{"id":1100870,"date":"2025-01-08T15:44:02","date_gmt":"2025-01-08T07:44:02","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1100870.html"},"modified":"2025-01-08T15:44:04","modified_gmt":"2025-01-08T07:44:04","slug":"python%e5%a6%82%e4%bd%95%e5%b0%86%e5%9b%be%e5%83%8f%e5%88%86%e6%88%90%e5%ad%90%e5%9d%97-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1100870.html","title":{"rendered":"python\u5982\u4f55\u5c06\u56fe\u50cf\u5206\u6210\u5b50\u5757"},"content":{"rendered":"

\"python\u5982\u4f55\u5c06\u56fe\u50cf\u5206\u6210\u5b50\u5757\"<\/p>\n

Python\u5982\u4f55\u5c06\u56fe\u50cf\u5206\u6210\u5b50\u5757\uff0c\u4f7f\u7528\u5e93\u5982OpenCV\u3001NumPy\u3001Pillow\u7b49\u8fdb\u884c\u56fe\u50cf\u5206\u5757\u3001\u5e76\u786e\u4fdd\u5b50\u5757\u6ca1\u6709\u91cd\u53e0\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528OpenCV\u8fdb\u884c\u56fe\u50cf\u5206\u5757\u7684\u8be6\u7ec6\u6b65\u9aa4\u3002<\/strong><\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528OpenCV\u5206\u5272\u56fe\u50cf<\/p>\n<\/p>\n

OpenCV\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5904\u7406\u56fe\u50cf\u3002\u8981\u5c06\u56fe\u50cf\u5206\u5272\u6210\u5b50\u5757\uff0c\u53ef\u4ee5\u4f7f\u7528OpenCV\u8bfb\u53d6\u56fe\u50cf\u5e76\u4f7f\u7528NumPy\u6570\u7ec4\u6765\u64cd\u4f5c\u56fe\u50cf\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u6b65\u9aa4\u6765\u5b9e\u73b0\u8fd9\u4e00\u76ee\u6807\uff1a<\/p>\n<\/p>\n

\u5b89\u88c5\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n

\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86OpenCV\u548cNumPy\u5e93\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\u8fd9\u4e9b\u5e93\uff1a<\/p>\n<\/p>\n

pip install opencv-python<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bfb\u53d6\u56fe\u50cf\u5e76\u5206\u5272\u6210\u5b50\u5757<\/h3>\n<\/p>\n
\u4f7f\u7528OpenCV\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u5206\u5272\u6210\u5b50\u5757\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('path_to_your_image.jpg')<\/p>\n
\u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8<\/strong><\/h2>\n
height, width, _ = image.shape<\/p>\n
\u5b9a\u4e49\u5b50\u5757\u7684\u5c3a\u5bf8<\/strong><\/h2>\n
block_size = (100, 100)<\/p>\n
\u8ba1\u7b97\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
num_blocks_y = height \/\/ block_size[0]<\/p>\n
num_blocks_x = width \/\/ block_size[1]<\/p>\n
\u5206\u5272\u56fe\u50cf\u6210\u5b50\u5757<\/strong><\/h2>\n
blocks = []<\/p>\n
for y in range(0, height, block_size[0]):<\/p>\n
    for x in range(0, width, block_size[1]):<\/p>\n
        block = image[y:y + block_size[0], x:x + block_size[1]]<\/p>\n
        blocks.append(block)<\/p>\n
\u6253\u5370\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
print(f'Total blocks: {len(blocks)}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/strong> \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u8bfb\u53d6\u56fe\u50cf\u5e76\u83b7\u53d6\u5176\u5c3a\u5bf8\u3002\u7136\u540e\uff0c\u5b9a\u4e49\u5b50\u5757\u7684\u5c3a\u5bf8\uff0c\u5e76\u8ba1\u7b97\u56fe\u50cf\u5728y\u65b9\u5411\u548cx\u65b9\u5411\u4e0a\u53ef\u4ee5\u5206\u5272\u6210\u591a\u5c11\u4e2a\u5b50\u5757\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528\u5d4c\u5957\u7684for\u5faa\u73af\u904d\u5386\u56fe\u50cf\uff0c\u5c06\u5176\u5206\u5272\u6210\u5b50\u5757\u5e76\u5b58\u50a8\u5728\u5217\u8868\u4e2d\u3002\u6700\u540e\uff0c\u6253\u5370\u51fa\u5b50\u5757\u7684\u6570\u91cf\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528Pillow\u5206\u5272\u56fe\u50cf<\/p>\n<\/p>\n
Pillow\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u5904\u7406\u56fe\u50cf\u5e76\u8fdb\u884c\u5404\u79cd\u56fe\u50cf\u64cd\u4f5c\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528Pillow\u5206\u5272\u56fe\u50cf\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
\u5b89\u88c5Pillow\u5e93<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86Pillow\u5e93\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\u8fd9\u4e2a\u5e93\uff1a<\/p>\n<\/p>\n
pip install pillow<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528Pillow\u8bfb\u53d6\u56fe\u50cf\u5e76\u5206\u5272\u6210\u5b50\u5757<\/h3>\n<\/p>\n
\u4f7f\u7528Pillow\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u5206\u5272\u6210\u5b50\u5757\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = Image.open('path_to_your_image.jpg')<\/p>\n
\u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8<\/strong><\/h2>\n
width, height = image.size<\/p>\n
\u5b9a\u4e49\u5b50\u5757\u7684\u5c3a\u5bf8<\/strong><\/h2>\n
block_size = (100, 100)<\/p>\n
\u8ba1\u7b97\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
num_blocks_x = width \/\/ block_size[0]<\/p>\n
num_blocks_y = height \/\/ block_size[1]<\/p>\n
\u5206\u5272\u56fe\u50cf\u6210\u5b50\u5757<\/strong><\/h2>\n
blocks = []<\/p>\n
for y in range(num_blocks_y):<\/p>\n
    for x in range(num_blocks_x):<\/p>\n
        left = x * block_size[0]<\/p>\n
        upper = y * block_size[1]<\/p>\n
        right = left + block_size[0]<\/p>\n
        lower = upper + block_size[1]<\/p>\n
        block = image.crop((left, upper, right, lower))<\/p>\n
        blocks.append(block)<\/p>\n
\u6253\u5370\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
print(f'Total blocks: {len(blocks)}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/strong> \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528Pillow\u8bfb\u53d6\u56fe\u50cf\u5e76\u83b7\u53d6\u5176\u5c3a\u5bf8\u3002\u7136\u540e\uff0c\u5b9a\u4e49\u5b50\u5757\u7684\u5c3a\u5bf8\uff0c\u5e76\u8ba1\u7b97\u56fe\u50cf\u5728x\u65b9\u5411\u548cy\u65b9\u5411\u4e0a\u53ef\u4ee5\u5206\u5272\u6210\u591a\u5c11\u4e2a\u5b50\u5757\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528\u5d4c\u5957\u7684for\u5faa\u73af\u904d\u5386\u56fe\u50cf\uff0c\u5c06\u5176\u5206\u5272\u6210\u5b50\u5757\u5e76\u5b58\u50a8\u5728\u5217\u8868\u4e2d\u3002\u6700\u540e\uff0c\u6253\u5370\u51fa\u5b50\u5757\u7684\u6570\u91cf\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528NumPy\u5206\u5272\u56fe\u50cf<\/p>\n<\/p>\n
NumPy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u7ec4\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u7528\u6765\u5904\u7406\u56fe\u50cf\u6570\u636e\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528NumPy\u5206\u5272\u56fe\u50cf\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
\u5b89\u88c5\u5fc5\u8981\u7684\u5e93<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u4f60\u5df2\u7ecf\u5b89\u88c5\u4e86NumPy\u5e93\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u6765\u5b89\u88c5\u8fd9\u4e2a\u5e93\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528NumPy\u8bfb\u53d6\u56fe\u50cf\u5e76\u5206\u5272\u6210\u5b50\u5757<\/h3>\n<\/p>\n
