예제 #1
0
파일: main.py 프로젝트: dilawar/algorithms
def process(filename):
    fs = fr.read_frames(filename)
    kernel = np.ones((3,3), np.uint8)
    newFs = []
    for f in fs:
        f = cv2.resize(f, None, fx=0.4, fy=0.4)
        f = cv2.equalizeHist(f)
        f1 = f.copy()
        #  f = cv2.medianBlur(f, 3)
        #  cnts = cd.contours(f)
        #  cv2.drawContours(f, cnts, -1, 255)
        f[f < f.max()-10] = 0
        f = cv2.morphologyEx(f, cv2.MORPH_OPEN, kernel)
        #  show_frame(f)
        newFs.append(f)

    # params for ShiTomasi corner detection
    feature_params = dict( maxCorners = 100,
            qualityLevel = 0.3,
            minDistance = 7,
            blockSize = 7 )
    # Parameters for lucas kanade optical flow
    lk_params = dict( winSize  = (15,15),
            maxLevel = 2,
            criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
    p0 = cv2.goodFeaturesToTrack(newFs[0], mask = None, **feature_params)

    # Create a mask image for drawing purposes
    mask = np.zeros_like(newFs[0])

    # Create some random colors
    color = np.random.randint(0,255,(100,3))

    for fo, fn in zip(newFs, newFs[1:]):
        # calculate optical flow
        p1, st, err = cv2.calcOpticalFlowPyrLK(fo, fn, p0, None, **lk_params)
        # Select good points
        good_new = p1[st==1]
        good_old = p0[st==1]
        # draw the tracks
        for i,(new,old) in enumerate(zip(good_new,good_old)):
            a,b = new.ravel()
            c,d = old.ravel()
            cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
            cv2.circle(fn,(a,b),5,color[i].tolist(),-1)

        fn = cv2.add(fn, mask)

        show_frame(fn)
        # Now update the previous frame and previous points
        fo = fn.copy()
        p0 = good_new.reshape(-1,1,2)
def init():
    global axes_, lines_
    global cap_, args_
    global data_
    global frames_
    global stride_
    global t_, y_
    global fps_

    videoFile = args_['video']
    print('[INFO] Processing %s' % videoFile )
    frames_ = fr.read_frames( videoFile )
    fps_ =  args_['fps']
    tmax = len(frames_) / float(fps_)
    stride_ = len(y_) / len(frames_)
    print('[INFO] Total frames: %s' % len(frames_))
    print('[INFO] Computed fps: %s' % fps_)
    print('[INFO] Stride to take: %s' % stride_)
    return lines_.values()
예제 #3
0
def init():
    global axes_, lines_
    global cap_, args_
    global data_
    global frames_
    global stride_
    global t_, y_
    global fps_

    videoFile = args_['video']
    frames_ = fr.read_frames( videoFile )
    data_ = scipy.io.loadmat( args_['activity'] )
    data_ = data_['final']
    t_, y_ = data_[:,0], data_[:,1]
    fps_ =  args_['fps']
    tmax = len(frames_) / float(fps_)
    stride_ = len(y_) / len(frames_)
    print('[INFO] Total frames: %s' % len(frames_))
    print('[INFO] Computed fps: %s' % fps_)
    print('[INFO] Stride to take: %s' % stride_)
    axes_['activity'].set_xlim([0, tmax])
    axes_['activity'].set_ylim([y_.min() - 2, y_.max() + 2])
    return lines_.values()
예제 #4
0
def main( inputfile, **kwargs):
    logger.info('Processing %s ' % inputfile)
    frames = fr.read_frames( inputfile, 10 )
    fShape = frames[0].shape
    logger.info('Got total %s frames' % len(frames))
    print("Done reading frames")
    data = np.dstack( frames )
    rows, cols = data.shape[0], data.shape[1]
    pixals = []
    for r in range(rows):
        for c in range(cols):
            vec = data[r, c, :]
            pixals.append( Pixal(vec, (r,c)) )
    
    for p in pixals:
        add_pixal( p )

    print("Total nice pixals: %s" % len(pixal_of_interest_))
    img = np.zeros( shape = fShape )
    for loc in pixal_of_interest_:
        img[loc] = pixal_of_interest_[loc].max
    
    pylab.imshow( img )
    pylab.show()
예제 #5
0
def main(inputfile, **kwargs):
    logger.info('Processing %s ' % inputfile)
    frames = fr.read_frames(inputfile, 10)
    fShape = frames[0].shape
    logger.info('Got total %s frames' % len(frames))
    print("Done reading frames")
    data = np.dstack(frames)
    rows, cols = data.shape[0], data.shape[1]
    pixals = []
    for r in range(rows):
        for c in range(cols):
            vec = data[r, c, :]
            pixals.append(Pixal(vec, (r, c)))

    for p in pixals:
        add_pixal(p)

    print("Total nice pixals: %s" % len(pixal_of_interest_))
    img = np.zeros(shape=fShape)
    for loc in pixal_of_interest_:
        img[loc] = pixal_of_interest_[loc].max

    pylab.imshow(img)
    pylab.show()