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replace np.int with np.int32 #2367

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2 changes: 1 addition & 1 deletion alignment/coordinate_reg/image_infer.py
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Expand Up @@ -15,7 +15,7 @@
color = (200, 160, 75)
for face in faces:
lmk = face.landmark_2d_106
lmk = np.round(lmk).astype(np.int)
lmk = np.round(lmk).astype(np.int32)
for i in range(lmk.shape[0]):
p = tuple(lmk[i])
cv2.circle(tim, p, 1, color, 1, cv2.LINE_AA)
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2 changes: 1 addition & 1 deletion alignment/heatmap/metric.py
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Expand Up @@ -51,7 +51,7 @@ def cal_nme(self, label, pred_label):
ind_gt = np.array(ind_gt)
else:
ind_gt = label[b][p]
#ind_gt = ind_gt.astype(np.int)
#ind_gt = ind_gt.astype(np.int32)
#print(ind_gt)
heatmap_pred = pred_label[b][p]
heatmap_pred = cv2.resize(
Expand Down
2 changes: 1 addition & 1 deletion alignment/synthetics/datasets/augs.py
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Expand Up @@ -24,7 +24,7 @@ def apply(self, image, border_size_limit, **params):
border_size[2] *= image.shape[1]
border_size[1] *= image.shape[0]
border_size[3] *= image.shape[0]
border_size = border_size.astype(np.int)
border_size = border_size.astype(np.int32)
image[:,:border_size[0],:] = self.fill_value
image[:border_size[1],:,:] = self.fill_value
image[:,-border_size[2]:,:] = self.fill_value
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4 changes: 2 additions & 2 deletions alignment/synthetics/test_synthetics.py
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Expand Up @@ -85,10 +85,10 @@
outf.write(' ')
outf.write(' '.join(["%.5f"%x for x in kps.flatten()]))
outf.write("\n")
box = bbox.astype(np.int)
box = bbox.astype(np.int32)
color = (0, 0, 255)
cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)
kps = kps.astype(np.int)
kps = kps.astype(np.int32)
#print(landmark.shape)
for l in range(kps.shape[0]):
color = (0, 0, 255)
Expand Down
4 changes: 2 additions & 2 deletions alignment/synthetics/tools/prepare_synthetics.py
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Expand Up @@ -48,11 +48,11 @@
_scale = output_size / (max(w, h)*1.5)
aimg, M = face_align.transform(dimg, center, output_size, _scale, rotate)
pred = face_align.trans_points(pred, M)
#box = bbox.astype(np.int)
#box = bbox.astype(np.int32)
#color = (0, 0, 255)
#cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)

#kps = pred.astype(np.int)
#kps = pred.astype(np.int32)
#for l in range(kps.shape[0]):
# color = (0, 0, 255)
# cv2.circle(aimg, (kps[l][0], kps[l][1]), 1, color, 2)
Expand Down
4 changes: 2 additions & 2 deletions body/human_pose/ambiguity_aware/scripts/eval_lsp.py
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Expand Up @@ -93,8 +93,8 @@
else:
color = "darkorange"
cv_color = (89, 141, 252)
x1, y1 = joints_2d[i].astype(np.int)
x2, y2 = joints_2d[j].astype(np.int)
x1, y1 = joints_2d[i].astype(np.int32)
x2, y2 = joints_2d[j].astype(np.int32)

cv2.line(image, (x1, y1), (x2, y2), cv_color, 2)
x1, y1, z1 = joints_3d_pre[i]
Expand Down
4 changes: 2 additions & 2 deletions body/human_pose/ambiguity_aware/scripts/inference.py
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Expand Up @@ -103,8 +103,8 @@
else:
color = "darkorange"
cv_color = (89, 141, 252)
x1, y1 = joints_2d[i].astype(np.int)
x2, y2 = joints_2d[j].astype(np.int)
x1, y1 = joints_2d[i].astype(np.int32)
x2, y2 = joints_2d[j].astype(np.int32)

cv2.line(image, (x1, y1), (x2, y2), cv_color, 2)
x1, y1, z1 = joints_3d_pre[i]
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6 changes: 3 additions & 3 deletions detection/retinaface/rcnn/PY_OP/cascade_refine.py
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Expand Up @@ -344,13 +344,13 @@ def forward(self, is_train, req, in_data, out_data, aux):
assert anchors_t1.shape[0] == self.ori_anchors.shape[0]

