opencv-Python 图像变换命令

目录

 

crop img

draw_circle

drawfigure

flip

mutil_scal_image

openAndsave

rotation

resize

---

crop img

import cv2

image = cv2.imread("konglong.jpg")
cv2.imshow("Original", image)
print(image.shape)

cropImg = image[0:300, 0:400]
cv2.imshow("crop", cropImg)
cv2.waitKey(0)
cv2.destroyAllWindows()

draw_circle

import cv2

img = cv2.imread("konglong.jpg",1)
cv2.circle(img,(100,100),5,(255,0,0),-1)
winname = 'example'
cv2.namedWindow(winname)
cv2.imshow(winname, img)
cv2.waitKey(0)
cv2.destroyWindow(winname)

drawfigure

import numpy as np
import cv2

# Create a black image
# img=np.zeros((512,512,3), np.uint8)
img = cv2.imread("konglong.jpg",1)
# Draw a diagonal blue line with thickness of 5 px
cv2.line(img,(0,0),(511,511),(255,0,0),5)

cv2.rectangle(img,(384,0),(510,128),(0,255,0),3)

font=cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,'OpenCV',(300,100), font, 1,(0,0,0),2)

winname = 'example'
cv2.namedWindow(winname)
cv2.imshow(winname, img)
cv2.waitKey(0)
cv2.destroyWindow(winname)

flip

# 导入库
import argparse
import cv2

# 构造参数解析器
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required=True, help = "Path to the image")
# args = vars(ap.parse_args())

# 加载图像并显示
image = cv2.imread("konglong.jpg")
cv2.imshow("Original", image)

# 横向翻转图像
flipped = cv2.flip(image, 1)
cv2.imshow("Flipped Horizontally", flipped)

# 纵向翻转图像
flipped = cv2.flip(image, 0)
cv2.imshow("Flipped Vertically", flipped)

# 同时在横向和纵向翻转图像
flipped = cv2.flip(image, -1)
cv2.imshow("Flipped Horizontally & Vertically", flipped)
cv2.waitKey(0)

mutil_scal_image

import numpy as np
import cv2

image = cv2.imread("konglong.jpg")
cv2.imshow("Original", image)
print(image.shape)

scales = []
factor = 0.793700526
largest = min(2,4000/max(image.shape[0:2]))
scale = largest
minD = largest*min(image.shape[0:2])
print(minD)
while minD >=227:
    scales.append(scale)
    scale *= factor
    minD *= factor
print(scales)

i = 0
for scale in scales:
    scale_img = cv2.resize(image,((int(image.shape[0]*scale),int(image.shape[1]*scale))))
    cv2.imwrite("scale"+str(i)+".jpg",scale_img)
    i+=1
# cv2.waitKey(0)

openAndsave

import cv2

img = cv2.imread("konglong.jpg",1)
cv2.imshow('image',img)
k = cv2.waitKey(0)
if k == 27:
    cv2.destroyAllWindows()
elif k == ord('s'):
    cv2.imwrite('newkonglong.png',img)
    cv2.destroyAllWindows()

rotation

# 导入库
import numpy as np
import argparse
import cv2

# 定义旋转rotate函数
def rotate(image, angle, center=None, scale=1.0):
    # 获取图像尺寸
    (h, w) = image.shape[:2]

    # 若未指定旋转中心，则将图像中心设为旋转中心
    if center is None:
        center = (w / 2, h / 2)

    # 执行旋转
    M = cv2.getRotationMatrix2D(center, angle, scale)
    rotated = cv2.warpAffine(image, M, (w, h))

    # 返回旋转后的图像
    return rotated

# 构造参数解析器
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required=True, help="Path to the image")
# args = vars(ap.parse_args())

# 加载图像并显示
image = cv2.imread("konglong.jpg")
cv2.imshow("Original", image)
print(image.shape)

# 将原图旋转不同角度
rotated = rotate(image, 45)
cv2.imshow("Rotated by 45 Degrees", rotated)
print(rotated.shape)
rotated = rotate(image, -45)
cv2.imshow("Rotated by -45 Degrees", rotated)
rotated = rotate(image, 90)
cv2.imshow("Rotated by 90 Degrees", rotated)
rotated = rotate(image, -90)
cv2.imshow("Rotated by -90 Degrees", rotated)
rotated = rotate(image, 180)
cv2.imshow("Rotated by 180 Degrees", rotated)
cv2.waitKey(0)

resize

# 导入库
"""
在一般情况下， cv2.INTER_NEAREST速度最快，但质量不高。如果资源非常有限，可以考虑使用。否则不选这个，尤其在上采样（增加）图像的大小时。
当增加（上采样）图像的大小时，可以考虑使用 cv2.INTER_LINEAR 和 cv2.INTER_CUBIC两种插值方法。 cv2.INTER_LINEAR 方法比 cv2.INTER_CUBIC 方法稍快，但无论哪个效果都不错。
当减小（下采样）的图像的大小，OpenCV的文档建议使用 cv2.INTER_AREA。PS.我感觉其实下采样各种方法都差不多，取速度最快的（cv2.INTER_NEAREST）就好。
"""
import argparse
import cv2

# 定义缩放resize函数
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    # 初始化缩放比例，并获取图像尺寸
    dim = None
    (h, w) = image.shape[:2]

    # 如果宽度和高度均为0，则返回原图
    if width is None and height is None:
        return image

    # 宽度是0
    if width is None:
        # 则根据高度计算缩放比例
        r = height / float(h)
        dim = (int(w * r), height)

    # 如果高度为0
    else:
        # 根据宽度计算缩放比例
        r = width / float(w)
        dim = (width, int(h * r))

    # 缩放图像
    resized = cv2.resize(image, dim, interpolation=inter)

    # 返回缩放后的图像
    return resized


# 构造参数解析器
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required=True, help="Path to the image")
# args = vars(ap.parse_args())

# 加载参数并显示（由于原图较大，我们先将原图缩小）
image = cv2.imread("konglong.jpg")
cv2.imshow("Original", image)

# 创建插值方法数组
methods = [
    ("cv2.INTER_NEAREST", cv2.INTER_NEAREST),
    ("cv2.INTER_LINEAR", cv2.INTER_LINEAR),
    ("cv2.INTER_AREA", cv2.INTER_AREA),
    ("cv2.INTER_CUBIC", cv2.INTER_CUBIC),
    ("cv2.INTER_LANCZOS4", cv2.INTER_LANCZOS4)]

# 循环执行插值方法数组中的各个方法
for (name, method) in methods:
    # 放大3倍
    resized = resize(image, width=image.shape[1] * 3, inter=method)
    cv2.imshow("ZoomIn Method: {}".format(name), resized)
    # 缩小2倍
    resized = resize(image, width=image.shape[1] //2, inter=method)
    cv2.imshow("ZoomOut Method: {}".format(name), resized)
cv2.waitKey(0)