week1 基本圖像處理

week1 基本圖像處理

a=[1,2,3]

print("Hello friends，welcome week1's class  .........",29*"-","%s"%(a))

環境配置

!pip install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple

import cv2
import numpy as np
import matplotlib

matplotlib 使用例子

https://www.cnblogs.com/yanghailin/p/11611333.html,

import matplotlib.pyplot as plt

i=0

print(image_data[i%10])
plt.imshow(image_data[i%10],cmap = 'gray')
i=i+1

plt.imshow(cv2.imread('lena.jpg'))

img  = cv2.imread("lena.jpg")
img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

直接用生成矩陣的方式生成圖片

img0 = np.array([[0,0,1],[0,1,0],[1,0,0]])
#img0 = np.array([[0,0,1],[0,1,0],[1,0,0]])

查看矩陣數值以及大小

print(img0)

print(img0.shape)
print("img0 size = %s,%s"%(img0.shape[0],img0.shape[1]))

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() 
 加一個%matplotlib inline就會顯示


## 從攝像頭採集圖像

# read camera
cap = cv2.VideoCapture(0)
# read video
#cap  = cv2.VideoCapture("/Users/zhaomingming/Documents/HTC/核心課/CVFundamentals/week1/How Computer Vision Works.mp4")
#cap = cv2.VideoCapture("../How Computer Vision Works.mp4")
cap = cv2.VideoCapture("How Computer Vision Works.mp4")



print(cap.isOpened())

return_value=True
if 1:
while return_value:
    return_value,frame = cap.read()
    print(cap.isOpened())

    # frame 圖像幀
    #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？

print(frame.shape)

# 關閉capture
cap.release()



## 從文件讀取圖像數據

img  = cv2.imread("lena.jpg")

#cv2.cvtColor()
#cv2.COLOR_BGR2RGB
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

print(img.shape)

# range of instrest

roi = img[100:200,300:400]

plt.imshow(img)
#plt.imshow(roi)

## 圖像顏色空間變換

# 黑白圖像

img_gray=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray.shape

plt.imshow(img_gray,cmap='gray')

### 彩色圖像的顏色空間轉換

# https://blog.csdn.net/zhang_cherry/article/details/88951259

#### rgb2hsv

import cv2
img_BGR = cv2.imread('lena.jpg')
img_hsv=cv2.cvtColor(img_BGR,cv2.COLOR_BGR2HSV)
plt.imshow(img_hsv,cmap='')

#### 二值化

# 二值化
import cv2
img =  cv2.imread('lena.jpg')

plt.imshow(cv2.threshold(img,128,200,cv2.THRESH_BINARY))

## 圖像的剛體變換

### 圖像的放大與縮小

# 列，行
img =cv2.resize(img,(50,30))
plt.imshow(img)
img.shape


# 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

### 獲取感興趣區域

# roi
img_roi =img[100:300,0:200]
plt.imshow(img_roi)

import numpy as np
# 移動：
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])


### 圖像的旋轉變換與拉伸變換

#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]])
# 變換矩陣，平移，斜切，旋轉
# affine
cols=800
rows=800
dst = cv2.warpAffine(img,M,(cols,rows))
plt.imshow(dst)

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)
# 拉伸變換後者透視變換
dst = cv2.warpPerspective(img,M,(300,300))
plt.imshow(dst)

## 圖像模糊與銳化

img= cv2.GaussianBlur(img, (11, 11), 1, 0)  # 高斯模糊
plt.imshow(img)

cv2.Canny(pil_img3,30,150)

### 圖像濾波/卷積

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)

#dst=cv.filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]])；當ddepth=-1時，表示輸出圖像與原圖像有相同的深度。
print(img.shape)
result = cv2.filter2D(img,-1,kernel)
result.shape
print(result[0,0])
plt.imshow(result*255)

result = cv2.filter2D(result,-1,kernel)

plt.imshow(result)
result.shape

## add watermask

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)