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How to use ignored method in elementium
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kitti_eval_numba.py
Source:kitti_eval_numba.py 
1import io as sysio2import time3import numba4import numpy as np5from scipy.interpolate import interp1d6# from second.core.non_max_suppression.nms_gpu import rotate_iou_gpu_eval7def get_mAP(prec):8 sums = 09 for i in range(0, len(prec), 4):10 sums += prec[i]11 return sums / 11 * 10012@numba.jit13def get_thresholds(scores: np.ndarray, num_gt, num_sample_pts=41):14 scores.sort()15 scores = scores[::-1]16 current_recall = 017 thresholds = []18 for i, score in enumerate(scores):19 l_recall = (i + 1) / num_gt20 if i < (len(scores) - 1):21 r_recall = (i + 2) / num_gt22 else:23 r_recall = l_recall24 if (((r_recall - current_recall) < (current_recall - l_recall))25 and (i < (len(scores) - 1))):26 continue27 # recall = l_recall28 thresholds.append(score)29 current_recall += 1 / (num_sample_pts - 1.0)30 # print(len(thresholds), len(scores), num_gt)31 return thresholds32def clean_data(gt_anno, dt_anno, current_class, difficulty):33 CLASS_NAMES = [34 'Car', 'Van', 'Truck', 'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram'35 ]36 MIN_HEIGHT = [40, 25, 25]37 MAX_OCCLUSION = [0, 1, 2]38 MAX_TRUNCATION = [0.15, 0.3, 0.5]39 dc_bboxes, ignored_gt, ignored_dt = [], [], []40 current_cls_name = CLASS_NAMES[current_class].lower()41 num_gt = len(gt_anno["name"])42 num_dt = len(dt_anno["name"])43 num_valid_gt = 044 for i in range(num_gt):45 # for each gt in one image46 bbox = gt_anno["bbox"][i]47 gt_name = gt_anno["name"][i].lower()48 height = bbox[3] - bbox[1]49 valid_class = -150 if (gt_name == current_cls_name):51 valid_class = 152 elif (current_cls_name == "Pedestrian".lower()53 and "Person_sitting".lower() == gt_name):54 valid_class = 055 elif (current_cls_name == "Car".lower() and "Van".lower() == gt_name):56 valid_class = 057 else:58 valid_class = -159 ignore = False60 if ((gt_anno["occluded"][i] > MAX_OCCLUSION[difficulty])61 or (gt_anno["truncated"][i] > MAX_TRUNCATION[difficulty])62 or (height <= MIN_HEIGHT[difficulty])):63 # if gt_anno["difficulty"][i] > difficulty or gt_anno["difficulty"][i] == -1:64 ignore = True65 if valid_class == 1 and not ignore:66 ignored_gt.append(0)67 num_valid_gt += 168 elif (valid_class == 0 or (ignore and (valid_class == 1))):69 ignored_gt.append(1)70 else:71 ignored_gt.append(-1)72 # for i in range(num_gt):73 if gt_anno["name"][i] == "DontCare":74 dc_bboxes.append(gt_anno["bbox"][i])75 for i in range(num_dt):76 # for each pred in one image77 if (dt_anno["name"][i].lower() == current_cls_name):78 valid_class = 179 else:80 valid_class = -181 height = abs(dt_anno["bbox"][i, 3] - dt_anno["bbox"][i, 1])82 if height < MIN_HEIGHT[difficulty]:83 ignored_dt.append(1)84 elif valid_class == 1:85 ignored_dt.append(0)86 else:87 ignored_dt.append(-1)88 return num_valid_gt, ignored_gt, ignored_dt, dc_bboxes89@numba.jit(nopython=True)90def image_box_overlap(boxes, query_boxes, criterion=-1):91 N = boxes.shape[0]92 K = query_boxes.shape[0]93 overlaps = np.zeros((N, K), dtype=boxes.dtype)94 for k in range(K):95 qbox_area = ((query_boxes[k, 2] - query_boxes[k, 0]) *96 (query_boxes[k, 3] - query_boxes[k, 1]))97 for n in range(N):98 iw = (min(boxes[n, 2], query_boxes[k, 2]) - max(99 boxes[n, 0], query_boxes[k, 0]))100 if iw > 0:101 ih = (min(boxes[n, 3], query_boxes[k, 3]) - max(102 boxes[n, 1], query_boxes[k, 1]))103 if ih > 0:104 if criterion == -1:105 ua = (106 (boxes[n, 2] - boxes[n, 0]) *107 (boxes[n, 3] - boxes[n, 1]) + qbox_area - iw * ih)108 elif criterion == 0:109 ua = ((boxes[n, 2] - boxes[n, 0]) *110 (boxes[n, 3] - boxes[n, 1]))111 elif criterion == 1:112 ua = qbox_area113 else:114 ua = 1.0115 overlaps[n, k] = iw * ih / ua116 return overlaps117# def bev_box_overlap(boxes, qboxes, criterion=-1):118# riou = rotate_iou_gpu_eval(boxes, qboxes, criterion)119# return riou120#@numba.jit(nopython=True, parallel=True)121# def d3_box_overlap_kernel(boxes,122# qboxes,123# rinc,124# criterion=-1,125# z_axis=1,126# z_center=1.0):127# """128# z_axis: the z (height) axis.129# z_center: unified z (height) center of box.130# """131# N, K = boxes.shape[0], qboxes.shape[0]132# for i in range(N):133# for j in range(K):134# if rinc[i, j] > 0:135# min_z = min(136# boxes[i, z_axis] + boxes[i, z_axis + 3] * (1 - z_center),137# qboxes[j, z_axis] + qboxes[j, z_axis + 3] * (1 - z_center))138# max_z = max(139# boxes[i, z_axis] - boxes[i, z_axis + 3] * z_center,140# qboxes[j, z_axis] - qboxes[j, z_axis + 3] * z_center)141# iw = min_z - max_z142# if iw > 0:143# area1 = boxes[i, 3] * boxes[i, 4] * boxes[i, 5]144# area2 = qboxes[j, 3] * qboxes[j, 4] * qboxes[j, 5]145# inc = iw * rinc[i, j]146# if criterion == -1:147# ua = (area1 + area2 - inc)148# elif criterion == 0:149# ua = area1150# elif criterion == 1:151# ua = area2152# else:153# ua = 1.0154# rinc[i, j] = inc / ua155# else:156# rinc[i, j] = 0.0157# def d3_box_overlap(boxes, qboxes, criterion=-1, z_axis=1, z_center=1.0):158# """kitti camera format z_axis=1.159# """160# bev_axes = list(range(7))161# bev_axes.pop(z_axis + 3)162# bev_axes.pop(z_axis)163# rinc = rotate_iou_gpu_eval(boxes[:, bev_axes], qboxes[:, bev_axes], 2)164# d3_box_overlap_kernel(boxes, qboxes, rinc, criterion, z_axis, z_center)165# return rinc166@numba.jit(nopython=True)167def compute_statistics_jit(overlaps,168 gt_datas,169 dt_datas,170 ignored_gt,171 ignored_det,172 dc_bboxes,173 metric,174 min_overlap,175 thresh=0,176 compute_fp=False,177 compute_aos=False):178 det_size = dt_datas.shape[0]179 gt_size = gt_datas.shape[0]180 dt_scores = dt_datas[:, -1]181 #dt_alphas = dt_datas[:, 4]182 gt_alphas = gt_datas[:, 4]183 dt_bboxes = dt_datas[:, :4]184 # gt_bboxes = gt_datas[:, :4]185 assigned_detection = [False] * det_size186 ignored_threshold = [False] * det_size187 if compute_fp:188 for i in range(det_size):189 if (dt_scores[i] < thresh):190 ignored_threshold[i] = True191 NO_DETECTION = -10000000192 tp, fp, fn, similarity = 0, 0, 0, 0193 # thresholds = [0.0]194 # delta = [0.0]195 thresholds = np.zeros((gt_size, ))196 thresh_idx = 0197 delta = np.zeros((gt_size, ))198 delta_idx = 0199 for i in range(gt_size):200 if ignored_gt[i] == -1:201 continue202 det_idx = -1203 valid_detection = NO_DETECTION204 max_overlap = 0205 assigned_ignored_det = False206 for j in range(det_size):207 if (ignored_det[j] == -1):208 continue209 if (assigned_detection[j]):210 continue211 if (ignored_threshold[j]):212 continue213 overlap = overlaps[j, i]214 dt_score = dt_scores[j]215 if (not compute_fp and (overlap > min_overlap)216 and dt_score > valid_detection):217 det_idx = j218 valid_detection = dt_score219 elif (compute_fp and (overlap > min_overlap)220 and (overlap > max_overlap or assigned_ignored_det)221 and ignored_det[j] == 0):222 max_overlap = overlap223 det_idx = j224 valid_detection = 1225 assigned_ignored_det = False226 elif (compute_fp and (overlap > min_overlap)227 and (valid_detection == NO_DETECTION)228 and ignored_det[j] == 1):229 det_idx = j230 valid_detection = 1231 assigned_ignored_det = True232 if (valid_detection == NO_DETECTION) and ignored_gt[i] == 0:233 fn += 