{"id":1098204,"date":"2025-01-08T15:19:04","date_gmt":"2025-01-08T07:19:04","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1098204.html"},"modified":"2025-01-08T15:19:07","modified_gmt":"2025-01-08T07:19:07","slug":"python%e5%a6%82%e4%bd%95%e8%8e%b7%e5%8f%96%e5%9b%be%e7%89%87%e6%af%8f%e4%b8%aa%e7%82%b9%e7%9a%84%e5%83%8f%e7%b4%a0-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1098204.html","title":{"rendered":"Python\u5982\u4f55\u83b7\u53d6\u56fe\u7247\u6bcf\u4e2a\u70b9\u7684\u50cf\u7d20"},"content":{"rendered":"

\"Python\u5982\u4f55\u83b7\u53d6\u56fe\u7247\u6bcf\u4e2a\u70b9\u7684\u50cf\u7d20\"<\/p>\n

\u4f7f\u7528Python\u83b7\u53d6\u56fe\u7247\u6bcf\u4e2a\u70b9\u7684\u50cf\u7d20\uff0c\u53ef\u4ee5\u4f7f\u7528PIL\uff08Python Imaging Library\uff09\u5e93\u3001OpenCV\u5e93\u3001numpy\u5e93\uff0c\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1a\u8bfb\u53d6\u56fe\u50cf\u3001\u8bbf\u95ee\u50cf\u7d20\u3001\u904d\u5386\u50cf\u7d20\u3001\u4fee\u6539\u50cf\u7d20\u3002<\/strong> \u6211\u4eec\u5c06\u5177\u4f53\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u5e93\u6765\u5b8c\u6210\u8fd9\u4e9b\u4efb\u52a1\u3002<\/p>\n<\/p>\n

\n

\u4e00\u3001\u4f7f\u7528PIL\uff08Python Imaging Library\uff09<\/h2>\n<\/p>\n

PIL\uff08\u73b0\u4e3aPillow\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u56fe\u50cf\u5904\u7406\u5e93\uff0c\u53ef\u4ee5\u8f7b\u677e\u5730\u8bfb\u53d6\u3001\u4fee\u6539\u548c\u4fdd\u5b58\u56fe\u50cf\u3002<\/p>\n<\/p>\n

1. \u5b89\u88c5Pillow\u5e93<\/h3>\n<\/p>\n

\u9996\u5148\uff0c\u9700\u8981\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86Pillow\u5e93\u3002\u5982\u679c\u6ca1\u6709\u5b89\u88c5\uff0c\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n

pip install Pillow<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6\u56fe\u50cf<\/h3>\n<\/p>\n
\u4f7f\u7528Pillow\u5e93\u8bfb\u53d6\u56fe\u50cf\u975e\u5e38\u7b80\u5355\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
\u6253\u5f00\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n
image = Image.open("path_to_image.jpg")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u50cf\u7d20\u4e4b\u524d\uff0c\u6211\u4eec\u53ef\u4ee5\u83b7\u53d6\u56fe\u50cf\u7684\u5c3a\u5bf8\uff1a<\/p>\n<\/p>\n
width, height = image.size<\/p>\n
print(f"\u56fe\u50cf\u5bbd\u5ea6: {width}, \u56fe\u50cf\u9ad8\u5ea6: {height}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u8bbf\u95ee\u548c\u904d\u5386\u50cf\u7d20<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528load<\/code>\u65b9\u6cd5\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u50cf\u7d20\u8bbf\u95ee\u5bf9\u8c61\uff0c\u7136\u540e\u53ef\u4ee5\u4f7f\u7528\u8be5\u5bf9\u8c61\u8bbf\u95ee\u6bcf\u4e2a\u50cf\u7d20\u3002<\/p>\n<\/p>\n
pixels = image.load()<\/p>\n
for y in range(height):<\/p>\n
    for x in range(width):<\/p>\n
        r, g, b = pixels[x, y]<\/p>\n
        print(f"\u50cf\u7d20\u4f4d\u7f6e: ({x},{y}), \u50cf\u7d20\u503c: (R: {r}, G: {g}, B: {b})")<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u4fee\u6539\u50cf\u7d20<\/h3>\n<\/p>\n
