目标检测的发展历程大致如下:
对于目标识别任务,比如判断一张图片中有没有车、是什么车,一般需要解决两个问题:目标检测、目标识别。而目标检测任务中通常需要先通过某种方法做图像分割,事先得到候选框;直观的做法是:给定窗口,对整张图片滑动扫描,结束后改变窗口大小重复上面步骤,缺点很明显:重复劳动耗费资源、精度和质量不高等等。 针对上面的问题,一种解决方案是借鉴启发式搜索的方法,充分利用人类的先验知识。J.R.R. Uijlings在《Selective Search for Object Recoginition》提出一种方法:基于数据驱动,与具体类别无关的多种策略融合的启发式生成方法。图片包含各种丰富信息,例如:大小、形状、颜色、纹理、物体重叠关系等,如果只使用一种信息往往不能解决大部分问题,例如:
左边的两只猫可以通过颜色区别而不是通过纹理,右面的变色龙却只能通过纹理区别而不是颜色。
所以概括来说:
基于以上准则设计Selective Search算法:
最终相似度为所有策略加权和,文中采用等权方式:
训练过程包含:提取候选框、提取特征、生成正负样本、训练模型,图示如下:
早期图像特征提取往往是各种HOG特征或BoW特征,现在CNN特征几乎一统天下。 检测定位效果评价采用Average Best Overlap(ABO)和Mean Average Best Overlap(MABO):
其中:为类别标注、为类别下的ground truth,为通过Selective Search生成的候选框。
)
参见AlpacaDB。
# -*- coding: utf-8 -*-
import skimage.io
import skimage.feature
import skimage.color
import skimage.transform
import skimage.util
import skimage.segmentation
import numpy
# "Selective Search for Object Recognition" by J.R.R. Uijlings et al.
#
# - Modified version with LBP extractor for texture vectorization
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_orig
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))
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
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 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
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 = 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
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 rt
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, right, bottom),
'labels': [...]
},
...
]
'''
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(), cmp=lambda a, b: cmp(a[1], b[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 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 filter(lambda a: a != (i, j), key_to_delete):
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 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
# -*- coding: utf-8 -*-
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import skimage.data
import skimage.io
from skimage.io import use_plugin,imread
import matplotlib.patches as mpatches
from matplotlib.pyplot import savefig
import selectivesearch
def main():
# loading astronaut image
#img = skimage.data.astronaut()
use_plugin('pil')
img = imread('car.jpg', as_grey=False)
# perform selective search
img_lbl, regions = selectivesearch.selective_search(
img, scale=500, sigma=0.9, min_size=10)
candidates = set()
for r in regions:
# excluding same rectangle (with different segments)
if r['rect'] in candidates:
continue
# excluding regions smaller than 2000 pixels
if r['size'] < 2000:
continue
# distorted rects
x, y, w, h = r['rect']
if w / h > 1.2 or h / w > 1.2:
continue
candidates.add(r['rect'])
# draw rectangles on the original image
plt.figure()
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
ax.imshow(img)
for x, y, w, h in candidates:
print x, y, w, h
rect = mpatches.Rectangle(
(x, y), w, h, fill=False, edgecolor='red', linewidth=1)
ax.add_patch(rect)
#plt.show()
savefig('MyFig.jpg')
if __name__ == "__main__":
main()
car.jpg原图如下:
结果图如下: