add

#coding:utf-8 from PIL import Image import os import sys #创建底图 target = Image.new('RGBA', (300, 300), (0, 0, 0, 0)) #打开头像 #nike_image = Image.open("./image1.png") nike_image = Image.open("./0.png") nike_image = nike_image.resize((300, 300)) #打开装饰 #hnu_image = Image.open("./hnu.png") hnu_image = Image.open("./water1.png") hnu_image = hnu_image.resize((300, 300)) # 分离透明通道 #r,g,b,a = hnu_image.split() r,g,b = hnu_image.split() # 将头像贴到底图 import pdb pdb.set_trace() nike_image.convert("RGBA") target.paste(nike_image, (0,0)) #将装饰贴到底图 hnu_image.convert("RGBA") target.paste(hnu_image,(0,0), mask=r*0.1) # 保存图片 target.save("f.png")

image_process.py

#coding:utf-8 # code for week2,recognize_computer_vision.py # houchangligong,zhaomingming,20200520, import numpy as np import cv2 def generate_data(): # 本函数生成0-9，10个数字的图片矩阵 image_data=[] num_0 =np.array( [[0,0,1,1,0,0], [0,1,0,0,1,0], [0,1,0,0,1,0], [0,1,0,0,1,0], [0,0,1,1,0,0], [0,0,0,0,0,0]]) image_data.append(num_0) num_1 = np.array( [[0,0,0,1,0,0], [0,0,1,1,0,0], [0,0,0,1,0,0], [0,0,0,1,0,0], [0,0,1,1,1,0], [0,0,0,0,0,0]]) image_data.append(num_1) num_2 = np.array( [[0,0,1,1,0,0], [0,1,0,0,1,0], [0,0,0,1,0,0], [0,0,1,0,0,0], [0,1,1,1,1,0], [0,0,0,0,0,0]]) image_data.append(num_2) num_3 = np.array( [[0,0,1,1,0,0], [0,0,0,0,1,0], [0,0,1,1,0,0], [0,0,0,0,1,0], [0,0,1,1,0,0], [0,0,0,0,0,0]]) image_data.append(num_3) num_4 = np.array( [ [0,0,0,0,1,0], [0,0,0,1,1,0], [0,0,1,0,1,0], [0,1,1,1,1,1], [0,0,0,0,1,0], [0,0,0,0,0,0]]) image_data.append(num_4) num_5 = np.array( [ [0,1,1,1,0,0], [0,1,0,0,0,0], [0,1,1,1,0,0], [0,0,0,0,1,0], [0,1,1,1,0,0], [0,0,0,0,0,0]]) image_data.append(num_5) num_6 = np.array( [[0,0,1,1,0,0], [0,1,0,0,0,0], [0,1,1,1,0,0], [0,1,0,0,1,0], [0,0,1,1,0,0], [0,0,0,0,0,0]]) image_data.append(num_6) num_7 = np.array( [ [0,1,1,1,1,0], [0,0,0,0,1,0], [0,0,0,1,0,0], [0,0,0,1,0,0], [0,0,0,1,0,0], [0,0,0,0,0,0]]) image_data.append(num_7) num_8 = np.array( [[0,0,1,1,0,0], [0,1,0,0,1,0], [0,0,1,1,0,0], [0,1,0,0,1,0], [0,0,1,1,0,0], [0,0,0,0,0,0]]) image_data.append(num_8) num_9 = np.array( [[0,0,1,1,1,0], [0,1,0,0,1,0], [0,0,1,1,1,0], [0,1,0,0,1,0], [0,0,0,0,1,0], [0,0,0,0,0,0]]) image_data.append(num_9) return image_data if __name__=="__main__": image_data = generate_data() # 打印出0的图像 print("数字0对应的图片是:") print(image_data[0]) cv2.imwrite("0.png",image_data[0]*255) print("-"*20) import pdb pdb.set_trace() # 打印出8的图像 print("数字8对应的图片是:") print(image_data[8]) print("-"*20)