\u53ef\u4ee5\u4f7f\u7528\u540c\u6837\u7684\u50cf\u7d20\u8bbf\u95ee\u5bf9\u8c61\u6765\u4fee\u6539\u50cf\u7d20\u7684\u503c\uff1a<\/p>\n<\/p>\n
for y in range(height):<\/p>\n
    for x in range(width):<\/p>\n
        pixels[x, y] = (255, 255, 255)  # \u5c06\u50cf\u7d20\u8bbe\u7f6e\u4e3a\u767d\u8272<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528OpenCV\u5e93<\/h2>\n<\/p>\n
OpenCV\u662f\u4e00\u4e2a\u5f00\u6e90\u8ba1\u7b97\u673a\u89c6\u89c9\u5e93\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u56fe\u50cf\u548c\u89c6\u9891\u5904\u7406\u9886\u57df\u3002\u4e0ePillow\u5e93\u7c7b\u4f3c\uff0cOpenCV\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8bfb\u53d6\u3001\u4fee\u6539\u548c\u4fdd\u5b58\u56fe\u50cf\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5OpenCV\u5e93<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86OpenCV\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install opencv-python<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u8bfb\u53d6\u56fe\u50cf<\/h3>\n<\/p>\n
\u4f7f\u7528OpenCV\u5e93\u8bfb\u53d6\u56fe\u50cf\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
\u8bfb\u53d6\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n
image = cv2.imread("path_to_image.jpg")<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8<\/h3>\n<\/p>\n
\u5728\u5904\u7406\u50cf\u7d20\u4e4b\u524d\uff0c\u6211\u4eec\u53ef\u4ee5\u83b7\u53d6\u56fe\u50cf\u7684\u5c3a\u5bf8\uff1a<\/p>\n<\/p>\n
height, width, channels = image.shape<\/p>\n
print(f"\u56fe\u50cf\u5bbd\u5ea6: {width}, \u56fe\u50cf\u9ad8\u5ea6: {height}, \u901a\u9053\u6570: {channels}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u8bbf\u95ee\u548c\u904d\u5386\u50cf\u7d20<\/h3>\n<\/p>\n
\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7numpy\u6570\u7ec4\u7d22\u5f15\u6765\u8bbf\u95ee\u548c\u904d\u5386\u50cf\u7d20\uff1a<\/p>\n<\/p>\n
for y in range(height):<\/p>\n
    for x in range(width):<\/p>\n
        b, g, r = image[y, x]<\/p>\n
        print(f"\u50cf\u7d20\u4f4d\u7f6e: ({x},{y}), \u50cf\u7d20\u503c: (R: {r}, G: {g}, B: {b})")<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u4fee\u6539\u50cf\u7d20<\/h3>\n<\/p>\n
\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7numpy\u6570\u7ec4\u7d22\u5f15\u6765\u4fee\u6539\u50cf\u7d20\u7684\u503c\uff1a<\/p>\n<\/p>\n
for y in range(height):<\/p>\n
    for x in range(width):<\/p>\n
        image[y, x] = [255, 255, 255]  # \u5c06\u50cf\u7d20\u8bbe\u7f6e\u4e3a\u767d\u8272<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528numpy\u5e93<\/h2>\n<\/p>\n
numpy\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u503c\u8ba1\u7b97\u5e93\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u6570\u7ec4\u64cd\u4f5c\u3002\u7ed3\u5408Pillow\u6216OpenCV\uff0c\u53ef\u4ee5\u4f7f\u7528numpy\u6765\u5904\u7406\u56fe\u50cf\u50cf\u7d20\u3002<\/p>\n<\/p>\n
1. \u5b89\u88c5numpy\u5e93<\/h3>\n<\/p>\n
\u9996\u5148\uff0c\u786e\u4fdd\u5df2\u7ecf\u5b89\u88c5\u4e86numpy\u5e93\u3002\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\uff1a<\/p>\n<\/p>\n
pip install numpy<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528Pillow\u5e93\u7ed3\u5408numpy\u5904\u7406\u56fe\u50cf<\/h3>\n<\/p>\n
\u8bfb\u53d6\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3anumpy\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
from PIL import Image<\/p>\n
import numpy as np<\/p>\n
\u6253\u5f00\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n
image = Image.open("path_to_image.jpg")<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3anumpy\u6570\u7ec4<\/strong><\/h2>\n
image_np = np.array(image)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f7f\u7528OpenCV\u5e93\u7ed3\u5408numpy\u5904\u7406\u56fe\u50cf<\/h3>\n<\/p>\n
\u8bfb\u53d6\u56fe\u50cf\u5e76\u8f6c\u6362\u4e3anumpy\u6570\u7ec4\uff1a<\/p>\n<\/p>\n
import cv2<\/p>\n
import numpy as np<\/p>\n
\u8bfb\u53d6\u56fe\u50cf\u6587\u4ef6<\/strong><\/h2>\n
image = cv2.imread("path_to_image.jpg")<\/p>\n
\u56fe\u50cf\u5df2\u7ecf\u662fnumpy\u6570\u7ec4\uff0c\u65e0\u9700\u8f6c\u6362<\/strong><\/h2>\n
image_np = image<\/p>\n
<\/code><\/pre>\n<\/p>\n
4. \u83b7\u53d6\u56fe\u50cf\u5c3a\u5bf8<\/h3>\n<\/p>\n
\u53ef\u4ee5\u76f4\u63a5\u4ecenumpy\u6570\u7ec4\u4e2d\u83b7\u53d6\u56fe\u50cf\u7684\u5c3a\u5bf8\uff1a<\/p>\n<\/p>\n
height, width, channels = image_np.shape<\/p>\n
print(f"\u56fe\u50cf\u5bbd\u5ea6: {width}, \u56fe\u50cf\u9ad8\u5ea6: {height}, \u901a\u9053\u6570: {channels}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
5. \u8bbf\u95ee\u548c\u904d\u5386\u50cf\u7d20<\/h3>\n<\/p>\n
\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7numpy\u6570\u7ec4\u7d22\u5f15\u6765\u8bbf\u95ee\u548c\u904d\u5386\u50cf\u7d20\uff1a<\/p>\n<\/p>\n
for y in range(height):<\/p>\n
    for x in range(width):<\/p>\n
        r, g, b = image_np[y, x]<\/p>\n
        print(f"\u50cf\u7d20\u4f4d\u7f6e: ({x},{y}), \u50cf\u7d20\u503c: (R: {r}, G: {g}, B: {b})")<\/p>\n
<\/code><\/pre>\n<\/p>\n
6. \u4fee\u6539\u50cf\u7d20<\/h3>\n<\/p>\n
\u53ef\u4ee5\u76f4\u63a5\u901a\u8fc7numpy\u6570\u7ec4\u7d22\u5f15\u6765\u4fee\u6539\u50cf\u7d20\u7684\u503c\uff1a<\/p>\n<\/p>\n
for y in range(height):<\/p>\n
    for x in range(width):<\/p>\n
        image_np[y, x] = [255, 255, 255]  # \u5c06\u50cf\u7d20\u8bbe\u7f6e\u4e3a\u767d\u8272<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u6027\u80fd\u4f18\u5316<\/h2>\n<\/p>\n
\u5728\u5904\u7406\u5927\u56fe\u50cf\u6216\u8fdb\u884c\u590d\u6742\u7684\u56fe\u50cf\u64cd\u4f5c\u65f6\uff0c\u6027\u80fd\u53ef\u80fd\u6210\u4e3a\u4e00\u4e2a\u95ee\u9898\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u6027\u80fd\u4f18\u5316\u7684\u5efa\u8bae\uff1a<\/p>\n<\/p>\n
1. \u4f7f\u7528\u77e2\u91cf\u5316\u64cd\u4f5c<\/h3>\n<\/p>\n
\u5c3d\u91cf\u4f7f\u7528numpy\u7684\u77e2\u91cf\u5316\u64cd\u4f5c\u6765\u66ff\u4ee3\u5faa\u73af\u3002\u77e2\u91cf\u5316\u64cd\u4f5c\u901a\u5e38\u6bd4\u5faa\u73af\u66f4\u9ad8\u6548\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u77e2\u91cf\u5316\u64cd\u4f5c\u5c06\u6240\u6709\u50cf\u7d20\u8bbe\u7f6e\u4e3a\u767d\u8272<\/p>\n
