num=400):
    from MHOF_Extraction import MHOF_Extraction
    from MHOF_histogram_block import MHOF_histogram_block
    from sklearn.decomposition import MiniBatchDictionaryLearning
    import numpy as np
    import cv2
    import video
    cam=video.create_capture('Crowd-Activity-All.avi')
    height_block_num=4
    width_block_num=5
    bin_num=16
    ret,prev=cam.read()
    ret,img=cam.read()
    flow_H=MHOF_Extraction(prev,img)
    flow_hist_H=MHOF_histogram_block(flow_H,height_block_num,width_block_num,bin_num)
    flow_hist_H=np.reshape(flow_hist_H,[1,flow_hist_H.size])
    #  error!!!!
    dico=MiniBatchDictionaryLearning(1,alpha=1,n_iter=500)
    dic=dico.fit(flow_hist_H).components_
    for i in range(training_samples_num):
        ret,img=cam.read()
        flow_H=MHOF_Extraction(prev,img)
        flow_hist_H=MHOF_histogram_block(flow_H,height_block_num,width_block_num,bin_num)
        dico=MiniBatchDictionaryLearing(i+1,alpha=1,n_iter=500,dict_init=dic)
        dic=dico.fit(flow_hist_H).components
    return dic

        

def main():
    try:
        video_src = sys.argv[1]
    except:
        video_src = 0

    cam = video.create_capture(video_src)
    mser = cv.MSER_create()

    while True:
        ret, img = cam.read()
        if ret == 0:
            break
        gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
        vis = img.copy()

        regions, _ = mser.detectRegions(gray)
        hulls = [cv.convexHull(p.reshape(-1, 1, 2)) for p in regions]
        cv.polylines(vis, hulls, 1, (0, 255, 0))

        cv.imshow('img', vis)
        if cv.waitKey(5) == 27:
            break

    print('Done')

 def __init__(self, video_src, paused = False):
     self.cap = video.create_capture(video_src)
     _, self.frame = self.cap.read()
     cv2.imshow('frame', self.frame)
     self.rect_sel = RectSelector('frame', self.onrect)
     self.trackers = []
     self.paused = paused

 def __init__(self, video_src, skipFrame):
     self.track_len = 10
     self.detect_interval = 5
     self.tracks = []
     self.cam = video.create_capture(video_src)
     self.frame_idx = 0
     self.skipFrame = skipFrame

def main():
    try:
        fn = sys.argv[1]
    except:
        fn = 0

    def nothing(*arg):
        pass

    cv.namedWindow('edge')
    cv.createTrackbar('thrs1', 'edge', 2000, 5000, nothing)
    cv.createTrackbar('thrs2', 'edge', 4000, 5000, nothing)

    cap = video.create_capture(fn)
    while True:
        flag, img = cap.read()
        gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
        thrs1 = cv.getTrackbarPos('thrs1', 'edge')
        thrs2 = cv.getTrackbarPos('thrs2', 'edge')
        edge = cv.Canny(gray, thrs1, thrs2, apertureSize=5)
        vis = img.copy()
        vis = np.uint8(vis/2.)
        vis[edge != 0] = (0, 255, 0)
        cv.imshow('edge', vis)
        ch = cv.waitKey(5)
        if ch == 27:
            break

    print('Done')

    def __init__(self, src, threshold = 25, doRecord=True, showWindows=True):
        self.doRecord = doRecord
        self.show = showWindows
        self.frame = None
        
        self.cap = video.create_capture(src)
        self.cap.set(3,1280)
        self.cap.set(4,2316)
        self.ret, self.frame = self.cap.read() #Take a frame to init recorder
        self.frame_rate = self.cap.get(5)
        print self.frame_rate
        self.gray_frame = np.zeros((self.cap.get(3), self.cap.get(4), 1), np.uint8)
        self.average_frame = np.zeros((self.cap.get(3), self.cap.get(4), 3), np.float32)
        self.absdiff_frame = None
        self.previous_frame = None
        
        self.surface = self.cap.get(3) * self.cap.get(4)
        self.currentsurface = 0
        self.currentcontours = None
        self.threshold = threshold
        self.isRecording = False

        self.tracks = []
        self.tracks_dist = []
        self.track_len = 3
        self.frame_idx = 0
        self.detect_interval = 5
        
        # self.font = cv.InitFont(cv.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 2, 8) #Creates a font

        self.trigger_time = 0
        if showWindows:
            cv2.namedWindow("Image", cv2.WINDOW_AUTOSIZE)

    def detectFace(self):
        print "stuff"
        import sys, getopt
        print help_message

        args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
        try: video_src = video_src[0]
        except: video_src = 0
        args = dict(args)
        # cascade_fn = args.get('--cascade', "haarcascade_frontalface_alt.xml")
        cascade = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
        
        cam = create_capture(video_src, fallback='synth:bg=lena.jpg:noise=0.05')
        found = False
        
        while True:
            ret, img = cam.read()
            rects = self.detect(img, cascade)
            vis = img.copy()
            if(rects != [] and found == False):
                print "here"
                if goodOrBad(img,cascade):
                    found = True

            cv2.imshow('facedetect', vis)

            if 0xFF & cv2.waitKey(5) == 27:
                break
        cv2.destroyAllWindows()