\u4f7f\u7528NumPy\u8bfb\u53d6\u56fe\u50cf\u5e76\u5c06\u5176\u5206\u5272\u6210\u5b50\u5757\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import numpy as np<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('path_to_your_image.jpg')<\/p>\n
\u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8<\/strong><\/h2>\n
height, width, _ = image.shape<\/p>\n
\u5b9a\u4e49\u5b50\u5757\u7684\u5c3a\u5bf8<\/strong><\/h2>\n
block_size = (100, 100)<\/p>\n
\u8ba1\u7b97\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
num_blocks_y = height \/\/ block_size[0]<\/p>\n
num_blocks_x = width \/\/ block_size[1]<\/p>\n
\u5206\u5272\u56fe\u50cf\u6210\u5b50\u5757<\/strong><\/h2>\n
blocks = []<\/p>\n
for y in range(num_blocks_y):<\/p>\n
    for x in range(num_blocks_x):<\/p>\n
        block = image[y*block_size[0]:(y+1)*block_size[0], x*block_size[1]:(x+1)*block_size[1]]<\/p>\n
        blocks.append(block)<\/p>\n
\u6253\u5370\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
print(f'Total blocks: {len(blocks)}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/strong> \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u4f7f\u7528OpenCV\u8bfb\u53d6\u56fe\u50cf\u5e76\u83b7\u53d6\u5176\u5c3a\u5bf8\u3002\u7136\u540e\uff0c\u5b9a\u4e49\u5b50\u5757\u7684\u5c3a\u5bf8\uff0c\u5e76\u8ba1\u7b97\u56fe\u50cf\u5728y\u65b9\u5411\u548cx\u65b9\u5411\u4e0a\u53ef\u4ee5\u5206\u5272\u6210\u591a\u5c11\u4e2a\u5b50\u5757\u3002\u63a5\u4e0b\u6765\uff0c\u4f7f\u7528\u5d4c\u5957\u7684for\u5faa\u73af\u904d\u5386\u56fe\u50cf\uff0c\u5c06\u5176\u5206\u5272\u6210\u5b50\u5757\u5e76\u5b58\u50a8\u5728\u5217\u8868\u4e2d\u3002\u6700\u540e\uff0c\u6253\u5370\u51fa\u5b50\u5757\u7684\u6570\u91cf\u3002<\/p>\n<\/p>\n
\u56db\u3001\u4fdd\u5b58\u5b50\u5757<\/p>\n<\/p>\n
\u5c06\u5206\u5272\u597d\u7684\u5b50\u5757\u4fdd\u5b58\u5230\u78c1\u76d8\u4e0a\uff0c\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import os<\/p>\n
\u521b\u5efa\u4fdd\u5b58\u5b50\u5757\u7684\u76ee\u5f55<\/strong><\/h2>\n
output_dir = 'output_blocks'<\/p>\n
if not os.path.exists(output_dir):<\/p>\n
    os.makedirs(output_dir)<\/p>\n
\u4fdd\u5b58\u5b50\u5757<\/strong><\/h2>\n
for i, block in enumerate(blocks):<\/p>\n
    cv2.imwrite(f'{output_dir}\/block_{i}.jpg', block)<\/p>\n
print('Blocks saved successfully!')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/strong> \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u521b\u5efa\u4e00\u4e2a\u76ee\u5f55\u6765\u4fdd\u5b58\u5b50\u5757\u3002\u5982\u679c\u76ee\u5f55\u4e0d\u5b58\u5728\uff0c\u5219\u521b\u5efa\u8be5\u76ee\u5f55\u3002\u7136\u540e\uff0c\u904d\u5386\u5b50\u5757\u5217\u8868\uff0c\u5c06\u6bcf\u4e2a\u5b50\u5757\u4fdd\u5b58\u5230\u6307\u5b9a\u76ee\u5f55\u4e2d\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u5e94\u7528\u573a\u666f<\/p>\n<\/p>\n
\u56fe\u50cf\u5206\u5757\u5728\u8bb8\u591a\u5e94\u7528\u573a\u666f\u4e2d\u90fd\u975e\u5e38\u6709\u7528\u3002\u4f8b\u5982\uff0c\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\uff0c\u56fe\u50cf\u5206\u5757\u53ef\u4ee5\u7528\u4e8e\u56fe\u50cf\u5904\u7406\u3001\u76ee\u6807\u68c0\u6d4b\u3001\u56fe\u50cf\u5206\u7c7b\u7b49\u4efb\u52a1\u3002\u5728\u533b\u5b66\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u56fe\u50cf\u5206\u5757\u53ef\u4ee5\u7528\u4e8e\u5206\u6790\u548c\u5904\u7406\u4e0d\u540c\u7684\u533a\u57df\u3002\u5728\u536b\u661f\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u56fe\u50cf\u5206\u5757\u53ef\u4ee5\u7528\u4e8e\u5206\u6790\u548c\u5904\u7406\u4e0d\u540c\u7684\u5730\u7406\u533a\u57df\u3002<\/p>\n<\/p>\n