1234 elif ((valid_detection != NO_DETECTION)235 and (ignored_gt[i] == 1 or ignored_det[det_idx] == 1)):236 assigned_detection[det_idx] = True237 elif valid_detection != NO_DETECTION:238 # only a tp add a threshold.239 tp += 1240 # thresholds.append(dt_scores[det_idx])241 thresholds[thresh_idx] = dt_scores[det_idx]242 thresh_idx += 1243 # if compute_aos:244 # delta.append(gt_alphas[i] - dt_alphas[det_idx])245 # delta[delta_idx] = gt_alphas[i] - dt_alphas[det_idx]246 # delta_idx += 1247 assigned_detection[det_idx] = True248 if compute_fp:249 for i in range(det_size):250 if (not (assigned_detection[i] or ignored_det[i] == -1251 or ignored_det[i] == 1 or ignored_threshold[i])):252 fp += 1253 nstuff = 0254 if metric == 0:255 overlaps_dt_dc = image_box_overlap(dt_bboxes, dc_bboxes, 0)256 for i in range(dc_bboxes.shape[0]):257 for j in range(det_size):258 if (assigned_detection[j]):259 continue260 if (ignored_det[j] == -1 or ignored_det[j] == 1):261 continue262 if (ignored_threshold[j]):263 continue264 if overlaps_dt_dc[j, i] > min_overlap:265 assigned_detection[j] = True266 nstuff += 1267 fp -= nstuff268 if compute_aos:269 tmp = np.zeros((fp + delta_idx, ))270 # tmp = [0] * fp271 for i in range(delta_idx):272 tmp[i + fp] = (1.0 + np.cos(delta[i])) / 2.0273 # tmp.append((1.0 + np.cos(delta[i])) / 2.0)274 # assert len(tmp) == fp + tp275 # assert len(delta) == tp276 if tp > 0 or fp > 0:277 similarity = np.sum(tmp)278 else:279 similarity = -1280 return tp, fp, fn, similarity, thresholds[:thresh_idx]281def get_split_parts(num, num_part):282 same_part = num // num_part283 remain_num = num % num_part284 if remain_num == 0:285 return [same_part] * num_part286 else:287 return [same_part] * num_part + [remain_num]288@numba.jit(nopython=True)289def fused_compute_statistics(overlaps,290 pr,291 gt_nums,292 dt_nums,293 dc_nums,294 gt_datas,295 dt_datas,296 dontcares,297 ignored_gts,298 ignored_dets,299 metric,300 min_overlap,301 thresholds,302 compute_aos=False):303 gt_num = 0304 dt_num = 0305 dc_num = 0306 for i in range(gt_nums.shape[0]):307 for t, thresh in enumerate(thresholds):308 overlap = overlaps[dt_num:dt_num + dt_nums[i], gt_num:gt_num +309 gt_nums[i]]310 gt_data = gt_datas[gt_num:gt_num + gt_nums[i]]311 dt_data = dt_datas[dt_num:dt_num + dt_nums[i]]312 ignored_gt = ignored_gts[gt_num:gt_num + gt_nums[i]]313 ignored_det = ignored_dets[dt_num:dt_num + dt_nums[i]]314 dontcare = dontcares[dc_num:dc_num + dc_nums[i]]315 tp, fp, fn, similarity, _ = compute_statistics_jit(316 overlap,317 gt_data,318 dt_data,319 ignored_gt,320 ignored_det,321 dontcare,322 metric,323 min_overlap=min_overlap,324 thresh=thresh,325 compute_fp=True,326 compute_aos=compute_aos)327 pr[t, 0] += tp328 pr[t, 1] += fp329 pr[t, 2] += fn330 if similarity != -1:331 pr[t, 3] += similarity332 gt_num += gt_nums[i]333 dt_num += dt_nums[i]334 dc_num += dc_nums[i]335def calculate_iou_partly(gt_annos,336 dt_annos,337 metric,338 num_parts=50,339 z_axis=1,340 z_center=1.0):341 """fast iou algorithm. this function can be used independently to342 do result analysis. 