image_np[:, :] = [255, 255, 255]<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b<\/h3>\n<\/p>\n
\u5728\u67d0\u4e9b\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528\u591a\u7ebf\u7a0b\u6216\u591a\u8fdb\u7a0b\u6765\u52a0\u901f\u56fe\u50cf\u5904\u7406\u3002<\/p>\n<\/p>\n
import concurrent.futures<\/p>\n
def process_pixel(x, y):<\/p>\n
    # \u5904\u7406\u50cf\u7d20<\/p>\n
    image_np[y, x] = [255, 255, 255]<\/p>\n
with concurrent.futures.ThreadPoolExecutor() as executor:<\/p>\n
    futures = []<\/p>\n
    for y in range(height):<\/p>\n
        for x in range(width):<\/p>\n
            futures.append(executor.submit(process_pixel, x, y))<\/p>\n
    concurrent.futures.wAI<\/a>t(futures)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f7f\u7528GPU\u52a0\u901f<\/h3>\n<\/p>\n
\u5982\u679c\u9700\u8981\u5904\u7406\u5927\u91cf\u56fe\u50cf\u6216\u8fdb\u884c\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\uff0c\u53ef\u4ee5\u8003\u8651\u4f7f\u7528GPU\u52a0\u901f\u3002\u53ef\u4ee5\u4f7f\u7528\u5e93\u5982CuPy\u6216TensorFlow\u6765\u8fdb\u884cGPU\u52a0\u901f\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
import cupy as cp<\/p>\n
\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3aCuPy\u6570\u7ec4<\/strong><\/h2>\n
image_cp = cp.array(image_np)<\/p>\n
\u4f7f\u7528CuPy\u8fdb\u884cGPU\u52a0\u901f\u8ba1\u7b97<\/strong><\/h2>\n
image_cp[:, :] = [255, 255, 255]<\/p>\n
\u5c06\u7ed3\u679c\u8f6c\u6362\u56denumpy\u6570\u7ec4<\/strong><\/h2>\n
image_np = cp.asnumpy(image_cp)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u56fe\u50cf\u4fdd\u5b58<\/h2>\n<\/p>\n
\u5b8c\u6210\u56fe\u50cf\u5904\u7406\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Pillow\u6216OpenCV\u5c06\u5904\u7406\u540e\u7684\u56fe\u50cf\u4fdd\u5b58\u5230\u6587\u4ef6\u3002<\/p>\n<\/p>\n
1. \u4f7f\u7528Pillow\u4fdd\u5b58\u56fe\u50cf<\/h3>\n<\/p>\n
# \u5c06numpy\u6570\u7ec4\u8f6c\u6362\u56dePillow\u56fe\u50cf<\/p>\n
processed_image = Image.fromarray(image_np)<\/p>\n
\u4fdd\u5b58\u56fe\u50cf\u5230\u6587\u4ef6<\/strong><\/h2>\n
processed_image.save("processed_image.jpg")<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u4f7f\u7528OpenCV\u4fdd\u5b58\u56fe\u50cf<\/h3>\n<\/p>\n
# \u4fdd\u5b58\u56fe\u50cf\u5230\u6587\u4ef6<\/p>\n
cv2.imwrite("processed_image.jpg", image_np)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\n
\u901a\u8fc7\u4ee5\u4e0a\u65b9\u6cd5\uff0c\u53ef\u4ee5\u4f7f\u7528Python\u8f7b\u677e\u5730\u8bfb\u53d6\u3001\u8bbf\u95ee\u3001\u904d\u5386\u3001\u4fee\u6539\u548c\u4fdd\u5b58\u56fe\u50cf\u7684\u6bcf\u4e2a\u50cf\u7d20\u3002\u7ed3\u5408Pillow\u3001OpenCV\u548cnumpy\u5e93\uff0c\u53ef\u4ee5\u5b9e\u73b0\u9ad8\u6548\u7684\u56fe\u50cf\u5904\u7406\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u4f7f\u7528Python\u8bfb\u53d6\u548c\u5904\u7406\u56fe\u50cf\u7684\u50cf\u7d20\u6570\u636e\uff1f<\/strong>
