[英]NON MAXIMUM SUPPRESSION FOR TENSORFLOW OBJECT DETECTION API
我正在Tensorflow對象檢測API中實現Faster RCNN v2 Inception。 為了刪除多余的重疊檢測,我讀到應該應用NMS。
一種方法是在配置文件first_stage_nms_iou_threshold
調整NMS IOU閾值。
問題
first_stage_nms_iou_threshold
? 為什么只有第一階段? 我無法回答您的第一個和第二個問題,但是重疊框有相同的問題,請使用以下代碼手動修復它們...您必須知道框的x1,y1,x2,y2坐標重疊...
# import the necessary packages
from nms import non_max_suppression_slow
import numpy as np
import cv2
# path to your image
# and the coordinates x1,x2,y1,y2 of the overlapping bounding boxes
images = [
("path/to/your/image", np.array([
(664, 0, 988, 177),
(670, 10, 1000, 188),
(685, 20, 1015, 193),
(47, 100, 357, 500),
(55, 105, 362, 508),
(68, 120, 375, 520),
(978, 80, 1093, 206)]))]
# loop over the images
for (imagePath, boundingBoxes) in images:
# load the image and clone it
print("[x] %d initial bounding boxes" % (len(boundingBoxes)))
image = cv2.imread(imagePath)
orig = image.copy()
# loop over the bounding boxes for each image and draw them
for (startX, startY, endX, endY) in boundingBoxes:
cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 0, 255), 2)
# perform non-maximum suppression on the bounding boxes
pick = non_max_suppression_slow(boundingBoxes, 0.3)
print("[x] after applying non-maximum, %d bounding boxes" % (len(pick)))
# loop over the picked bounding boxes and draw them
for (startX, startY, endX, endY) in pick:
cv2.rectangle(image, (startX, startY), (endX, endY), (0, 255, 0), 2)
# display the images
cv2.imshow("Original", orig)
cv2.imshow("After NMS", image)
cv2.waitKey(0)
仍然需要這個:
# import the necessary packages
import numpy as np
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]
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