基于opencv的图像阴影消除

详细代码在这！！！

最大滤波

def max_filtering(N, I_temp):wall = np.full((I_temp.shape[0]+(N//2)*2, I_temp.shape[1]+(N//2)*2), -1)wall[(N//2):wall.shape[0]-(N//2), (N//2):wall.shape[1]-(N//2)] = I_temp.copy()temp = np.full((I_temp.shape[0]+(N//2)*2, I_temp.shape[1]+(N//2)*2), -1)for y in range(0,wall.shape[0]):for x in range(0,wall.shape[1]):if wall[y,x]!=-1:window = wall[y-(N//2):y+(N//2)+1,x-(N//2):x+(N//2)+1]num = np.amax(window)temp[y,x] = numA = temp[(N//2):wall.shape[0]-(N//2), (N//2):wall.shape[1]-(N//2)].copy()return A

np.full：返回一个指定形状、类型和数值的数组

numpy.full(shape, fill_value, dtype=None, order=‘C’)

shape
fill_value

前三步图示一下

 wall = np.full((I_temp.shape[0]+(N//2)*2, I_temp.shape[1]+(N//2)*2), -1)

+(N//2)*2有点类似于padding

最小滤波

def min_filtering(N, A):wall_min = np.full((A.shape[0]+(N//2)*2, A.shape[1]+(N//2)*2), 300)wall_min[(N//2):wall_min.shape[0]-(N//2), (N//2):wall_min.shape[1]-(N//2)] = A.copy()temp_min = np.full((A.shape[0]+(N//2)*2, A.shape[1]+(N//2)*2), 300)for y in range(0,wall_min.shape[0]):for x in range(0,wall_min.shape[1]):if wall_min[y,x]!=300:window_min = wall_min[y-(N//2):y+(N//2)+1,x-(N//2):x+(N//2)+1]num_min = np.amin(window_min)temp_min[y,x] = num_minB = temp_min[(N//2):wall_min.shape[0]-(N//2), (N//2):wall_min.shape[1]-(N//2)].copy()return B

def background_subtraction(I, B):
O = I - B
norm_img = cv2.normalize(O, None, 0,255, norm_type=cv2.NORM_MINMAX)
return norm_img

最小滤波和最大滤波的顺序：

①
②

def min_max_filtering(M, N, I):if M == 0:#max_filteringA = max_filtering(N, I)#min_filteringB = min_filtering(N, A)#subtractionnormalised_img = background_subtraction(I, B)elif M == 1:#min_filteringA = min_filtering(N, I)#max_filteringB = max_filtering(N, A)#subtractionnormalised_img = background_subtraction(I, B)return normalised_img

