整个项目源码：GitHub
整个项目数据集：车辆数据集、无车辆数据集

引言

本次分享主要介绍，如何对道路上的汽车进行识别与跟踪。这里我们实现一个简单的demo。后续我们还会对前面的代码及功能进行重构，从而进一步丰富我们的功能。

项目软件框架

下图是车辆检测的实现流程图：

具体内容如下：

• 在有标签的训练数据集上进行Histogram of Oriented Gradients(HOG)特征提取
• Normalize 这些特征，并随机化数据集
• 训练线性SVM分类器
• 实现一个滑动窗口技术，并使用训练好的分类器在图片中寻找车辆
• 实现一个流处理机制，并通过一帧一帧地创建循环检测的热图来去除异常值及跟踪车辆
• 为检测到的车辆估计一个边界框

Features

本项目，我们使用一些有标签的训练数据：汽车图片、无汽车图片，训练数据在all文件夹中可以找到
有汽车地图片标签为1，无汽车的图片标签为0
我们先读取数据，看下数据的分布

# import libs
import glob
import matplotlib.image as mpimg
import numpy as np
from skimage.feature import hog
import cv2
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
import time
import pickle
SEED = 2018
%matplotlib inline
# Read dataset image
vehicle_images = glob.glob('all/vehicles/GTI*/*.png')
none_vehicle_images = glob.glob('all/non-vehicles/*/*.png')
cars = []
notcars = []
for image in vehicle_images:cars.append(image)
for image in none_vehicle_images:notcars.append(image)
print('Dataset size:Cars {} | NotCars {}'.format(len(cars),len(notcars)))
rate = len(cars)/len(notcars)*1.0
if rate<2 and rate>0.5:print('DataSet is balance')
else:print('DataSet is not balance')

Dataset size:Cars 2826 | NotCars 8968
DataSet is not balance

接下来我们分别随机选取一张有汽车及无汽车的图片

# random choose
rand_car = np.random.choice(len(cars))
rand_notcar = np.random.choice(len(notcars))
this_car = mpimg.imread(cars[rand_car])
this_notcar = mpimg.imread(notcars[rand_notcar])print('The size of car is {}'.format(len(this_car)))
print('The size of notcar is {}'.format(len(this_notcar)))
plt.figure(1)
plt.subplot(121)
plt.title('Car:class 1')
plt.imshow(this_car)
plt.subplot(122)
plt.title('Not a Car:class 0')
plt.imshow(this_notcar)
plt.show()

The size of car is 64
The size of notcar is 64

HOG feature extraction

接下来，我们来提取Histogram of oriented Gradients(HOG)特征。
有关HOG特征相关的信息，大家可以参考：HOG
提取HOG特征的基本步骤如下：

• 第一阶段为了减少图像亮度的影响需要对图片做一个全局的归一化。
• 第二阶段计算图像的一阶导数，从而捕捉图像的轮廓及纹理信息。
• 第三阶段旨在产生对局部图像内容敏感的编码（cell）
• 第四阶段，归一化（block）
• 最后将HOG descriptor 转化成分类器需要的特征向量