#for i in range(_gt_boxes.shape[0]):
# box = _gt_boxes[i].astype(np.int)
# box = _gt_boxes[i].astype(np.int32)
# print('%d: gt%d'%(self.nbatch, i), box)
# #color = (0,0,255)
# #cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
#for i in range(anchors_t1.shape[0]):
# box1 = self.ori_anchors[i].astype(np.int)
# box2 = anchors_t1[i].astype(np.int)
# box1 = self.ori_anchors[i].astype(np.int32)
# box2 = anchors_t1[i].astype(np.int32)
# print('%d %d: anchorscompare %d'%(self.nbatch, self.stride, i), box1, box2)
#color = (255,255,0)
#cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
Expand Down
2 changes: 1 addition & 1 deletion detection/retinaface/rcnn/core/loader.py
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Expand Up @@ -247,7 +247,7 @@ def get_batch(self):
print('DEBUG SHAPE', data['data'].shape,
label['gt_boxes'].shape)

box = label['gt_boxes'].copy()[0][0:4].astype(np.int)
box = label['gt_boxes'].copy()[0][0:4].astype(np.int32)
cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]),
(0, 255, 0), 2)
filename = './debugout/%d.png' % (self._debug_id)
Expand Down
2 changes: 1 addition & 1 deletion detection/retinaface/rcnn/cython/cpu_nms.pyx
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Expand Up @@ -26,7 +26,7 @@ def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):

cdef int ndets = dets.shape[0]
cdef np.ndarray[np.int_t, ndim=1] suppressed = \
np.zeros((ndets), dtype=np.int)
np.zeros((ndets), dtype=np.int32)

# nominal indices
cdef int _i, _j
Expand Down
2 changes: 1 addition & 1 deletion detection/retinaface/rcnn/dataset/ds_utils.py
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Expand Up @@ -4,7 +4,7 @@
def unique_boxes(boxes, scale=1.0):
""" return indices of unique boxes """
v = np.array([1, 1e3, 1e6, 1e9])
hashes = np.round(boxes * scale).dot(v).astype(np.int)
hashes = np.round(boxes * scale).dot(v).astype(np.int32)
_, index = np.unique(hashes, return_index=True)
return np.sort(index)

