| show | version | enable_checker |
|---|---|---|
step |
1.0 |
true |
- 上次研究的是HSV
- HSV是一种色彩模式
| 简写 | 名称 | 含义 |
|---|---|---|
| H | Hue | 色相 |
| S | Saturation | 饱和度 |
| V | Value | 亮度 |
- 可以通过滑块对于HSV的图像进行控制
- 还有什么可以玩的东西吗？
import cv2
img = cv2.imread("/home/shiyanlou/kun1.png")
dst1 = cv2.flip(img,0)#flip-x
dst2 = cv2.flip(img,1)#flip-y
dst3 = cv2.flip(img,-1)#flip-xy
cv2.imshow("img",img)
cv2.imshow("dst1",dst1)
cv2.imshow("dst2",dst2)
cv2.imshow("dst3",dst3)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
import cv2
import numpy as np
img = cv2.imread("/home/shiyanlou/kun1.png")
rows = len(img)
cols = len(img[0])
M = np.float32([[1,0,50] #50 to left
,[0,1,100]])#100 to bottom
dst1 = cv2.warpAffine(img,M,(cols,rows))#flip-x
cv2.imshow("img",img)
cv2.imshow("dst1",dst1)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
import cv2
img = cv2.imread("/home/shiyanlou/kun1.png")
ball = img[115:192,262:328]
ball = cv2.flip(ball,1)
img[138:215,103:169] = ball[:,:]
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
import cv2
import numpy as np
img = cv2.imread("/home/shiyanlou/kun1.png")
rows = len(img)
cols = len(img[0])
center = (rows/2,cols/2)
M = cv2.getRotationMatrix2D(center,30,0.8)
dst1 = cv2.warpAffine(img,M,(cols,rows))
cv2.imshow("img",img)
cv2.imshow("dst1",dst1)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
import cv2
import numpy as np
img = cv2.imread("/home/shiyanlou/kun1.png")
rows = len(img)
cols = len(img[0])
p1 = np.zeros((4,2),np.float32)#original picture 3 points
p1[0] = [0, 0]#left top point of original pictrure
p1[1] = [cols -1 , 0]#right top point of original pictrure
p1[2] = [0, rows-1]# left bottom point of original pictrure
p1[3] = [cols - 1, rows-1]# right bottom point of original pictrure
p2 = np.zeros((4,2),np.float32)
p2[0] = [90,0]
p2[1] = [cols -90 ,0]
p2[2] = [0, rows - 1]
p2[3] = [cols - 1, rows-1]
M = cv2.getPerspectiveTransform(p1, p2)
dst = cv2.warpPerspective(img, M, (cols, rows))
cv2.imshow("img",img)
cv2.imshow("dst",dst)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
- 可以把已经透视的图片
- 变回 没有透视的 状态吗？
- 标记点
- 具体怎么变形呢？
import cv2
import numpy as np
img = cv2.imread("/home/shiyanlou/notice.png")
rows = len(img)
cols = len(img[0])
p1 = np.zeros((4,2),np.float32)#original picture 3 points
p1[0] = [0, 0]#left top point of original pictrure
p1[1] = [cols -1 , 0]#right top point of original pictrure
p1[2] = [0, rows-1]# left bottom point of original pictrure
p1[3] = [cols - 1, rows-1]# right bottom point of original pictrure
p2 = np.zeros((4,2),np.float32)
p2[0] = [150,20]
p2[1] = [350 ,40]
p2[2] = [162, 311]
p2[3] = [320, 270]
M = cv2.getPerspectiveTransform(p2, p1)
dst = cv2.warpPerspective(img, M, (cols, rows))
cv2.imshow("img",img)
cv2.imshow("dst",dst)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
- 这种类型包括了近大远小的透视变形
- getPerspectiveTransform
import cv2
img = cv2.imread("/home/shiyanlou/kun1.png")
ball = img[115:192,262:328]
rows = len(ball)
cols = len(ball[0])
center = (rows/2,cols/2)
M = cv2.getRotationMatrix2D(center,60,0.7)
dst = cv2.warpAffine(ball,M,(cols,rows))
print(dst.shape)
for y in range(0,77):
for x in range(0,66):
print("y=",40+y,"x=",250+x,"img",img[40+y,250+x])
print("y=",y,"x=",x,"dst",dst[y,x])
if dst[y,x,0] == 0 and dst[y,x,1]==0 and dst[y,x,2]==0:
continue
img[70+y,250+x] = dst[y,x]
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
import numpy as np
import cv2
img = cv2.imread("/home/shiyanlou/kun1.png")
img = cv2.cv2.cvtColor(img,cv2.COLOR_BGR2BGRA)
y2 = 192
y1 = 115
x2 = 328
x1 = 262
ball = img[y1:y2,x1:x2]
print(ball)
rows = len(ball)
cols = len(ball[0])
center = (rows/2,cols/2)
M = cv2.getRotationMatrix2D(center,60,0.7)
dst = cv2.warpAffine(ball,M,(cols,rows),borderValue=[230,229,245,0])
cv2.imshow("dst",dst)
cv2.imwrite("d.png",dst)
print(dst.shape)
dy = y2 - y1
dx = x2 - x1
for y in range(0,dy):
for x in range(0,dx):
#print("y=",40+y,"x=",250+x,"img",img[40+y,250+x])
#print("y=",y,"x=",x,"dst",dst[y,x])
if dst[y,x,3] == 0:
print(dst)
continue
img[70+y,250+x] = dst[y,x]
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
import cv2
from math import sin,cos
import numpy as np
def rotate_bound_white_bg(image, angle):
# grab the dimensions of the image and then determine the
# center
(h, w) = image.shape[:2]
(cX, cY) = (w // 2, h // 2)
# grab the rotation matrix (applying the negative of the
# angle to rotate clockwise), then grab the sine and cosine
# (i.e., the rotation components of the matrix)
M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
print(M)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# compute the new bounding dimensions of the image
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# adjust the rotation matrix to take into account translation
M[0, 2] += (nW / 2) - cX
M[1, 2] += (nH / 2) - cY
print(M)
# perform the actual rotation and return the image
return cv2.warpAffine(image, M, (nW, nH),borderValue=(255,255,255))
# return cv2.warpAffine(image, M, (nW, nH))
img = cv2.imread("/mnt/cgshare/kun1.png")
for i in range(0,8):
imgRotation = rotate_bound_white_bg(img, 45*i-45)
cv2.imshow("imgRotation",imgRotation)
cv2.imwrite("img_" + str(i) + ".png",imgRotation)
cv2.waitKey(0)
- 拼出九宫格环绕
import cv2
import numpy as np
img = cv2.imread("/home/shiyanlou/kun1.png")
rows = len(img)
cols = len(img[0])
p1 = np.zeros((3,2),np.float32)#original picture 3 points
p1[0] = [0, 0]#left top point of original pictrure
p1[1] = [cols -1 , 0]#right top point of original pictrure
p1[2] = [0, rows-1]# left bottom point of original pictrure
p2 = np.zeros((3,2),np.float32)
p2[0] = [50,0]
p2[1] = [cols -1 ,0]
p2[2] = [0, rows - 1]
M = cv2.getAffineTransform(p1, p2)
dst = cv2.warpAffine(img, M, (cols, rows))
cv2.imshow("img",img)
cv2.imshow("dst",dst)
cv2.waitKey()
cv2.destroyAllWindows()
- 效果
- 这种方式被称为斜切
- shear
- 这次研究了图像的各种类型的变型方法
- 旋转
- 翻转
- 透视
- 倾斜
- 仿射变形
- 透视和仿射有什么区别呢？？
- 我们下次再说 👋