\u516d\u3001\u63d0\u9ad8\u6027\u80fd<\/p>\n<\/p>\n
\u5728\u5904\u7406\u5927\u56fe\u50cf\u65f6\uff0c\u5206\u5272\u56fe\u50cf\u53ef\u80fd\u9700\u8981\u5927\u91cf\u7684\u8ba1\u7b97\u8d44\u6e90\u3002\u4e3a\u4e86\u63d0\u9ad8\u6027\u80fd\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u5e76\u884c\u8ba1\u7b97\u6765\u52a0\u901f\u56fe\u50cf\u5206\u5272\u8fc7\u7a0b\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528\u591a\u7ebf\u7a0b\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
from concurrent.futures import ThreadPoolExecutor<\/p>\n
\u8bfb\u53d6\u56fe\u50cf<\/strong><\/h2>\n
image = cv2.imread('path_to_your_image.jpg')<\/p>\n
\u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8<\/strong><\/h2>\n
height, width, _ = image.shape<\/p>\n
\u5b9a\u4e49\u5b50\u5757\u7684\u5c3a\u5bf8<\/strong><\/h2>\n
block_size = (100, 100)<\/p>\n
\u8ba1\u7b97\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
num_blocks_y = height \/\/ block_size[0]<\/p>\n
num_blocks_x = width \/\/ block_size[1]<\/p>\n
\u5206\u5272\u56fe\u50cf\u6210\u5b50\u5757\u7684\u51fd\u6570<\/strong><\/h2>\n
def get_block(y, x):<\/p>\n
    return image[y*block_size[0]:(y+1)*block_size[0], x*block_size[1]:(x+1)*block_size[1]]<\/p>\n
\u4f7f\u7528\u591a\u7ebf\u7a0b\u5206\u5272\u56fe\u50cf\u6210\u5b50\u5757<\/strong><\/h2>\n
with ThreadPoolExecutor() as executor:<\/p>\n
    blocks = list(executor.map(lambda args: get_block(*args), [(y, x) for y in range(num_blocks_y) for x in range(num_blocks_x)]))<\/p>\n
\u6253\u5370\u5b50\u5757\u7684\u6570\u91cf<\/strong><\/h2>\n
print(f'Total blocks: {len(blocks)}')<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8be6\u7ec6\u63cf\u8ff0\uff1a<\/strong> \u5728\u8fd9\u6bb5\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5b9a\u4e49\u4e86\u4e00\u4e2a\u51fd\u6570\u6765\u83b7\u53d6\u5b50\u5757\u3002\u7136\u540e\uff0c\u4f7f\u7528ThreadPoolExecutor\u521b\u5efa\u4e00\u4e2a\u7ebf\u7a0b\u6c60\uff0c\u5e76\u4f7f\u7528map\u51fd\u6570\u5e76\u884c\u5730\u5206\u5272\u56fe\u50cf\u6210\u5b50\u5757\u3002\u6700\u540e\uff0c\u6253\u5370\u51fa\u5b50\u5757\u7684\u6570\u91cf\u3002<\/p>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528Python\u8f7b\u677e\u5730\u5c06\u56fe\u50cf\u5206\u5272\u6210\u5b50\u5757\uff0c\u5e76\u5728\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u4e2d\u4f7f\u7528\u8fd9\u4e9b\u5b50\u5757\u3002\u65e0\u8bba\u662f\u4f7f\u7528OpenCV\u3001Pillow\u8fd8\u662fNumPy\uff0c\u8fd9\u4e9b\u5e93\u90fd\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u529f\u80fd\u6765\u5904\u7406\u548c\u64cd\u4f5c\u56fe\u50cf\u6570\u636e\u3002<\/p>\n<\/p>\n
\u4e03\u3001\u603b\u7ed3<\/p>\n<\/p>\n
\u56fe\u50cf\u5206\u5757\u662f\u56fe\u50cf\u5904\u7406\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u6b65\u9aa4\uff0c\u53ef\u4ee5\u7528\u4e8e\u5404\u79cd\u5e94\u7528\u573a\u666f\u3002\u4f7f\u7528Python\u4e2d\u7684OpenCV\u3001Pillow\u548cNumPy\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u5c06\u56fe\u50cf\u5206\u5272\u6210\u5b50\u5757\u3002<\/strong> \u5728\u5904\u7406\u5927\u56fe\u50cf\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u5e76\u884c\u8ba1\u7b97\u6765\u63d0\u9ad8\u6027\u80fd\u3002\u6b64\u5916\uff0c\u5c06\u5206\u5272\u597d\u7684\u5b50\u5757\u4fdd\u5b58\u5230\u78c1\u76d8\u4e0a\uff0c\u4ee5\u4fbf\u540e\u7eed\u4f7f\u7528\u3002\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u4f60\u53ef\u4ee5\u638c\u63e1\u5982\u4f55\u4f7f\u7528Python\u5c06\u56fe\u50cf\u5206\u5272\u6210\u5b50\u5757\uff0c\u5e76\u5728\u4e0d\u540c\u7684\u5e94\u7528\u573a\u666f\u4e2d\u4f7f\u7528\u8fd9\u4e9b\u5b50\u5757\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u5c06\u56fe\u50cf\u5206\u5272\u6210\u5b50\u5757\uff1f<\/strong>
