OpenCV on ubuntu using Python

import cv2 import numpy as np import math ##输入结果库 pic_1 = cv2.imread('v1.png') pic_2 = cv2.imread('v2.png') pic_3 = cv2.imread('v3.png') ##摄像机输入 cap = cv2.VideoCapture(0) while( cap.isOpened() ) :     ret,img = cap.read()     gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) blur = cv2.GaussianBlur(gray,(5,5),0) ##阈值分割     ret,thresh1 = cv2.threshold(blur,70,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)   	 aa,contours, hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE) ##深复制     drawing = np.zeros(img.shape,np.uint8)      max_area=0    	##找轮廓     for i in range(len(contours)):             cnt=contours[i]             area = cv2.contourArea(cnt)             if(area>max_area):                 max_area=area                 ci=i     cnt=contours[ci]     hull = cv2.convexHull(cnt)#0621     ##meanshift求质心     moments = cv2.moments(cnt)     #print len(cnt)     #print hull 	##求质心公式     if moments['m00']!=0:                 cx = int(moments['m10']/moments['m00']) # cx = M10/M00                 cy = int(moments['m01']/moments['m00']) # cy = M01/M00                    centr=(cx,cy)            cv2.circle(img,centr,5,[0,0,255],2)     #cv2.circle(img,centr,5,[0,255,255],2)#0621     #cv2.rectangle(original, p1, p2, (77, 255, 9), 1, 1)#0621         cv2.drawContours(drawing,[cnt],0,(255,255,0),2)      #cv2.drawContours(drawing,[hull],0,(0,0,255),2)                 cnt = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)     hull = cv2.convexHull(cnt,returnPoints = False)      ndefects = 0 #0622 for counting finger_number ###根据图像中凹凸点中的 (开始点, 结束点, 远点)的坐标, 可利用余弦定理计算两根手指之间的夹角,     if(1):                defects = cv2.convexityDefects(cnt,hull)                #mind=0                #maxd=0                for i in range(defects.shape[0]):                     s,e,f,d = defects[i,0]                     start = tuple(cnt[s][0])                     end = tuple(cnt[e][0])                     far = tuple(cnt[f][0])                     #dist = cv2.pointPolygonTest(cnt,centr,True) 		    a = np.sqrt(np.square(start[0]-end[0])+np.square(start[1]-end[1]))#0622 		    b = np.sqrt(np.square(start[0]-far[0])+np.square(start[1]-far[1]))#0622 		    c = np.sqrt(np.square(end[0]-far[0])+np.square(end[1]-far[1]))#0622                       angle   = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c)) # * 57#0622 	##其必为锐角, 根据锐角的个数判别手势                     if angle <= math.pi/2 :#90:#0622                           ndefects = ndefects + 1#0622                      #cv2.line(img,start,end,[0,255,255],2)                     cv2.line(img,start,centr,[0,255,255],2)                     cv2.circle(img,start,20,[0,255,255],4)                       #cv2.circle(img,end,20,[0,255,0],4)                		                     cv2.circle(img,far,5,[0,0,255],-1)                #print(i) 	       print ndefects                i=0              	       if ndefects == 0: 			print 07 			cv2.imshow("RESULT",pic_3) 	       else: 		 if ndefects == 1: 			print 27 			cv2.imshow("RESULT",pic_2) 		 else:  			if ndefects == 4: 				print 57 				cv2.imshow("RESULT",pic_1) 			else:  			 	print 0000 	            cv2.imshow('output',drawing)     cv2.imshow('input',img)                      k = cv2.waitKey(10) #Esc     if k == 27: 		break

import cv2 import numpy as np  cap = cv2.VideoCapture(0) while( cap.isOpened() ) : 	ret,img = cap.read() 	# load an original image 	#img = cv2.imread(imgFile) 	rows,cols,channels = img.shape 	# convert color space from rgb to ycbcr 	imgYcc = cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB)  	# convert color space from bgr to rgb                         	img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  	# prepare an empty image space 	imgSkin = np.zeros(img.shape, np.uint8) 	# copy original image/深复制 	imgSkin = img.copy()     	for r in range(rows): 	    for c in range(cols): 		 		# non-skin area if skin equals 0, skin area otherwise         		skin = 0        		 		# get values from rgb color space 		R = img.item(r,c,0) 		G = img.item(r,c,1) 		B = img.item(r,c,2) 		 		# get values from ycbcr color space    		Y = imgYcc.item(r,c,0) 		Cr = imgYcc.item(r,c,1) 		Cb = imgYcc.item(r,c,2)                                                                                                                                         	 	 		# skin color detection 		 		if R > G and R > B: 		        if (133 <= Cr and Cr <= 173) and (77 <= Cb and Cb <= 127): 		            skin = 1 		            # print 'Skin detected!' 		 		if 0 == skin: 		    imgSkin.itemset((r,c,0),0) 		    imgSkin.itemset((r,c,1),0)                 		    imgSkin.itemset((r,c,2),0) 		if 1 == skin: 		    imgSkin.itemset((r,c,0),255) 		    imgSkin.itemset((r,c,1),255)                 		    imgSkin.itemset((r,c,2),255)  	cv2.imshow("RESULT",imgSkin)  	k = cv2.waitKey(10) 	if k == 27: 		break