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24 changes: 12 additions & 12 deletions detection/retinaface/rcnn/io/image.py
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Expand Up @@ -95,7 +95,7 @@ def get_image(roidb, scale=False):
if 'boxes_mask' in roi_rec:
im = im.astype(np.float32)
boxes_mask = roi_rec['boxes_mask'].copy() * im_scale
boxes_mask = boxes_mask.astype(np.int)
boxes_mask = boxes_mask.astype(np.int32)
for j in range(boxes_mask.shape[0]):
m = boxes_mask[j]
im_tensor[:, :, m[1]:m[3], m[0]:m[2]] = 0.0
Expand Down Expand Up @@ -156,7 +156,7 @@ def __get_crop_image(roidb):
if 'boxes_mask' in roi_rec:
#im = im.astype(np.float32)
boxes_mask = roi_rec['boxes_mask'].copy()
boxes_mask = boxes_mask.astype(np.int)
boxes_mask = boxes_mask.astype(np.int32)
for j in range(boxes_mask.shape[0]):
m = boxes_mask[j]
im[m[1]:m[3], m[0]:m[2], :] = 0
Expand Down Expand Up @@ -197,7 +197,7 @@ def __get_crop_image(roidb):
fy=im_scale,
interpolation=cv2.INTER_LINEAR)
new_rec['boxes'] = roi_rec['boxes'].copy() * im_scale
box_scale = new_rec['boxes'][box_ind].copy().astype(np.int)
box_scale = new_rec['boxes'][box_ind].copy().astype(np.int32)
ul_min = box_scale[2:4] - SIZE
ul_max = box_scale[0:2]
assert ul_min[0] <= ul_max[0]
Expand All @@ -215,15 +215,15 @@ def __get_crop_image(roidb):
im = cv2.warpAffine(im,
M, (SIZE, SIZE),
borderValue=tuple(config.PIXEL_MEANS))
#tbox = np.array([left, left+SIZE, up, up+SIZE], dtype=np.int)
#tbox = np.array([left, left+SIZE, up, up+SIZE], dtype=np.int32)
#im_new = np.zeros( (SIZE, SIZE,3), dtype=im.dtype)
#for i in range(3):
# im_new[:,:,i] = config.PIXEL_MEANS[i]
new_rec['boxes'][:, 0] -= left
new_rec['boxes'][:, 2] -= left
new_rec['boxes'][:, 1] -= up
new_rec['boxes'][:, 3] -= up
box_trans = new_rec['boxes'][box_ind].copy().astype(np.int)
box_trans = new_rec['boxes'][box_ind].copy().astype(np.int32)
#print('sel box', im_scale, box, box_scale, box_trans, file=sys.stderr)
#print('before', new_rec['boxes'].shape[0])
boxes_new = []
Expand All @@ -249,7 +249,7 @@ def __get_crop_image(roidb):
if TMP_ID < 10:
tim = im.copy()
for i in range(new_rec['boxes'].shape[0]):
box = new_rec['boxes'][i].copy().astype(np.int)
box = new_rec['boxes'][i].copy().astype(np.int32)
cv2.rectangle(tim, (box[0], box[1]), (box[2], box[3]),
(255, 0, 0), 1)
filename = './trainimages/train%d.png' % TMP_ID
Expand Down Expand Up @@ -320,7 +320,7 @@ def get_crop_image1(roidb):
if 'boxes_mask' in roi_rec:
#im = im.astype(np.float32)
boxes_mask = roi_rec['boxes_mask'].copy()
boxes_mask = boxes_mask.astype(np.int)
boxes_mask = boxes_mask.astype(np.int32)
for j in range(boxes_mask.shape[0]):
m = boxes_mask[j]
im[m[1]:m[3], m[0]:m[2], :] = 127
Expand Down Expand Up @@ -437,7 +437,7 @@ def get_crop_image1(roidb):
if TMP_ID >= 0 and TMP_ID < 10:
tim = im.copy().astype(np.uint8)
for i in range(new_rec['boxes'].shape[0]):
box = new_rec['boxes'][i].copy().astype(np.int)
box = new_rec['boxes'][i].copy().astype(np.int32)
cv2.rectangle(tim, (box[0], box[1]), (box[2], box[3]),
(255, 0, 0), 1)
print('draw box:', box)
Expand All @@ -447,7 +447,7 @@ def get_crop_image1(roidb):
if landmark[0][2] < 0:
print('zero', landmark)
continue
landmark = landmark.astype(np.int)
landmark = landmark.astype(np.int32)
print('draw landmark', landmark)
for k in range(5):
color = (0, 0, 255)
Expand Down Expand Up @@ -498,7 +498,7 @@ def get_crop_image2(roidb):
if 'boxes_mask' in roi_rec:
#im = im.astype(np.float32)
boxes_mask = roi_rec['boxes_mask'].copy()
boxes_mask = boxes_mask.astype(np.int)
boxes_mask = boxes_mask.astype(np.int32)
for j in range(boxes_mask.shape[0]):
m = boxes_mask[j]
im[m[1]:m[3], m[0]:m[2], :] = 0
Expand Down Expand Up @@ -697,7 +697,7 @@ def get_crop_image2(roidb):
if TMP_ID >= 0 and TMP_ID < 10:
tim = im.copy().astype(np.uint8)
for i in range(new_rec['boxes'].shape[0]):
box = new_rec['boxes'][i].copy().astype(np.int)
box = new_rec['boxes'][i].copy().astype(np.int32)
cv2.rectangle(tim, (box[0], box[1]), (box[2], box[3]),
(255, 0, 0), 1)
print('draw box:', box)
Expand All @@ -707,7 +707,7 @@ def get_crop_image2(roidb):
if landmark[10] == 0.0:
print('zero', landmark)
continue
landmark = landmark.astype(np.int)
landmark = landmark.astype(np.int32)
print('draw landmark', landmark)
for k in range(5):
color = (0, 0, 255)
Expand Down
4 changes: 2 additions & 2 deletions detection/retinaface/test.py
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Expand Up @@ -44,12 +44,12 @@
print('find', faces.shape[0], 'faces')
for i in range(faces.shape[0]):
#print('score', faces[i][4])
box = faces[i].astype(np.int)
box = faces[i].astype(np.int32)
#color = (255,0,0)
color = (0, 0, 255)
cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
if landmarks is not None:
landmark5 = landmarks[i].astype(np.int)
landmark5 = landmarks[i].astype(np.int32)
#print(landmark.shape)
for l in range(landmark5.shape[0]):
color = (0, 0, 255)
Expand Down
2 changes: 1 addition & 1 deletion detection/retinaface/test_widerface.py
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Expand Up @@ -128,7 +128,7 @@ def get_boxes(roi, pyramid):
font = cv2.FONT_HERSHEY_SIMPLEX
for i in range(boxes.shape[0]):
box = boxes[i]
ibox = box[0:4].copy().astype(np.int)
ibox = box[0:4].copy().astype(np.int32)
cv2.rectangle(im, (ibox[0], ibox[1]), (ibox[2], ibox[3]),
(255, 0, 0), 2)
#print('box', ibox)
Expand Down
4 changes: 2 additions & 2 deletions detection/retinaface_anticov/test.py
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Expand Up @@ -46,7 +46,7 @@
for i in range(faces.shape[0]):
#print('score', faces[i][4])
face = faces[i]
box = face[0:4].astype(np.int)
box = face[0:4].astype(np.int32)
mask = face[5]
print(i, box, mask)
#color = (255,0,0)
Expand All @@ -55,7 +55,7 @@
else:
color = (0, 255, 0)
cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
landmark5 = landmarks[i].astype(np.int)
landmark5 = landmarks[i].astype(np.int32)
#print(landmark.shape)
for l in range(landmark5.shape[0]):
color = (255, 0, 0)
Expand Down
6 changes: 3 additions & 3 deletions detection/scrfd/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py
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Expand Up @@ -72,7 +72,7 @@ def sample_via_interval(self, max_overlaps, full_set, num_expected):
tmp_sampled_set = self.random_choice(tmp_inds,
per_num_expected)
else:
tmp_sampled_set = np.array(tmp_inds, dtype=np.int)
tmp_sampled_set = np.array(tmp_inds, dtype=np.int32)
sampled_inds.append(tmp_sampled_set)

