def __call__(self, img, boxes, labels):
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return img, boxes, labels
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(
int(left),
int(top),
int(left + new_w),
int(top + new_h),
)
)
overlaps = bbox_overlaps(
patch.reshape(-1, 4), boxes.reshape(-1, 4)
).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (
(center[:, 0] > patch[0])
* (center[:, 1] > patch[1])
* (center[:, 0] < patch[2])
* (center[:, 1] < patch[3])
)
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1] : patch[3], patch[0] : patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
return img, boxes, labels
def box_voting(top_dets, all_dets, thresh, scoring_method='ID', beta=1.0):
"""Apply bounding-box voting to refine `top_dets` by voting with `all_dets`.
See: https://arxiv.org/abs/1505.01749. Optional score averaging (not in the
referenced paper) can be applied by setting `scoring_method` appropriately.
"""
# top_dets is [N, 5] each row is [x1 y1 x2 y2, sore]
# all_dets is [N, 5] each row is [x1 y1 x2 y2, sore]
top_dets_out = top_dets.copy()
top_boxes = top_dets[:, :4]
all_boxes = all_dets[:, :4]
all_scores = all_dets[:, 4]
top_to_all_overlaps = bbox_overlaps(top_boxes, all_boxes)
for k in range(top_dets_out.shape[0]):
inds_to_vote = np.where(top_to_all_overlaps[k] >= thresh)[0]
boxes_to_vote = all_boxes[inds_to_vote, :]
ws = all_scores[inds_to_vote]
top_dets_out[k, :4] = np.average(boxes_to_vote, axis=0, weights=ws)
if scoring_method == 'ID':
# Identity, nothing to do
pass
elif scoring_method == 'TEMP_AVG':
# Average probabilities (considered as P(detected class) vs.
# P(not the detected class)) after smoothing with a temperature
# hyperparameter.
P = np.vstack((ws, 1.0 - ws))
P_max = np.max(P, axis=0)
X = np.log(P / P_max)
X_exp = np.exp(X / beta)
P_temp = X_exp / np.sum(X_exp, axis=0)
P_avg = P_temp[0].mean()
top_dets_out[k, 4] = P_avg
elif scoring_method == 'AVG':
# Combine new probs from overlapping boxes
top_dets_out[k, 4] = ws.mean()
elif scoring_method == 'IOU_AVG':
P = ws
ws = top_to_all_overlaps[k, inds_to_vote]
P_avg = np.average(P, weights=ws)
top_dets_out[k, 4] = P_avg
elif scoring_method == 'GENERALIZED_AVG':
P_avg = np.mean(ws**beta)**(1.0 / beta)
top_dets_out[k, 4] = P_avg
elif scoring_method == 'QUASI_SUM':
top_dets_out[k, 4] = ws.sum() / float(len(ws))**beta
else:
raise NotImplementedError(
'Unknown scoring method {}'.format(scoring_method)
)
return top_dets_out
def analyze_per_img_dets(confusion_matrix,
gt_bboxes,
gt_labels,
result,
score_thr=0,
tp_iou_thr=0.5,
nms_iou_thr=None):
"""Analyze detection results on each image.
Args:
confusion_matrix (ndarray): The confusion matrix,
has shape (num_classes + 1, num_classes + 1).
gt_bboxes (ndarray): Ground truth bboxes, has shape (num_gt, 4).
gt_labels (ndarray): Ground truth labels, has shape (num_gt).
result (ndarray): Detection results, has shape
(num_classes, num_bboxes, 5).
score_thr (float): Score threshold to filter bboxes.
Default: 0.
tp_iou_thr (float): IoU threshold to be considered as matched.
Default: 0.5.
nms_iou_thr (float|optional): nms IoU threshold, the detection results
have done nms in the detector, only applied when users want to
change the nms IoU threshold. Default: None.
"""
true_positives = np.zeros_like(gt_labels)
for det_label, det_bboxes in enumerate(result):
if nms_iou_thr:
det_bboxes, _ = nms(
det_bboxes[:, :4],
det_bboxes[:, -1],
nms_iou_thr,
score_threshold=score_thr)
ious = bbox_overlaps(det_bboxes[:, :4], gt_bboxes)
for i, det_bbox in enumerate(det_bboxes):
score = det_bbox[4]
det_match = 0
if score >= score_thr:
for j, gt_label in enumerate(gt_labels):
if ious[i, j] >= tp_iou_thr:
det_match += 1
if gt_label == det_label:
true_positives[j] += 1 # TP
confusion_matrix[gt_label, det_label] += 1
if det_match == 0: # BG FP
confusion_matrix[-1, det_label] += 1
for num_tp, gt_label in zip(true_positives, gt_labels):
if num_tp == 0: # FN
confusion_matrix[gt_label, -1] += 1
def acc_single_video(results,
gts,
iou_thr=0.5,
ignore_iof_thr=0.5,
ignore_by_classes=False):
"""Accumulate results in a single video."""
num_classes = len(results[0])
accumulators = [
mm.MOTAccumulator(auto_id=True) for i in range(num_classes)
]
for result, gt in zip(results, gts):
if ignore_by_classes:
gt_ignore = outs2results(bboxes=gt['bboxes_ignore'],
labels=gt['labels_ignore'],
num_classes=num_classes)['bbox_results']
else:
gt_ignore = [gt['bboxes_ignore'] for i in range(num_classes)]
gt = outs2results(bboxes=gt['bboxes'],
labels=gt['labels'],
ids=gt['instance_ids'],
num_classes=num_classes)['bbox_results']
for i in range(num_classes):
gt_ids, gt_bboxes = gt[i][:, 0].astype(np.int), gt[i][:, 1:]
pred_ids, pred_bboxes = result[i][:, 0].astype(
np.int), result[i][:, 1:-1]
dist = bbox_distances(gt_bboxes, pred_bboxes, iou_thr)
if gt_ignore[i].shape[0] > 0:
# 1. assign gt and preds
fps = np.ones(pred_bboxes.shape[0]).astype(np.bool)
row, col = linear_sum_assignment(dist)
for m, n in zip(row, col):
if not np.isfinite(dist[m, n]):
continue
fps[n] = False
# 2. ignore by iof
iofs = bbox_overlaps(pred_bboxes, gt_ignore[i], mode='iof')
ignores = (iofs > ignore_iof_thr).any(axis=1)
# 3. filter preds
valid_inds = ~(fps & ignores)
pred_ids = pred_ids[valid_inds]
dist = dist[:, valid_inds]
if dist.shape != (0, 0):
accumulators[i].update(gt_ids, pred_ids, dist)
return accumulators
def calc_tpfpfn(det_bboxes, gt_bboxes, iou_thr=0.5):
"""Check if detected bboxes are true positive or false positive and if gt bboxes are false negative.
Args:
det_bboxes (ndarray): Detected bboxes of this image, of shape (m, 5).
gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
iou_thr (float): IoU threshold to be considered as matched.
Default: 0.5.
Returns:
float: (tp, fp, fn).