## 
def get_hog_features(img,orient,pix_per_cell,cell_per_block,vis=False,feature_vec=True):'''function:Extract HOG image and HOG features of a given imageorient: number of bins for the orientationpix_per_cell: size of a cellcell_per_block: nber of cells per blockvis(Boolean) :visualize the HOG imagefeature_vec(Boolean):return the features as a feature vectorBy default,the function uses transform_sqrt(apply power law compression to normalize the image before processing)'''if vis == True:features,hog_image = hog(img,orientations=orient,pixels_per_cell=(pix_per_cell,pix_per_cell),cells_per_block = (cell_per_block,cell_per_block),transform_sqrt=True,visualise=vis,feature_vector=feature_vec)return features,hog_imageelse:features = hog(img,orientations=orient,pixels_per_cell=(pix_per_cell,pix_per_cell),cells_per_block=(cell_per_block,cell_per_block),transform_sqrt=True,visualise=vis,feature_vector=feature_vec)return featuresdef bin_spatial(img,size=(32,32)):'''Binned Color Featureimg:original imagesize:target size of the imageoutput:feature vector'''features = cv2.resize(img,size).ravel()#print(cv2.resize(img,size).shape)(8,8,3)=>192return featuresdef color_hist(img,nbins=32,bins_range=(0,256)):'''Color histogram features for each channel of the original imageimg: original imagenbins: number of bins of the histogramoutput:concatenate feature vector'''channel1_hist = np.histogram(img[:,:,0],bins=nbins,range=bins_range)channel2_hist = np.histogram(img[:,:,1],bins=nbins,range=bins_range)channel3_hist = np.histogram(img[:,:,2],bins=nbins,range=bins_range)#Concatenate the histograms into a sigle feature vectorhist_features = np.concatenate((channel1_hist[0],channel2_hist[0],channel3_hist[0]))#48#print(channel1_hist)# Return the individual histograms into a single feature vector return hist_featuresdef extract_features(imgs,color_space="RGB",spatial_size=(32,32),hist_bins=32,orient=9,pix_per_cell=8,cell_per_block=2,hog_channel=0,spatial_feat=True,hist_feat=True,hog_feat=True,hog_vis=False):'''Feature extractor:extract features from a list of imagesThe function calls bin_spatial(),color_hist() and get_hog_features'''#create a list to append feature vectors tofeatures = []# Iterate through the list of imagesfor file in imgs:file_features = []# Read in each one by oneimage = mpimg.imread(file)if hog_vis == False:image = image.astype(np.float32)/255# apply color conversion if other than 'RGB'# color conversionif color_space in ['HSV','LUV','HLS','YUV','YCrCb']:feature_image = cv2.cvtColor(image,eval('cv2.COLOR_RGB2'+color_space))else: feature_image = np.copy(image)# Image size: add all pixels of reduced image as vectorif spatial_feat == True:spatial_features = bin_spatial(feature_image,size=spatial_size)#print('spatial features shape:',spatial_features.shape)file_features.append(spatial_features)# Histogram of reduced image: add histogram as a vectorif hist_feat == True:hist_features = color_hist(feature_image,nbins=hist_bins)file_features.append(hist_features)#HOG of reduced image: add HOG as feature vectorif hog_feat == True:# Call get_hog_features() with vis=False ,feature_vec = Trueif hog_channel == 'ALL':hog_features = []for channel in range(feature_image.shape[2]):if hog_vis:hog_feature,hog_image = get_hog_features(feature_image[:,:,channel],orient,pix_per_cell,cell_per_block,vis=True,feature_vec=True)#print(cv2.cvtColor(image,cv2.COLOR_RGB2GRAY).dtype)res = cv2.addWeighted(cv2.cvtColor(image,cv2.COLOR_RGB2GRAY),0.1,((hog_image/np.max(hog_image))*255).astype(np.float32),0.1,0.0)# Plot the examplesfig = plt.figure()plt.title(channel)plt.subplot(131)plt.imshow(image,cmap='gray')plt.title('Original Image')plt.subplot(132)plt.imshow(hog_image,cmap='gray')plt.title('HOG')plt.subplot(133)plt.imshow(res,cmap='gray')plt.title('overlapped')plt.show()else:hog_feature = get_hog_features(feature_image[:,:,channel],orient,pix_per_cell,cell_per_block,vis=False,feature_vec=True)#print('hog feature shape:',hog_feature.shape)hog_features.append(hog_feature)hog_features = np.ravel(hog_features)else:hog_features = get_hog_features(feature_image[:,:,hog_channel],orient,pix_per_cell,cell_per_block,vis=False,feature_vec = True)#Append the new feature vector to the features list#print('hog features shape:',hog_features.shape)file_features.append(hog_features)features.append(np.concatenate(file_features))#print(np.concatenate(file_features).shape)# return list of feature vectorsreturn features

Settings for feature extraction

color_space = 'YCrCb'  # ['RGB','HSV','LUV','HLS','YUV',''YCrCb']
orient = 12#HOG orientations
pix_per_cell = 8#HOG pixels per cell
cell_per_block = 2 #HOG cells per block
hog_channel = 'ALL'  # ['0','1','ALL']
spatial_size = (8,8) #Spatial binning dimensions
hist_bins = 16  #Number of histogram bins
hist_range = bins_range = (0,256)
spatial_feat = True #spatial features
hist_feat = False # histogram features
hog_feat = True # hog features

