video_jpeg_rolls_flow_saliency.py 文件源码

def extract_optical_flow(fn, n_frames=34):
    img = dd.image.load(fn)
    if img.shape != (128*34, 128, 3):
        return []
    frames = np.array_split(img, 34, axis=0)
    grayscale_frames = [fr.mean(-1) for fr in frames]
    mags = []
    skip_frames = np.random.randint(34 - n_frames + 1)
    middle_frame = frames[np.random.randint(skip_frames, skip_frames+n_frames)]
    im0 = grayscale_frames[skip_frames]
    for f in range(1+skip_frames, 1+skip_frames+n_frames-1):
        im1 = grayscale_frames[f]
        flow = cv2.calcOpticalFlowFarneback(im0, im1,
                    None, # flow
                    0.5, # pyr_scale
                    3, # levels
                    np.random.randint(3, 20), # winsize
                    3, #iterations
                    5, #poly_n 
                    1.2, #poly_sigma
                    0 # flags
        )
        mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])
        mags.append(mag)
        im0 = im1
    mag = np.sum(mags, 0)
    mag = mag.clip(min=0)
    #norm_mag = np.tanh(mag * 10000)
    norm_mag = (mag - mag.min()) / (mag.max() - mag.min() + 1e-5)
    outputs = []
    outputs.append((middle_frame, norm_mag))
    return outputs