"""
num_dets = det_bboxes.shape[0]
num_gts = gt_bboxes.shape[0]
tp = 0
fp = 0
# if there is no gt bboxes in this image, then all det bboxes
# within area range are false positives
if num_gts == 0:
fp = num_dets
return tp, fp, 0
ious: np.ndarray = bbox_overlaps(det_bboxes, gt_bboxes)
# sort all dets in descending order by scores
sort_inds = np.argsort(-det_bboxes[:, -1])
gt_covered = np.zeros(num_gts, dtype=bool)
for i in sort_inds:
uncovered_ious = ious[i, gt_covered == 0]
if len(uncovered_ious):
iou_argmax = uncovered_ious.argmax()
iou_max = uncovered_ious[iou_argmax]
if iou_max >= iou_thr:
gt_covered[[x[iou_argmax]
for x in np.where(gt_covered == 0)]] = True
tp += 1
else:
fp += 1
else:
fp += 1
fn = (gt_covered == 0).sum()
return tp, fp, fn
def verify_match():
data_root = 'data/fabu3d'
out_dir = '/private/ningqingqun/results/centernet2_results/match'
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
split_file = os.path.join(data_root, 'pkl', 'val.pkl')
dataset = pkl.load(open(split_file, 'rb'), encoding='latin1')
for _, label in tqdm(dataset.items()):
imf = os.path.join(data_root, 'images', label['filename'])
im = cv2.imread(imf)
reiszed_im = cv2.resize(im, (1280, 800))
gt_im = reiszed_im.copy()
inputs = preprocess(reiszed_im)
results = model(inputs.cuda(), return_loss=False, try_dummy=False)
out_file = os.path.join(out_dir, os.path.basename(imf))
det_bboxes = get_det_bboxes(results)
gt_bboxes = label['ann']['bboxes'] / 1.5
ious = bbox_overlaps(det_bboxes, gt_bboxes)
ious_argmax = ious.argmax(axis=1)
ious_max = ious.max(axis=1)
rand_colors = np.random.rand(100, 3) * 255
# draw det bboxes
for i in range(len(det_bboxes)):
b = det_bboxes[i]
if ious_max[i] > 0.2:
color = rand_colors[ious_argmax[i]]
else:
color = (0, 0, 0)
cv2.rectangle(
reiszed_im, (b[0], b[1]), (b[2], b[3]), color, thickness=2)
# draw gt bboxes
for i in range(len(gt_bboxes)):
b = gt_bboxes[i]
color = rand_colors[i]
cv2.rectangle(
gt_im, (b[0], b[1]), (b[2], b[3]), color, thickness=2)
display_im = np.vstack([reiszed_im, gt_im])
cv2.imwrite(out_file, display_im)
def __call__(self, results):
if np.random.rand() <= self.p:
for i in range(np.random.randint(1, self.n_crops + 1)):
filepath = random.choice(self.crop_paths)
crop = mmcv.imread(filepath)
crop = self.aug(image=crop)["image"]
crop_h, crop_w = crop.shape[:2]
h, w = results["img"].shape[:2]
if 3 <= crop_w < w and 3 <= crop_h < h:
for _ in range(10):
y_center = np.random.randint(crop_h // 2,
h - crop_h // 2)
x_center = np.random.randint(crop_w // 2,
w - crop_w // 2)
crop_bbox = np.array([
x_center - crop_w // 2,
y_center - crop_h // 2,
x_center + crop_w // 2,
y_center + crop_h // 2,
]).reshape(1, 4)
ious = bbox_overlaps(results["gt_bboxes"],
crop_bbox,
mode="iof")
if max(ious) < self.iou_threshold:
crop_mask = 255 * np.ones(crop.shape, crop.dtype)
results["img"] = cv2.seamlessClone(
src=crop,
dst=results["img"],
mask=crop_mask,
p=(x_center, y_center),
flags=cv2.NORMAL_CLONE,
)
results["gt_bboxes"] = np.concatenate(
[results["gt_bboxes"], crop_bbox])
results["gt_labels"] = np.concatenate([
results["gt_labels"],
np.full(len(crop_bbox), self.crop_label)
])
break
return results
def calc_tpfpfn(gt_bboxes, pred_bboxes, iou_thr=0.5):
"""Calculate tp, fp, fn.
gt_bboxes: (N, 4) np.array in xyxy format
pred_bboxes: (N, 5) np.array in xyxy+conf format
"""
if len(gt_bboxes) == 0 and len(pred_bboxes) == 0:
tps, fps, fns = 0, 0, 0
return tps, fps, fns
elif len(gt_bboxes) == 0:
tps, fps, fns = 0, len(pred_bboxes), 0
return tps, fps, fns
elif len(pred_bboxes) == 0:
tps, fps, fns = 0, 0, len(gt_bboxes)
return tps, fps, fns
# sort by conf
pred_bboxes = pred_bboxes[pred_bboxes[:, 4].argsort()[::-1]]
gt_bboxes = gt_bboxes.copy()
tp = 0
fp = 0
for k, pred_bbox in enumerate(pred_bboxes):
ious = bbox_overlaps(gt_bboxes, pred_bbox[None, :4])
max_iou = ious.max()
if max_iou > iou_thr:
tp += 1
gt_bboxes = np.delete(gt_bboxes, ious.argmax(), axis=0)
else:
fp += 1
if len(gt_bboxes) == 0:
fp += len(pred_bboxes) - (k + 1)
break
fn = len(gt_bboxes)
return tp, fp, fn
def score(gt_boxes,anchors):
overlaps=bbox_overlaps(gt_boxes,anchors)
best=overlaps.max(1)
#print(best)
return best
def __call__(self, img, boxes, labels):
h, w, c = img.shape
while True:
# import cv2
# import neptune
# im2 = img.copy()
# for i in range(boxes.shape[0]):
# cv2.rectangle(im2, tuple(map(int, boxes[i][:2])), tuple(map(int, boxes[i][-2:])), (255, 0, 0), 4)
# neptune.log_image('mosaics', im2)
if random.randint(0, 1):
return img, boxes, labels
for i in range(50):
# keep the labeled id
idx_labeled = labels[:, 1] > -1
if not any(idx_labeled):
idx_labeled = ~idx_labeled
boxes_labeled = boxes[idx_labeled]
x1 = min(boxes_labeled[:, 0])
y1 = min(boxes_labeled[:, 1])
x2 = max(boxes_labeled[:, 2])
y2 = max(boxes_labeled[:, 3])
# based on above, random choose coord
new_x1 = random.uniform(0, x1)
new_y1 = random.uniform(0, y1)
new_x2 = random.uniform(x2, w)
new_y2 = random.uniform(y2, h)
new_w = new_x2 - new_x1
new_h = new_y2 - new_y1
# keep the nearby ratio
if new_h / new_w < h / w - self.range_ratio or new_h / new_w > h / w + self.range_ratio:
continue
patch = np.array(
(int(new_x1), int(new_y1), int(new_x2), int(new_y2)))
overlaps = bbox_overlaps(patch.reshape(-1, 4),
boxes.reshape(-1, 4),
mode='iof1').reshape(-1)
if any((self.range_overlaps[0] < overlaps)
& (overlaps < self.range_overlaps[1])):
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] > patch[0]) * (
center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
center[:, 1] < patch[3])
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
return img, boxes, labels
def tpfp_imagenet(det_bboxes,
gt_bboxes,
gt_bboxes_ignore=None,
default_iou_thr=0.5,
area_ranges=None):
"""Check if detected bboxes are true positive or false positive.
Args:
det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
of shape (k, 4). Default: None
default_iou_thr (float): IoU threshold to be considered as matched for
medium and large bboxes (small ones have special rules).
Default: 0.5.
area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,
in the format [(min1, max1), (min2, max2), ...]. Default: None.
