python字符验证码识别

Question

有没有兄弟可以用python帮我写一份6个字符的包含数字和字母大小写的验证码图片灰度二分化预处理，切割成单个字符，然后用差不多1000组数据自己训练库测试一一比对得到模型的识别代码，⚽️⚽️了

Answer 1

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为了解决你的问题，我将提供以下步骤和代码来完成使用Python进行字符验证码识别。

步骤 1: 准备数据

1. 准备一些包含数字和字母大小写的验证码图片。确保图片包含明确的字符和背景，并保存在一个文件夹中。

步骤 2: 图片预处理

1. 导入必要的库：

from PIL import Image
import numpy as np

1. 创建一个函数来处理验证码图片。首先加载图片，然后将其转换为灰度图像。接下来，使用二值化将图像转换为黑白图像。最后，返回二值化后的图像。

def preprocess_image(image_path):
    image = Image.open(image_path).convert('L')
    binary_image = image.point(lambda x: 0 if x < 128 else 255, '1')
    return binary_image

步骤 3: 字符切割

1. 创建一个函数来切割二值化后的验证码图像。通过设置一个阈值，确定字符开始和结束的位置。使用PIL库的crop函数将字符切割成单个字符，并将其保存为单独的图像文件。

def split_characters(image, threshold=128, min_width=6):
    width, height = image.size
    pixels = np.array(image.getdata()).reshape((height, width))

    in_character = False
    start = 0
    end = 0

    for x in range(width):
        if not in_character and np.mean(pixels[:, x]) < threshold:
            in_character = True
            start = x
        elif in_character and (np.mean(pixels[:, x]) > threshold or x == width - 1):
            in_character = False
            if x - start > min_width:
                end = x + 1
                character_image = image.crop((start, 0, end, height))
                character_image.save(f"character_{start}.png")

    return

步骤 4: 数据处理与模型训练

1. 确保你已经收集了足够的验证码图片，并将其切割成单个字符。
2. 创建一个函数来读取切割后的字符图像，并将其转换为特征矩阵用于训练模型。

def process_data():
    X = []
    y = []

    for file in glob.glob("character_*.png"):
        image = Image.open(file)
        data = np.array(image.getdata()).reshape((image.height, image.width))
        X.append(data.flatten())
        y.append(file.split("_")[1][:-4])

    return np.array(X), np.array(y)

1. 使用sklearn库的train_test_split函数将数据划分为训练集和测试集。

from sklearn.model_selection import train_test_split

X, y = process_data()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

1. 使用sklearn库的MLPClassifier类创建一个多层感知机模型，并在训练集上训练模型。

from sklearn.neural_network import MLPClassifier

model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=200)
model.fit(X_train, y_train)

1. 使用测试集评估模型的性能。

accuracy = model.score(X_test, y_test)
print("模型准确率:", accuracy)

1. 完整代码示例：

from PIL import Image
import numpy as np
import glob
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

def preprocess_image(image_path):
    image = Image.open(image_path).convert('L')
    binary_image = image.point(lambda x: 0 if x < 128 else 255, '1')
    return binary_image

def split_characters(image, threshold=128, min_width=6):
    width, height = image.size
    pixels = np.array(image.getdata()).reshape((height, width))

    in_character = False
    start = 0
    end = 0

    for x in range(width):
        if not in_character and np.mean(pixels[:, x]) < threshold:
            in_character = True
            start = x
        elif in_character and (np.mean(pixels[:, x]) > threshold or x == width - 1):
            in_character = False
            if x - start > min_width:
                end = x + 1
                character_image = image.crop((start, 0, end, height))
                character_image.save(f"character_{start}.png")

    return

def process_data():
    X = []
    y = []

    for file in glob.glob("character_*.png"):
        image = Image.open(file)
        data = np.array(image.getdata()).reshape((image.height, image.width))
        X.append(data.flatten())
        y.append(file.split("_")[1][:-4])

    return np.array(X), np.array(y)

def train_model():
    X, y = process_data()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=200)
    model.fit(X_train, y_train)

    accuracy = model.score(X_test, y_test)
    print("模型准确率:", accuracy)


if __name__ == "__main__":
    # 图片预处理和字符切割
    image_path = "验证码图片.png"
    binary_image = preprocess_image(image_path)
    split_characters(binary_image)

    # 数据处理和模型训练
    train_model()

希望这些步骤和代码能够帮助你解决问题。如果需要更多帮助，请随时提问！