python opencv 裁剪图片_OpenCV图像处理以在Python中裁剪图像的倾斜部分
这是一个图像提取小部件,可让您旋转图像并通过单击并拖动鼠标来选择ROI.想法是使用鼠标选择边界框窗口,在这里我们可以使用Numpy切片来裁剪图像.由于OpenCV不允许您绘制成角度的矩形,因此可以通过首先旋转图像来绕过该矩形.
一旦选择了ROI,就可以使用边界框坐标裁剪图像.如果我们将(0,0)视为图像的左上角,从左至右作为x方向,从上至下作为y方向,则将(x1,y1)作为顶部-左顶点和(x2,y2)作为ROI的右下角顶点,我们可以通过以下方式裁剪图像：
ROI = image[y1:y2,x1:x2]
由于图像在OpenCV中存储为Numpy数组,因此我们能够做到这一点. Here是Numpy数组索引和切片的重要资源.
要使用小部件：
>鼠标左键拖动-选择ROI
>右键单击-重置图像
> r-顺时针旋转图像5度
> e-逆时针旋转图像5度
> c-裁剪所选的ROI
> q-退出程序
import cv2
import numpy as np
class ExtractImageWidget(object):
def __init__(self):
self.original_image = cv2.imread('plane.PNG')
# Resize image,remove if you want raw image size
self.original_image = cv2.resize(self.original_image,(640,556))
self.clone = self.original_image.copy()
cv2.namedWindow('image')
cv2.setMouseCallback('image',self.extract_coordinates)
# Bounding box reference points and boolean if we are extracting coordinates
self.image_coordinates = []
self.angle = 0
self.extract = False
self.selected_ROI = False
def extract_coordinates(self,event,x,flags,parameters):
# Record starting (x,y) coordinates on left mouse button click
if event == cv2.EVENT_LBUTTONDOWN:
self.image_coordinates = [(x,y)]
self.extract = True
# Record ending (x,y) coordintes on left mouse bottom release
elif event == cv2.EVENT_LBUTTONUP:
self.image_coordinates.append((x,y))
self.extract = False
self.selected_ROI = True
self.crop_ROI()
# Draw rectangle around ROI
cv2.rectangle(self.clone,self.image_coordinates[0],self.image_coordinates[1],(0,255,2)
cv2.imshow("image",self.clone)
# Clear drawing boxes on right mouse button click and reset angle
elif event == cv2.EVENT_RBUTTONDOWN:
self.clone = self.original_image.copy()
self.angle = 0
self.selected_ROI = False
def show_image(self):
return self.clone
def rotate_image(self,angle):
# Grab the dimensions of the image and then determine the center
(h,w) = self.original_image.shape[:2]
(cX,cY) = (w / 2,h / 2)
self.angle += angle
# 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),-self.angle,1.0)
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
# Perform the actual rotation and return the image
self.clone = cv2.warpAffine(self.original_image,M,(nW,nH))
self.selected_ROI = False
def crop_ROI(self):
if self.selected_ROI:
self.cropped_image = self.clone.copy()
x1 = self.image_coordinates[0][0]
y1 = self.image_coordinates[0][1]
x2 = self.image_coordinates[1][0]
y2 = self.image_coordinates[1][1]
self.cropped_image = self.cropped_image[y1:y2,x1:x2]
print('Cropped image: {} {}'.format(self.image_coordinates[0],self.image_coordinates[1]))
else:
print('Select ROI to crop before cropping')
def show_cropped_ROI(self):
cv2.imshow('cropped image',self.cropped_image)
if __name__ == '__main__':
extract_image_widget = ExtractImageWidget()
while True:
cv2.imshow('image',extract_image_widget.show_image())
key = cv2.waitKey(1)
# Rotate clockwise 5 degrees
if key == ord('r'):
extract_image_widget.rotate_image(5)
# Rotate counter clockwise 5 degrees
if key == ord('e'):
extract_image_widget.rotate_image(-5)
# Close program with keyboard 'q'
if key == ord('q'):
cv2.destroyAllWindows()
exit(1)
# Crop image
if key == ord('c'):
extract_image_widget.show_cropped_ROI()