Returns:
tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of
each array is (num_scales, m).
"""
# an indicator of ignored gts
gt_ignore_inds = np.concatenate((np.zeros(gt_bboxes.shape[0],
dtype=np.bool),
np.ones(gt_bboxes_ignore.shape[0],
dtype=np.bool)))
# stack gt_bboxes and gt_bboxes_ignore for convenience
gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
num_dets = det_bboxes.shape[0]
num_gts = gt_bboxes.shape[0]
if area_ranges is None:
area_ranges = [(None, None)]
num_scales = len(area_ranges)
# tp and fp are of shape (num_scales, num_gts), each row is tp or fp
# of a certain scale.
tp = np.zeros((num_scales, num_dets), dtype=np.float32)
fp = np.zeros((num_scales, num_dets), dtype=np.float32)
if gt_bboxes.shape[0] == 0:
if area_ranges == [(None, None)]:
fp[...] = 1
else:
det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0]) * (
det_bboxes[:, 3] - det_bboxes[:, 1])
for i, (min_area, max_area) in enumerate(area_ranges):
fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
return tp, fp
ious = bbox_overlaps(det_bboxes, gt_bboxes - 1)
gt_w = gt_bboxes[:, 2] - gt_bboxes[:, 0]
gt_h = gt_bboxes[:, 3] - gt_bboxes[:, 1]
iou_thrs = np.minimum((gt_w * gt_h) / ((gt_w + 10.0) * (gt_h + 10.0)),
default_iou_thr)
# sort all detections by scores in descending order
sort_inds = np.argsort(-det_bboxes[:, -1])
for k, (min_area, max_area) in enumerate(area_ranges):
gt_covered = np.zeros(num_gts, dtype=bool)
# if no area range is specified, gt_area_ignore is all False
if min_area is None:
gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
else:
gt_areas = gt_w * gt_h
gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
for i in sort_inds:
max_iou = -1
matched_gt = -1
# find best overlapped available gt
for j in range(num_gts):
# different from PASCAL VOC: allow finding other gts if the
# best overlaped ones are already matched by other det bboxes
if gt_covered[j]:
continue
elif ious[i, j] >= iou_thrs[j] and ious[i, j] > max_iou:
max_iou = ious[i, j]
matched_gt = j
# there are 4 cases for a det bbox:
# 1. it matches a gt, tp = 1, fp = 0
# 2. it matches an ignored gt, tp = 0, fp = 0
# 3. it matches no gt and within area range, tp = 0, fp = 1
# 4. it matches no gt but is beyond area range, tp = 0, fp = 0
if matched_gt >= 0:
gt_covered[matched_gt] = 1
if not (gt_ignore_inds[matched_gt]
or gt_area_ignore[matched_gt]):
tp[k, i] = 1
elif min_area is None:
fp[k, i] = 1
else:
bbox = det_bboxes[i, :4]
area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
if area >= min_area and area < max_area:
fp[k, i] = 1
return tp, fp
def __call__(self, img, gt_bboxes, gt_labels, gt_masks,
gt_ignore_bboxes, gt_ignore_masks, img_shape, pad_shape):
pad_h, pad_w = pad_shape[:2]
img_h, img_w = img_shape[:2]
crp_h, crp_w = self.crop_size
# img [c, H, W] -> [H, W, c]
img = img.transpose(1, 2, 0).copy()
assert img.shape[0] == pad_h and img_shape[1] == pad_w
if pad_h == crp_h and pad_w == crp_w:
img = img.transpose(2, 0, 1)
return img, gt_bboxes, gt_labels, gt_ignore_bboxes, \
gt_masks, gt_ignore_masks, img_shape, pad_shape
tar_size = tuple(map(lambda x, y: x if x < y else y, img_shape[:2], self.crop_size[:2]))
label = np.zeros(img_shape[:2], dtype=np.uint8)
for idx in range(len(gt_masks)):
mask = gt_masks[idx][:img_h, :img_w]
label[mask > 0] = 1
count = 50
while count > 0:
# randomly select a patch.
if random.random() > 3.0 / 8.0 and np.max(label) > 0:
tl = tuple(np.maximum(np.min(np.where(label > 0), axis=1) - tar_size, 0))
br = tuple(np.maximum(np.max(np.where(label > 0), axis=1) - tar_size, 0))
br[0] = min(br[0], img_h - tar_size[0])
br[1] = min(br[1], img_w - tar_size[1])
i = random.randint(tl[0], br[0]) if tl[0] < br[0] else 0
j = random.randint(tl[1], br[1]) if tl[1] < br[1] else 1
else:
i = random.randint(img_h - tar_size[0]) if img_h > tar_size[0] else 0
j = random.randint(img_w - tar_size[1]) if img_w > tar_size[1] else 0
patch = np.array((int(j), int(i), int(j + tar_size[1]), int(i + tar_size[0])))
overlaps = bbox_overlaps(
patch.reshape(-1, 4), gt_bboxes.reshape(-1, 4), mode='iob').reshape(-1)
if len(gt_masks) > 0 and (0 < overlaps.min() < 0.3):
count -= 1
continue
else:
break
""" do not select the center..."""
new_gt_bbox, new_gt_mask, new_gt_labels = [], [], []
for idx in range(len(gt_masks)):
mask = gt_masks[idx]
mask = mask[i:i + tar_size[0], j:j + tar_size[1]]
if np.max(mask) > 0:
cnt = get_cnt_from_mask(mask)
if cnt is not None:
box = get_rect_from_cnt(cnt)
new_gt_bbox.append(box.copy())
mask_p = cv2.copyMakeBorder(mask, 0, crp_h - tar_size[0],
0, crp_w - tar_size[1], borderType=cv2.BORDER_CONSTANT, value=(0,))
new_gt_mask.append(mask_p.copy())
new_gt_labels.append(gt_labels[idx])
new_gt_ignore_bbox = []
new_gt_ignore_mask = []
for idx in range(len(gt_ignore_masks)):
mask = gt_ignore_masks[idx]
mask = mask[i:i + tar_size[0], j:j + tar_size[1]]
if np.max(mask) > 0:
cnt = get_cnt_from_mask(mask)
if cnt is not None:
box = get_rect_from_cnt(cnt)
new_gt_ignore_bbox.append(box.copy())
mask_p = cv2.copyMakeBorder(mask, 0, crp_h - tar_size[0], 0, crp_w - tar_size[1],
borderType=cv2.BORDER_CONSTANT,
value=(0,))
new_gt_ignore_mask.append(mask_p.copy())
if new_gt_bbox:
new_gt_bbox = np.array(new_gt_bbox, dtype=np.float32)
new_gt_bbox = clip_box(new_gt_bbox, img_w=tar_size[1], img_h=tar_size[0])
new_gt_labels = np.array(new_gt_labels, dtype=np.int64)
new_gt_mask = np.stack(new_gt_mask, axis=0)
else:
new_gt_bbox = np.zeros((0, 4), dtype=np.float32)
new_gt_labels = np.array([], dtype=np.int64)
if new_gt_ignore_mask:
new_gt_ignore_bbox = np.array(new_gt_ignore_bbox, dtype=np.float32)
new_gt_ignore_bbox = clip_box(new_gt_ignore_bbox, img_w=tar_size[1], img_h=tar_size[0])
new_gt_ignore_mask = np.stack(new_gt_ignore_mask, axis=0)
else:
new_gt_ignore_bbox = np.zeros((0, 4), dtype=np.float32)
img_crp = img[i:i + tar_size[0], j:j + tar_size[1]]
img_shape_new = img_crp.shape
img_p = cv2.copyMakeBorder(img_crp, 0, crp_h - tar_size[0], 0, crp_w - tar_size[1], borderType=cv2.BORDER_CONSTANT,
value=(0, 0, 0))
pad_shape_new = img_p.shape
img_p = img_p.transpose(2, 0, 1)
assert len(img_p.shape) == 3 and img_p.shape[0] == 3
return img_p, new_gt_bbox, new_gt_labels, new_gt_ignore_bbox, \
new_gt_mask, new_gt_ignore_mask, img_shape_new, pad_shape_new
def bbox_distances(bboxes1, bboxes2, iou_thr=0.5):
"""Calculate the IoU distances of two sets of boxes."""