week1_class_code.py

#!/usr/bin/env python # coding: utf-8 # In[9]: a=[1,2,3] # In[10]: print("Hello friends，welcome week1's class .........",29*"-","%s"%(a)) # In[11]: import cv2 import numpy as np # # 直接用生成矩阵的方式生成图片 # In[12]: img0 = np.array([[0,0,1],[0,1,0],[1,0,0]]) #img0 = np.array([[0,0,1],[0,1,0],[1,0,0]]) # # 查看矩阵数值以及大小 # In[13]: print(img0) # In[17]: print(img0.shape) print("img0 size = %s,%s"%(img0.shape[0],img0.shape[1])) # In[227]: import matplotlib.pyplot as plt plt.imshow(img0,cmap = 'gray' ) # # 彩色图像的颜色空间转换 # In[ ]: # https://blog.csdn.net/zhang_cherry/article/details/88951259 # # 从摄像头采集图像 # In[189]: #cap = cv2.VideoCapture(0) cap = cv2.VideoCapture("../How Computer Vision Works.mp4") # In[226]: ret,frame = cap.read() print(cap.isOpened()) plt.imshow(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)) # In[173]: cap.release() # In[ ]: # # 从文件读取图像数据 # In[139]: img = cv2.imread("lena.jpg") # In[144]: print(img.shape) # In[ ]: # range of instrest # In[ ]: roi = img[100:200,300:400] # In[230]: # 黑白图像 # In[ ]: # 彩色图像 # In[231]: img_RGB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2RGB # In[ ]: cv2.cvtColor(img_BGR, cv2.COLOR_BGR2GRAY) # In[ ]: cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HSV # In[ ]: # 肤色检测 # In[235]: # 二值化 # In[242]: plt.imshow(cv2.threshold(img,128,200,cv2.THRESH_BINARY)) # In[228]: # 图像的放大与缩小 # In[243]: plt.imshow(cv2.resize(img,(300,500))) # In[247]: # 移动： M = np.float32([[1,0,30],[0,1,30]]) plt.imshow(cv2.warpAffine(img,M,(500,300))) # In[229]: # 图像的旋转与拉伸 # In[260]: pts1 = np.float32([[50,50],[200,50],[50,200]]) pts2 = np.float32([[10,100],[200,50],[100,250]]) M = cv2.getAffineTransform(pts1,pts2) print(M) theta=1 M = np.float32([[np.cos(theta),-np.sin(theta),500],[np.sin(theta),np.cos(theta),100]]) cols=800 rows=800 dst = cv2.warpAffine(img,M,(cols,rows)) plt.imshow(dst) # In[ ]: pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]]) pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]]) M = cv2.getPerspectiveTransform(pts1,pts2) dst = cv2.warpPerspective(img,M,(300,300)) # In[261]: # 图像模糊与锐化 # In[ ]: lena_gaussian_blur = cv2.GaussianBlur(lena_RGB, (5, 5), 1, 0) # 高斯模糊 # In[ ]: cv2.Canny(pil_img3,30,150) # In[ ]: kernel = np.ones((9,9),np.float32)/81 result = cv2.filter2D(img,-1,kernel) # In[ ]: # 形态学运算 # In[ ]: # In[ ]: # In[ ]: # 灰度直方图 # In[ ]: # In[ ]: # In[ ]: # 直方图均衡化 # In[ ]: # In[ ]: # In[ ]: # 加水印，保护版权 # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[3]: import torch # In[10]: import matplotlib # In[11]: get_ipython().system(u'pip install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple') # In[13]: from image_process import * image_data = generate_data() # # matplotlib 使用例子 # https://www.cnblogs.com/yanghailin/p/11611333.html, # In[14]: import matplotlib.pyplot as plt # In[ ]: i=0 # In[120]: print(image_data[i%10]) plt.imshow(image_data[i%10],cmap = 'gray') i=i+1 # In[83]: plt.imshow(cv2.imread('lena.jpg')) # In[129]: img = cv2.imread("lena.jpg") # # add watermask # In[234]: wm = cv2.imread("water1.png") wm = cv2.resize(wm,(300,300)) wm = 255-wm img1 = cv2.resize(img,(300,300)) img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB) print(wm.shape) plt.imshow(cv2.add(wm,img1)) plt.imshow(cv2.addWeighted(wm,1,img1,0.2,0)) #plt.imshow(wm) # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]:

week1_class_code_after_class.py

#!/usr/bin/env python # coding: utf-8 # In[9]: a=[1,2,3] # In[10]: print("Hello friends，welcome week1's class .........",29*"-","%s"%(a)) # In[11]: import cv2 import numpy as np # # 直接用生成矩阵的方式生成图片 # In[12]: img0 = np.array([[0,0,1],[0,1,0],[1,0,0]]) #img0 = np.array([[0,0,1],[0,1,0],[1,0,0]]) # # 查看矩阵数值以及大小 # In[13]: print(img0) # In[17]: print(img0.shape) print("img0 size = %s,%s"%(img0.shape[0],img0.shape[1])) # In[7]: import matplotlib.pyplot as plt # import matplotlib.pylab as plt plt.imshow(img0,cmap = 'gray' ) # color map #plt.imshow(img0) # pycharm要加一句：plt.show() # 加一个%matplotlib inline就会显示 # # 彩色图像的颜色空间转换 # In[ ]: # https://blog.csdn.net/zhang_cherry/article/details/88951259 # # 从摄像头采集图像 # In[98]: # read camera #cap = cv2.VideoCapture(0) # read video #cap = cv2.VideoCapture("/Users/zhaomingming/Documents/HTC/核心课/CVFundamentals/week1/How Computer Vision Works.mp4") #cap = cv2.VideoCapture("../How Computer Vision Works.mp4") cap = cv2.VideoCapture("How Computer Vision Works.mp4") # In[99]: print(cap.isOpened()) # In[ ]: return_value=True while return_value: return_value,frame = cap.read() print(cap.isOpened()) # frame 图像帧 #cv2.cvtColor() #cv2.COLOR_BGR2RGB plt.imshow(frame,cmap = 'gray') #plt.imshow(cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)) # # frame 的大小是多少？ # In[66]: print(frame.shape) # In[75]: # 关闭capture cap.release() # In[ ]: # # 从文件读取图像数据 # In[8]: img = cv2.imread("lena.jpg") #cv2.cvtColor() #cv2.COLOR_BGR2RGB img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # In[86]: print(img.shape) # In[87]: # range of instrest # In[88]: roi = img[100:200,300:400] # In[89]: plt.imshow(img) #plt.imshow(roi) # In[230]: # 黑白图像 # In[96]: img_gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img_gray.shape # In[95]: plt.imshow(img_gray,cmap='gray') # In[ ]: # 彩色图像 # In[ ]: # # rgb2hsv # In[10]: import cv2 img_BGR = cv2.imread('lena.jpg') img_hsv=cv2.cvtColor(img_BGR,cv2.COLOR_BGR2HSV) plt.imshow(img_hsv,cmap='') # In[ ]: # 肤色检测 # In[5]: # 二值化 import cv2 img = cv2.imread('lena.jpg') # In[6]: plt.imshow(cv2.threshold(img,128,200,cv2.THRESH_BINARY)) # In[228]: # 图像的放大与缩小 # In[15]: # 列，行 img =cv2.resize(img,(50,30)) plt.imshow(img) img.shape # In[18]: # 列，行 img = cv2.imread('lena.jpg') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img =cv2.resize(img,(500,300)) plt.imshow(img) img.shape # In[22]: # roi img_roi =img[100:300,0:200] plt.imshow(img_roi) # In[31]: import numpy as np # 移动： M = np.float32([[1,0,300],[0,1,200]]) print(img[0+50,0]) img_1=cv2.warpAffine(img,M,(1000,1000)) plt.imshow(img_1) print(img_1[200+50,300]) # In[229]: # 图像的旋转变换与拉伸变换 # In[56]: #pts1 = np.float32([[50,50],[200,50],[50,200]]) #pts2 = np.float32([[10,100],[200,50],[100,250]]) #M = cv2.getAffineTransform(pts1,pts2) #print(M) #180/3.14 theta=0.5 #M = np.float32([[np.cos(theta),-np.sin(theta),100],[np.sin(theta),np.cos(theta),200]]) M = np.float32([[0.1,0,100],[0,2,100]]) # 变换矩阵，平移，斜切，旋转 # affine cols=800 rows=800 dst = cv2.warpAffine(img,M,(cols,rows)) plt.imshow(dst) # In[59]: pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]]) pts2 = np.float32([[0,0],[100,0],[0,300],[300,300]]) M = cv2.getPerspectiveTransform(pts1,pts2) print(M) # 拉伸变换后者透视变换 dst = cv2.warpPerspective(img,M,(300,300)) plt.imshow(dst) # In[261]: # 图像模糊与锐化 # In[62]: img= cv2.GaussianBlur(img, (11, 11), 1, 0) # 高斯模糊 plt.imshow(img) # In[ ]: cv2.Canny(pil_img3,30,150) # # 图像滤波/卷积 # In[139]: kernel = np.ones((3,3),np.float32)/8 kernel=-kernel kernel[0,:]=[-1,-1,-1] kernel[1,:]=[0,0,0] kernel[2,:]=[1,1,1] print(kernel) plt.imshow(img) # In[140]: #dst=cv.filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]])；当ddepth=-1时，表示输出图像与原图像有相同的深度。 print(img.shape) result = cv2.filter2D(img,-1,kernel) result.shape print(result[0,0]) plt.imshow(result*255) # In[118]: result = cv2.filter2D(result,-1,kernel) plt.imshow(result) result.shape # In[101]: # 形态学运算 # In[ ]: # In[ ]: # In[ ]: # 灰度直方图 # In[ ]: # In[ ]: # In[ ]: # 直方图均衡化 # In[ ]: # In[ ]: # In[ ]: # 加水印，保护版权 # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[ ]: # In[3]: import torch # In[10]: import matplotlib # In[11]: get_ipython().system(u'pip install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple') # In[13]: from image_process import * image_data = generate_data() # # matplotlib 使用例子 # https://www.cnblogs.com/yanghailin/p/11611333.html, # In[14]: import matplotlib.pyplot as plt # In[ ]: i=0 # In[120]: print(image_data[i%10]) plt.imshow(image_data[i%10],cmap = 'gray') i=i+1 # In[83]: plt.imshow(cv2.imread('lena.jpg')) # In[148]: img = cv2.imread("lena.jpg") img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # # add watermask # In[152]: wm = cv2.imread("water1.png") wm = cv2.resize(wm,(300,300)) wm = 255-wm img1 = cv2.resize(img,(300,300)) #img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB) print(wm.shape) plt.imshow(cv2.add(wm,img1)) plt.imshow(cv2.addWeighted(wm,0.9,img1,0.5,0)) #plt.imshow(wm)