Visualization of Hog Image

# randomly select example
rand_img = np.random.choice(np.arange(0,len(notcars),1))print('Image adress:',notcars[rand_img])
feat = extract_features([notcars[rand_img]],color_space=color_space,spatial_size=spatial_size,hist_bins=hist_bins,orient=orient,pix_per_cell=pix_per_cell,cell_per_block=cell_per_block,hog_channel=hog_channel,spatial_feat=spatial_feat,hist_feat=hist_feat,hog_feat=hog_feat,hog_vis=True)

Image adress: all/non-vehicles/GTI/image1686.png/home/ora/anaconda3/envs/tensorflow/lib/python3.6/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15'be changed to `L2-Hys` in v0.15', skimage_deprecation)

Build Dataset with feature extraction

car_features = extract_features(cars,color_space=color_space,spatial_size=spatial_size,hist_bins=hist_bins,orient=orient,pix_per_cell=pix_per_cell,cell_per_block=cell_per_block,hog_channel=hog_channel,spatial_feat=spatial_feat,hist_feat=hist_feat,hog_feat=hog_feat)notcar_features = extract_features(notcars,color_space=color_space,spatial_size=spatial_size,hist_bins=hist_bins,orient=orient,pix_per_cell=pix_per_cell,cell_per_block=cell_per_block,hog_channel=hog_channel,spatial_feat=spatial_feat,hist_feat=hist_feat,hog_feat=hog_feat)
# Group cars and notcars images in a single array
X = np.vstack((car_features,notcar_features)).astype(np.float64)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)),np.zeros(len(notcar_features))))
#Normalize data:fit a per-column scaler
X_scaler = StandardScaler().fit(X)
scaled_X = X_scaler.transform(X)#Split up data into randomized training and test sets(shuffe included)
X_train,X_test,y_train,y_test = train_test_split(scaled_X,y,test_size=0.2,random_state=SEED)print('Using:',orient,'orientations',pix_per_cell,'pixels per cell and ',cell_per_block,'cells per block')
print('Feature vector length:',len(X_train[0]))
print('Mean of example 0{}|std {}'.format(np.mean(X_train[10]),np.std(X_train[0])))

/home/ora/anaconda3/envs/tensorflow/lib/python3.6/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15'be changed to `L2-Hys` in v0.15', skimage_deprecation)Using: 12 orientations 8 pixels per cell and  2 cells per block
Feature vector length: 7248
Mean of example 0-0.05479098608161728|std 0.8436106482861411

Run classifier

SVC

这里我们运行线性支持向量机

svc = LinearSVC()
# Check the training time for the SVC
t = time.time()
svc.fit(X_train,y_train)
t2 = time.time()print(round(t2-t,2),'Seconds to train SVC...')
# Check the score of the SVC
print('Train Accuracy of SVC=',round(svc.score(X_train,y_train),4))
print('Test Accuracy of SVC=',round(svc.score(X_test,y_test),4))
# Check the prediction time for a single sample
t = time.time()
n_predict = 10
print('My SVC predicts:',svc.predict(X_test[0:n_predict]))
print('For these',n_predict,'labels:',y_test[0:n_predict])
t2 = time.time()
print(round(t2-t,5),'Seconds to predict',n_predict,'labels with SVC')

22.9 Seconds to train SVC...
Train Accuracy of SVC= 1.0
Test Accuracy of SVC= 0.9818
My SVC predicts: [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
For these 10 labels: [0. 1. 0. 0. 1. 1. 0. 0. 1. 0.]
0.00101 Seconds to predict 10 labels with SVC

