first commit

Author: bruceyang2012

images/After_NMS_0.jpg

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+
+# import the necessary packages
+import numpy as np
+
+#  Felzenszwalb et al.
+def non_max_suppression_slow(boxes, overlapThresh):
+    # if there are no boxes, return an empty list
+    if len(boxes) == 0:
+        return []
+
+    # initialize the list of picked indexes
+    pick = []
+
+    # grab the coordinates of the bounding boxes
+    x1 = boxes[:,0]
+    y1 = boxes[:,1]
+    x2 = boxes[:,2]
+    y2 = boxes[:,3]
+
+    # compute the area of the bounding boxes and sort the bounding
+    # boxes by the bottom-right y-coordinate of the bounding box
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    idxs = np.argsort(y2)
+
+    # keep looping while some indexes still remain in the indexes
+    # list
+    while len(idxs) > 0:
+        # grab the last index in the indexes list, add the index
+        # value to the list of picked indexes, then initialize
+        # the suppression list (i.e. indexes that will be deleted)
+        # using the last index
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+        suppress = [last]
+
+        # loop over all indexes in the indexes list
+        for pos in range(0, last):
+            # grab the current index
+            j = idxs[pos]
+
+            # find the largest (x, y) coordinates for the start of
+            # the bounding box and the smallest (x, y) coordinates
+            # for the end of the bounding box
+            xx1 = max(x1[i], x1[j])
+            yy1 = max(y1[i], y1[j])
+            xx2 = min(x2[i], x2[j])
+            yy2 = min(y2[i], y2[j])
+
+            # compute the width and height of the bounding box
+            w = max(0, xx2 - xx1 + 1)
+            h = max(0, yy2 - yy1 + 1)
+
+            # compute the ratio of overlap between the computed
+            # bounding box and the bounding box in the area list
+            overlap = float(w * h) / area[j]
+
+            # if there is sufficient overlap, suppress the
+            # current bounding box
+            if overlap > overlapThresh:
+                suppress.append(pos)
+
+        # delete all indexes from the index list that are in the
+        # suppression list
+        idxs = np.delete(idxs, suppress)
+
+    # return only the bounding boxes that were picked
+    return boxes[pick]
+
+def non_max_suppression_fast(boxes, probs=None, overlapThresh=0.3):
+    
+    # if there are no boxes, return an empty list
+    if len(boxes) == 0:
+        return []
+
+    # if the bounding boxes are integers, convert them to floats -- this
+    # is important since we'll be doing a bunch of divisions
+    if boxes.dtype.kind == "i":
+        boxes = boxes.astype("float")
+
+    # initialize the list of picked indexes
+    pick = []
+
+    # grab the coordinates of the bounding boxes
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+
+    # compute the area of the bounding boxes and grab the indexes to sort
+    # (in the case that no probabilities are provided, simply sort on the
+    # bottom-left y-coordinate)
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    idxs = y2
+
+    # if probabilities are provided, sort on them instead
+    if probs is not None:
+        idxs = probs
+
+    # sort the indexes
+    idxs = np.argsort(idxs)
+
+    # keep looping while some indexes still remain in the indexes list
+    while len(idxs) > 0:
+        # grab the last index in the indexes list and add the index value
+        # to the list of picked indexes
+        last = len(idxs) - 1
+        i = idxs[last]
+        pick.append(i)
+
+        # find the largest (x, y) coordinates for the start of the bounding
+        # box and the smallest (x, y) coordinates for the end of the bounding
+        # box
+        xx1 = np.maximum(x1[i], x1[idxs[:last]])
+        yy1 = np.maximum(y1[i], y1[idxs[:last]])
+        xx2 = np.minimum(x2[i], x2[idxs[:last]])
+        yy2 = np.minimum(y2[i], y2[idxs[:last]])
+
+        # compute the width and height of the bounding box
+        w = np.maximum(0, xx2 - xx1 + 1)
+        h = np.maximum(0, yy2 - yy1 + 1)
+
+        # compute the ratio of overlap
+        overlap = (w * h) / area[idxs[:last]]
+
+        # delete all indexes from the index list that have overlap greater
+        # than the provided overlap threshold
+        idxs = np.delete(idxs, np.concatenate(([last],
+            np.where(overlap > overlapThresh)[0])))
+
+    # return only the bounding boxes that were picked
+    return boxes[pick].astype("int")