sample_patches_combined.py 文件源码

def sample_hard_negatives(img, roi_mask, out_dir, img_id, abn,  
                          patch_size=256, neg_cutoff=.35, nb_bkg=100, 
                          start_sample_nb=0,
                          bkg_dir='background', verbose=False):
    '''WARNING: the definition of hns may be problematic.
    There has been study showing that the context of an ROI is also useful
    for classification.
    '''
    bkg_out = os.path.join(out_dir, bkg_dir)
    basename = '_'.join([img_id, str(abn)])

    img = add_img_margins(img, patch_size/2)
    roi_mask = add_img_margins(roi_mask, patch_size/2)
    # Get ROI bounding box.
    roi_mask_8u = roi_mask.astype('uint8')
    ver = (cv2.__version__).split('.')
    if int(ver[0]) < 3:
        contours,_ = cv2.findContours(
            roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    else:
        _,contours,_ = cv2.findContours(
            roi_mask_8u.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    cont_areas = [ cv2.contourArea(cont) for cont in contours ]
    idx = np.argmax(cont_areas)  # find the largest contour.
    rx,ry,rw,rh = cv2.boundingRect(contours[idx])
    if verbose:
        M = cv2.moments(contours[idx])
        cx = int(M['m10']/M['m00'])
        cy = int(M['m01']/M['m00'])
        print "ROI centroid=", (cx,cy); sys.stdout.flush()

    rng = np.random.RandomState(12345)
    # Sample hard negative samples.
    sampled_bkg = start_sample_nb
    while sampled_bkg < start_sample_nb + nb_bkg:
        x1,x2 = (rx - patch_size/2, rx + rw + patch_size/2)
        y1,y2 = (ry - patch_size/2, ry + rh + patch_size/2)
        x1 = crop_val(x1, patch_size/2, img.shape[1] - patch_size/2)
        x2 = crop_val(x2, patch_size/2, img.shape[1] - patch_size/2)
        y1 = crop_val(y1, patch_size/2, img.shape[0] - patch_size/2)
        y2 = crop_val(y2, patch_size/2, img.shape[0] - patch_size/2)
        x = rng.randint(x1, x2)
        y = rng.randint(y1, y2)
        if not overlap_patch_roi((x,y), patch_size, roi_mask, cutoff=neg_cutoff):
            patch = img[y - patch_size/2:y + patch_size/2, 
                        x - patch_size/2:x + patch_size/2]
            patch = patch.astype('int32')
            patch_img = toimage(patch, high=patch.max(), low=patch.min(), 
                                mode='I')
            filename = basename + "_%04d" % (sampled_bkg) + ".png"
            fullname = os.path.join(bkg_out, filename)
            patch_img.save(fullname)
            sampled_bkg += 1
            if verbose:
                print "sampled a hns patch at (x,y) center=", (x,y)
                sys.stdout.flush()