Logistic Regression Classifier

接下来我们运行逻辑回归分类器

from sklearn.linear_model import LogisticRegression
lrc = LogisticRegression(max_iter=10)
t = time.time()
lrc.fit(X_train,y_train)
t2 = time.time()
print(round(t2-t,2),'Seconds to train LRC...')
# Check the score of the LRC
print('Train Accuracy of LRC=',round(lrc.score(X_train,y_train),4))
print('Test Accuracy of LRC=',round(lrc.score(X_test,y_test),4))
# Check the prediction time for a single sample
t = time.time()
n_predict = 10
print('My LRC predicts:',lrc.predict(X_test[0:n_predict]))
print('For these',n_predict,'labels:',y_test[0:n_predict])
t2 = time.time()
print(round(t2-t,5),'Seconds to predict',n_predict,'labels with LRC')

27.1 Seconds to train LRC...
Train Accuracy of LRC= 1.0
Test Accuracy of LRC= 0.9852
My LRC predicts: [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
For these 10 labels: [0. 1. 0. 0. 1. 1. 0. 0. 1. 0.]
0.00169 Seconds to predict 10 labels with LRC

Multi-Layer Perceptron Classifer

最后我们来运行多层感知分类器

from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(random_state=SEED)
t = time.time()
mlp.fit(X_train,y_train)
t2 = time.time()
print(round(t2-t,2),'Seconds to train MLP...')
# Check the score of the LRC
print('Train Accuracy of MLP=',round(mlp.score(X_train,y_train),4))
print('Test Accuracy of MLP=',round(mlp.score(X_test,y_test),4))
# Check the prediction time for a single sample
t = time.time()
n_predict = 10
print('My MLP predicts:',mlp.predict(X_test[0:n_predict]))
print('For these',n_predict,'labels:',y_test[0:n_predict])
t2 = time.time()
print(round(t2-t,5),'Seconds to predict',n_predict,'labels with LRC')

21.28 Seconds to train MLP...
Train Accuracy of MLP= 1.0
Test Accuracy of MLP= 0.9953
My MLP predicts: [0. 1. 0. 0. 0. 1. 0. 0. 1. 0.]
For these 10 labels: [0. 1. 0. 0. 1. 1. 0. 0. 1. 0.]
0.00294 Seconds to predict 10 labels with LRC

Save the model

保存模型

model_combine = 'model.p'
try:with open(model_combine,'wb') as pfile:pickle.dump({'X_dataset':X,'y_dataset':y,'svc':svc,'lrc':lrc,'mlp':mlp,'X_scaler':X_scaler,'color_space':color_space,'spatial_size':spatial_size,'hist_bins':hist_bins,'orient':orient,'pix_per_cell':pix_per_cell,'cell_per_block':cell_per_block,'hog_channel':hog_channel,'spatial_feat':spatial_feat,'hist_feat':hist_feat,'hog_feat':hog_feat},pfile,pickle.HIGHEST_PROTOCOL)
except Exception as e:print('Unable to save data to',model,':',e)raise

Vechicle Detection and Tracking

import glob
import matplotlib.image as mpimg
import numpy as np
from skimage.feature import hog
import cv2
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from scipy.ndimage.measurements import label
import time 
from sklearn.externals import joblib
import pickle
from moviepy.editor import VideoFileClip
from IPython.display import HTML
# from skimage import measure
SEED = 2018
%matplotlib inline