ious = bbox_overlaps(bboxes1, bboxes2, mode='iou')
distances = 1 - ious
distances = np.where(distances > iou_thr, np.nan, distances)
return distances
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ("img", "gt_bboxes", "gt_labels")
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(patch.reshape(-1, 4),
boxes.reshape(-1, 4)).reshape(-1)
if len(overlaps) > 0 and overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
# only adjust boxes and instance masks when the gt is not empty
if len(overlaps) > 0:
# adjust boxes
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = ((center[:, 0] > patch[0]) *
(center[:, 1] > patch[1]) *
(center[:, 0] < patch[2]) *
(center[:, 1] < patch[3]))
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results["gt_bboxes"] = boxes
results["gt_labels"] = labels
if "gt_masks" in results:
valid_masks = [
results["gt_masks"][i] for i in range(len(mask))
if mask[i]
]
# here the valid_masks is not empty
results["gt_masks"] = np.stack([
gt_mask[patch[1]:patch[3], patch[0]:patch[2]]
for gt_mask in valid_masks
])
# adjust the img no matter whether the gt is empty before crop
img = img[patch[1]:patch[3], patch[0]:patch[2]]
results["img"] = img
# not tested
if "gt_semantic_seg" in results:
results["gt_semantic_seg"] = results["gt_semantic_seg"][
patch[1]:patch[3], patch[0]:patch[2]]
return results
def __call__(self, results, state=None):
if state is not None:
return self._call_with_state(results, state)
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
# Force return origin for no annotation.
if len(boxes) == 0:
mode = 1
state = dict(mode=mode)
if mode == 1:
return results, state
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(patch.reshape(-1, 4),
boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] > patch[0]) * (
center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
center[:, 1] < patch[3])
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
state['new_w'] = new_w
state['new_h'] = new_h
state['left'] = left
state['top'] = top
return results, state
def main():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
cfg.data.test.test_mode = True
config_file = args.config
checkpoint_file = args.checkpoint
# build the model from a config file and a checkpoint file
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = init_detector(config_file, checkpoint_file, device=device)
model.CLASSES = Ultra4CocoDataset.CLASSES
cls2id = dict(zip(model.CLASSES, range(0, len(model.CLASSES))))
acc = 0.0
tot = 0.0
gt_cls_num = np.zeros((len(model.CLASSES)))
tp = np.zeros((len(model.CLASSES)))
fp = np.zeros((len(model.CLASSES)))
fn = np.zeros((len(model.CLASSES)))
tn = np.zeros((len(model.CLASSES)))
with open(test_path, "r") as f:
filenames = f.readlines()
for filename in filenames:
img_file = filename.strip() + ".jpg"
xml_file = filename.strip() + ".xml"
img = cv2.imread(os.path.join(test_img_path, img_file))
if img is not None:
xml_path = os.path.join(test_xml_path, xml_file)
coords = read_xml(xml_path)
if len(coords) is 0:
print("No annotations\n")
continue
gt_bboxes = [coord[:4] for coord in coords]
gt_labels = [coord[4] for coord in coords]
for label in gt_labels:
if label in cls2id.keys():
gt_cls_num[cls2id[label]] += 1
tot += 1
result = inference_detector(model, img)
det_bboxes, det_labels, det_scores = get_result(result,
score_thr=0.5)
ious = bbox_overlaps(np.array(det_bboxes), np.array(gt_bboxes))
ious_max = ious.max(axis=1)
ious_argmax = ious.argmax(axis=1)
gt_matched_det = np.ones((len(gt_bboxes))) * -1
det_matched_gt = np.ones((len(det_bboxes))) * -1
gt_matched_scores = np.zeros((len(gt_bboxes)))
for i in range(0, len(det_bboxes)):
if ious_max[i] > 0.5:
target_gt = ious_argmax[i]
if gt_matched_scores[target_gt] < det_scores[i]:
gt_matched_scores[target_gt] = det_scores[i]
gt_matched_det[target_gt] = i
det_matched_gt[i] = target_gt
else:
fp[cls2id[model.CLASSES[det_labels[i]]]] += 1
for i in range(0, len(det_matched_gt)):
gt = int(det_matched_gt[i])
if gt > -1:
if model.CLASSES[det_labels[i]] == gt_labels[gt]:
tp[cls2id[model.CLASSES[det_labels[i]]]] += 1
acc += 1
else:
fp[cls2id[model.CLASSES[det_labels[i]]]] += 1
for i in range(0, len(model.CLASSES)):
fn[i] = gt_cls_num[i] - tp[i]
tn[i] = gt_cls_num.sum() - fn[i] - tp[i] - fp[i]
print("accuracy: %f" % (acc / tot))
mat = np.zeros((len(model.CLASSES), len(TABLE_HEAD)))
for i in range(0, len(model.CLASSES)):
mat[i][0] = i
mat[i][1] = gt_cls_num[i]
mat[i][2] = tp[i]
mat[i][3] = fp[i]
mat[i][4] = fn[i]
mat[i][5] = tp[i] / (tp[i] + fp[i])
mat[i][6] = tp[i] / (tp[i] + fn[i])
print("%s: %.0f gt, %.0f det, %.0f tp, precision: %.6f, recall: %.6f" %
(model.CLASSES[i], gt_cls_num[i], tp[i] + fp[i], tp[i], tp[i] / (tp[i] + fp[i]), tp[i] / (tp[i] + fn[i])))\
if os.path.exists(output_xml_name):
os.remove(output_xml_name)
workbook = openpyxl.Workbook(output_xml_name)
sheet = workbook.create_sheet("sheet")
sheet.append(TABLE_HEAD)
for i in range(0, len(model.CLASSES)):
label = model.CLASSES[i]
sheet.append([
label,
"%.0f" % gt_cls_num[i],
"%.0f" % tp[i],
"%.0f" % fp[i],
"%.0f" % fn[i],
"%.6f" % (tp[i] / (tp[i] + fp[i])),
"%.6f" % (tp[i] / (tp[i] + fn[i]))