343 Args:344 gt_annos: dict, must from get_label_annos() in kitti_common.py345 dt_annos: dict, must from get_label_annos() in kitti_common.py346 metric: eval type. 0: bbox, 1: bev, 2: 3d347 num_parts: int. a parameter for fast calculate algorithm348 z_axis: height axis. kitti camera use 1, lidar use 2.349 """350 assert len(gt_annos) == len(dt_annos)351 total_dt_num = np.stack([len(a["name"]) for a in dt_annos], 0)352 total_gt_num = np.stack([len(a["name"]) for a in gt_annos], 0)353 num_examples = len(gt_annos)354 split_parts = get_split_parts(num_examples, num_parts)355 parted_overlaps = []356 example_idx = 0357 #bev_axes = list(range(3))358 #bev_axes.pop(z_axis)359 for num_part in split_parts:360 gt_annos_part = gt_annos[example_idx:example_idx + num_part]361 dt_annos_part = dt_annos[example_idx:example_idx + num_part]362 if metric == 0:363 gt_boxes = np.concatenate([a["bbox"] for a in gt_annos_part], 0)364 dt_boxes = np.concatenate([a["bbox"] for a in dt_annos_part], 0)365 overlap_part = image_box_overlap(gt_boxes, dt_boxes)366 elif metric == 1:367 loc = np.concatenate(368 [a["location"][:, bev_axes] for a in gt_annos_part], 0)369 dims = np.concatenate(370 [a["dimensions"][:, bev_axes] for a in gt_annos_part], 0)371 rots = np.concatenate([a["rotation_y"] for a in gt_annos_part], 0)372 gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],373 axis=1)374 loc = np.concatenate(375 [a["location"][:, bev_axes] for a in dt_annos_part], 0)376 dims = np.concatenate(377 [a["dimensions"][:, bev_axes] for a in dt_annos_part], 0)378 rots = np.concatenate([a["rotation_y"] for a in dt_annos_part], 0)379 dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],380 axis=1)381 overlap_part = bev_box_overlap(gt_boxes,382 dt_boxes).astype(np.float64)383 elif metric == 2:384 loc = np.concatenate([a["location"] for a in gt_annos_part], 0)385 dims = np.concatenate([a["dimensions"] for a in gt_annos_part], 0)386 rots = np.concatenate([a["rotation_y"] for a in gt_annos_part], 0)387 gt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],388 axis=1)389 loc = np.concatenate([a["location"] for a in dt_annos_part], 0)390 dims = np.concatenate([a["dimensions"] for a in dt_annos_part], 0)391 rots = np.concatenate([a["rotation_y"] for a in dt_annos_part], 0)392 dt_boxes = np.concatenate([loc, dims, rots[..., np.newaxis]],393 axis=1)394 overlap_part = d3_box_overlap(395 gt_boxes, dt_boxes, z_axis=z_axis,396 z_center=z_center).astype(np.float64)397 else:398 raise ValueError("unknown metric")399 parted_overlaps.append(overlap_part)400 example_idx += num_part401 overlaps = []402 example_idx = 0403 for j, num_part in enumerate(split_parts):404 gt_annos_part = gt_annos[example_idx:example_idx + num_part]405 dt_annos_part = dt_annos[example_idx:example_idx + num_part]406 gt_num_idx, dt_num_idx = 0, 0407 for i in range(num_part):408 gt_box_num = total_gt_num[example_idx + i]409 dt_box_num = total_dt_num[example_idx + i]410 overlaps.append(411 parted_overlaps[j][gt_num_idx:gt_num_idx +412 gt_box_num, dt_num_idx:dt_num_idx +413 dt_box_num])414 gt_num_idx += gt_box_num415 dt_num_idx += dt_box_num416 example_idx += num_part417 return overlaps, parted_overlaps, total_gt_num, total_dt_num418def _prepare_data(gt_annos, dt_annos, current_class, difficulty):419 gt_datas_list = []420 dt_datas_list = []421 total_dc_num = []422 ignored_gts, ignored_dets, dontcares = [], [], []423 total_num_valid_gt = 0424 for i in range(len(gt_annos)):425 # for each image426 rets = clean_data(gt_annos[i], dt_annos[i], current_class, difficulty)427 num_valid_gt, ignored_gt, ignored_det, dc_bboxes = rets428 ignored_gts.append(np.array(ignored_gt, dtype=np.int64))429 ignored_dets.append(np.array(ignored_det, dtype=np.int64))430 if len(dc_bboxes) == 0:431 dc_bboxes = np.zeros((0, 4)).astype(np.float64)432 else:433 dc_bboxes = np.stack(dc_bboxes, 0).astype(np.float64)434 total_dc_num.append(dc_bboxes.shape[0])435 dontcares.append(dc_bboxes)436 total_num_valid_gt += num_valid_gt437 gt_datas = np.concatenate(438 [gt_annos[i]["bbox"], gt_annos[i]["alpha"][..., np.newaxis]], 1)439 dt_datas = np.concatenate([440 dt_annos[i]["bbox"],441 