Feature extractor functions

def get_hog_features(img,orient,pix_per_cell,cell_per_block,vis=False,feature_vector=True):'''function:Extract HOG image and HOG features of a given imageorient: number of bins for the orientationpix_per_cell: size of a cellcell_per_block: nber of cells per blockvis(Boolean) :visualize the HOG imagefeature_vec(Boolean):return the features as a feature vectorBy default,the function uses transform_sqrt(apply power law compression to normalize the image before processing)'''if vis == True:features,hog_image = hog(img,orientations=orient,pixels_per_cell=(pix_per_cell,pix_per_cell),cells_per_block = (cell_per_block,cell_per_block),transform_sqrt=True,visualise=vis,feature_vector=feature_vector)return features,hog_imageelse:features = hog(img,orientations=orient,pixels_per_cell=(pix_per_cell,pix_per_cell),cells_per_block=(cell_per_block,cell_per_block),transform_sqrt=True,visualise=vis,feature_vector=feature_vector)return featuresdef bin_spatial(img,size=(32,32)):'''Binned Color Featureimg:original imagesize:target size of the imageoutput:feature vector'''features = cv2.resize(img,size).ravel()return featuresdef color_hist(img,nbins=32,bins_range=(0,256)):'''Color histogram features for each channel of the original imageimg: original imagenbins: number of bins of the histogramoutput:concatenate feature vector'''channel1_hist = np.histogram(img[:,:,0],bins=nbins,range=bins_range)channel2_hist = np.histogram(img[:,:,1],bins=nbins,range=bins_range)channel3_hist = np.histogram(img[:,:,2],bins=nbins,range=bins_range)#Concatenate the histograms into a sigle feature vectorhist_features = np.concatenate((channel1_hist[0],channel2_hist[0],channel3_hist[0]))# Return the individual histograms into a single feature vector return hist_featuresdef color_cvt(img,cspace):'''image conversion to different color spacecspace avaliable:'HSV','LUV','YUV','YCrCb''''if cspace in ['HSV','LUV','HLS','YUV','YCrCb']:return cv2.cvtColor(img,eval('cv2.COLOR_RGB2'+cspace))else:return np.copy(img)

Load SVC Classifier and Feature settings

这里选用svc分类器

data_file = 'model.p'
with open(data_file,mode='rb') as f:data = pickle.load(f)svc = data['svc']
X_scaler = data['X_scaler']
color_space = data['color_space']
spatial_size = data['spatial_size']
hist_bins = data['hist_bins']
orient = data['orient']
pix_per_cell = data['pix_per_cell']
cell_per_block = data['cell_per_block']
hog_channel = data['hog_channel']
spatial_feat = data['spatial_feat']
hist_feat = data['hist_feat']
hog_feat = data['hog_feat']

Smoothing

# 此处列表的更新，可以使用固定长的队列存储，这里是固定更新的
class Buffer():def __init__(self,buffer_sz):self.buffer_sz = buffer_szself.hot_windows = []self.heat_mframe = []self.hotwindows_mframe = []self.nwindow_mframe = []def add_hotwindows(self,new_val):self.hot_windows.append(new_val)def update_hotwindows_historic(self,new_val):self.hotwindows_mframe.extend(new_val)self.nwindow_mframe.append(len(new_val))if len(self.nwindow_mframe) > self.buffer_sz:self.hotwindows_mframe = self.hotwindows_mframe[self.nwindow_mframe[0]:]self.nwindow_mframe = self.nwindow_mframe[-self.buffer_sz:]def update_heat_historic(self,new_heat):self.heat_mframe.append(new_heat)if len(self.heat_mframe) > self.buffer_sz:self.heat_mframe = self.heat_mframe[-self.buffer_sz:]
buffer = Buffer(buffer_sz=40)

