Fix: numpy.int/float are depracated, replace them to int32 and float32 respectively

alignment/coordinate_reg/image_infer.py

@@ -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)

alignment/heatmap/metric.py

@@ -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(

alignment/synthetics/datasets/augs.py

@@ -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

alignment/synthetics/test_synthetics.py

@@ -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)

alignment/synthetics/tools/prepare_synthetics.py

@@ -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)

detection/retinaface/rcnn/PY_OP/cascade_refine.py

@@ -127,8 +127,8 @@ def assign_anchor_fpn(self,
         if gt_boxes.size > 0:
             # overlap between the anchors and the gt boxes
             # overlaps (ex, gt)
-            overlaps = bbox_overlaps(anchors.astype(np.float),
-                                     gt_boxes.astype(np.float))
+            overlaps = bbox_overlaps(anchors.astype(np.float32),
+                                     gt_boxes.astype(np.float32))
             argmax_overlaps = overlaps.argmax(axis=1)
             #print('AAA', argmax_overlaps.shape)
             max_overlaps = overlaps[np.arange(num_anchors), argmax_overlaps]
@@ -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)

detection/retinaface/rcnn/core/loader.py

@@ -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)

detection/retinaface/rcnn/core/tester.py

@@ -238,8 +238,8 @@ def test_proposals(predictor, test_data, imdb, roidb, vis=False):
                                                             gt_boxes[:, 1] + 1)
         num_pos += gt_boxes.shape[0]
 
-        overlaps = bbox_overlaps(boxes.astype(np.float),
-                                 gt_boxes.astype(np.float))
+        overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                 gt_boxes.astype(np.float32))
         #print(im_info, gt_boxes.shape, boxes.shape, overlaps.shape, file=sys.stderr)
 
         _gt_overlaps = np.zeros((gt_boxes.shape[0]))

detection/retinaface/rcnn/dataset/ds_utils.py

@@ -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)
 

detection/retinaface/rcnn/dataset/imdb.py

@@ -133,8 +133,8 @@ def create_roidb_from_box_list(self, box_list, gt_roidb):
                 gt_boxes = gt_roidb[i]['boxes']
                 gt_classes = gt_roidb[i]['gt_classes']
                 # n boxes and k gt_boxes => n * k overlap
-                gt_overlaps = bbox_overlaps(boxes.astype(np.float),
-                                            gt_boxes.astype(np.float))
+                gt_overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                            gt_boxes.astype(np.float32))
                 # for each box in n boxes, select only maximum overlap (must be greater than zero)
                 argmaxes = gt_overlaps.argmax(axis=1)
                 maxes = gt_overlaps.max(axis=1)
@@ -285,8 +285,8 @@ def evaluate_recall(self, roidb, candidate_boxes=None, thresholds=None):
                 if boxes.shape[0] == 0:
                     continue
 
-                overlaps = bbox_overlaps(boxes.astype(np.float),
-                                         gt_boxes.astype(np.float))
+                overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                         gt_boxes.astype(np.float32))
 
                 _gt_overlaps = np.zeros((gt_boxes.shape[0]))
                 # choose whatever is smaller to iterate

detection/retinaface/rcnn/io/image.py

@@ -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
@@ -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
@@ -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]
@@ -223,7 +223,7 @@ def __get_crop_image(roidb):
         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 = []
@@ -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
@@ -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
@@ -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)
@@ -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)
@@ -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
@@ -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)
@@ -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)

