NumPy ROI区域操作的详细教程

img[ y1:y2 , x1:x2 ]

import numpy as np

# 创建一个 6x6 测试数组（模拟图像）
img = np.arange(36).reshape(6,6)
print("原始数组：")
print(img)

# ====================
# 【入门1】截取固定区域 ROI
# ====================
# 截取 行1~行4，列2~列5
roi = img[1:5, 2:6]
print("\n截取 ROI（行1-4，列2-5）：")
print(roi)

# 创建 400x400 彩色图
img = np.zeros((400,400,3), dtype=np.uint8)

# ====================
# 截取 ROI：y1:y2, x1:x2
# ====================
roi = img[50:200, 100:300]  # 高50→200，宽100→300

print("ROI 形状：", roi.shape)  # (150行, 200列, 3通道)

import numpy as np

img = np.zeros((300,300,3), np.uint8)

# 截取ROI
roi = img[50:150, 50:250]

# 把ROI改成纯红色
roi[:,:] = [0,0,255]  # 全部赋值

# 查看原图 → 已经被修改！
print(img[50,50])  # [0,0,255]

# 方法1：.copy()
roi = img[50:150,50:150].copy()

# 方法2：np.copy()
roi = np.copy(img[50:150,50:150])

# 现在修改 roi 不会影响原图！
roi[:,:] = [0,255,0]

import numpy as np

# 创建大图 + 小图
img = np.zeros((400,400,3), np.uint8)
logo = np.ones((100,100,3), np.uint8) * 255  # 白色小图

# ====================
# 把小图粘贴到大图的 ROI 位置
# ====================
img[50:150, 50:150] = logo

img = np.random.randint(0,255,(200,200,3), np.uint8)

# 提取红色通道 > 100 的区域
mask = img[:,:,2] > 100

# 只保留满足条件的区域
roi = img[mask]

# 给ROI周围加10像素黑色边框
img = np.zeros((200,200,3), np.uint8)
roi = img[50:100,50:100]

# 上下左右各扩展10像素，填充黑色
roi_pad = np.pad(roi, ((10,10),(10,10),(0,0)), mode='constant')

img = np.zeros((500,500,3), np.uint8)
rois = []

# 循环截取多个ROI
for y in range(0,500,100):
    for x in range(0,500,100):
        roi = img[y:y+100, x:x+100]
        rois.append(roi)

import cv2
import numpy as np

# 读取图片
img = cv2.imread("test.jpg")
height, width = img.shape[:2]

# ====================
# 1. 截取人脸区域（模拟）
# ====================
x1, y1 = 100, 80
x2, y2 = 300, 350
face_roi = img[y1:y2, x1:x2]

# ====================
# 2. 把ROI区域调亮
# ====================
face_roi = cv2.add(face_roi, 50)

# ====================
# 3. 粘贴回原图
# ====================
img[y1:y2, x1:x2] = face_roi

# 显示
cv2.imshow("ROI结果", img)
cv2.waitKey(0)

# 截取
roi = img[y1:y2, x1:x2]

# 独立副本
roi = img[...].copy()

# 粘贴
img[y1:y2, x1:x2] = roi

# 通道
roi = img[..., 2]  # 红色通道