\u4f7f\u7528Python\u8fdb\u884c\u56fe\u50cf\u5904\u7406\u65f6\uff0c\u53ef\u4ee5\u5229\u7528\u5e93\u5982Pillow\u6216OpenCV\u6765\u5206\u5272\u56fe\u50cf\u3002\u9996\u5148\uff0c\u52a0\u8f7d\u56fe\u50cf\u5e76\u786e\u5b9a\u9700\u8981\u5206\u5272\u7684\u5b50\u5757\u5927\u5c0f\u3002\u63a5\u4e0b\u6765\uff0c\u5faa\u73af\u904d\u5386\u56fe\u50cf\u7684\u6bcf\u4e2a\u90e8\u5206\uff0c\u5e76\u5c06\u6bcf\u4e2a\u5b50\u5757\u4fdd\u5b58\u4e3a\u65b0\u7684\u56fe\u50cf\u6587\u4ef6\u3002\u4f8b\u5982\uff0c\u4f7f\u7528Pillow\u5e93\u53ef\u4ee5\u901a\u8fc7crop()<\/code>\u65b9\u6cd5\u5b9e\u73b0\u8fd9\u4e00\u8fc7\u7a0b\u3002<\/p>\n
\u6211\u8be5\u5982\u4f55\u9009\u62e9\u56fe\u50cf\u5206\u5757\u7684\u5927\u5c0f\uff1f<\/strong>
\u9009\u62e9\u56fe\u50cf\u5206\u5757\u5927\u5c0f\u4e3b\u8981\u53d6\u51b3\u4e8e\u5e94\u7528\u573a\u666f\u548c\u76ee\u6807\u3002\u4f8b\u5982\uff0c\u5982\u679c\u4f60\u5e0c\u671b\u5728\u56fe\u50cf\u5206\u6790\u4e2d\u63d0\u53d6\u7ec6\u8282\uff0c\u8f83\u5c0f\u7684\u5b50\u5757\u53ef\u80fd\u66f4\u6709\u6548\u3002\u7136\u800c\uff0c\u8fc7\u5c0f\u7684\u5b50\u5757\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5904\u7406\u6548\u7387\u4f4e\u4e0b\u3002\u5728\u4e00\u822c\u60c5\u51b5\u4e0b\uff0c\u5efa\u8bae\u6839\u636e\u56fe\u50cf\u7684\u5206\u8fa8\u7387\u548c\u5904\u7406\u80fd\u529b\u8fdb\u884c\u8bd5\u9a8c\uff0c\u627e\u5230\u4e00\u4e2a\u5408\u9002\u7684\u5e73\u8861\u70b9\u3002<\/p>\n
\u4f7f\u7528OpenCV\u5206\u5272\u56fe\u50cf\u662f\u5426\u6bd4\u4f7f\u7528Pillow\u66f4\u6709\u4f18\u52bf\uff1f<\/strong>
OpenCV\u5728\u5904\u7406\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\u65f6\u5f80\u5f80\u66f4\u4e3a\u9ad8\u6548\uff0c\u7279\u522b\u662f\u6d89\u53ca\u5230\u5b9e\u65f6\u5904\u7406\u6216\u8ba1\u7b97\u673a\u89c6\u89c9\u9879\u76ee\u65f6\u3002\u5b83\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u4f18\u5316\u7684\u7b97\u6cd5\uff0c\u9002\u5408\u5904\u7406\u5927\u89c4\u6a21\u7684\u56fe\u50cf\u64cd\u4f5c\u3002Pillow\u5219\u66f4\u9002\u5408\u4e8e\u7b80\u5355\u7684\u56fe\u50cf\u5904\u7406\u4efb\u52a1\uff0c\u6613\u4e8e\u4e0a\u624b\u3002\u56e0\u6b64\uff0c\u9009\u62e9\u54ea\u4e2a\u5e93\u53d6\u51b3\u4e8e\u5177\u4f53\u9700\u6c42\u548c\u9879\u76ee\u590d\u6742\u6027\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u5982\u4f55\u5c06\u56fe\u50cf\u5206\u6210\u5b50\u5757\uff0c\u4f7f\u7528\u5e93\u5982OpenCV\u3001NumPy\u3001Pillow\u7b49\u8fdb\u884c\u56fe\u50cf\u5206\u5757\u3001\u5e76\u786e\u4fdd\u5b50\u5757\u6ca1\u6709\u91cd […]","protected":false},"author":3,"featured_media":1100876,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1100870"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1100870"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1100870\/revisions"}],"predecessor-version":[{"id":1100877,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1100870\/revisions\/1100877"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1100876"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1100870"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1100870"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1100870"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}