dt_annos[i]["score"][..., np.newaxis]442 ], 1)443 gt_datas_list.append(gt_datas)444 dt_datas_list.append(dt_datas)445 total_dc_num = np.stack(total_dc_num, axis=0)446 return (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets, dontcares,447 total_dc_num, total_num_valid_gt)448def eval_class(gt_annos,449 dt_annos,450 current_classes,451 difficultys,452 metric,453 min_overlaps,454 compute_aos=False,455 z_axis=1,456 z_center=1.0,457 num_parts=50):458 """Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP.459 Args:460 gt_annos: dict, must from get_label_annos() in kitti_common.py461 dt_annos: dict, must from get_label_annos() in kitti_common.py462 current_class: int, 0: car, 1: pedestrian, 2: cyclist463 difficulty: int. eval difficulty, 0: easy, 1: normal, 2: hard464 metric: eval type. 0: bbox, 1: bev, 2: 3d465 min_overlap: float, min overlap. official: 466 [[0.7, 0.5, 0.5], [0.7, 0.5, 0.5], [0.7, 0.5, 0.5]] 467 format: [metric, class]. choose one from matrix above.468 num_parts: int. a parameter for fast calculate algorithm469 Returns:470 dict of recall, precision and aos471 """472 assert len(gt_annos) == len(dt_annos)473 num_examples = len(gt_annos)474 split_parts = get_split_parts(num_examples, num_parts)475 rets = calculate_iou_partly(476 dt_annos,477 gt_annos,478 metric,479 num_parts,480 z_axis=z_axis,481 z_center=z_center)482 overlaps, parted_overlaps, total_dt_num, total_gt_num = rets483 N_SAMPLE_PTS = 41484 num_minoverlap = len(min_overlaps)485 num_class = len(current_classes)486 num_difficulty = len(difficultys)487 precision = np.zeros(488 [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])489 recall = np.zeros(490 [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])491 aos = np.zeros([num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])492 all_thresholds = np.zeros([num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS])493 for m, current_class in enumerate(current_classes):494 for l, difficulty in enumerate(difficultys):495 rets = _prepare_data(gt_annos, dt_annos, current_class, difficulty)496 (gt_datas_list, dt_datas_list, ignored_gts, ignored_dets,497 dontcares, total_dc_num, total_num_valid_gt) = rets498 for k, min_overlap in enumerate(min_overlaps[:, metric, m]):499 thresholdss = []500 for i in range(len(gt_annos)):501 rets = compute_statistics_jit(502 overlaps[i],503 gt_datas_list[i],504 dt_datas_list[i],505 ignored_gts[i],506 ignored_dets[i],507 dontcares[i],508 metric,509 min_overlap=min_overlap,510 thresh=0.0,511 compute_fp=False)512 tp, fp, fn, similarity, thresholds = rets513 thresholdss += thresholds.tolist()514 thresholdss = np.array(thresholdss)515 thresholds = get_thresholds(thresholdss, total_num_valid_gt)516 thresholds = np.array(thresholds)517 all_thresholds[m, l, k, :len(thresholds)] = thresholds518 pr = np.zeros([len(thresholds), 4])519 idx = 0520 for j, num_part in enumerate(split_parts):521 gt_datas_part = np.concatenate(522 gt_datas_list[idx:idx + num_part], 0)523 dt_datas_part = np.concatenate(524 dt_datas_list[idx:idx + num_part], 0)525 dc_datas_part = np.concatenate(526 dontcares[idx:idx + num_part], 0)527 ignored_dets_part = np.concatenate(528 ignored_dets[idx:idx + num_part], 0)529 ignored_gts_part = np.concatenate(530 ignored_gts[idx:idx + num_part], 0)531 fused_compute_statistics(532 parted_overlaps[j],533 pr,534 total_gt_num[idx:idx + num_part],535 total_dt_num[idx:idx + num_part],536 total_dc_num[idx:idx + num_part],537 gt_datas_part,538 dt_datas_part,539 dc_datas_part,540 ignored_gts_part,541 ignored_dets_part,542 metric,543 min_overlap=min_overlap,544 