import cv2 #opencv函數大多數都在cv2模塊內，並不是針對2.x.x版本的

MJ: https://www.yiibai.com/numpy/numpy_array_from_numerical_ranges.html net: https://blog.csdn.net/scorpion_zs/article/details/52982872  numpy.zeros（np.zeros）使用方法--python学习笔记31 https://blog.csdn.net/qq_26948675/article/details/54318917  ndarray :n维数组 py文件名字不能叫numpy.py  x = np.arange(5) print x result(in terminal): [0  1  2  3  4]  ... numpy.ndarray.item  np.random.randint产生一个范围内的数据 owners = np.random.randint(15000, high=73001, size=398, dtype='l') 官方文档中给出的用法是：numpy.random.randint(low,high=None,size=None,dtype) 生成在半开半闭区间[low,high)上离散均匀分布的整数值;若high=None，则取值区间变为[0,low)  eg: x = np.random.randint(9, size=(3, 3))  print x print x.item(3) print x.item(7) print x.item((0, 1)) 默认high=none 取值区间[0，9），生成3*3的数组 比如随机生成了： [[5 7 3]  [6 6 7]  [4 5 5]] print x.item(3)即从0位开始数，0;1;2;；3;4;5；；6;7;8； “3”对应的是6 print x.item(7)对应是5 print x.item((0, 1))即从第0行第0列开始，则0行1列是7   Z = img.reshape((-1,3)) numpy.reshape https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.reshape.html https://blog.csdn.net/qq_28618765/article/details/78083895  # convert to np.float32 Z = np.float32(Z) Numpy 中文用户指南 3.1 数据类型: https://segmentfault.com/a/1190000004681332

b=[[1,1],[1,1]]#定义了一个2*2的，且初始为0的二维数组？ from Python数组定义方法_python_脚本之家 c = [[0]*10]*10 #定义10*10初始为0的二维数组 src = np.zeros((100, 100)) #使用np.zeros可以构造一个相应大小的全部由0组成的矩阵 100行100列 這用了numpy src[49][49] = 255  #第49行49列的元素爲255

just need one order：  cap = cv2.VideoCapture(1) while( cap.isOpened() ) :     ret,img = cap.read()     then use the img

https://www.2cto.com/kf/201611/568821.html中的  	cv2.imshow('draw_result',result) 	cv2.moveWindow('draw_result',1000,500)

solution:https://blog.csdn.net/u011333059/article/details/38079833 代码如下：  [python] view plain copy  import matplotlib.pyplot as plt   input_image = root/to/your/image  plt.imshow(input_image)    运行后发现图片无法显示，  解决方法是： 首先import pylab 在需要显示图片的代码下一行加上 pylab.show()    这种方法可以显示图片，但是不关掉这个图片窗口，代码就不会继续运行下去，暂时还没有找到更合适的方法。 希望有知道解决方法的可以告诉我，谢谢！  问题来了，为什么显示的颜色与原图不同呢？ 后来网上搜索后才知道，对于opencv的像素是BGR顺序，然而matplotlib所遵循的是RGB顺序。 opencv的一个像素为：[B,G,R] ,matplotlib的一个像素为：[R,G,B]。这就是为什么本来发红的区域变得有些发蓝了。 from 博客 ：https://www.cnblogs.com/visionfeng/p/6094423.html

from https://stackoverflow.com/questions/14494101/using-other-keys-for-the-waitkey-function-of-opencv  You can use ord() function in Python for that. For example, if you want to trigger 'a' key press, do as follows :  if cv2.waitKey(33) == ord('a'):    print "pressed a" To find the key value for any key is to print the key value using a simple script as follows : import cv2 img = cv2.imread('sof.jpg') # load a dummy image while(1):     cv2.imshow('img',img)     k = cv2.waitKey(33)     if k==27:    # Esc key to stop         break     elif k==-1:  # normally -1 returned,so don't print it         continue     else:         print k # else print its value  With this code, I got following values : Upkey : 2490368 DownKey : 2621440 LeftKey : 2424832 RightKey: 2555904 Space : 32 Delete : 3014656

import cv2  stitcher = cv2.createStitcher(False) foo = cv2.imread("v1.jpg") bar = cv2.imread("v2.jpg") result = stitcher.stitch((foo,bar))  cv2.imwrite("v3.jpg", result[1])  cv2.imshow("v3.jpg", result[1]) cv2.waitKey(0)