python-opencv3计算机视觉学习笔记
七：目标检测与识别
梯度直方图（Histogram of Oriented Gradient）
图像金字塔 （image pyramid）
滑动窗口（sliding window）
7.1、目标检测与识别
HOG描述符（详情见opencv特征提取描述符）
将图像分成小单元（16*16），每个单元包含视觉表示，安八个方向（N,NW,W,SW,W,SW,S,SE,E,NE）计算颜色梯度
尺度： ？？
位置：检测目标可能位于图像任何地方，需扫描图像各个部分，找出感兴趣区域，并尝试检测目标
为解决位置、尺寸问题需熟悉图像金字塔和滑动窗口概念
非最大（极大)抑制：对图像同一区域相关的结果进行抑制，只关心结果最好的窗口，丢弃评分低的重叠窗口。
支持向量机SVM：
对于带有标签的训练数据，通过一个优化的超平面来对这些数据进行分类
2、检测人（opencv的HOG）
#检测行人
#!/usr/bin/python
# -*- coding: utf-8 -*-
# @Time : 2017/06/10
# @Author :
# @Site : 检测人
# @Software: PyCharm
import cv2
import numpy as np
#如果矩形被完全包含在另外一个矩形中，可确定该矩形应该被丢弃
def is_inside(o, i):
ox, oy, ow, oh = o
ix, iy, iw, ih = i
return ox > ix and oy > iy and ox + ow 另：上述方法对近景人检测效果不佳，下面来自一老外的方法，近景效果不错
#检测近景的效果与test_ObjectDetection.py效果好
# import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2
# left_camera1 = cv2.VideoCapture(0)
# construct the argument parse and parse the arguments
# ap = argparse.ArgumentParser()# the path to the directory that contains the list of images we are going to perform pedestrian detection on.
# ap.add_argument("-i", "--images", required=True, help="path to images directory")
# args = vars(ap.parse_args())
imagePath = "C:\\Software\\Python\\snapshotoutdoor\\rightRGB_11.jpg"
# initialize the HOG descriptor/person detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
# loop over the image paths
# for imagePath in paths.list_images(args["images"]):#start looping over the images in our --images directory
# load the image and resize it to (1) reduce detection time
# and (2) improve detection accuracy
while True:
# ret1, frame1 = left_camera1.read()
# cv2.imshow("frame1", frame1)
# cv2.waitKey(10)
# image = frame1
image = cv2.imread(imagePath)
# image = imutils.resize(image, width=min(400, image.shape[1]))
orig = image.copy()
# detect people in the image
(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
padding=(8, 8), scale=1.05)
# draw the original bounding boxes
for (x, y, w, h) in rects:
cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
# apply non-maxima suppression to the bounding boxes using a
# fairly large overlap threshold to try to maintain overlapping
# boxes that are still people
rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
# draw the final bounding boxes
for (xA, yA, xB, yB) in pick:
cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
# show some information on the number of bounding boxes
# filename = imagePath[imagePath.rfind("/") + 1:]
# print("[INFO] {}: {} original boxes, {} after suppression".format(
# filename, len(rects), len(pick)))
# show the output images
cv2.imshow("Before NMS", orig)
cv2.imshow("After NMS", image)
cv2.waitKey(1)3、创建和训练目标检测器
使用SVM和词袋（Bag of Word,BOW）
词袋：计算文档中每个词出现的次数，用该些次数构成的向量重新表示文档
示列如下:
I like opencv and i like python
I like c++ and python
I don’t like artichokes
可以用以下值来建立字
{
I:4,
Like:4,
Opencv:2,
And:2
Python:2
C++:1
Don’t:1
Artichokes:1
}
以上的三句话可以用以下向量表示:
[2,2,1,1,1,0,0,0]
[1,1,0,1,1,1,0,0]
[1,1,0,0,0,0,1,1]
代码实现？？？以后每学一个知识要留下学习过程并整理保存https://blog.csdn.net/lilai619/article/details/46740837
BoW(词袋模型)+python代码实现
？？？？
K-means聚类：数据分析的向量量化方法
？？？