])
workbook.save(output_xml_name)
def __call__(self, results):
if 'img_fields' in results:
assert results['img_fields'] == ['img'], \
'Only single img_fields is allowed'
img = results['img']
assert 'bbox_fields' in results
boxes = [results[key] for key in results['bbox_fields']]
boxes = np.concatenate(boxes, 0)
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
self.mode = mode
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
# Line or point crop is not allowed
if patch[2] == patch[0] or patch[3] == patch[1]:
continue
overlaps = bbox_overlaps(patch.reshape(-1, 4),
boxes.reshape(-1, 4)).reshape(-1)
if len(overlaps) > 0 and overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
# only adjust boxes and instance masks when the gt is not empty
if len(overlaps) > 0:
# adjust boxes
def is_center_of_bboxes_in_patch(boxes, patch):
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = ((center[:, 0] > patch[0]) *
(center[:, 1] > patch[1]) *
(center[:, 0] < patch[2]) *
(center[:, 1] < patch[3]))
return mask
mask = is_center_of_bboxes_in_patch(boxes, patch)
if not mask.any():
continue
for key in results.get('bbox_fields', []):
boxes = results[key].copy()
mask = is_center_of_bboxes_in_patch(boxes, patch)
boxes = boxes[mask]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results[key] = boxes
# labels
label_key = self.bbox2label.get(key)
if label_key in results:
results[label_key] = results[label_key][mask]
# mask fields
mask_key = self.bbox2mask.get(key)
if mask_key in results:
results[mask_key] = results[mask_key][
mask.nonzero()[0]].crop(patch)
# adjust the img no matter whether the gt is empty before crop
img = img[patch[1]:patch[3], patch[0]:patch[2]]
results['img'] = img
results['img_shape'] = img.shape
# seg fields
for key in results.get('seg_fields', []):
results[key] = results[key][patch[1]:patch[3],
patch[0]:patch[2]]
return results
def forward(self, mask_feats, mask_preds, classes, bbox_pair=None): # modified to take in classes as input
if not self.only_label:
assert len(mask_feats.size()) == 4 and mask_feats.size()[1:] == (512, 14, 14)
assert len(mask_preds.size()) == 4 and mask_preds.size()[1:] == (2, 28, 28)
assert len(classes.size()) == 2 and classes.size()[1:] == (2,)
if self.mode == 'feature+mask':
mask_preds = mask_preds.sigmoid()
mask_pred_pooled = self.max_pool(mask_preds)
x = torch.cat([mask_feats, mask_pred_pooled], dim=1)
for conv in self.convs:
x = self.relu(conv(x))
x = x.view(x.size(0), -1)
for fc in self.fcs:
x = self.relu(fc(x))
logits = self.fc_final(x)
return logits
if self.mode == 'feature+mask+label':
# one hot coding
classes = classes.long()
cls_pred = torch.zeros(classes.shape[0], 2 * self.num_class).cuda()
for i in range(classes.shape[0]):
cls_pred[i, [classes[i, 0], self.num_class + classes[i, 1]]] = 1
mask_preds = mask_preds.sigmoid()
mask_pred_pooled = self.max_pool(mask_preds)
x = torch.cat([mask_feats, mask_pred_pooled], dim=1)
for conv in self.convs:
x = self.relu(conv(x))
x = x.view(x.size(0), -1)
x = torch.cat([x, cls_pred], dim=-1)
for fc in self.fcs:
x = self.relu(fc(x))
logits = self.fc_final(x)
return logits
if self.mode == 'label':
# one hot coding
classes = classes.long()
cls_pred = torch.zeros(classes.shape[0], 2*self.num_class).cuda()
for i in range(classes.shape[0]):
cls_pred[i, [classes[i, 0], self.num_class+classes[i, 1]]] = 1
x = cls_pred
for fc in self.fcs:
x = self.relu(fc(x))
logits = self.fc_final(x)
return logits
if self.mode == 'stats':
classes = classes.int()
logits = []
for i in range(classes.shape[0]):
logits.append(self.cat_occ[classes[i,0]][classes[i,1]])
logits = torch.tensor(logits).float().cuda()
return logits
if self.mode == 'SHR':
logit_one = torch.ones(1).float().cuda()
if bbox_pair==None:
raise Exception("SHR is only available in testing")
# bbox_overlaps take bbox1 as foreground obj by default!
bbox1, bbox2 = [x.unsqueeze(dim=0).cpu().numpy() for x in bbox_pair]
if bbox_overlaps(bbox1, bbox2, mode='iof')[0] > self.min_thing_area:
return torch.stack([logit_one * 1, logit_one * 0])
if bbox_overlaps(bbox2, bbox1, mode='iof')[0] > self.min_thing_area:
return torch.stack([logit_one * 0, logit_one * 1])
return torch.stack([logit_one * -1, logit_one * -1])
def create_groundtruth_database(dataset_class_name,
data_path,
info_prefix,
info_path=None,
mask_anno_path=None,
used_classes=None,
database_save_path=None,
db_info_save_path=None,
relative_path=True,
add_rgb=False,
lidar_only=False,
bev_only=False,
coors_range=None,
with_mask=False):
"""Given the raw data, generate the ground truth database.
Args:
dataset_class_name （str): Name of the input dataset.
data_path (str): Path of the data.
info_prefix (str): Prefix of the info file.
info_path (str): Path of the info file.
Default: None.
mask_anno_path (str): Path of the mask_anno.
Default: None.
used_classes (list[str]): Classes have been used.
Default: None.
database_save_path (str): Path to save database.
Default: None.
db_info_save_path (str): Path to save db_info.
Default: None.
relative_path (bool): Whether to use relative path.
Default: True.
with_mask (bool): Whether to use mask.
Default: False.
