TensorFlow 对象检测 API 的非最大抑制 [英] NON MAXIMUM SUPPRESSION FOR TENSORFLOW OBJECT DETECTION API

问题描述

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

I am implementing a Faster RCNN v2 Inception in Tensorflow Object Detection API. To remove redundant overlapping detections, I read that NMS should be applied.

一种方法是在配置文件 first_stage_nms_iou_threshold 中调整 NMS IOU 阈值.

One way of doing this is adjusting the NMS IOU Threshold in the config file first_stage_nms_iou_threshold.

问题

1. 这个参数到底是什么?这个参数应该调整到什么值(默认为0.7)
2. 为什么叫first_stage_nms_iou_threshold?为什么只有第一阶段?
3. 是否有另一种简单有效的方法来消除冗余检测?

1. What is this parameter exactly? To what value should this parameter be adjusted to (default value is 0.7)
2. Why is it called first_stage_nms_iou_threshold? Why first stage only?
3. Is there another easy and more effective way of removing redundant detections?

推荐答案

我无法回答您的第一和第二个问题，但我遇到了重叠边界框的相同问题，并使用以下代码手动修复它们...必须知道重叠的边界框的 x1,y1,x2,y2 坐标...

I can't anwser your first and second question but i had the same problem with overlapping bounding boxes and use the following code to fix them manually... You have to know the x1,y1,x2,y2 coordinates of your bounding boxes which are overlapping...

# 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]

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