thresholds=thresholds,545 compute_aos=compute_aos)546 idx += num_part547 for i in range(len(thresholds)):548 precision[m, l, k, i] = pr[i, 0] / (pr[i, 0] + pr[i, 1])549 if compute_aos:550 aos[m, l, k, i] = pr[i, 3] / (pr[i, 0] + pr[i, 1])551 for i in range(len(thresholds)):552 precision[m, l, k, i] = np.max(553 precision[m, l, k, i:], axis=-1)554 if compute_aos:555 aos[m, l, k, i] = np.max(aos[m, l, k, i:], axis=-1)556 ret_dict = {557 # "recall": recall, # [num_class, num_difficulty, num_minoverlap, N_SAMPLE_PTS]558 "precision": precision,559 "orientation": aos,560 "thresholds": all_thresholds,561 "min_overlaps": min_overlaps,562 }563 return ret_dict564def get_mAP_v2(prec):565 sums = 0566 for i in range(0, prec.shape[-1], 4):567 sums = sums + prec[..., i]568 return sums / 11 * 100569def do_eval_v3(gt_annos,570 dt_annos,571 current_classes,572 min_overlaps,573 compute_aos=False,574 difficultys=(0, 1, 2),575 z_axis=1,576 z_center=1.0):577 # min_overlaps: [num_minoverlap, metric, num_class]578 types = ["bbox"]579 metrics = {}580 for i in range(len(types)):581 ret = eval_class(582 gt_annos,583 dt_annos,584 current_classes,585 difficultys,586 i,587 min_overlaps,588 compute_aos,589 z_axis=z_axis,590 z_center=z_center)591 metrics[types[i]] = ret592 return metrics593def print_str(value, *arg, sstream=None):594 if sstream is None:595 sstream = sysio.StringIO()596 sstream.truncate(0)597 sstream.seek(0)598 print(value, *arg, file=sstream)599 return sstream.getvalue()600def get_official_eval_result(gt_annos,601 dt_annos,602 current_classes,603 difficultys=[0, 1, 2],604 z_axis=1,605 z_center=1.0):606 """607 gt_annos and dt_annos must contains following keys:608 [bbox, location, dimensions, rotation_y, score]609 """610 overlap_mod = np.array([[0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],611 [0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7],612 [0.7, 0.5, 0.5, 0.7, 0.5, 0.7, 0.7, 0.7]])613 overlap_easy = np.array([[0.7, 0.5, 0.5, 0.7, 0.5, 0.5, 0.5, 0.5],614 [0.5, 0.25, 0.25, 0.5, 0.25, 0.5, 0.5, 0.5],615 [0.5, 0.25, 0.25, 0.5, 0.25, 0.5, 0.5, 0.5]])616 min_overlaps = np.stack([overlap_mod, overlap_easy], axis=0) # [2, 3, 5]617 class_to_name = {618 0: 'Car',619 1: 'Van',620 2: 'Truck',621 3: 'Pedestrian',622 4: 'Person_sitting',623 5: 'Cyclist',624 6: 'Tram',625 }626 name_to_class = {v: n for n, v in class_to_name.items()}627 if not isinstance(current_classes, (list, tuple)):628 current_classes = [current_classes]629 current_classes_int = []630 for curcls in current_classes:631 if isinstance(curcls, str):632 current_classes_int.append(name_to_class[curcls])633 else:634 current_classes_int.append(curcls)635 current_classes = current_classes_int636 min_overlaps = min_overlaps[:, :, current_classes]637 result = ''638 # check whether alpha is valid639 compute_aos = False640 # for anno in dt_annos:641 # if anno['alpha'].shape[0] != 0:642 # if anno['alpha'][0] != -10:643 # compute_aos = True644 # break645 metrics = do_eval_v3(646 gt_annos,647 dt_annos,648 current_classes,649 min_overlaps,650 compute_aos,651 difficultys,652 z_axis=z_axis,653 z_center=z_center)654 for j, curcls in enumerate(current_classes):655 # mAP threshold array: [num_minoverlap, metric, class]656 # mAP result: [num_class, num_diff, num_minoverlap]657 for i in range(min_overlaps.shape[0]):658 mAPbbox = get_mAP_v2(metrics["bbox"]["precision"][j, :, i])659 mAPbbox = ", ".join("{:.2f}".format(v) for v in mAPbbox)660 #mAPbev = get_mAP_v2(metrics["bev"]["precision"][j, :, i])661 #mAPbev = ", ".join("{:.2f}".format(v) for v in mAPbev)662 #mAP3d = get_mAP_v2(metrics["3d"]["precision"][j, :, i])663 #mAP3d = ", ".join("{:.2f}".format(v) for v in mAP3d)664 result += print_str(665 ( class_to_name[curcls] + 666 "AP(Average Precision)@{:.2f}, {:.2f}, {:.2f}:".format(*min_overlaps[i, :, j])))667 result += print_str("bbox AP:{}".format(mAPbbox))668 #result += print_str("bev AP:{}".format(mAPbev))669 #result += print_str("3d AP:{}".format(mAP3d))670 # if compute_aos:671 # mAPaos = get_mAP_v2(metrics["bbox"]["orientation"][j, :, i])672 # mAPaos = ", ".join("{:.2f}".format(v) for v in mAPaos)673 # result += print_str("aos AP:{}".format(mAPaos))...
wait.py
Source:wait.py
1#!/usr/bin/python2#3# Copyright 2011 Software Freedom Conservancy.4#5# Licensed under the Apache License, Version 2.0 (the "License");6# you may not use this file except in compliance with the License.7# You may obtain a copy of the License at8#9# http://www.apache.org/licenses/LICENSE-2.010#11# Unless required by applicable law or agreed to in writing, software12# distributed under the License is distributed on an "AS IS" BASIS,13# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.14# See the License for the specific language governing permissions and15# limitations under the License.16import time17from selenium.common.exceptions import NoSuchElementException18from selenium.common.exceptions import TimeoutException19POLL_FREQUENCY = 0.5 # How long to sleep inbetween calls to the method20IGNORED_EXCEPTIONS = [NoSuchElementException] # list of exceptions ignored during calls to the method21class WebDriverWait(object):22 def __init__(self, driver, timeout, poll_frequency=POLL_FREQUENCY, ignored_exceptions=None):23 """Constructor, takes a WebDriver instance and timeout in seconds.24 25 :Args:26 - driver - Instance of WebDriver (Ie, Firefox, Chrome or Remote)27 - timeout - Number of seconds before timing out28 - poll_frequency - sleep interval between calls29 By default, it is 0.5 second.30 - ignored_exceptions - iterable structure of exception classes ignored during calls.31 By default, it contains NoSuchElementException only.32 Example:33 from selenium.webdriver.support.ui import WebDriverWait \n34 element = WebDriverWait(driver, 10).until(lambda x: x.find_element_by_id("someId")) \n35 is_disappeared = WebDriverWait(driver, 30, 1, (ElementNotVisibleException)).\ \n36 until_not(lambda x: x.find_element_by_id("someId").is_displayed())37 """38 self._driver = driver39 self._timeout = timeout40 self._poll = poll_frequency41 # avoid the divide by zero42 if self._poll == 0:43 self._poll = POLL_FREQUENCY44 exceptions = IGNORED_EXCEPTIONS45 if ignored_exceptions is not None:46 try:47 exceptions.extend(iter(ignored_exceptions))48 except TypeError: # ignored_exceptions is not iterable49 exceptions.append(ignored_exceptions)50 self._ignored_exceptions = tuple(exceptions)51 def until(self, method, message=''):52 """Calls the method provided with the driver as an argument until the \53 return value is not False."""54 end_time = time.time() + self._timeout55 while(True):56 try:57 value = method(self._driver)58 if value:59 return value60 except self._ignored_exceptions:61 pass62 time.sleep(self._poll)63 if(time.time() > end_time):64 break65 raise TimeoutException(message)66 def until_not(self, method, message=''):67 """Calls the method provided with the driver as an argument until the \68 return value is False."""69 end_time = time.time() + self._timeout70 while(True):71 try:72 value = method(self._driver)73 if not value:74 return value75 except self._ignored_exceptions:76 return True77 time.sleep(self._poll)78 if(time.time() > end_time):79 break...
