目录
-
1 前言
在目标检测时，为了定位到目标的具体位置，通常会把图像分成许多子块（sub-regions / patches），然后把子块作为输入，送到目标识别的模型中。selective search就是一种选择子块的启发式方法。
-
2 Selective Search算法
主要思路：输入一张图片，首先通过图像分割的方法（如大名鼎鼎的felzenszwalb算法）获得很多小的区域，然后对这些小的区域不断进行合并，一直到无法合并为止。此时这些原始的小区域和合并得到的区域的就是我们得到的bounding box.
算法分为如下几个大步：
1. 生成原始的区域集R（利用felzenszwalb算法）
2. 计算区域集R里每个相邻区域的相似度S={s1,s2,…}
3. 找出相似度最高的两个区域，将其合并为新集，添加进R
4. 从S中移除所有与第3步中有关的子集
5. 计算新集与所有子集的相似度
6.跳至第三步，不断循环，合并，直至S为空（到不能再合并时为止）
-
3 Python源码分析
Github上有一个选择性搜索的简单实现 -- selectivesearch ，可以帮助大家理解
import skimage.data
import selectivesearch
img = skimage.data.astronaut()
img_lbl, regions = selectivesearch.selective_search(img, scale=500, sigma=0.9, min_size=10)
regions[:10]
=>
[{'labels': [0.0], 'rect': (0, 0, 15, 24), 'size': 260},
{'labels': [1.0], 'rect': (13, 0, 1, 12), 'size': 23},
{'labels': [2.0], 'rect': (0, 15, 15, 11), 'size': 30},
{'labels': [3.0], 'rect': (15, 14, 0, 0), 'size': 1},
{'labels': [4.0], 'rect': (0, 0, 61, 153), 'size': 4927},
{'labels': [5.0], 'rect': (0, 12, 61, 142), 'size': 177},
{'labels': [6.0], 'rect': (7, 54, 6, 17), 'size': 8},
{'labels': [7.0], 'rect': (28, 50, 18, 32), 'size': 22},
{'labels': [8.0], 'rect': (2, 99, 7, 24), 'size': 24},
{'labels': [9.0], 'rect': (14, 118, 79, 117), 'size': 4008}]
1. 用户生成原始区域集的函数，其中用到了felzenszwalb图像分割算法。每一个区域都有一个编号，将编号并入图片中，方便后面的操作
def _generate_segments(im_orig, scale, sigma, min_size):
"""
segment smallest regions by the algorithm of Felzenswalb and
Huttenlocher
"""
# open the Image
im_mask = skimage.segmentation.felzenszwalb(
skimage.util.img_as_float(im_orig), scale=scale, sigma=sigma,
min_size=min_size)
# merge mask channel to the image as a 4th channel
im_orig = numpy.append(
im_orig, numpy.zeros(im_orig.shape[:2])[:, :, numpy.newaxis], axis=2)
im_orig[:, :, 3] = im_mask
return im_orig2. 计算两个区域的相似度
论文中考虑了四种相似度 -- 颜色，纹理，尺寸，以及交叠。
其中颜色和纹理相似度，通过获取两个区域的直方图的交集，来判断相似度。
最后的相似度是四种相似度的加和。
def _sim_colour(r1, r2):
"""
calculate the sum of histogram intersection of colour
"""
return sum([min(a, b) for a, b in zip(r1["hist_c"], r2["hist_c"])])
def _sim_texture(r1, r2):
"""
calculate the sum of histogram intersection of texture
"""
return sum([min(a, b) for a, b in zip(r1["hist_t"], r2["hist_t"])])
def _sim_size(r1, r2, imsize):
"""
calculate the size similarity over the image
"""
return 1.0 - (r1["size"] + r2["size"]) / imsize
def _sim_fill(r1, r2, imsize):
"""
calculate the fill similarity over the image
"""
bbsize = (
(max(r1["max_x"], r2["max_x"]) - min(r1["min_x"], r2["min_x"]))
* (max(r1["max_y"], r2["max_y"]) - min(r1["min_y"], r2["min_y"]))
)
return 1.0 - (bbsize - r1["size"] - r2["size"]) / imsize
def _calc_sim(r1, r2, imsize):
return (_sim_colour(r1, r2) + _sim_texture(r1, r2)
+ _sim_size(r1, r2, imsize) + _sim_fill(r1, r2, imsize))3. 用于计算颜色和纹理的直方图的函数
颜色直方图：将色彩空间转为HSV，每个通道下以bins=25计算直方图，这样每个区域的颜色直方图有25*3=75个区间。 对直方图除以区域尺寸做归一化后使用下式计算相似度：
纹理相似度：论文采用方差为1的高斯分布在8个方向做梯度统计，然后将统计结果（尺寸与区域大小一致）以bins=10计算直方图。直方图区间数为8*3*10=240（使用RGB色彩空间）。这里是用了LBP（local binary pattern）获取纹理特征，建立直方图，其余相同
