opencvpython截取圆形区域的实现

import cv2
import numpy as np

image = cv2.imread('F:\Siamese-PyTorch-master\datasets\images_background\quebian_Hander/20220724-112303-336.jpg')
image=cv2.resize(image,(800,600))#缩放一下
img=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# 灰度图
# 二值化  这个位置要注意二值化和反二值化，看你原图是什么样子的底色
# ret , thresh = cv2.threshold(img, 230, 255, cv2.THRESH_BINARY_INV)
# ret , thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)
ret , thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHaiN_APPROX_SIMPLE)
dot=[]  # 用来保存所有轮廓返回的坐标点。
for c in contours:
    # 找到边界坐标
    min_list=[] # 保存单个轮廓的信息，x,y,w,h,area。 x,y 为起始点坐标
    x, y, w, h = cv2.boundingRect(c)  # 计算点集最外面的矩形边界
    min_list.append(x)
    min_list.append(y)
    min_list.append(w)
    min_list.append(h)
    min_list.append(w*h) # 把轮廓面积也添加到 dot 中
    dot.append(min_list)

# 找出最大矩形的 x,y,w,h,area
max_area=dot[0][4] # 把第一个矩形面积当作最大矩形面积
for inlist in dot:
    area=inlist[4]
    if area >= max_area:
        x=inlist[0]
        y=inlist[1]
        w=inlist[2]
        h=inlist[3]
        max_area=area
# 在原图上画出最大的矩形  这部分实际上是对边缘再扩展一下，避免剪切的圆不够完整
print(x,y,w,h)
if y>=60:
    new_w=60
elif y>=50:
    new_w=50
elif y>=40:
    new_w=40
elif y>=30:
    new_w=30
elif y>=20:
    new_w=20
elif y>=10:
    new_w=10

x0=x-int(new_w/2)
y0=y-int(new_w/2)
w=w+new_w
h=h+new_w
print(x0,y0,w,h)
# cv2.rectangle(image, (x0, y0), (x + w, y + h), (0, 255, 0), 1)

crop = image[y0:y0+h, x0:x0+w]
cv2.imshow('crop',crop)
cv2.imwrite("crop.jpg",crop)
cv2.waiTKEy(0)
cv2.destroyWindow()

# -*- coding:utf-8 -*-
# from re import X
import cv2
import numpy as np

from glob import glob
import os

img_path = glob("F:\Siamese-pytorch-master\datasets\images_background\character01/*.jpg")
path_save = "F:\Siamese-pytorch-master\datasets\images_background\quebian_Hander/"


for i,file in enumerate(img_path):
    name = os.path.join(path_save, "%d.jpg"%i)
    image = cv2.imread(file)
    print(file,i)
    image=cv2.resize(image,(800,600))
    img=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    img = cv2.bilateralFilter(img,9,75,75)
    ret , thresh = cv2.threshold(img,127,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    dot=[]  # 用来保存所有轮廓返回的坐标点。
    for c in contours:
        # 找到边界坐标
        min_list=[] # 保存单个轮廓的信息，x,y,w,h,area。 x,y 为起始点坐标
        x, y, w, h = cv2.boundingRect(c)  # 计算点集最外面的矩形边界
        min_list.append(x)
        min_list.append(y)
        min_list.append(w)
        min_list.append(h)
        min_list.append(w*h) # 把轮廓面积也添加到 dot 中
        dot.append(min_list)

# 找出最大矩形的 x,y,w,h,area
    max_area=dot[0][4] # 把第一个矩形面积当作最大矩形面积
    for inlist in dot:
        area=inlist[4]
        if area >= max_area:
            x=inlist[0]
            y=inlist[1]
            w=inlist[2]
            h=inlist[3]
            max_area=area
# 在原图上画出最大的矩形
    print(x,y,w,h)
    if y>=60:
        new_w=60
    elif y>=50:
        new_w=50
    elif y>=40:
        new_w=40
    elif y>=30:
        new_w=30
    elif y>=20:
        new_w=20
    elif y>=10:
        new_w=10
    elif y>=5:
        new_w=5
    else:
        new_w=0

    x0=x-int(new_w/2)
    y0=y-int(new_w/2)
    w=w+new_w
    h=h+new_w
    print(x0,y0,w,h)
    # cv2.rectangle(image, (x0, y0), (x0 + w , y0 + h), (0, 255, 0), 1)
    crop = image[y0:y0+h, x0:x0+w]
    cv2.imwrite(name,crop)

import cv2
import numpy as np
from glob import glob
import os
 

img_path = glob("F:\DEMO_CODE\demo\ML\qieyuan/*.jpg")
path_save = "F:\DEMO_CODE\demo\ML\qieyuan/"

for i,file in enumerate(img_path):
    name = os.path.join(path_save, "%d.jpg"%i)
    image = cv2.imread(file)
    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
    _, binary = cv2.threshold(gray,0,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)
    contours, _ = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    for cont in contours:
        (x, y), radius = cv2.minEnclosinGCircle(cont)
        if radius>200:
            ming=cv2.circle(image,(int(x),int(y)),int(radius), (0, 0, 255), 2)
            print("radius is ")
            print(radius)
            print((x, y))   
            roi = np.zeros(image.shape[:2], np.uint8)
            roi = cv2.circle(roi,(int(x),int(y)), int(radius), 255, cv2.FILLED)
            mask = np.ones_like(image) * 255
            mask = cv2.bitwise_and(mask, image, mask=roi) + cv2.bitwise_and(mask, mask, mask=~roi)
            cv2.imwrite(name,mask)
            print(i,name)