Replaced depracated np.int and np.float references

deep_sort/detection.py

@@ -29,7 +29,7 @@ class Detection(object):
     """
 
     def __init__(self, tlwh, confidence, class_name, feature):
-        self.tlwh = np.asarray(tlwh, dtype=np.float)
+        self.tlwh = np.asarray(tlwh, dtype=np.float64)
         self.confidence = float(confidence)
         self.class_name = class_name
         self.feature = np.asarray(feature, dtype=np.float32)

deep_sort/preprocessing.py

@@ -38,7 +38,7 @@ def non_max_suppression(boxes, classes, max_bbox_overlap, scores=None):
     if len(boxes) == 0:
         return []
 
-    boxes = boxes.astype(np.float)
+    boxes = boxes.astype(np.float64)
     pick = []
 
     x1 = boxes[:, 0]

tools/generate_detections.py

@@ -60,7 +60,7 @@ def extract_image_patch(image, bbox, patch_shape):
 
     # convert to top left, bottom right
     bbox[2:] += bbox[:2]
-    bbox = bbox.astype(np.int)
+    bbox = bbox.astype(np.int64)
 
     # clip at image boundaries
     bbox[:2] = np.maximum(0, bbox[:2])
@@ -164,9 +164,9 @@ def generate_detections(encoder, mot_dir, output_dir, detection_dir=None):
         detections_in = np.loadtxt(detection_file, delimiter=',')
         detections_out = []
 
-        frame_indices = detections_in[:, 0].astype(np.int)
-        min_frame_idx = frame_indices.astype(np.int).min()
-        max_frame_idx = frame_indices.astype(np.int).max()
+        frame_indices = detections_in[:, 0].astype(np.int64)
+        min_frame_idx = frame_indices.astype(np.int64).min()
+        max_frame_idx = frame_indices.astype(np.int64).max()
         for frame_idx in range(min_frame_idx, max_frame_idx + 1):
             print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
             mask = frame_indices == frame_idx

tracking_helpers.py

@@ -86,7 +86,7 @@ def extract_image_patch(image, bbox, patch_shape):
 
     # convert to top left, bottom right
     bbox[2:] += bbox[:2]
-    bbox = bbox.astype(np.int)
+    bbox = bbox.astype(int)
 
     # clip at image boundaries
     bbox[:2] = np.maximum(0, bbox[:2])
@@ -190,9 +190,9 @@ def generate_detections(encoder, mot_dir, output_dir, detection_dir=None):
         detections_in = np.loadtxt(detection_file, delimiter=',')
         detections_out = []
 
-        frame_indices = detections_in[:, 0].astype(np.int)
-        min_frame_idx = frame_indices.astype(np.int).min()
-        max_frame_idx = frame_indices.astype(np.int).max()
+        frame_indices = detections_in[:, 0].astype(np.int64)
+        min_frame_idx = frame_indices.astype(np.int64).min()
+        max_frame_idx = frame_indices.astype(np.int64).max()
         for frame_idx in range(min_frame_idx, max_frame_idx + 1):
             print("Frame %05d/%05d" % (frame_idx, max_frame_idx))
             mask = frame_indices == frame_idx

utils/datasets.py

@@ -415,7 +415,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r
                 x[:, 0] = 0
 
         n = len(shapes)  # number of images
-        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
+        bi = np.floor(np.arange(n) / batch_size).astype(np.int64)  # batch index
         nb = bi[-1] + 1  # number of batches
         self.batch = bi  # batch index of image
         self.n = n
@@ -443,7 +443,7 @@ def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, r
                 elif mini > 1:
                     shapes[i] = [1, 1 / mini]
 
-            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride
+            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int64) * stride
 
         # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
         self.imgs = [None] * n
@@ -1200,7 +1200,7 @@ def pastein(image, labels, sample_labels, sample_images, sample_masks):
                 r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h))
                 temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w]
                 m_ind = r_mask > 0
-                if m_ind.astype(np.int).sum() > 60:
+                if m_ind.astype(np.int64).sum() > 60:
                     temp_crop[m_ind] = r_image[m_ind]
                     #print(sample_labels[sel_ind])
                     #print(sample_images[sel_ind].shape)
@@ -1283,7 +1283,7 @@ def extract_boxes(path='../coco/'):  # from utils.datasets import *; extract_box
                     b = x[1:] * [w, h, w, h]  # box
                     # b[2:] = b[2:].max()  # rectangle to square
                     b[2:] = b[2:] * 1.2 + 3  # pad
-                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int)
+                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int64)
 
                     b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
                     b[[1, 3]] = np.clip(b[[1, 3]], 0, h)

utils/general.py

@@ -219,7 +219,7 @@ def labels_to_class_weights(labels, nc=80):
         return torch.Tensor()
 
     labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
-    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
+    classes = labels[:, 0].astype(np.int64)  # labels = [class xywh]
     weights = np.bincount(classes, minlength=nc)  # occurrences per class
 
     # Prepend gridpoint count (for uCE training)
@@ -234,7 +234,7 @@ def labels_to_class_weights(labels, nc=80):
 
 def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
     # Produces image weights based on class_weights and image contents
-    class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels])
+    class_counts = np.array([np.bincount(x[:, 0].astype(np.int64), minlength=nc) for x in labels])
     image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
     # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
     return image_weights