sample_patches_combined.py 文件源码

def sample_blob_negatives(img, roi_mask, out_dir, img_id, abn, blob_detector, 
                          patch_size=256, neg_cutoff=.35, nb_bkg=100, 
                          start_sample_nb=0,
                          bkg_dir='background', verbose=False):
    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()

    # Sample blob negative samples.
    key_pts = blob_detector.detect((img/img.max()*255).astype('uint8'))
    rng = np.random.RandomState(12345)
    key_pts = rng.permutation(key_pts)
    sampled_bkg = 0
    for kp in key_pts:
        if sampled_bkg >= nb_bkg:
            break
        x,y = int(kp.pt[0]), int(kp.pt[1])
        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" % (start_sample_nb + sampled_bkg) + ".png"
            fullname = os.path.join(bkg_out, filename)
            patch_img.save(fullname)
            if verbose:
                print "sampled a blob patch at (x,y) center=", (x,y)
                sys.stdout.flush()
            sampled_bkg += 1
    return sampled_bkg

#### End of function definition ####