接下来实现一个函数来提取特征及作出预测

def find_cars(img,ystart,ystop,cells_per_step,scale,svc,X_scale,cspace,orient,pix_per_cell,cell_per_block,spatial_feat,spatial_size,hist_feat,hist_bins):'''uses a single HOG feature extraction on the entire imagesliding_window = {'scale':[0.6, 0.8, 1.2, 1.6, 2, 2.2], 'ystart':[400, 400, 400, 350, 350, 350], 'ystop': [520, 520, 620, 620, 656, 656], 'cells_per_step': [3, 3, 1, 1, 1, 1]}img.shape: (720,1280,3)'''draw_img = np.copy(img)#Normalize pixel intensityimg = img.astype(np.float32)/255#确定搜索车辆的区域img_tosearch = img[ystart:ystop,700::]#print(img_tosearch.shape)ctrans_tosearch = color_cvt(img_tosearch,cspace=cspace)if scale!=1:imshape = ctrans_tosearch.shapectrans_tosearch = cv2.resize(ctrans_tosearch,(np.int(imshape[1]/scale),np.int(imshape[0]/scale)))ch1 = ctrans_tosearch[:,:,0]ch2 = ctrans_tosearch[:,:,1]ch3 = ctrans_tosearch[:,:,2]#print(ch1.shape[1])# Define blocks and steps as above(//地板除法，取整数)nxblocks = (ch1.shape[1]//(pix_per_cell))-1nyblocks = (ch1.shape[0]//(pix_per_cell))-1#nfeat_per_block = orient*cell_per_block**2#64 was the original sampling rate with 8 cells and 8 pix per cellwindow = 64nblocks_per_window = (window//(pix_per_cell))-1#cells_per_step = 2 instead of overlap ,define how many cells to step cells=>blocknxsteps = (nxblocks-nblocks_per_window)//cells_per_stepnysteps = (nyblocks-nblocks_per_window)//cells_per_step#print('nxsteps:{},nysteps:{}'.format(nxsteps,nysteps))# Compute individual channel HOG features for the entire imagehog1 = get_hog_features(ch1,orient,pix_per_cell,cell_per_block,feature_vector=False)hog2 = get_hog_features(ch2,orient,pix_per_cell,cell_per_block,feature_vector=False)hog3 = get_hog_features(ch3,orient,pix_per_cell,cell_per_block,feature_vector=False)current_hot_windows = []for xb in range(nxsteps):for yb in range(nysteps):ypos = yb*cells_per_stepxpos = xb*cells_per_step#Extract HOG for this patchhog_feat1 = hog1[ypos:ypos+nblocks_per_window,xpos:xpos+nblocks_per_window].ravel()hog_feat2 = hog2[ypos:ypos+nblocks_per_window,xpos:xpos+nblocks_per_window].ravel()hog_feat3 = hog3[ypos:ypos+nblocks_per_window,xpos:xpos+nblocks_per_window].ravel()hog_features = np.hstack((hog_feat1,hog_feat2,hog_feat3))xleft = xpos*pix_per_cellytop = ypos*pix_per_cell#Extract the image patchsubimg = cv2.resize(ctrans_tosearch[ytop:ytop+window,xleft:xleft+window],(64,64))#Get color featuresif spatial_feat == True:spatial_features = bin_spatial(subimg,size=spatial_size)if hist_feat == True:hist_features = color_hist(subimg,nbins=hist_bins)#Scale features and make a predictionif (spatial_feat== True) and (hist_feat==True) and (hog_feat==True):test_feature = X_scaler.transform(np.hstack((spatial_features,hist_features,hog_features)).reshape(1,-1))elif (spatial_feat==True) and (hist_feat==False) and (hog_feat==True):test_features = X_scaler.transform(np.hstack((spatial_features,hog_features)).reshape(1,-1))test_prediction = svc.predict(test_features)if test_prediction ==1.:#这里scale系数需要还原xbox_left = np.int(xleft*scale) + 700ytop_draw = np.int(ytop*scale)+ystartwin_draw = np.int(window*scale)buffer.add_hotwindows(((xbox_left,ytop_draw),(xbox_left+win_draw,ytop_draw+win_draw)))cv2.rectangle(draw_img,(xbox_left,ytop_draw),(xbox_left+win_draw,ytop_draw+win_draw),(0,0,255),6)return draw_img

Filters

前面代码中，我们将检测到汽车的位置存储在hot_windows中。通过hot_windows我们来画出热点图，并在热点图上应用阈值检测来清除错误检测的坐标

def add_heat(heatmap,bbox_list):'''iterate through list of positive sliding windows (bbox_list) and add heat'''for box in bbox_list:# Add +=1 for all pixels inside each bbox# Assuming each 'box' takes the form ((x1,y1),(x2,y2))heatmap[box[0][1]:box[1][1],box[0][0]:box[1][0]]+=1# return updated heatmapreturn heatmap# Iterate throughdef apply_threshold(heatmap,threshold):'''Appy threshold on heatmapreturn thresholded heatmap where all values below threshold are set to 0'''# Zero out pixles below the thresholdheatmap[heatmap<=threshold] = 0# return thresholded mapreturn heatmapdef draw_labeled_bboxes(img,labels):#Iterate through all detected carsfor car_number in range(1,labels[1]+1):#find pixels with each car_number label valuenonzero = (labels[0]==car_number).nonzero()# Identify x and y values of those pixelsnonezeroy = np.array(nonzero[0])nonezerox = np.array(nonzero[1])# Define a bounding box based on min/max x and ybbox = ((np.min(nonezerox),np.min(nonezeroy)),(np.max(nonezerox),np.max(nonezeroy)))# Check car validtion ==> too small then ignoreif(abs(bbox[0][0]-bbox[1][0])>50 and abs(bbox[0][1]-bbox[1][1])>50):#too small rect are ignorecv2.rectangle(img,bbox[0],bbox[1],(0,255,0),6)# return imagereturn img