其中，
是直方图中第
个bin的值。
def _calc_colour_hist(img):
"""
calculate colour histogram for each region
the size of output histogram will be BINS * COLOUR_CHANNELS(3)
number of bins is 25 as same as [uijlings_ijcv2013_draft.pdf]
extract HSV
"""
BINS = 25
hist = numpy.array([])
for colour_channel in (0, 1, 2):
# extracting one colour channel
c = img[:, colour_channel]
# calculate histogram for each colour and join to the result
hist = numpy.concatenate(
[hist] + [numpy.histogram(c, BINS, (0.0, 255.0))[0]])
# L1 normalize
hist = hist / len(img)
return hist
def _calc_texture_gradient(img):
"""
calculate texture gradient for entire image
The original SelectiveSearch algorithm proposed Gaussian derivative
for 8 orientations, but we use LBP instead.
output will be [height(*)][width(*)]
"""
ret = numpy.zeros((img.shape[0], img.shape[1], img.shape[2]))
for colour_channel in (0, 1, 2):
ret[:, :, colour_channel] = skimage.feature.local_binary_pattern(
img[:, :, colour_channel], 8, 1.0)
return ret
def _calc_texture_hist(img):
"""
calculate texture histogram for each region
calculate the histogram of gradient for each colours
the size of output histogram will be
BINS * ORIENTATIONS * COLOUR_CHANNELS(3)
"""
BINS = 10
hist = numpy.array([])
for colour_channel in (0, 1, 2):
# mask by the colour channel
fd = img[:, colour_channel]
# calculate histogram for each orientation and concatenate them all
# and join to the result
hist = numpy.concatenate(
[hist] + [numpy.histogram(fd, BINS, (0.0, 1.0))[0]])
# L1 Normalize
hist = hist / len(img)
return hist
4. 提取区域的尺寸，颜色和纹理特征
def _extract_regions(img):
R = {}
# get hsv image
hsv = skimage.color.rgb2hsv(img[:, :, :3])
# pass 1: count pixel positions
for y, i in enumerate(img):
for x, (r, g, b, l) in enumerate(i):
# initialize a new region
if l not in R:
R[l] = {
"min_x": 0xffff, "min_y": 0xffff,
"max_x": 0, "max_y": 0, "labels": [l]}
# bounding box
if R[l]["min_x"] > x:
R[l]["min_x"] = x
if R[l]["min_y"] > y:
R[l]["min_y"] = y
if R[l]["max_x"] < x:
R[l]["max_x"] = x
if R[l]["max_y"] < y:
R[l]["max_y"] = y
# pass 2: calculate texture gradient
tex_grad = _calc_texture_gradient(img)
# pass 3: calculate colour histogram of each region
for k, v in list(R.items()):
# colour histogram
masked_pixels = hsv[:, :, :][img[:, :, 3] == k]
R[k]["size"] = len(masked_pixels / 4)
R[k]["hist_c"] = _calc_colour_hist(masked_pixels)
# texture histogram
R[k]["hist_t"] = _calc_texture_hist(tex_grad[:, :][img[:, :, 3] == k])
return R
5. 找邻居 -- 通过计算每个区域与其余的所有区域是否有相交，来判断是不是邻居
def _extract_neighbours(regions):
def intersect(a, b):
if (a["min_x"] < b["min_x"] < a["max_x"]
and a["min_y"] < b["min_y"] < a["max_y"]) or (
a["min_x"] < b["max_x"] < a["max_x"]
and a["min_y"] < b["max_y"] < a["max_y"]) or (
a["min_x"] < b["min_x"] < a["max_x"]
and a["min_y"] < b["max_y"] < a["max_y"]) or (
a["min_x"] < b["max_x"] < a["max_x"]
