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如果我想实现连续多个数字的图片识别，要如何实现？
如何解决这个思路呢?
我们可以不修改单个 CNN 数字解析，然后实现这个功能。
要支持**“一张图片中包含多个数字”（比如 123），我们可以不改模型结构，
而是在推理阶段**（inference）做「多数字分割 + 批量识别」。
也就是说，CNN 依然是识别“单个数字”，我们让它对每个数字小块依次预测。
这样你不需要重新训练新模型，也能直接识别「连续多个数字」。
完整实现：支持多数字识别（改进版）
下面这份是你代码的直接改进版，核心改动只有两个地方：
识别前，用 OpenCV 找轮廓 自动切割出每个数字；
逐个送入 CNN 模型识别后，拼接成结果字符串。
import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
import torchvision.transforms as transforms
# ======================
# ✅ 1. 定义 CNN 模型结构
# ======================
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.dropout = nn.Dropout(0.25)
self.fc1 = nn.Linear(64 * 14 * 14, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = torch.relu(self.conv1(x))
x = torch.relu(self.conv2(x))
x = self.pool(x)
x = self.dropout(x)
x = x.view(-1, 64 * 14 * 14)
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# ======================
# ✅ 2. 加载模型
# ======================
model = CNN()
model.load_state_dict(torch.load("model/mnist_cnn.pth", map_location="cpu"))
model.eval()
# ======================
# ✅ 3. 图像预处理
# ======================
def preprocess_image_for_mnist(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bitwise_not(gray)
pil = Image.fromarray(gray)
try:
resample = Image.Resampling.LANCZOS
except AttributeError:
resample = Image.ANTIALIAS
pil = pil.resize((28, 28), resample)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
tensor = transform(pil).unsqueeze(0)
return tensor
# ======================
# ✅ 4. 分割多个数字
# ======================
def segment_digits(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bitwise_not(gray)
_, thresh = cv2.threshold(gray, 30, 255, cv2.THRESH_BINARY)
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
boxes = [cv2.boundingRect(c) for c in contours]
boxes = sorted(boxes, key=lambda x: x[0]) # 按 x 坐标从左到右排序
digits = []
for (x, y, w, h) in boxes:
if w > 10 and h > 10: # 过滤噪点
digit_img = gray[y:y+h, x:x+w]
# 在方框内补白背景，让比例居中
size = max(w, h)
square = np.ones((size, size), dtype=np.uint8) * 0
offset_x = (size - w) // 2
offset_y = (size - h) // 2
square[offset_y:offset_y + h, offset_x:offset_x + w] = digit_img
square = cv2.bitwise_not(square)
digits.append(square)
return digits
# ======================
# ✅ 5. 绘图窗口
# ======================
canvas = np.ones((280, 560, 3), dtype=np.uint8) * 255
drawing = False
last_point = None
prediction_text = None
def draw(event, x, y, flags, param):
global drawing, last_point
if event == cv2.EVENT_LBUTTONDOWN:
drawing = True
last_point = (x, y)
elif event == cv2.EVENT_MOUSEMOVE and drawing:
cv2.line(canvas, last_point, (x, y), (0, 0, 0), 12)
last_point = (x, y)
elif event == cv2.EVENT_LBUTTONUP:
drawing = False
last_point = None
cv2.namedWindow("🖌 MNIST Multi-Digit Board", cv2.WINDOW_NORMAL)
cv2.setMouseCallback("🖌 MNIST Multi-Digit Board", draw)
print("🎨 用鼠标左键画多个数字（例如：1234）")
print("✅ 按 's' 识别所有数字")
print("🧹 按 'c' 清空画布")
print("❌ 按 'q' 退出")
# ======================
# ✅ 6. 主循环
# ======================
while True:
display = canvas.copy()
if prediction_text is not None:
cv2.putText(display, f"RES: {prediction_text}", (10, 60),
cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)
cv2.imshow("🖌 MNIST Multi-Digit Board", display)
key = cv2.waitKey(10) & 0xFF
if key == ord('q'):
break
elif key == ord('c'):
canvas[:] = 255
prediction_text = None
elif key == ord('s'):
img_for_pred = canvas.copy()
digits = segment_digits(img_for_pred)
if not digits:
print("⚠️ 没检测到数字，请重试！")
continue
preds = []
for d in digits:
tensor = preprocess_image_for_mnist(cv2.cvtColor(d, cv2.COLOR_GRAY2BGR))
with torch.no_grad():
outputs = model(tensor)
_, predicted = torch.max(outputs, 1)
preds.append(str(predicted.item()))
prediction_text = "".join(preds)
print(f"🧠 Predicted digits: {prediction_text}")
cv2.destroyAllWindows()
效果
效果还行，下一步可以直接试一下重新训练一个支持多个字符的模型