sampled_inds = np.concatenate(sampled_inds)
Expand Down Expand Up @@ -137,13 +137,13 @@ def _sample_neg(self, assign_result, num_expected, **kwargs):
iou_sampling_neg_inds, num_expected_iou_sampling)
else:
iou_sampled_inds = np.array(
iou_sampling_neg_inds, dtype=np.int)
iou_sampling_neg_inds, dtype=np.int32)
num_expected_floor = num_expected - len(iou_sampled_inds)
if len(floor_neg_inds) > num_expected_floor:
sampled_floor_inds = self.random_choice(
floor_neg_inds, num_expected_floor)
else:
sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int)
sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int32)
sampled_inds = np.concatenate(
(sampled_floor_inds, iou_sampled_inds))
if len(sampled_inds) < num_expected:
Expand Down
6 changes: 3 additions & 3 deletions detection/scrfd/mmdet/core/evaluation/widerface.py
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Expand Up @@ -317,7 +317,7 @@ def image_eval(pred, gt, ignore, iou_thresh, mpp):

def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall):
pr_info = np.zeros((thresh_num, 2)).astype('float')
fp = np.zeros((pred_info.shape[0],), dtype=np.int)
fp = np.zeros((pred_info.shape[0],), dtype=np.int32)
last_info = [-1, -1]
for t in range(thresh_num):

Expand Down Expand Up @@ -429,7 +429,7 @@ def wider_evaluation(pred, gt_path, iou_thresh=0.5, debug=False):
#if len(keep_index) != 0:
# ignore[keep_index-1] = 1
#assert len(keep_index)>0
ignore = np.zeros(gt_boxes.shape[0], dtype=np.int)
ignore = np.zeros(gt_boxes.shape[0], dtype=np.int32)
if len(keep_index) != 0:
ignore[keep_index-1] = 1
pred_info = np_round(pred_info,1)
Expand Down Expand Up @@ -523,7 +523,7 @@ def get_widerface_gts(gt_path):
#if len(keep_index) != 0:
# ignore[keep_index-1] = 1
#assert len(keep_index)>0
#ignore = np.zeros(gt_boxes.shape[0], dtype=np.int)
#ignore = np.zeros(gt_boxes.shape[0], dtype=np.int32)
#if len(keep_index) != 0:
# ignore[keep_index-1] = 1
#print('ignore:', len(ignore), len(np.where(ignore==1)[0]))
Expand Down
2 changes: 1 addition & 1 deletion detection/scrfd/mmdet/datasets/custom.py
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Expand Up @@ -139,7 +139,7 @@ def get_cat_ids(self, idx):
list[int]: All categories in the image of specified index.
"""