and a["min_y"] < b["min_y"] < a["max_y"]):
return True
return False
R = list(regions.items())
neighbours = []
for cur, a in enumerate(R[:-1]):
for b in R[cur + 1:]:
if intersect(a[1], b[1]):
neighbours.append((a, b))
return neighbours
6. 合并两个区域的函数
def _merge_regions(r1, r2):
new_size = r1["size"] + r2["size"]
rt = {
"min_x": min(r1["min_x"], r2["min_x"]),
"min_y": min(r1["min_y"], r2["min_y"]),
"max_x": max(r1["max_x"], r2["max_x"]),
"max_y": max(r1["max_y"], r2["max_y"]),
"size": new_size,
"hist_c": (
r1["hist_c"] * r1["size"] + r2["hist_c"] * r2["size"]) / new_size,
"hist_t": (
r1["hist_t"] * r1["size"] + r2["hist_t"] * r2["size"]) / new_size,
"labels": r1["labels"] + r2["labels"]
}
return rt7. 主函数 -- Selective Search
scale：图像分割的集群程度。值越大，意味集群程度越高，分割的越少，获得子区域越大。默认为1
signa: 图像分割前，会先对原图像进行高斯滤波去噪，sigma即为高斯核的大小。默认为0.8
min_size : 最小的区域像素点个数。当小于此值时，图像分割的计算就停止，默认为20
每次选出相似度最高的一组区域（如编号为100和120的区域），进行合并，得到新的区域（如编号为300）。然后计算新的区域300与区域100的所有邻居和区域120的所有邻居的相似度，加入区域集S。不断循环，知道S为空，此时最后只剩下一个区域，而且它的像素数会非常大，接近原始图片的像素数，因此无法继续合并。最后退出程序。
def selective_search(
im_orig, scale=1.0, sigma=0.8, min_size=50):
'''Selective Search
Parameters
----------
im_orig : ndarray
Input image
scale : int
Free parameter. Higher means larger clusters in felzenszwalb segmentation.
sigma : float
Width of Gaussian kernel for felzenszwalb segmentation.
min_size : int
Minimum component size for felzenszwalb segmentation.
Returns
-------
img : ndarray
image with region label
region label is stored in the 4th value of each pixel [r,g,b,(region)]
regions : array of dict
[
{
'rect': (left, top, width, height),
'labels': [...],
'size': component_size
},
...
]
'''
assert im_orig.shape[2] == 3, "3ch image is expected"
# load image and get smallest regions
# region label is stored in the 4th value of each pixel [r,g,b,(region)]
img = _generate_segments(im_orig, scale, sigma, min_size)
if img is None:
return None, {}
imsize = img.shape[0] * img.shape[1]
R = _extract_regions(img)
# extract neighbouring information
neighbours = _extract_neighbours(R)
# calculate initial similarities
S = {}
for (ai, ar), (bi, br) in neighbours:
S[(ai, bi)] = _calc_sim(ar, br, imsize)
# hierarchal search
while S != {}:
# get highest similarity
i, j = sorted(S.items(), key=lambda i: i[1])[-1][0]
# merge corresponding regions
t = max(R.keys()) + 1.0
R[t] = _merge_regions(R[i], R[j])
# mark similarities for regions to be removed
key_to_delete = []
for k, v in list(S.items()):
if (i in k) or (j in k):
key_to_delete.append(k)
# remove old similarities of related regions
for k in key_to_delete:
del S[k]
# calculate similarity set with the new region
for k in [a for a in key_to_delete if a != (i, j)]:
n = k[1] if k[0] in (i, j) else k[0]
S[(t, n)] = _calc_sim(R[t], R[n], imsize)
regions = []
for k, r in list(R.items()):
regions.append({
'rect': (
r['min_x'], r['min_y'],
r['max_x'] - r['min_x'], r['max_y'] - r['min_y']),
'size': r['size'],
'labels': r['labels']
})
return img, regions