TensorFlow 对象检测 API 的非最大抑制

Question

我正在 Tensorflow 对象检测 API 中实现 Faster RCNN v2 Inception。为了去除多余的重叠检测，我读到应该应用 NMS。

一种方法是调整配置文件first_stage_nms_iou_threshold中的NMS IOU阈值。

问题

1. 这个参数到底是什么？这个参数应该调整到什么值（默认值为0.7）
2. 为什么叫first_stage_nms_iou_threshold？为什么只有第一阶段？
3. 还有其他简单有效的方法来消除冗余检测吗？

Answer 1

我无法回答您的第一个和第二个问题，但我在重叠边界框方面​​遇到了同样的问题，并使用以下代码手动修复它们...您必须知道您的 x1,y1,x2,y2 坐标重叠的边界框...

# 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)

仍然需要这个：

# 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]