[自动驾驶]CarND-Vehicle-Detection

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我是靠谱客的博主难过鞋垫，这篇文章主要介绍[自动驾驶]CarND-Vehicle-Detection，现在分享给大家，希望可以做个参考。

听到Vehicle-Detection，我还以为是YOLO，SSD啥的，结果课程里用的是传统的HOG。Excuse me？就是各种论文里被用来踩一脚的SIFT、HOG？那也好吧，深刻体会体会传统方法有多弱鸡。

HOG

HOG之前简单了解过，不要既然要动手干活，还是得了解一些具体的计算细节。http://www.learnopencv.com/histogram-of-oriented-gradients/这篇文章讲得挺好的。

简单来看，HOG提取到的特征有点像低清版图像边缘，有一定鲁棒性，能容忍物体一些轻微光影变换和平移吧。但是特征降维得有点厉害，跟DL提取的特征没法比。不过如果精度要求不高，又想节约计算资源，HOG还是可以用用。

特征提取

用惯了opencv，一开始就查的opencv的api。

这里有tutorial，照猫画虎调用就行 http://www.learnopencv.com/handwritten-digits-classification-an-opencv-c-python-tutorial/ opencv得升级到3.*，科学上网的原因，因为破环境搞半了天。

不过里面说是opensv的svm有bug啥的，而且我更喜欢用sklearn，所以还是用opencv提特征，用sklearn来learn，术业有专攻。

后来仔细看了课程要求才发现提示可以用skimage，感觉封装得比较方便。

hog模版+svc模版，基本长这样：

复制代码

import
glob
img_positive=[ cv2.cvtColor(cv2.imread(f), cv2.COLOR_BGR2YCrCb) for f in glob.glob("./data/vehicles/*/*.png")]
img_negative=[ cv2.cvtColor( cv2.imread(f), cv2.COLOR_BGR2YCrCb) for f in glob.glob("./data/non-vehicles/*/*.png")]
winSize = (64,64)
blockSize = (16,16)
blockStride = (8,8)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = -1.
histogramNormType = 0
L2HysThreshold = 0.2
gammaCorrection = 1
nlevels = 64
useSignedGradients = True
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,histogramNormType,L2HysThreshold,gammaCorrection,nlevels, useSignedGradients)
feature_positive=np.array([(hog.compute(img[:, :, 0]) - 0.14275211)/0.085549206 for img in img_positive])
feature_negative=np.array([ (hog.compute(img[:, :, 0])- 0.14275211)/0.085549206 for img in img_negative])
SAMPLE_NUM=2000
small_feature_positive=feature_positive[:SAMPLE_NUM]
small_feature_negative=feature_negative[:SAMPLE_NUM]
small_X=np.concatenate((small_feature_positive, small_feature_negative), axis=0)
# remove last 1L dimension (1000L, 1764L, 1L)
small_X = small_X.squeeze()
small_y=np.ones((SAMPLE_NUM*2))
small_y[SAMPLE_NUM:]=0
#svc
parameters = {'nu': (0.05, ) , 'gamma':[6e-4, ]}
svc_clf=NuSVC(nu=0.1, kernel='rbf', verbose=True )
gs_clf =
GridSearchCV(svc_clf, parameters, n_jobs=1, verbose=True )
gs_clf.fit( small_X, small_y)
for params, mean_score, scores in gs_clf.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() * 2, params))

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import
glob
img_positive=[ cv2.cvtColor(cv2.imread(f), cv2.COLOR_BGR2YCrCb) for f in glob.glob("./data/vehicles/*/*.png")]
img_negative=[ cv2.cvtColor( cv2.imread(f), cv2.COLOR_BGR2YCrCb) for f in glob.glob("./data/non-vehicles/*/*.png")]
winSize = (64,64)
blockSize = (16,16)
blockStride = (8,8)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = -1.
histogramNormType = 0
L2HysThreshold = 0.2
gammaCorrection = 1
nlevels = 64
useSignedGradients = True
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,histogramNormType,L2HysThreshold,gammaCorrection,nlevels, useSignedGradients)
feature_positive=np.array([(hog.compute(img[:, :, 0]) - 0.14275211)/0.085549206 for img in img_positive])
feature_negative=np.array([ (hog.compute(img[:, :, 0])- 0.14275211)/0.085549206 for img in img_negative])
SAMPLE_NUM=2000
small_feature_positive=feature_positive[:SAMPLE_NUM]
small_feature_negative=feature_negative[:SAMPLE_NUM]
small_X=np.concatenate((small_feature_positive, small_feature_negative), axis=0)
# remove last 1L dimension (1000L, 1764L, 1L)
small_X = small_X.squeeze()
small_y=np.ones((SAMPLE_NUM*2))
small_y[SAMPLE_NUM:]=0
#svc
parameters = {'nu': (0.05, ) , 'gamma':[6e-4, ]}
svc_clf=NuSVC(nu=0.1, kernel='rbf', verbose=True )
gs_clf =
GridSearchCV(svc_clf, parameters, n_jobs=1, verbose=True )
gs_clf.fit( small_X, small_y)
for params, mean_score, scores in gs_clf.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() * 2, params))