"""
print(f'Create GT Database of {dataset_class_name}')
dataset_cfg = dict(type=dataset_class_name,
data_root=data_path,
ann_file=info_path)
if dataset_class_name == 'KittiDataset':
file_client_args = dict(backend='disk')
dataset_cfg.update(test_mode=False,
split='training',
modality=dict(
use_lidar=True,
use_depth=False,
use_lidar_intensity=True,
use_camera=with_mask,
),
pipeline=[
dict(type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=4,
use_dim=4,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
file_client_args=file_client_args)
])
elif dataset_class_name == 'NuScenesDataset':
dataset_cfg.update(use_valid_flag=True,
pipeline=[
dict(type='LoadPointsFromFile',
load_dim=5,
use_dim=5),
dict(type='LoadPointsFromMultiSweeps',
coord_type='LIDAR',
sweeps_num=10,
use_dim=[0, 1, 2, 3, 4],
pad_empty_sweeps=True,
remove_close=True),
dict(type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True)
])
elif dataset_class_name == 'WaymoDataset':
file_client_args = dict(backend='disk')
dataset_cfg.update(test_mode=False,
split='training',
modality=dict(
use_lidar=True,
use_depth=False,
use_lidar_intensity=True,
use_camera=False,
),
pipeline=[
dict(type='LoadPointsFromFile',
coord_type='LIDAR',
load_dim=6,
use_dim=5,
file_client_args=file_client_args),
dict(type='LoadAnnotations3D',
with_bbox_3d=True,
with_label_3d=True,
file_client_args=file_client_args)
])
dataset = build_dataset(dataset_cfg)
if database_save_path is None:
database_save_path = osp.join(data_path, f'{info_prefix}_gt_database')
if db_info_save_path is None:
db_info_save_path = osp.join(data_path,
f'{info_prefix}_dbinfos_train.pkl')
mmcv.mkdir_or_exist(database_save_path)
all_db_infos = dict()
if with_mask:
coco = COCO(osp.join(data_path, mask_anno_path))
imgIds = coco.getImgIds()
file2id = dict()
for i in imgIds:
info = coco.loadImgs([i])[0]
file2id.update({info['file_name']: i})
group_counter = 0
for j in track_iter_progress(list(range(len(dataset)))):
input_dict = dataset.get_data_info(j)
dataset.pre_pipeline(input_dict)
example = dataset.pipeline(input_dict)
annos = example['ann_info']
image_idx = example['sample_idx']
points = example['points'].tensor.numpy()
gt_boxes_3d = annos['gt_bboxes_3d'].tensor.numpy()
names = annos['gt_names']
group_dict = dict()
if 'group_ids' in annos:
group_ids = annos['group_ids']
else:
group_ids = np.arange(gt_boxes_3d.shape[0], dtype=np.int64)
difficulty = np.zeros(gt_boxes_3d.shape[0], dtype=np.int32)
if 'difficulty' in annos:
difficulty = annos['difficulty']
num_obj = gt_boxes_3d.shape[0]
point_indices = box_np_ops.points_in_rbbox(points, gt_boxes_3d)
if with_mask:
# prepare masks
gt_boxes = annos['gt_bboxes']
img_path = osp.split(example['img_info']['filename'])[-1]
if img_path not in file2id.keys():
print(f'skip image {img_path} for empty mask')
continue
img_id = file2id[img_path]
kins_annIds = coco.getAnnIds(imgIds=img_id)
kins_raw_info = coco.loadAnns(kins_annIds)
kins_ann_info = _parse_coco_ann_info(kins_raw_info)
h, w = annos['img_shape'][:2]
gt_masks = [
_poly2mask(mask, h, w) for mask in kins_ann_info['masks']
]
# get mask inds based on iou mapping
bbox_iou = bbox_overlaps(kins_ann_info['bboxes'], gt_boxes)
mask_inds = bbox_iou.argmax(axis=0)
valid_inds = (bbox_iou.max(axis=0) > 0.5)
# mask the image
# use more precise crop when it is ready
# object_img_patches = np.ascontiguousarray(
# np.stack(object_img_patches, axis=0).transpose(0, 3, 1, 2))
# crop image patches using roi_align
# object_img_patches = crop_image_patch_v2(
# torch.Tensor(gt_boxes),
# torch.Tensor(mask_inds).long(), object_img_patches)
object_img_patches, object_masks = crop_image_patch(
gt_boxes, gt_masks, mask_inds, annos['img'])
for i in range(num_obj):
filename = f'{image_idx}_{names[i]}_{i}.bin'
abs_filepath = osp.join(database_save_path, filename)
rel_filepath = osp.join(f'{info_prefix}_gt_database', filename)
# save point clouds and image patches for each object
gt_points = points[point_indices[:, i]]
gt_points[:, :3] -= gt_boxes_3d[i, :3]
if with_mask:
if object_masks[i].sum() == 0 or not valid_inds[i]:
# Skip object for empty or invalid mask
continue
img_patch_path = abs_filepath + '.png'
mask_patch_path = abs_filepath + '.mask.png'
mmcv.imwrite(object_img_patches[i], img_patch_path)
mmcv.imwrite(object_masks[i], mask_patch_path)
with open(abs_filepath, 'w') as f:
gt_points.tofile(f)
if (used_classes is None) or names[i] in used_classes:
db_info = {
'name': names[i],
'path': rel_filepath,
'image_idx': image_idx,
'gt_idx': i,
'box3d_lidar': gt_boxes_3d[i],
'num_points_in_gt': gt_points.shape[0],
'difficulty': difficulty[i],
}
local_group_id = group_ids[i]
# if local_group_id >= 0:
if local_group_id not in group_dict:
group_dict[local_group_id] = group_counter
group_counter += 1
db_info['group_id'] = group_dict[local_group_id]
if 'score' in annos:
db_info['score'] = annos['score'][i]
if with_mask:
db_info.update({'box2d_camera': gt_boxes[i]})
if names[i] in all_db_infos:
all_db_infos[names[i]].append(db_info)
else:
all_db_infos[names[i]] = [db_info]
for k, v in all_db_infos.items():
print(f'load {len(v)} {k} database infos')
with open(db_info_save_path, 'wb') as f:
pickle.dump(all_db_infos, f)
def main():
args = parse_args()
rgcfg = mmcv.Config.fromfile(args.configrg)
rgcfg.data.test.test_mode = True
abcfg = mmcv.Config.fromfile(args.configab)
abcfg.data.test.test_mode = True
rg_config_file = args.configrg
rg_checkpoint_file = args.checkpointrg
ab_config_file = args.configab
ab_checkpoint_file = args.checkpointab
# build the model from a config file and a checkpoint file
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
modelrg = init_detector(rg_config_file, rg_checkpoint_file, device=device)
modelrg.CLASSES = RoseGoldDataset.CLASSES
modelab = init_detector(ab_config_file, ab_checkpoint_file, device=device)
modelab.CLASSES = UltraABDataset.CLASSES
acc = 0.0
tot = 0.0
gt_cls_num = np.zeros((len(modelrg.CLASSES) + len(modelab.CLASSES) - 1))
tp = np.zeros((len(modelrg.CLASSES) + len(modelab.CLASSES) - 1))
fp = np.zeros((len(modelrg.CLASSES) + len(modelab.CLASSES) - 1))
fn = np.zeros((len(modelrg.CLASSES) + len(modelab.CLASSES) - 1))
tn = np.zeros((len(modelrg.CLASSES) + len(modelab.CLASSES) - 1))
cls2id = {
'壳牌恒护超凡喜力欧系专属 5W-30 1L': 0,
'壳牌恒护超凡喜力欧系专属 5W-30 4L': 1,
'壳牌恒护超凡喜力欧系专属 5W-40 1L': 2,
'壳牌恒护超凡喜力欧系专属 5W-40 4L': 3,
'壳牌恒护超凡喜力亚系专属 5W-30 1L': 4,
'壳牌恒护超凡喜力亚系专属 5W-30 4L': 5,
'壳牌先锋超凡喜力 SN PLUS 天然气全合成油 0W-20 4L': 6,
'壳牌先锋超凡喜力 SN PLUS 天然气全合成油 0W-20 1L': 7,
'壳牌先锋超凡喜力 SN PLUS 天然气全合成油 0W-30 4L': 8,
'壳牌先锋超凡喜力 SN PLUS 天然气全合成油 0W-30 1L': 9,
'壳牌先锋超凡喜力 ACEA C5 天然气全合成油 0W-20 4L': 10,
'壳牌先锋超凡喜力 ACEA C5 天然气全合成油 0W-20 1L': 11,
'壳牌先锋超凡喜力 ACEA C2 / C3 天然气全合成油 0W-30 4L': 12,
'壳牌先锋超凡喜力 ACEA C2 / C3 天然气全合成油 0W-30 1L': 13,
'壳牌先锋超凡喜力 ACEA A3 / B4 天然气全合成油 0W-40 4L': 14,
'壳牌先锋超凡喜力 ACEA A3 / B4 天然气全合成油 0W-40 1L': 15,
'其他': 16
}
id2cls = {value: key for key, value in cls2id.items()}
with open(test_path, "r") as f:
filenames = f.readlines()
for filename in filenames:
img_file = filename.strip() + ".jpg"
xml_file = filename.strip() + ".xml"
img = cv2.imread(os.path.join(test_img_path, img_file))
if img is not None:
xml_path = os.path.join(test_xml_path, xml_file)
coords = read_xml(xml_path)
if len(coords) is 0:
print("No annotations\n")
continue
gt_bboxes = [coord[:4] for coord in coords]
gt_labels = [coord[4] for coord in coords]
for label in gt_labels:
gt_cls_num[cls2id[label]] += 1
tot += 1
resultrg = inference_detector(modelrg, img)
resultab = inference_detector(modelab, img)
det_bboxes, det_labels, det_scores = get_result(
resultrg,
resultab,
modelrg.CLASSES,
modelab.CLASSES,
score_thr=0.5)
ious = bbox_overlaps(np.array(det_bboxes), np.array(gt_bboxes))
ious_max = ious.max(axis=1)
ious_argmax = ious.argmax(axis=1)
gt_matched_det = np.ones((len(gt_bboxes))) * -1
det_matched_gt = np.ones((len(det_bboxes))) * -1
gt_matched_scores = np.zeros((len(gt_bboxes)))
for i in range(0, len(det_bboxes)):
if ious_max[i] > 0.5:
target_gt = ious_argmax[i]
if gt_matched_scores[target_gt] < det_scores[i]:
gt_matched_scores[target_gt] = det_scores[i]
gt_matched_det[target_gt] = i
det_matched_gt[i] = target_gt
else:
fp[cls2id[det_labels[i]]] += 1
for i in range(0, len(det_matched_gt)):
gt = int(det_matched_gt[i])
if gt > -1:
if det_labels[i] == gt_labels[gt]:
tp[cls2id[det_labels[i]]] += 1
acc += 1
else:
fp[cls2id[det_labels[i]]] += 1
for i in range(0, len(cls2id)):
fn[i] = gt_cls_num[i] - tp[i]
tn[i] = gt_cls_num.sum() - fn[i] - tp[i] - fp[i]
print("accuracy: %f" % (acc / tot))
mat = np.zeros((len(cls2id), len(TABLE_HEAD)))
for i in range(0, len(cls2id)):
mat[i][0] = i
mat[i][1] = gt_cls_num[i]
mat[i][2] = tp[i]
mat[i][3] = fp[i]
mat[i][4] = fn[i]
mat[i][5] = tp[i] / (tp[i] + fp[i])
mat[i][6] = tp[i] / (tp[i] + fn[i])
print("%s: %.0f gt, %.0f det, %.0f tp, precision: %.6f, recall: %.6f" %
(id2cls[i], gt_cls_num[i], tp[i] + fp[i], tp[i], tp[i] / (tp[i] + fp[i]), tp[i] / (tp[i] + fn[i])))\
if os.path.exists("shell_statistics.xlsx"):
os.remove("shell_statistics.xlsx")
workbook = openpyxl.Workbook("shell_statistics.xlsx")
sheet = workbook.create_sheet("sheet")
sheet.append(TABLE_HEAD)
for i in range(0, len(id2cls)):
label = id2cls[i]
sheet.append([
label,
"%.0f" % gt_cls_num[i],
"%.0f" % tp[i],
"%.0f" % fp[i],
"%.0f" % fn[i],
"%.6f" % (tp[i] / (tp[i] + fp[i])),
"%.6f" % (tp[i] / (tp[i] + fn[i]))
])
workbook.save("shell_statistics.xlsx")
def test_corner_head_encode_and_decode_heatmap():
"""Tests corner head generating and decoding the heatmap."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3),
'border': (0, 0, 0, 0)
}]
gt_bboxes = [
torch.Tensor([[10, 20, 200, 240], [40, 50, 100, 200],
[10, 20, 200, 240]])
]
gt_labels = [torch.LongTensor([1, 1, 2])]
self = CornerHead(num_classes=4, in_channels=1, corner_emb_channels=1)
feat = [
torch.rand(1, 1, s // 4, s // 4) for _ in range(self.num_feat_levels)
]
targets = self.get_targets(gt_bboxes,
gt_labels,
feat[0].shape,
img_metas[0]['pad_shape'],
with_corner_emb=self.with_corner_emb)
gt_tl_heatmap = targets['topleft_heatmap']
gt_br_heatmap = targets['bottomright_heatmap']
gt_tl_offset = targets['topleft_offset']
gt_br_offset = targets['bottomright_offset']
embedding = targets['corner_embedding']
[top, left], [bottom, right] = embedding[0][0]
gt_tl_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4])
gt_br_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4])
gt_tl_embedding_heatmap[0, 0, top, left] = 1
gt_br_embedding_heatmap[0, 0, bottom, right] = 1
batch_bboxes, batch_scores, batch_clses = self.decode_heatmap(
tl_heat=gt_tl_heatmap,
br_heat=gt_br_heatmap,
tl_off=gt_tl_offset,
br_off=gt_br_offset,
tl_emb=gt_tl_embedding_heatmap,
br_emb=gt_br_embedding_heatmap,
img_meta=img_metas[0],
k=100,
kernel=3,
distance_threshold=0.5)
bboxes = batch_bboxes.view(-1, 4)
scores = batch_scores.view(-1, 1)
clses = batch_clses.view(-1, 1)
idx = scores.argsort(dim=0, descending=True)
bboxes = bboxes[idx].view(-1, 4)
scores = scores[idx].view(-1)
clses = clses[idx].view(-1)
valid_bboxes = bboxes[torch.where(scores > 0.05)]
valid_labels = clses[torch.where(scores > 0.05)]
max_coordinate = valid_bboxes.max()
offsets = valid_labels.to(valid_bboxes) * (max_coordinate + 1)
gt_offsets = gt_labels[0].to(gt_bboxes[0]) * (max_coordinate + 1)
offset_bboxes = valid_bboxes + offsets[:, None]
offset_gtbboxes = gt_bboxes[0] + gt_offsets[:, None]
iou_matrix = bbox_overlaps(offset_bboxes.numpy(), offset_gtbboxes.numpy())
assert (iou_matrix == 1).sum() == 3
def tpfp_default(det_bboxes,
gt_bboxes,
gt_bboxes_ignore=None,
iou_thr=0.5,
area_ranges=None,
class_index=None,
gt_bboxes_all_classes=None):
"""Check if detected bboxes are true positive or false positive.
Args:
det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
of shape (k, 4). Default: None
iou_thr (float): IoU threshold to be considered as matched.
Default: 0.5.
area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,
in the format [(min1, max1), (min2, max2), ...]. Default: None.
Returns:
tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of
each array is (num_scales, m).