\u4f7f\u7528Python\u5904\u7406\u56fe\u50cf\u50cf\u7d20\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u5e93\u5b9e\u73b0\u3002\u6700\u5e38\u7528\u7684\u5305\u62ecPIL\uff08Pillow\uff09\u3001OpenCV\u548cNumPy\u3002\u4ee5Pillow\u4e3a\u4f8b\uff0c\u9996\u5148\u9700\u8981\u5b89\u88c5\u5e93\uff0c\u7136\u540e\u53ef\u4ee5\u4f7f\u7528Image.open()<\/code>\u65b9\u6cd5\u52a0\u8f7d\u56fe\u50cf\u3002\u63a5\u7740\uff0c\u53ef\u4ee5\u4f7f\u7528getpixel()<\/code>\u65b9\u6cd5\u83b7\u53d6\u7279\u5b9a\u5750\u6807\u7684\u50cf\u7d20\u503c\uff0c\u6216\u8005\u4f7f\u7528load()<\/code>\u65b9\u6cd5\u52a0\u8f7d\u6574\u4e2a\u56fe\u50cf\u6570\u636e\u4e3a\u4e00\u4e2a\u53ef\u64cd\u4f5c\u7684\u6570\u7ec4\u3002<\/p>\n
\u5728Python\u4e2d\u83b7\u53d6\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u662f\u5426\u6709\u6027\u80fd\u8003\u8651\uff1f<\/strong>
\u5904\u7406\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u65f6\uff0c\u6027\u80fd\u53ef\u80fd\u6210\u4e3a\u4e00\u4e2a\u95ee\u9898\u3002\u4f7f\u7528NumPy\u53ef\u4ee5\u6781\u5927\u63d0\u9ad8\u6548\u7387\uff0c\u56e0\u4e3a\u5b83\u80fd\u5c06\u56fe\u50cf\u6570\u636e\u8f6c\u5316\u4e3a\u6570\u7ec4\uff0c\u4ece\u800c\u5b9e\u73b0\u6279\u91cf\u5904\u7406\u3002\u5982\u679c\u9700\u8981\u8fdb\u884c\u590d\u6742\u7684\u56fe\u50cf\u5904\u7406\u6216\u5206\u6790\uff0c\u8003\u8651\u4f7f\u7528OpenCV\uff0c\u5b83\u63d0\u4f9b\u4e86\u66f4\u591a\u7684\u529f\u80fd\u548c\u66f4\u9ad8\u7684\u5904\u7406\u901f\u5ea6\uff0c\u7279\u522b\u662f\u5728\u5927\u89c4\u6a21\u56fe\u50cf\u6570\u636e\u5904\u7406\u65f6\u3002<\/p>\n
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\u83b7\u53d6\u50cf\u7d20\u6570\u636e\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528Matplotlib\u7b49\u53ef\u89c6\u5316\u5e93\u5c55\u793a\u56fe\u50cf\u7684\u50cf\u7d20\u503c\u5206\u5e03\u3002\u901a\u8fc7\u7ed8\u5236\u76f4\u65b9\u56fe\u6216\u70ed\u56fe\uff0c\u53ef\u4ee5\u76f4\u89c2\u5730\u5206\u6790\u56fe\u50cf\u7684\u4eae\u5ea6\u3001\u5bf9\u6bd4\u5ea6\u7b49\u7279\u6027\u3002\u6b64\u5916\uff0c\u53ef\u4ee5\u7ed3\u5408NumPy\u8fdb\u884c\u6570\u5b66\u8fd0\u7b97\uff0c\u5b9e\u73b0\u66f4\u590d\u6742\u7684\u56fe\u50cf\u5206\u6790\uff0c\u6bd4\u5982\u8fb9\u7f18\u68c0\u6d4b\u6216\u989c\u8272\u7a7a\u95f4\u8f6c\u6362\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u4f7f\u7528Python\u83b7\u53d6\u56fe\u7247\u6bcf\u4e2a\u70b9\u7684\u50cf\u7d20\uff0c\u53ef\u4ee5\u4f7f\u7528PIL\uff08Python Imaging Library\uff09\u5e93\u3001Ope […]","protected":false},"author":3,"featured_media":1098215,"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\/1098204"}],"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=1098204"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1098204\/revisions"}],"predecessor-version":[{"id":1098219,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1098204\/revisions\/1098219"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1098215"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1098204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1098204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1098204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}