detection/retinaface/rcnn/io/rcnn.py

@@ -128,8 +128,8 @@ def sample_rois(rois,
     :return: (labels, rois, bbox_targets, bbox_weights)
     """
     if labels is None:
-        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                 gt_boxes[:, :4].astype(np.float))
+        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                 gt_boxes[:, :4].astype(np.float32))
         gt_assignment = overlaps.argmax(axis=1)
         overlaps = overlaps.max(axis=1)
         labels = gt_boxes[gt_assignment, 4]
@@ -345,8 +345,8 @@ def sample_rois(rois,
             overlaps = np.zeros((len(rois), ))
             labels = np.zeros((len(rois), ))
         else:
-            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                     gt_boxes[:, :4].astype(np.float))
+            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                     gt_boxes[:, :4].astype(np.float32))
             gt_assignment = overlaps.argmax(axis=1)
             overlaps = overlaps.max(axis=1)
             labels = gt_boxes[gt_assignment, 4]
@@ -465,8 +465,8 @@ def sample_rois_fpn(rois,
             overlaps = np.zeros((len(rois), ))
             labels = np.zeros((len(rois), ))
         else:
-            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                     gt_boxes[:, :4].astype(np.float))
+            overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                     gt_boxes[:, :4].astype(np.float32))
             gt_assignment = overlaps.argmax(axis=1)
             overlaps = overlaps.max(axis=1)
             labels = gt_boxes[gt_assignment, 4]
@@ -617,8 +617,8 @@ def get_rois(rois,
     if labels is None:
         if len(gt_boxes) == 0:
             gt_boxes = np.array([[1, 1, 1, 1, 0]])
-        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float),
-                                 gt_boxes[:, :4].astype(np.float))
+        overlaps = bbox_overlaps(rois[:, 1:].astype(np.float32),
+                                 gt_boxes[:, :4].astype(np.float32))
         gt_assignment = overlaps.argmax(axis=1)
         overlaps = overlaps.max(axis=1)
         labels = gt_boxes[gt_assignment, 4]

detection/retinaface/rcnn/io/rpn.py

@@ -245,8 +245,8 @@ def _unmap(data, count, inds, fill=0):
     if gt_boxes.size > 0:
         # overlap between the anchors and the gt boxes
         # overlaps (ex, gt)
-        overlaps = bbox_overlaps(anchors.astype(np.float),
-                                 gt_boxes.astype(np.float))
+        overlaps = bbox_overlaps(anchors.astype(np.float32),
+                                 gt_boxes.astype(np.float32))
         argmax_overlaps = overlaps.argmax(axis=1)
         #print('AAA', argmax_overlaps.shape)
         max_overlaps = overlaps[np.arange(len(inds_inside)), argmax_overlaps]
@@ -618,8 +618,8 @@ def assign_anchor_fpn(self,
         if gt_boxes.size > 0:
             # overlap between the anchors and the gt boxes
             # overlaps (ex, gt)
-            overlaps = bbox_overlaps(anchors.astype(np.float),
-                                     gt_boxes.astype(np.float))
+            overlaps = bbox_overlaps(anchors.astype(np.float32),
+                                     gt_boxes.astype(np.float32))
             argmax_overlaps = overlaps.argmax(axis=1)
             #print('AAA', argmax_overlaps.shape)
             max_overlaps = overlaps[np.arange(len(inds_inside)),

detection/retinaface/test.py

@@ -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)

detection/retinaface/test_widerface.py

@@ -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)
@@ -191,8 +191,8 @@ def test(args):
                         1) * (gt_boxes[:, 3] - gt_boxes[:, 1] + 1)
             num_pos += gt_boxes.shape[0]
 
-            overlaps = bbox_overlaps(boxes.astype(np.float),
-                                     gt_boxes.astype(np.float))
+            overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                     gt_boxes.astype(np.float32))
             #print(im_info, gt_boxes.shape, boxes.shape, overlaps.shape, file=sys.stderr)
 
             _gt_overlaps = np.zeros((gt_boxes.shape[0]))

detection/retinaface_anticov/test.py

@@ -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)
@@ -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)

detection/scrfd/mmdet/datasets/custom.py

@@ -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."""

detection/scrfd/mmdet/models/roi_heads/bbox_heads/bbox_head.py

@@ -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, []))

detection/scrfd/tools/scrfd.py

@@ -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)

examples/person_detection/scrfd_person.py

@@ -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()