sliding_window = {'scale':[0.6, 0.8, 1.2, 1.6, 2, 2.2], 'ystart':[400, 400, 400, 350, 350, 350], 'ystop': [520, 520, 620, 620, 656, 656], 'cells_per_step': [3, 3, 1, 1, 1, 1]}

def pipline_test(image):'''takes an image and returns a image'''#initialize for heatmap of current frameheat_sframe = np.zeros_like(image[:,:,0]).astype(np.float)#initialize hot_windows recoderbuffer.hot_windows = []threshold = 50for idx ,scale in enumerate(sliding_window['scale']):ystart = sliding_window['ystart'][idx]ystop = sliding_window['ystop'][idx]cells_per_step = sliding_window['cells_per_step'][idx]out_img = find_cars(image,ystart,ystop,cells_per_step,scale,svc,X_scaler,color_space,orient,pix_per_cell,cell_per_block,spatial_feat,spatial_size,hist_feat,hist_bins)  plt.imshow(out_img)plt.title('Find cars function output')plt.show()#Add heat to each box in box list#print(buffer.hot_windows)heat_sframe = add_heat(heat_sframe,buffer.hot_windows)heat_sframe = apply_threshold(heat_sframe,threshold)buffer.update_heat_historic(heat_sframe)smooth_heat = np.zeros_like(image[:,:,0]).astype(np.float)for h in buffer.heat_mframe:smooth_heat +=hsmooth_heat = smooth_heat/len(buffer.heat_mframe)heatmap = np.clip(smooth_heat,0,255)plt.imshow(heatmap,cmap='hot')plt.title('Heat Map')plt.show()labels = label(heatmap)new = draw_labeled_bboxes(np.copy(image),labels)plt.imshow(new)plt.title('Result image')plt.show()return new
# Read test image
test_data = glob.glob('test_images/*.jpg')
for file in test_data:image = mpimg.imread(file)new_image = pipline_test(image)

#接下来实现 vehicle detector pipelinedef pipline(image):'''takes an image and returns a image'''#initialize for heatmap of current frameheat_sframe = np.zeros_like(image[:,:,0]).astype(np.float)#initialize hot_windows recoderbuffer.hot_windows = []threshold = 50for idx ,scale in enumerate(sliding_window['scale']):ystart = sliding_window['ystart'][idx]ystop = sliding_window['ystop'][idx]cells_per_step = sliding_window['cells_per_step'][idx]out_img = find_cars(image,ystart,ystop,cells_per_step,scale,svc,X_scaler,color_space,orient,pix_per_cell,cell_per_block,spatial_feat,spatial_size,hist_feat,hist_bins)heat_sframe = add_heat(heat_sframe,buffer.hot_windows)heat_sframe = apply_threshold(heat_sframe,threshold)buffer.update_heat_historic(heat_sframe)smooth_heat = np.zeros_like(image[:,:,0]).astype(np.float)for h in buffer.heat_mframe:smooth_heat +=hsmooth_heat = smooth_heat/len(buffer.heat_mframe)heatmap = np.clip(smooth_heat,0,255)labels = label(heatmap)new = draw_labeled_bboxes(np.copy(image),labels)return new

# Run pipeline on video
video_output = 'project_solution.mp4'
clip1 = VideoFileClip('project_video.mp4')
white_clip = clip1.fl_image(pipline)
%time white_clip.write_videofile(video_output,audio=False)

[MoviePy] >>>> Building video project_solution.mp4
[MoviePy] Writing video project_solution.mp4100%|█████████▉| 1260/1261 [12:31<00:00,  1.70it/s][MoviePy] Done.
[MoviePy] >>>> Video ready: project_solution.mp4 CPU times: user 12min 32s, sys: 2.17 s, total: 12min 34s
Wall time: 12min 32s