    def __init__(self, video_src):
        self.track_len = 25
        self.detect_interval = 1
        self.tracks = []

        self.capture = cv2.VideoCapture(video_src)

        self.frame_width = int(self.capture.get(cv.CV_CAP_PROP_FRAME_WIDTH) / 2)
        self.frame_height = int(self.capture.get(cv.CV_CAP_PROP_FRAME_HEIGHT) / 2)
        self.frame_size = (self.frame_width, self.frame_height)
        self.grid_width = int(self.frame_width / 8 / 2)
        self.grid_height = int(self.frame_height / 8 / 2)
        self.grid_size = (self.grid_width, self.grid_height)
        self.total_frame_count = int(self.capture.get(cv.CV_CAP_PROP_FRAME_COUNT))
        print self.frame_size
        print self.grid_size
        print self.total_frame_count

        self.data_path = str(video_src) + ".oflw"
        print self.data_path
        self.fp = np.memmap(self.data_path, dtype="float32", mode="w+", shape=(self.total_frame_count, (512 + 128)))

        print "FP shape: ", self.fp.shape
        self.cam = video.create_capture(video_src)
        self.frame_idx = 0

    def __init__(self, src):
        self.cap = video.create_capture(src)
        self.frame = None
        self.rect_obj = None

        cv2.namedWindow('plane')
        self.rect_sel = common.RectSelector('plane', self.on_rect)

def compare(face_to_check,learn=False):
	import sys, getopt
	detected_time = 0
	detected_time_max = 10
	
	video_src = 0
	cascade_fn = os.path.join('data','haarcascades','haarcascade_frontalface_alt2.xml')

	cascade = cv2.CascadeClassifier(cascade_fn)

	cam = create_capture(video_src, fallback='synth:bg=../cpp/lena.jpg:noise=0.05')
	
	while True:
		ret, img1 = cam.read()
		gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
		gray = cv2.equalizeHist(gray)
		
		t = clock()
		rects = detect(gray, cascade)
		
		if len(rects):
			if detected_time<detected_time_max:
				detected_time+=1
			else:
				_found_size = (rects[0][0],rects[0][1],rects[0][2]-rects[0][0],
					rects[0][3]-rects[0][1])
				
				_found_face = cv.GetImage(cv.fromarray(img1))
				
				cv.SetImageROI(_found_face,_found_size)
				
				current_face = cv.CreateImage(cv.GetSize(_found_face),
					_found_face.depth,
					_found_face.nChannels)
				
				if learn:
					cv.Copy(_found_face, current_face, None)
					cv.SaveImage(os.path.join('data','images',face_to_check),current_face)
				
				cv.ResetImageROI(cv.GetImage(cv.fromarray(img1)))
				
				img2 = cv.LoadImage(os.path.join('data','images',face_to_check))
				
				dest_face = cv.CreateImage(cv.GetSize(img2),
					img2.depth,
					img2.nChannels)
				
				cv.Resize(_found_face, dest_face)
				
				if cv.Norm(dest_face,img2)<=30000:
					return True
				else:
					return False
				
				sys,exit()
		else:
			detected_time = 0
		
		dt = clock() - t

def main():
    cap = video.create_capture()

    classifier_fn = 'digits_svm.dat'
    if not os.path.exists(classifier_fn):
        print '"%s" not found, run digits.py first' % classifier_fn
        return 
    
    model = SVM()
    model.load('digits_svm.dat')

    while True:
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        bin = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 31, 10)
        bin = cv2.medianBlur(bin, 3)
        contours, heirs = cv2.findContours( bin.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
        rects = map(cv2.boundingRect, contours)
        valid_flags = [ 16 <= h <= 64  and w <= 1.2*h  for x, y, w, h in rects]

        for i, cnt in enumerate(contours):
            if not valid_flags[i]:
                continue
            _, _, _, outer_i = heirs[0, i]
            if outer_i >=0 and valid_flags[outer_i]:
                continue
            x, y, w, h = rects[i]
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0))
            sub = bin[y:,x:][:h,:w]
            #sub = ~cv2.equalizeHist(sub)
            #_, sub_bin = cv2.threshold(sub, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)

            s = 1.5*float(h)/SZ
            m = cv2.moments(sub)
            m00 = m['m00']
            if m00/255 < 0.1*w*h or m00/255 > 0.9*w*h:
                continue

            c1 = np.float32([m['m10'], m['m01']]) / m00
            c0 = np.float32([SZ/2, SZ/2])
            t = c1 - s*c0
            A = np.zeros((2, 3), np.float32)
            A[:,:2] = np.eye(2)*s
            A[:,2] = t
            sub1 = cv2.warpAffine(sub, A, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)
            sub1 = deskew(sub1)
            if x+w+SZ < frame.shape[1] and y+SZ < frame.shape[0]:
                frame[y:,x+w:][:SZ, :SZ] = sub1[...,np.newaxis]
                
            sample = preprocess_hog([sub1])
            digit = model.predict(sample)[0]
            cv2.putText(frame, '%d'%digit, (x, y), cv2.FONT_HERSHEY_PLAIN, 1.0, (200, 0, 0), thickness = 1)


        cv2.imshow('frame', frame)
        cv2.imshow('bin', bin)
        if cv2.waitKey(1) == 27:
            break