"""
# an indicator of ignored gts
gt_ignore_inds = np.concatenate((np.zeros(gt_bboxes.shape[0],
dtype=np.bool),
np.ones(gt_bboxes_ignore.shape[0],
dtype=np.bool)))
# stack gt_bboxes and gt_bboxes_ignore for convenience
gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
num_dets = det_bboxes.shape[0]
num_gts = gt_bboxes.shape[0]
if area_ranges is None:
area_ranges = [(None, None)]
num_scales = len(area_ranges)
# tp and fp are of shape (num_scales, num_gts), each row is tp or fp of
# a certain scale
tp = np.zeros((num_scales, num_dets), dtype=np.float32)
fp = np.zeros((num_scales, num_dets), dtype=np.float32)
fp_redundant = np.zeros((num_scales, num_dets), dtype=np.float32)
gt_covered = []
ious_max = -1 * np.ones(num_dets, dtype=np.float32)
ious_argmax = -1 * np.ones(num_dets, dtype=np.float32)
matched_classes = -1 * np.ones(num_dets, dtype=np.float32)
# if there is no gt bboxes in this image, then all det bboxes
# within area range are false positives
if gt_bboxes.shape[0] == 0:
if area_ranges == [(None, None)]:
fp[...] = 1
else:
det_areas = (det_bboxes[:, 2] - det_bboxes[:, 0]) * (
det_bboxes[:, 3] - det_bboxes[:, 1])
for i, (min_area, max_area) in enumerate(area_ranges):
fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
# return tp, fp, fp_redundant, gt_covered, ious_max, ious_argmax, -1 * np.ones(num_dets, dtype=np.float32), [], []
else:
ious = bbox_overlaps(det_bboxes, gt_bboxes)
# for each det, the max iou with all gts
ious_max = ious.max(axis=1)
# for each det, which gt overlaps most with it
ious_argmax = ious.argmax(axis=1)
# sort all dets in descending order by scores
sort_inds = np.argsort(-det_bboxes[:, -1])
for k, (min_area, max_area) in enumerate(area_ranges):
gt_covered = np.zeros(num_gts, dtype=bool)
# if no area range is specified, gt_area_ignore is all False
if min_area is None:
gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
else:
gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (
gt_bboxes[:, 3] - gt_bboxes[:, 1])
gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
for i in sort_inds:
if ious_max[i] >= iou_thr:
matched_gt = ious_argmax[i]
if not (gt_ignore_inds[matched_gt]
or gt_area_ignore[matched_gt]):
if not gt_covered[matched_gt]:
gt_covered[matched_gt] = True
tp[k, i] = 1
else:
fp[k, i] = 1
fp_redundant[k, i] = 1
# otherwise ignore this detected bbox, tp = 0, fp = 0
elif min_area is None:
fp[k, i] = 1
else:
bbox = det_bboxes[i, :4]
area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
if area >= min_area and area < max_area:
fp[k, i] = 1
if gt_bboxes.shape[0] != 0:
matched_classes[np.where(ious_max != 0)] = class_index
# Check if det matches gt from other class
if -1 in matched_classes and gt_bboxes_all_classes['bboxes'].shape[0] != 0:
unmatched_inds = np.where(matched_classes == -1)
ious_all_classes = bbox_overlaps(det_bboxes,
gt_bboxes_all_classes['bboxes'])
ious_max_all_classes = ious_all_classes.max(axis=1)[unmatched_inds]
ious_argmax_all_classes = ious_all_classes.argmax(
axis=1)[unmatched_inds]
labels = gt_bboxes_all_classes['labels'][ious_argmax_all_classes]
labels[np.where(ious_max_all_classes == 0)] = -1
matched_classes[unmatched_inds] = labels
new_iou_max = np.array(ious_max_all_classes, copy=True)
new_iou_max[np.where(ious_max_all_classes == 0)] = -1
ious_max[unmatched_inds] = new_iou_max
new_ious_argmax = np.array(ious_argmax_all_classes, copy=True)
new_ious_argmax[np.where(ious_max_all_classes == 0)] = -1
# Fix matched_gt_index. Find index within each class
for i in range(len(new_ious_argmax)):
index_all_gts = new_ious_argmax[i]
if index_all_gts != -1:
label = gt_bboxes_all_classes['labels'][index_all_gts]
new_ious_argmax[i] = np.where(
gt_bboxes_all_classes['labels'][:index_all_gts] ==
label)[0].shape[0]
ious_argmax[unmatched_inds] = new_ious_argmax
gt_iou_max, gt_iou_argmax = [], []
if det_bboxes.shape[0] != 0 and gt_bboxes.shape[0] != 0:
gt_iou_max, gt_iou_argmax = ious.max(axis=0), ious.argmax(axis=0)
unmatched_inds = np.where(gt_iou_max == 0)
gt_iou_max[unmatched_inds] = -1
gt_iou_argmax[unmatched_inds] = -1
return tp, fp, fp_redundant, gt_covered, ious_max, ious_argmax, matched_classes, gt_iou_max, gt_iou_argmax
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
h, w, c = img.shape
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w, w)
new_h = random.uniform(self.min_crop_size * h, h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(patch.reshape(-1, 4),
boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = ((center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) *
(center[:, 0] < patch[2]) * (center[:, 1] < patch[3]))
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
if 'gt_masks' in results:
valid_masks = [
results['gt_masks'][i] for i in range(len(mask))
if mask[i]
]
results['gt_masks'] = [
gt_mask[patch[1]:patch[3], patch[0]:patch[2]]
for gt_mask in valid_masks
]
# not tested
if 'gt_semantic_seg' in results:
results['gt_semantic_seg'] = results['gt_semantic_seg'][
patch[1]:patch[3], patch[0]:patch[2]]
return results
def __call__(self, results):
img, boxes, labels = [
results[k] for k in ('img', 'gt_bboxes', 'gt_labels')
]
results['raw_img'] = img
h, w, c = img.shape
max_box = [0, 0] # w, h
box_list = []
flag_sm = False
for ind, box in enumerate(results.get('gt_bboxes', [])):
box_w, box_h = int(box[2] - box[0]), int(box[3] - box[1])
if box_w > max_box[0]: max_box[0] = box_w # find max w h
if box_h > max_box[1]: max_box[1] = box_h
box_list.append(box)
if max_box[0] > w // 2 or max_box[1] > h // 2:
return results
# if max_box[0] < 100 and max_box[1] < 100:
# self.min_crop_size = self.sm_min_crop_size
# self.max_crop_size = self.sm_max_crop_size
#
# self.sample_mode = self.sm_min_ious
# flag_sm = True
while True:
mode = random.choice(self.sample_mode)
if mode == 1:
return results
min_iou = mode
for i in range(50):
new_w = random.uniform(self.min_crop_size * w,
self.max_crop_size * w)
new_h = random.uniform(self.min_crop_size * h,
self.max_crop_size * h)
# h / w in [0.5, 2]
if new_h / new_w < 0.5 or new_h / new_w > 2:
continue
left = random.uniform(w - new_w)
top = random.uniform(h - new_h)
if flag_sm:
sm_box = rchoice(box_list)
xmax, ymax = sm_box[0], sm_box[1]
xmin = round(xmax - new_w)
ymin = round(ymax - new_h)
b_w = w - new_w
b_h = h - new_h
top_line = max(xmin, 0) # x1
left_line = max(ymin, 0) # y1
bottom_line = min(xmax, b_w) # x2
right_line = min(ymax, b_h) # y2
left = random.uniform(top_line, bottom_line)
top = random.uniform(left_line, right_line)
patch = np.array(
(int(left), int(top), int(left + new_w), int(top + new_h)))
overlaps = bbox_overlaps(patch.reshape(-1, 4),
boxes.reshape(-1, 4)).reshape(-1)
if overlaps.min() < min_iou:
continue
# center of boxes should inside the crop img
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] > patch[0]) * (
center[:, 1] > patch[1]) * (center[:, 0] < patch[2]) * (
center[:, 1] < patch[3])
if not mask.any():
continue
boxes = boxes[mask]
labels = labels[mask]
# adjust boxes
img = img[patch[1]:patch[3], patch[0]:patch[2]]
boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
boxes -= np.tile(patch[:2], 2)
results['img'] = img
results['gt_bboxes'] = boxes
results['gt_labels'] = labels
return results