return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist()
return self.data_infos[idx]['ann']['labels'].astype(np.int32).tolist()

def pre_pipeline(self, results):
"""Prepare results dict for pipeline."""
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2 changes: 1 addition & 1 deletion detection/scrfd/mmdet/datasets/pipelines/transforms.py
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Expand Up @@ -907,7 +907,7 @@ def __call__(self, results):
top = random.randint(h - ch, 0)

patch = np.array(
(int(left), int(top), int(left + cw), int(top + ch)), dtype=np.int)
(int(left), int(top), int(left + cw), int(top + ch)), dtype=np.int32)

# center of boxes should inside the crop img
# only adjust boxes and instance masks when the gt is not empty
Expand Down
2 changes: 1 addition & 1 deletion detection/scrfd/mmdet/models/roi_heads/bbox_heads/bbox_head.py
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Expand Up @@ -262,7 +262,7 @@ def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
>>> labels = torch.randint(0, 2, (n_roi,)).long()
>>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
>>> # For each image, pretend random positive boxes are gts
>>> is_label_pos = (labels.numpy() > 0).astype(np.int)
>>> is_label_pos = (labels.numpy() > 0).astype(np.int32)
>>> lbl_per_img = kwarray.group_items(is_label_pos,
... img_ids.numpy())
>>> pos_per_img = [sum(lbl_per_img.get(gid, []))
Expand Down
4 changes: 2 additions & 2 deletions detection/scrfd/tools/scrfd.py
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Expand Up @@ -324,12 +324,12 @@ def scrfd_2p5gkps(**kwargs):
print(kpss.shape)
for i in range(bboxes.shape[0]):
bbox = bboxes[i]
x1,y1,x2,y2,score = bbox.astype(np.int)
x1,y1,x2,y2,score = bbox.astype(np.int32)
cv2.rectangle(img, (x1,y1) , (x2,y2) , (255,0,0) , 2)
if kpss is not None:
kps = kpss[i]
for kp in kps:
kp = kp.astype(np.int)
kp = kp.astype(np.int32)
cv2.circle(img, tuple(kp) , 1, (0,0,255) , 2)
filename = img_path.split('/')[-1]
print('output:', filename)
Expand Down
4 changes: 2 additions & 2 deletions examples/person_detection/scrfd_person.py
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Expand Up @@ -11,8 +11,8 @@

def detect_person(img, detector):
bboxes, kpss = detector.detect(img)
bboxes = np.round(bboxes[:,:4]).astype(np.int)
kpss = np.round(kpss).astype(np.int)
bboxes = np.round(bboxes[:,:4]).astype(np.int32)
kpss = np.round(kpss).astype(np.int32)
kpss[:,:,0] = np.clip(kpss[:,:,0], 0, img.shape[1])
kpss[:,:,1] = np.clip(kpss[:,:,1], 0, img.shape[0])
vbboxes = bboxes.copy()
Expand Down
4 changes: 2 additions & 2 deletions python-package/insightface/model_zoo/scrfd.py
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Expand Up @@ -335,12 +335,12 @@ def scrfd_2p5gkps(**kwargs):
print(kpss.shape)
for i in range(bboxes.shape[0]):
bbox = bboxes[i]
x1,y1,x2,y2,score = bbox.astype(np.int)
x1,y1,x2,y2,score = bbox.astype(np.int32)
cv2.rectangle(img, (x1,y1) , (x2,y2) , (255,0,0) , 2)
if kpss is not None:
kps = kpss[i]
for kp in kps:
kp = kp.astype(np.int)
kp = kp.astype(np.int32)
cv2.circle(img, tuple(kp) , 1, (0,0,255) , 2)
filename = img_path.split('/')[-1]
print('output:', filename)
Expand Down
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