Colab中使用cv2_imshow加载图片：无需文件路径的方法咨询

针对你在手写体模型测试流程中遇到的需求，这里提供几种不用手动写文件路径就能用cv2_imshow加载图片的实现方式：

1. 直接上传本地图片到会话内存 #

不用把图片存到Colab的文件系统，直接上传后从内存读取，完全不需要路径：

import cv2 as cv
import numpy as np
from google.colab.patches import cv2_imshow
from google.colab import files
from tensorflow.keras.models import load_model

# 触发本地文件上传窗口
uploaded = files.upload()

# 自动获取上传的第一个图片文件
img_name = next(iter(uploaded.keys()))
# 从内存解码图片数据，跳过文件路径步骤
img_color = cv.imdecode(np.frombuffer(uploaded[img_name], np.uint8), cv.IMREAD_COLOR)

# 原有的图片处理流程
img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY)
ret, img_binary = cv.threshold(img_gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
img_binary = cv.morphologyEx(img_binary, cv.MORPH_CLOSE, kernel)

cv2_imshow(img_binary)
cv.waitKey(0)

运行代码后会弹出本地文件选择窗口，选中要测试的手写体图片即可，自动处理后续步骤。

2. 调用摄像头实时捕获手写体图片 #

如果要直接拍摄手写体测试，不用提前准备图片文件，直接调用设备摄像头：

import cv2 as cv
import numpy as np
from google.colab.patches import cv2_imshow
from IPython.display import display, Javascript
from google.colab.output import eval_js
from base64 import b64decode

def take_photo(quality=0.8):
  js = Javascript('''
    async function takePhoto(quality) {
      const div = document.createElement('div');
      const capture = document.createElement('button');
      capture.textContent = 'Capture';
      div.appendChild(capture);

      const video = document.createElement('video');
      video.style.display = 'block';
      const stream = await navigator.mediaDevices.getUserMedia({video: true});

      document.body.appendChild(div);
      div.appendChild(video);
      video.srcObject = stream;
      await video.play();

      await new Promise((resolve) => capture.onclick = resolve);

      const canvas = document.createElement('canvas');
      canvas.width = video.videoWidth;
      canvas.height = video.videoHeight;
      canvas.getContext('2d').drawImage(video, 0, 0);
      stream.getVideoTracks()[0].stop();
      div.remove();
      return canvas.toDataURL('image/jpeg', quality);
    }
    ''')
  display(js)
  data = eval_js('takePhoto({})'.format(quality))
  binary = b64decode(data.split(',')[1])
  # 临时保存到Colab会话（不用手动设置路径）
  with open('temp_photo.jpg', 'wb') as f:
    f.write(binary)
  return 'temp_photo.jpg'

# 捕获摄像头图片
img_path = take_photo()
# 读取捕获的图片
img_color = cv.imread(img_path, cv.IMREAD_COLOR)

# 原有的图片处理流程
img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY)
ret, img_binary = cv.threshold(img_gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
img_binary = cv.morphologyEx(img_binary, cv.MORPH_CLOSE, kernel)

cv2_imshow(img_binary)
cv.waitKey(0)

运行后会请求摄像头权限，点击“Capture”按钮即可拍摄图片，自动进入后续处理，全程不用手动指定路径。

3. 从Google Drive自动读取图片 #

如果你的手写体图片存在Google Drive里，挂载Drive后可以自动遍历文件夹获取图片，不用手动输入单个文件路径：

import cv2 as cv
import numpy as np
from google.colab.patches import cv2_imshow
from google.colab import drive
import os

# 挂载Google Drive（按照提示完成授权）
drive.mount('/content/drive')

# 替换成你Drive里存放图片的文件夹路径，自动筛选图片文件
drive_img_dir = '/content/drive/MyDrive/手写体测试图片'
img_files = [f for f in os.listdir(drive_img_dir) if f.lower().endswith(('.jpg', '.png', '.jpeg'))]
# 取第一个图片文件（批量测试可以循环遍历img_files）
img_color = cv.imread(os.path.join(drive_img_dir, img_files[0]), cv.IMREAD_COLOR)

# 原有的图片处理流程
img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY)
ret, img_binary = cv.threshold(img_gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5))
img_binary = cv.morphologyEx(img_binary, cv.MORPH_CLOSE, kernel)

cv2_imshow(img_binary)
cv.waitKey(0)

只需要修改drive_img_dir为你Drive里的文件夹路径，代码会自动筛选出图片文件，不用手动写具体的图片文件名和路径。

内容的提问来源于stack exchange，提问作者이주현