style_controlword_list.py
Source:style_controlword_list.py
1# {"controlword": ["ignored/coded", "description", "section", "type"]2# filters: [see table spec pp.173-2073style_controlword_dict = {4 "spv": [],5 "li": [],6 "ri": [],7 "ql": [],8 "rin": [],9 "lin": [],10 "itap": [],11 "nowidctlpar": [],12 "wrapdefault": [],13}14"snext": ["snext", "Coded", "Style Sheet", "Value", "\\snext", "\\snext"],15 "additive": ["additive", "Coded", "Style Sheet", "Flag", "\\additive",16 "\\additive"]17test = {18 "\\'": ["Ignored", "Special Characters", "Symbol"],19 "\\-": ["Ignored", "Special Characters", "Symbol"],20 "\\*": ["Ignored", "Special Characters", "Symbol"],21 "\\:": ["Ignored", "Special Characters", "Symbol"],22 "\\": ["Ignored", "Special Characters", "Symbol"],23 "\\_": ["Ignored", "Special Characters", "Symbol"],24 "\\{": ["Ignored", "Special Characters", "Symbol"],25 "\\|": ["Ignored", "Special Characters", "Symbol"],26 "\\}": ["Ignored", "Special Characters", "Symbol"],27 "\\~": ["Ignored", "Special Characters", "Symbol"],28 "\\ab": ["Ignored", "Associated Character Properties", "Toggle"],29 "\\absh": ["Ignored", "Positioned Objects and Frames", "Value"],30 "\\abslock": ["Ignored", "Positioned Objects and Frames", "Flag"],31 "\\absnoovrlpN": ["Ignored", "Positioned Objects and Frames", "Toggle"],32 "\\absw": ["Ignored", "Positioned Objects and Frames", "Value"],33 "\\acaps": ["Ignored", "Associated Character Properties", "Toggle"],34 "\\acccomma": ["Ignored", "Font (Character^) Formatting Properties", "Toggle"],35 "\\accdot": ["Ignored", "Font (Character^) Formatting Properties", "Toggle"],36 "\\accnone": ["Ignored", "Font (Character^) Formatting Properties", "Toggle"],37 "\\acccircle": ["Ignored", "Font (Character^) Formatting Properties", "Toggle"],38 "\\accunderdot": ["Ignored", "Font (Character^) Formatting Properties", "Toggle"],39 "\\acf": ["Ignored", "Associated Character Properties", "Value"],40 "\\additive": ["Ignored", "Style Sheet", "Flag"],41 "\\adjustright": ["Ignored", "Section Formatting Properties", "Flag"],42 "\\adn": ["Ignored", "Associated Character Properties", "Value"],43 "\\aenddoc": ["Ignored", "Document Formatting Properties", "Flag"],44 "\\aendnotes": ["Ignored", "Document Formatting Properties", "Flag"],45 "\\aexpnd": ["Ignored", "Associated Character Properties", "Value"],46 "\\af": ["Ignored", "Associated Character Properties", "Value"],47 "\\affixed": ["Ignored", "Paragraph Formatting Properties", "Flag"],48 "\\afs": ["Ignored", "Associated Character Properties", "Value"],49 "\\aftnbj": ["Ignored", "Document Formatting Properties", "Flag"],50 "\\aftncn": ["Ignored", "Document Formatting Properties", "Destination"],51 "\\aftnnalc": ["Ignored", "Document Formatting Properties", "Flag"],52 "\\aftnnar": ["Ignored", "Document Formatting Properties", "Flag"],53 "\\aftnnauc": ["Ignored", "Document Formatting Properties", "Flag"],54 "\\aftnnchi": ["Ignored", "Document Formatting Properties", "Flag"],55 "\\aftnnchosung": ["Ignored", "Document Formatting Properties", "Flag"],56 "\\aftnncnum": ["Ignored", "Document Formatting Properties", "Flag"],57 "\\aftnndbar": ["Ignored", "Document Formatting Properties", "Flag"],58 "\\aftnndbnum": ["Ignored", "Document Formatting Properties", "Flag"],59 "\\aftnndbnumd": ["Ignored", "Document Formatting Properties", "Flag"]...
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