基于opencv的车辆变道检测

代码在基于opencv的车辆变道检测
整体结构如下：

angle.py解析

找到汽车的角度

import cv2
import mathclass Target:def __init__(self):self.capture = cv2.VideoCapture(0)cv2.namedWindow("Target", 1)cv2.NamedWindow("Threshold1",1)cv2.NamedWindow("Threshold2",1)cv2.NamedWindow("hsv",1)def run(self):#initiate fontfont = cv2.InitFont(cv2.CV_FONT_HERSHEY_SIMPLEX, 1, 1, 0, 3, 8)#instantiate imageshsv_img=cv2.CreateImage(cv2.GetSize(cv2.QueryFrame(self.capture)),8,3)threshold_img1 = cv2.CreateImage(cv2.GetSize(hsv_img),8,1)threshold_img1a = cv2.CreateImage(cv2.GetSize(hsv_img),8,1)threshold_img2 = cv2.CreateImage(cv2.GetSize(hsv_img),8,1)i=0writer=cv2.CreateVideoWriter("angle_tracking.avi",cv2.CV_FOURCC('M','J','P','G'),30,cv2.GetSize(hsv_img),1)while True:#capture the image from the camimg=cv2.QueryFrame(self.capture)#convert the image to HSVcv2.CvtColor(img,hsv_img,cv2.CV_BGR2HSV)#threshold the image to isolate two colorscv2.InRangeS(hsv_img,(165,145,100),(250,210,160),threshold_img1)  #redcv2.InRangeS(hsv_img,(0,145,100),(10,210,160),threshold_img1a)   #red againcv2.Add(threshold_img1,threshold_img1a,threshold_img1)               #this is combining the two limits for redcv2.InRangeS(hsv_img,(105,180,40),(120,260,100),threshold_img2) #blue#determine the moments of the two objectsthreshold_img1=cv2.GetMat(threshold_img1)threshold_img2=cv2.GetMat(threshold_img2)moments1=cv2.Moments(threshold_img1,0)moments2=cv2.Moments(threshold_img2,0)area1=cv2.GetCentralMoment(moments1,0,0)area2=cv2.GetCentralMoment(moments2,0,0)#initialize x and yx1,y1,x2,y2=(1,2,3,4)coord_list=[x1,y1,x2,y2]for x in coord_list:x=0#there can be noise in the video so ignore objects with small areasif (area1 >200000):#x and y coordinates of the center of the object is found by dividing the 1,0 and 0,1 moments by the areax1=int(cv2.GetSpatialMoment(moments1,1,0)/area1)y1=int(cv2.GetSpatialMoment(moments1,0,1)/area1)#draw circlecv2.Circle(img,(x1,y1),2,(0,255,0),20)#write x and y positioncv2.PutText(img,str(x1)+","+str(y1),(x1,y1+20),font, 255) #Draw the textif (area2 >100000):#x and y coordinates of the center of the object is found by dividing the 1,0 and 0,1 moments by the areax2=int(cv2.GetSpatialMoment(moments2,1,0)/area2)y2=int(cv2.GetSpatialMoment(moments2,0,1)/area2)#draw circlecv2.Circle(img,(x2,y2),2,(0,255,0),20)cv2.PutText(img,str(x2)+","+str(y2),(x2,y2+20),font, 255) #Draw the textcv2.Line(img,(x1,y1),(x2,y2),(0,255,0),4,cv2.CV_AA)#draw line and anglecv2.Line(img,(x1,y1),(cv2.GetSize(img)[0],y1),(100,100,100,100),4,cv2.CV_AA)x1=float(x1)y1=float(y1)x2=float(x2)y2=float(y2)angle = int(math.atan((y1-y2)/(x2-x1))*180/math.pi)cv2.PutText(img,str(angle),(int(x1)+50,(int(y2)+int(y1))/2),font,255)#cv2.WriteFrame(writer,img)#display frames to userscv2.ShowImage("Target",img)cv2.ShowImage("Threshold1",threshold_img1)cv2.ShowImage("Threshold2",threshold_img2)cv2.ShowImage("hsv",hsv_img)# Listen for ESC or ENTER keyc = cv2.WaitKey(7) % 0x100if c == 27 or c == 10:breakcv2.DestroyAllWindows()if __name__=="__main__":t = Target()t.run()

CreateImageHeader(size, depth, channels)
创建图像标头并分配图像数据

QueryFrame（capture）
在一次调用中结合了 VideoCapture：：grab（）和 VideoCapture：：retrieve（）。这是读取视频文件或从解码中捕获数据并返回刚刚抓取的帧的最方便的方法。如果未抓取任何帧（相机已断开连接，或者视频文件中没有更多帧），则方法返回 false，函数返回 NULL 指针。

将红色和蓝色单独分离开，红色在hsv中有两段，最终做一个加和

#threshold the image to isolate two colorscv2.InRangeS(hsv_img,(165,145,100),(250,210,160),threshold_img1)  #redcv2.InRangeS(hsv_img,(0,145,100),(10,210,160),threshold_img1a)   #red againcv2.Add(threshold_img1,threshold_img1a,threshold_img1)               #this is combining the two limits for redcv2.InRangeS(hsv_img,(105,180,40),(120,260,100),threshold_img2) #blue

cv.InRangeS（src， lower， upper， dst）
实际基本等价于InRange

 #determine the moments of the two objectsthreshold_img1=cv2.GetMat(threshold_img1)threshold_img2=cv2.GetMat(threshold_img2)moments1=cv2.Moments(threshold_img1,0)moments2=cv2.Moments(threshold_img2,0)area1=cv2.GetCentralMoment(moments1,0,0)area2=cv2.GetCentralMoment(moments2,0,0)

GetMat该函数返回输入数组的矩阵标头
Moments概念:轮廓矩代表了一条轮廓，一幅图像，一组点集（取自《learning opencv》的介绍）
当处理轮廓时，结果是轮廓的长度；当处理点集时，它代表的是点的数量

cv.Moments（arr， binary=0）

binary如果为 true，则所有非零图像像素都被视为 1。该参数仅用于图像。

GetCentralMoment
GetSpatialMoment

现在好像已经被淘汰，在文档已经找不到了， x2=int(cv2.GetSpatialMoment(moments2,1,0)/area2) y2=int(cv2.GetSpatialMoment(moments2,0,1)/area2)
功能是：通过将1,0和0,1矩除以面积，可以找到对象中心的x和y坐标

还是举一个例子说明不同版本下的使用:

//cv1：
moments = cv.Moments(thresholded_img, 0)
area = cv.GetCentralMoment(moments, 0, 0) #there can be noise in the video so ignore objects with small areas
if(area > 100000): #determine the x and y coordinates of the center of the object #we are tracking by dividing the 1, 0 and 0, 1 moments by the area x = cv.GetSpatialMoment(moments, 1, 0)/area y = cv.GetSpatialMoment(moments, 0, 1)/area //cv2
moments = cv2.moments(thresholded_img)
area = moments['m00'] #there can be noise in the video so ignore objects with small areas
if(area > 100000): #determine the x and y coordinates of the center of the object #we are tracking by dividing the 1, 0 and 0, 1 moments by the area x = moments['m10'] / areay = moments['m01'] / area

GetCentralMoment，GetSpatialMoment在opencv2可以直接使用索引进行获取

angle_pop.py详解

完整代码

import cv2
import numpy as np
import time
import tkinter as tk
from tkinter import ttkNORM_FONT= ("Verdana", 10)def popupmsg(msg):popup = tk.Tk()popup.wm_title("Message")label = ttk.Label(popup, text=msg, font=NORM_FONT)label.pack(side="top", fill="x", pady=10)B1 = ttk.Button(popup, text="Okay", command = popup.destroy)B1.pack()popup.mainloop()cascade_src = 'cascade/cars.xml'
video_src = 'dataset/cars.mp4'cap = cv2.VideoCapture(video_src)
car_cascade = cv2.CascadeClassifier(cascade_src)deltatsum = 0
n = 0
last_time = time.time()while(1):# Take each frame_, frame = cap.read()cars = car_cascade.detectMultiScale(frame, 1.1, 1)for (x,y,w,h) in cars:roi = cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)   #ROI is region of interest#blur the frame to get rid of noise. the kernel should be ODDframe = cv2.GaussianBlur(frame,(21,21),0)# Convert BGR to HSVhsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)# define range of color in HSVlower_limit = np.array([0,150,150])upper_limit = np.array([10,255,255])# Threshold the HSV image to get only the thresholded colors# mask is a binary imagemask = cv2.inRange(hsv, lower_limit, upper_limit)# The dimensions of the kernel must be odd!kernel = np.ones((3,3),np.uint8)kernel_lg = np.ones((15,15),np.uint8)# erode the mask to get rid of noisemask = cv2.erode(mask,kernel,iterations = 1)# dialate it back to regain some lost areamask = cv2.dilate(mask,kernel_lg,iterations = 1)    # Bitwise-AND the mask and grayscale image so we end up with our areas of interest and black everywhere elseresult = cv2.bitwise_and(frame,frame, mask= mask)thresh = maskcontours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)# define a minimum area for a contour - if it's below this, ignore it min_area = 1000cont_filtered = []    # filter out all contours below a min_areafor cont in contours:if cv2.contourArea(cont) > min_area:cont_filtered.append(cont)#print(cv2.contourArea(cont))try:cnt = cont_filtered[0]# draw the rectangle surrounding the filtered contourrect = cv2.minAreaRect(cnt)box = cv2.boxPoints(rect)box = np.int0(box)cv2.drawContours(frame,[box],0,(0,0,255),2)rows,cols = thresh.shape[:2][vx,vy,x,y] = cv2.fitLine(cnt, cv2.DIST_L2,0,0.01,0.01)lefty = int((-x*vy/vx) + y)righty = int(((cols-x)*vy/vx)+y)cv2.line(frame,(cols-1,righty),(0,lefty),(0,255,0),2)        # this would draw all the contours on the image, not just the ones from cont_filtered#cv2.drawContours(frame, cont_filtered, -1, (0,255,0), 3)M = cv2.moments(cnt)cx = int(M['m10']/M['m00'])cy = int(M['m01']/M['m00'])(x,y),(MA,ma),angle = cv2.fitEllipse(cnt)print('x= ', cx, '  y= ', cy, ' angle = ', round(rect[2],2))if(round(rect[2],2))<-45:popupmsg('Lane change detected')#print(contours)#print('there are contours')except:print('no contours')cv2.imshow('frame',frame)#cv2.imshow('mask', mask)#cv2.imshow('thresh',thresh)#cv2.imshow('im2', im2)cv2.imshow('result', result)k = cv2.waitKey(5) & 0xFFif k == ord('q'):breakdeltat = time.time() - last_timelast_time = time.time()    deltatsum += deltatn += 1freq = round(1/(deltatsum/n), 2)# print('Updating at ' + str(freq) + ' FPS\r', end='')# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()

模块函数,类解析

import tkinter as tk
from tkinter import ttk

使用了tkinter作为经典ui设计，详细介绍！！！

1.CascadeClassifier(级联分类器)

2.detectMultiScale函数
有多个重载形式，且c++和python传入参数形式有一定区别
输入为灰度形式

整体思想

获取图像,实例化级联分类器
搜索图像，预处理（滤波，阈值…）
找轮廓，筛选轮廓